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FSU computational scientist helps advance targeted drug-delivery systems using coding, modeling

A Florida State University computational scientist is paving the way for future medical breakthroughs by developing mathematical models and simulations to

Jun 24 • 9:56 AM EDT • Science • news.fsu.edu

Studying Science in an Anti-Scientific Time

This Chronicle Review Deep Cut was first published in 2025. By Julianne Werlin Thanks for reading The Chronicle Review! Subscribe for free to receive new posts and support my work. Subscribe The last two decades have not been kind to science studies. Already bruised and battered by the “science wars” of the 1990s, by the 2000s sociologists of science — who had long argued that science had to be understood in its human context — were encountering the funhouse-mirror version of their own views in right-wing critiques of climate scientists. It was enough to give some prominent scholars pause. “Was I wrong to participate in the invention of this field known as science studies?” asked Bruno Latour, the most famous sociologist of science in the world, in 2003. “Should we apologize for having been wrong all along? Or should we rather bring the sword of criticism to criticism itself and do a bit of soul-searching here: What were we really after when we were so intent on showing the social construction of scientific facts?” In response, Latour proposed a change of method and attitude. Science studies, now chastened and contrite, would no longer seek to strip facts of their authority, but to enrich and enhance them. What this meant in practice was never fully clear. But his diagnosis of problems resonated, even if his solutions did not. Latour’s exercise in public hand-wringing signaled a shift in mood. By 2016, with the first election of Donald Trump, the idea that skepticism of science was a right-wing position, not a left-wing one, had become a commonplace, discussed eagerly in The New York Times. For liberals, vaccine denial during the Covid era and Trump’s second-term attacks on the funding of scientific research, covered extensively in these pages, added a new urgency to the defense of science on both practical and principled grounds. There was little appetite for a sociology or philosophy of science that emphasized the messiness and missteps of the scientific process rather than its integrity and triumphs. The distinguished historian of science Peter Dear’s new book, The World as We Know It: From Natural Philosophy to Modern Science (Princeton University Press, 2025), which seeks to tell the story of 18th- and 19th-century science for a general public, may seem comfortably remote from these bitter contests. In its 16 short chapters, Dear draws swift, sure sketches of major discoveries from Newton to Einstein, ably guiding the reader through debates on stellar nebulae, animal taxonomy, atomic chemistry, evolution, and other flashpoints in the history of science. But though a work of history, Dear’s book must still negotiate our polarized present. In a blurb on its back cover, the historian of biology Lynn K. Nyhart alludes to the “present moment, when scientific knowledge is being swamped by misinformation and its institutions are under siege.” Dear himself, in his conclusion, refers to the “yard signs” and “bumper stickers” that proclaim “Science Is Real.” One antidote to both anti-science propaganda and to reductive slogans in science’s defense is a robust history and philosophy of science: exactly what Dear’s scholarship has long offered. But his new book illustrates how difficult such work has become. Dear, an emeritus professor of history at Cornell University, is best known as a creative and philosophically minded historian of the Scientific Revolution. His edited volume The Literary Structure of Scientific Argument (1991) analyzed the rhetoric of Galileo’s thought experiments to brilliant effect. Discipline and Experience: The Mathematical Way in the Scientific Revolution (1995) was a major contribution to the analysis of scientific experiments in early modern Europe and beyond, while Revolutionizing the Sciences: European Knowledge in Transition, 1500-1700 (2001), now in its third edition, remains one of the best overviews of the profound epistemological upheavals of 16th- and 17th-century science. But Dear has also ventured into later periods. Nearly two decades ago, in The Intelligibility of Nature: How Science Makes Sense of the World (2006), he turned to the 18th and 19th centuries to sketch a history and theory of scientific knowledge. Aimed at a generalist audience, Dear’s simple prose belied serious philosophical ambitions. Science, Dear argued, has two aspects: “natural philosophy,” the quest to understand the true nature of the universe, and “instrumentality,” the applied skills and technologies that allow us to manipulate the world for human advantage. Both strands are ancient, but the innovation of the phenomenon we call “science” was to yoke them together. As Francis Bacon wrote, “Human knowledge and human power meet in one; for where the cause is not known, the effect cannot be produced,” an insight typically abridged to “knowledge is power.” Or, in Bertrand Russell’s characteristically blunt phrase, science has two functions, “1. to enable us to know things, and 2., to enable us to do things.” So far, so conventional. But Dear took issue with this familiar story in one respect. Bacon’s formula implied that theory and practice could be fused in a single method. In fact, Dear argued, the quest to understand nature and the desire to manipulate it were coupled only loosely. They could come together, but they could also pull apart. Focusing on natural philosophy, the “knowing” half of the scientific circle, he used a series of case studies from cosmology, taxonomy, chemistry, electromagnetism, and quantum theory to show how theories changed over time, in dialogue with practice, but never reducible to it. Take the case of matter, the basic substrate of the physical world. In the 18th and 19th centuries, scientists not only disagreed about what it was, but also about how well it could be known. In 1789, the chemist Antoine Lavoisier dismissed speculation about the atomic composition of chemicals as “discussions entirely of a metaphysical nature,” as he sought to orient chemistry away from philosophical abstraction and toward laboratory results. But in the 19th century, atoms returned with a vengeance in the atomic chemistry of John Dalton, who wanted to explain the underlying structure of nature. It was not just that Dalton and Lavoisier had a different set of experimental results, or even a different theory, Dear showed. What counted as an explanation had changed. In arguing that scientific theories depended on their intelligibility to human beings with complex arrays of beliefs and commitments, Dear was in sympathy with science studies. Key figures such as the sociologist Steven Shapin emphasized the importance of psychological mechanisms like trust in the formation and acceptance of scientific ideas. But in focusing on the scientific pursuit of knowledge, not its politics or economics, Dear resisted the more cynical perspective of some of his colleagues. The Intelligibility of Nature strikes a very different note than Shapin’s collection of a few years later, Never Pure: Historical Studies of Science as If It Was Produced by People With Bodies, Situated in Time, Space, Culture, and Society, and Struggling for Credibility and Authority (2010), to name just one example. Walking a very fine line, Dear depicted science as driven at once by complex social dynamics and by the intellectual power of real discoveries. It was a testament not only to his own acumen but also to the vitality of the field that he was able to do so. Nearly two decades later, The World as We Know It returns to the questions broached in The Intelligibility of Nature. Like Dear’s earlier study, it begins with the Newtonian cosmos and ends with debates between Einstein and Bohr on quantum physics. Beyond its terminal figures, many of the same characters appear in both studies, including the naturalists John Ray and the Comte de Buffon; the chemists Étienne François Geoffroy, Antoine Lavoisier, and John Dalton; as well as Charles Darwin and Michael Faraday, among others. Its subject is nearly identical: As in The Intelligibility of Nature, The World as We Know It considers natural philosophy as opposed to instrumentality, here defined as the “desire to create a picture of what the world is really like — the world as God knows it — rather than simply having instrumental or operational control over it.” The two books also overlap in insights — and sometimes even in language. The reader who has learned in The Intelligibility of Nature that Buffon’s method of classifying animals “reflected his belief in the importance of the senses and empiricism in learning about nature (a doctrine associated with Newton and the philosopher John Locke)” will experience a sense of déjà vu on reading, in The World as We Know It, that the classification “reflects Buffon’s commitment to the epistemological stance of Newton and his philosophical underlaborer John Locke, who stressed the role of the senses in creating natural knowledge.” Later in the passage, we read that, “because of its stress on understanding the ways of life of animals in their environments, Buffon’s approach could almost be labeled (anachronistically) as ‘ecological.’” And again, “an anachronistic way of putting it would be to say that Buffon recommends an ecological mode of understanding.” Likewise, in The Intelligibility of Nature we read of Dalton that he was initially drawn to meteorology, and that: He did a bit of experimental work on these things, especially having to do with the water-holding capacity of gases, but his real interest lay in understanding what, at an underlying natural-philosophical level, was really going on physically.” Whereas, in The World as We Know It: He did experimental work on these things, especially concerning the water-holding capacity of gases — although not at a very refined level quantitatively — but he was continually trying to develop a theoretical conception of what was happening physically. The final full chapter of each book closes on the same resigned note. Intelligibility: These days, most serious work on the natural philosophical underpinnings and implications of quantum mechanics is performed not by physicists but by philosophers of science. Among scientific practitioners themselves, Bohr’s attempt to use instrumentality as sufficient grounds for a respectable science has met with a high degree of success. And again, in The World as We Know It: Bohr’s and Einstein’s concerns are now questions that are mostly discussed by philosophers of science rather than by physicists themselves. The marginalization of these sorts of questions amounts to the demise of natural philosophy, at least in physics, in the 20th century. Quantum mechanics “works,” and that’s good enough for most scientists who encounter it. Examples could be multiplied. When handling the same subject matter, some echoes are inevitable. Scholars should not feel that because they have written “Isaac Newton died in 1727” in one book, in the next they must describe him as perishing, expiring, or meeting his maker. But the repetitions of language and arguments (not to mention six reproduced images) threaded throughout The World as We Know It are more extensive than that. They should have been caught by Princeton University Press or its reviewers, and revised. Because the two books have so much in common, it is easy to see where they diverge. The World as We Know It is a less theoretical work than its predecessor. The sociology of knowledge that organized The Intelligibility of Nature has been reduced in scale and folded into readings and examples. The taut interplay between history and theory, which gave the earlier study so much interest, is there, but it is subtle. In the earlier book, the case studies demonstrated theoretical claims about the character and development of science. In the later work, each narrative of discovery is an end in itself. Insights emerge from the history, but they do not control the organization of the study. With the theory muted, it can be hard to understand the logic uniting Dear’s sprawling history of more than two centuries of scientific knowledge. The 16 brisk chapters cover an enormous amount of material. But the closer the volume edges to comprehensiveness, the more obvious the missing elements become. A chapter on “Institutions and Pedagogy,” describing the birth of the research university, illustrates the problem. Inserted roughly halfway through the book, the unnumbered chapter is designed as an “entr’acte,” in acknowledgement that it does not fit the study’s organization around scientific discoveries. Yet as Dear is well aware, no general history of the development of modern science can bracket its 19th-century institutionalization and professionalization. It was the 19th-century university, after all, that gave us the “scientist,” a term coined by the Cambridge philosopher William Whewell. Dear’s history of scientific discoveries implies a wider social and institutional history, but it remains largely subterranean, only occasionally extruded under the pressure of the narrative. It must be said that the balance is not all on the side of the earlier study. There are some advantages to the more historical, less theoretical organization of The World as We Know It. Dear’s accounts of paradigm shifts are remarkably lucid, no small accomplishment given that they span fields and centuries. He makes excellent use of the wealth of new scholarship on Darwin and his influences, to which he has himself contributed. It is satisfying to see puzzle pieces from Buffon’s natural history, Georges Cuvier’s animal taxonomy, Thomas Robert Malthus’s demography, and Charles Lyell’s geology recombined seamlessly in the Darwinian jigsaw. Likewise, Dear’s discussion of stellar nebulae, divided between chapters two and 15, shows vividly how Newton, Kant, Herschel, and Hubble reimagined insights and observations as they sought to discover whether the diffuse light that filtered through early telescopes was a glowing, gaseous substance or discrete but distant stars. Yet despite such pleasures, the book remains a more modest work than The Intelligibility of Nature. Dear has clearly not changed his views about the history of science. But in the two decades that have elapsed, the discipline and the world around it have changed, making it harder to write a sociologically and philosophically ambitious history of science for the general reader. That is a shame, because in the age of Silicon Valley and biotechnology, both the reciprocity and the tensions Dear identified between the epistemological and instrumental ambitions of science are more evident than ever. In recent years, some of the most exciting new books on the history of science in early modernity have focused precisely on this pressure point. Important studies such as Pamela H. Smith’s From Lived Experience to the Written Word (2022), James Poskett’s Horizons: The Global Origins of Modern Science (2022), and Vera Keller’s The Interlopers: Early Stuart Projects and the Undisciplining of Knowledge (2023) all shed new light on how early modern science negotiated the relationship between its instrumental and epistemological ambitions, or in Russell’s phrase, knowing and doing. There is every opportunity for a philosophically informed approach to draw new insights from this wealth of material. Dear, unfortunately, has not done so in The World as We Know It. But for future scholars of the history of science, his body of work over nearly four decades may provide just the model. Julianne Werlin is an associate professor of English at Duke University. Thanks for reading The Chronicle Review! Subscribe for free to receive new posts and support my work. Subscribe

Oct 2 • 2:52 PM EDT • Science • chronicleofhighereducation.substack.com

My Mathematical Development: Probability and Improbability

As it is, probabilities are everywhere in information theory, and yet probability theory and information theory have a very different feel.

Sep 28 • 9:33 AM EDT • Science • scienceandculture.com

Can researchers trust that AI models won’t leak their ideas?

Research funders are asking how they can stop confidential scientific ideas in grant proposals from leaking out through large language models (LLMs), for example if reviewers use AI to help them judge applications. Last week, a row erupted in mathematics after OpenAI announced it had solved a notoriously difficult problem, only for another mathematician to demand answers over whether the company had used ideas he had fed into its models. The spat, and the broader issue of idea leakage through AI models, was discussed by funders at an R&D policy conference held in Paris on September 14. At worst, it threatens to undermine the correct attribution of breakthroughs, one of the foundations of modern science. The mathematics dispute around OpenAI is “certainly something we're looking at” Jason Gallo, head of the newly established Office of Metascience at the US National Science Foundation (NSF), told the Blue Sky Forum, hosted by the Organisation for Economic Co-operation and Development. The controversy started on September 8, when OpenAI announced it had cracked a mathematical problem that had gone unsolved for 90 years. Using a swarm of AI agents, millions of dollars’ worth of computing power and a new internal model, the company said it had finally proved that a long-used formula for modelling fluid dynamics breaks down under certain conditions. However, Tristan Buckmaster, a New York University mathematician who had been independently working with collaborators on the same problem using OpenAI’s Codex tool, hit back with a statement questioning whether the company had snooped on his Codex sessions to gain an advantage. OpenAI has always denied it looked up the “specific user data” of Buckmaster and colleagues. However, the company initially admitted that “while unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models⁠,” although its proofs “differ significantly” from Buckmaster’s. The firm later said that, following an investigation, it concluded that Buckmaster’s Codex prompts “could not have influenced the [OpenAI] system in any way, including through training.”Funder concernYet OpenAI’s initial admission that its model might have ingested Buckmaster’s ideas, then spat them out to its own mathematicians, has scientistsworriedthat their work could leak out if shared with AI. Some AI tools give users an opt-out option to stop their sessions from training the model. If such guarantees can be trusted, this gives scientists some confidence they can use an LLM to develop an idea without the risk of it leaking out.But there’s a bigger potential vulnerability. To win money to investigate ideas, scientists have to share them with funding bodies. These grant proposals are normally kept strictly confidential, to prevent other scientists from scooping them, or to protect commercially valuable intellectual property. To preserve this confidentiality, when the NSF uses AI models, “we have to be sure that it's not making server calls out to an external party, leaking that data,” said Gallo to delegates in Paris. Leaks through reviewersHowever, funders normally use external academic reviewers to assess proposals. And there are growing worries that these reviewers are feeding grant ideas into AI models to help them make a judgement. Research funders have noticed that peer review reports and funding recommendations have “suddenly become much longer, more detailed and complex,” said Diogo Machado, head of the data science unit at Technopolis, a science and technology-focused consultancy. “The sense is that reviewers are increasingly using generative AI in their review work, even where guidelines sometimes forbid it,” he told Science|Business. “This is very difficult for funders to control or prove.” Happening at scaleThis sharing of confidential proposals with AI models is “happening at scale already,” said James Wilson, executive director of the Research on Research Institute, based at University College London, during the debate in Paris. In response, Gallo said the NSF had “strict guidance” around the uploading of proposals to AI models. But he added the NSF was mulling new types of assessment to make sure applicants were truly the originators of their ideas, such as following a short-form proposal with an oral presentation, which would allow the foundation to “really interrogate the investigator on their intellectual contribution.” Related articlesHow can Europe cope with a flood of AI-assisted research grant proposals?The risks and benefits of AI in screening research grant proposals Viewpoint: AI could make scientific overload worse“For us as funders, a lot of the ways to ensure that the application remains the applicant's own intellectual contribution, those ways are quite regressive,” Peter Clifford, head of strategy at Research Ireland, told delegates. “They're maybe ten or 15 or 20 years old, and they don't scale particularly well.” Disputes over the origin of ideas are as old as modern science. Isaac Newton and Gottfried Wilhelm Leibniz spent more than a decade arguing over who invented calculus, for example. And long before LLMs emerged, academics would agonise over how much of their early work to reveal at conferences, in case their colleagues scooped them, Jack Leahy, a senior metascience advisor to the UK government, told delegates. But AI was “accelerating” the “problems of the system,” he said.

Sep 17 • 8:00 AM EDT • Science • sciencebusiness.net

Ramanujan’s 100-year-old pi formula is still revealing the Universe

Ramanujan’s elegant formulas for calculating pi, developed more than a century ago, have unexpectedly resurfaced at the heart of modern physics. Researchers at IISc discovered that the same mathematical structures behind these formulas also describe real-world phenomena like turbulence, percolation, and even black holes. What once seemed like pure mathematics now appears deeply intertwined with the physical laws governing the universe.

Dec 16, 2025 • 7:00 AM EST • Science • sciencedaily.com

The Math Singularity Is Here, and Science May Be Next

OpenAI has released an extraordinary body of mathematical work produced by an unreleased frontier model: 722 manuscripts spanning 372 research families across fields including number theory, complexity theory, and mathematical physics. Each result required, on average, the equivalent of roughly three hours of ChatGPT Pro reasoning. A New Era of Discovery We may be witnessing the beginnings of a “math singularity”: a point at which machine-generated discovery accelerates beyond humanity’s ability to independently reproduce, verify, or fully absorb it. If frontier models can generate hundreds of meaningful results in the time it takes a human researcher to investigate one, the constraint will no longer be producing new mathematics. It will be our capacity to validate, interpret, and integrate it. This does not make mathematicians obsolete. It changes their role. They may increasingly serve as guides, critics, and meaning-makers. Increasingly, they will decide which questions matter, determining which results can be trusted, and explaining why they are important. Mathematical insight may become abundant, while human judgment becomes more valuable than ever. Mathematics May Be Only the Beginning Every scientific discipline could eventually experience a similar acceleration. AI systems could generate and test hypotheses, connect findings across specialized fields, and explore possibilities at a scale no human research community could match. In medicine, imagine models identifying overlooked disease mechanisms, designing drugs and clinical trials, personalizing treatments, or discovering cures in months rather than decades. In materials science, AI could help create better batteries and cleaner industrial processes. In climate science, it could accelerate advances in energy, forecasting, and carbon removal. In biology, it could uncover relationships hidden across enormous genomic and molecular datasets. The result would not simply be faster research. It could expand the frontier of what is scientifically possible. The New Bottleneck Scientific discovery, however, is not the same as scientific progress. A medical breakthrough must still survive laboratory testing, clinical trials, regulatory scrutiny, manufacturing constraints, and real-world deployment. As machine-generated ideas multiply, the bottleneck may shift from producing breakthroughs to validating them safely and translating them into practice. We may soon have more hypotheses than laboratories can test, more drugs than clinical trials can evaluate, and more mathematical results than experts can review. That imbalance will require better systems for automated verification, reproducible research, prioritization, peer review, and collaboration between AI and domain experts. The importance of OpenAI’s work is not merely the number of manuscripts produced. It offers a glimpse of a world in which intelligence can explore thousands of intellectual paths in parallel. If ideas cease to be scarce, humanity’s defining challenge will be deciding which are true, which matter, and how to turn them into progress. The math singularity is not the end of human inquiry. It is the beginning of a new kind—one in which machines expand the frontier of knowledge and humans decide where it should lead. Subscribe Share Leave a comment

Oct 8 • 4:22 AM EDT • Science • douglevin.substack.com

Chiefs Could Also Be Mathematically Eliminated From Playoffs On Sunday

Dec 10, 2025 • 8:41 AM EST • Sports • roundtable.io

Eight days key to Portsmouth's likely survival

While Portsmouth are not mathematically safe, their past three results in eight days have been crucial to their hopes of staying up.

Apr 18 • 12:07 PM EDT • Sports • sports.yahoo.com

Scientists find a way to slash computer memory energy use by orders of magnitude

Scientists have devised a new way to switch magnetic computer memory while using far less energy than today's leading technologies. By mathematically optimizing the pulses used to flip digital bits, the method could reduce energy consumption by several orders of magnitude. Simulations suggest it may bring future memory devices surprisingly close to the fundamental physical limit for processing information. The same idea could eventually work with electrical currents or ultrafast lasers.

Sep 6 • 8:00 AM EDT • Science • sciencedaily.com

Camms’ latest gift to Clemson School of Mathematical and Statistical Sciences recognizes the importance of faculty excellence

The gift establishes the Jeff '84 and Karen '82 Camm Endowed Professorship in Data Science and Decision Intelligence. The gift establishes the Jeff '84 and Karen '82 Camm Endowed Professorship in Data Science and Decision Intelligence.

Feb 23 • 11:23 AM EST • Science • news.clemson.edu

A cosmic rule just broke—could this discovery rewrite how we count stars? - Futura-Sciences

Mechanics of the initial mass function Stars form when giant clouds of interstellar gas and dust collapse under their own gravity. To calculate the total mass of distant galaxies, scientists use the Initial Mass Function (IMF). Developed in part by astrophysicist Edwin Salpeter, this mathematical tool describes the initial distribution...

Aug 18 • 3:00 PM EDT • Science • futura-sciences.com

Fermat's Last Theorem: still a must-read about a 350-year maths secret

Simon Singh's exploration of mathematical proof – in particular Pierre de Fermat's last theorem – remains an absolute treasure, almost three decades after it was first published

Apr 22 • 7:00 AM EDT • Science • newscientist.com

Behold the Manifold, the Concept that Changed How Mathematicians View Space

In the mid-19th century, Bernhard Riemann conceived of a new way to think about mathematical spaces, providing the foundation for modern geometry and physics.

Dec 28, 2025 • 2:00 AM EST • Technology • wired.com

Scientists discover hidden math secret inside Chinese money plant leaves

Scientists have uncovered a hidden mathematical secret inside the leaves of the Chinese money plant: a naturally occurring geometric pattern known as a Voronoi diagram, something typically associated with city planning, computer science, and network design. By mapping tiny pores and looping veins in the plant’s leaves, researchers discovered that the plant organizes itself using the same kind of elegant spatial logic humans use to solve complex distance problems — without ever “measuring” anything.

May 14 • 8:00 AM EDT • Science • sciencedaily.com

Mariners Game #149: Mariners (69-79) @ Athletics (60-88)

Still mathematically in the race

Sep 12 • 9:00 PM EDT • Sports • sports.yahoo.com

It’s the Great Fear of Our Time. I’m Mathematically Sure It Won’t Happen.

Let the movies explain.

May 25 • 5:45 AM EDT • Technology • slate.com

Engineers make magnets behave like graphene

Engineers have discovered an unexpected link between two very different realms of physics: the behavior of electrons in graphene and magnetic waves in specially engineered materials. By designing a thin magnetic film with a hexagonal pattern of holes—similar to graphene’s structure—the researchers showed that magnetic “spin waves” can follow the same mathematical rules as graphene’s famously unusual electrons. The surprising overlap reveals a deeper connection between electronic and magnetic systems and gives scientists a powerful new way to study complex magnetic materials.

Mar 8 • 8:00 AM EDT • Science • sciencedaily.com

Researchers find a Wordle strategy that wins 99% of the time

Researchers at Binghamton University have developed a mathematical strategy that can solve Wordle with a 99% success rate. Instead of simply guessing words packed with common letters or choosing the most likely answer, the method uses Shannon entropy, a measure from information theory, to identify guesses that reveal the most useful information. Each guess is designed to rapidly shrink the pool of possible answers, even when the guessed word itself seems unlikely to be correct.

Sep 12 • 8:00 AM EDT • Science • sciencedaily.com

String theory suddenly emerged from simple physics rules

Physicists may have uncovered a surprising new clue that string theory—the idea that the universe is built from unimaginably tiny vibrating strings—could be more than just a mathematical fantasy. Instead of assuming strings existed from the start, researchers began with a few simple rules about how particles behave at extreme energies and discovered that the equations naturally produced the telltale fingerprints of string theory all on their own.

May 18 • 8:00 PM EDT • Science • sciencedaily.com

We can create the future of science right now

Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.

Jun 8 • 4:44 PM EDT • Science • goodscience.substack.com

Fact-checking RFK Jr.'s claim there's more than one way to calculate a percentage decrease

President Donald Trump has regularly said that drug-price discounts on his watch are greater than 100%, which isn’t mathematically possible. Now his health and human services secretary, Robert F. Kennedy Jr., is trying to back up his faulty math.

Apr 24 • 3:51 PM EDT • Politics • pbs.org

The System That Decides What Science Gets Published Is Breaking Down

The peer review system that validates scientific research is trapped in a self-defeating cycle. A new mathematical model shows why—and what comes next.

Mar 25 • 8:00 AM EDT • Science • forbes.com

WIU Joins HL7’s Inaugural Break the Code Cohort

Western Illinois University’s School of Computing, Data, and Mathematical Sciences is proud to partner with HL7 International’s inaugural Break the Code program, giving WIU students a unique opportunity to gain hands-on experience with the technologies shaping the future of healthcare.

Sep 29 • 8:00 AM EDT • Science • wiu.edu

Figuring out why AIs get flummoxed by some games

When winning depends on intuiting a mathematical function, AIs come up short.

Mar 13 • 5:47 PM EDT • Technology • arstechnica.com

What do Spurs and West Ham need on final day?

Joining Wolves and Burnley in the Championship next season will be one of Spurs or West Ham. Tottenham in 17th do have a significant advantage going into the final day of the season. While mathematically not guaranteed, a draw for Spurs at home to Everton all but confirms their stay in the Premier League.

May 23 • 3:04 AM EDT • Sports • sports.yahoo.com

Audit spots some errors, but gives Wyoming Business Council passing grade

by Dustin Bleizeffer, WyoFile Though there were some instances of incomplete, misclassified reporting and mathematical errors, an audit of the

Jul 4 • 12:07 PM EDT • Business • capcity.news

Liberian official’s Minnesota group home accused of overbilling Medicaid

A new state investigation found Golden Touch Health Care billed Medicaid for 45 hours of services per day — a number that was mathematically impossible on many days based on its staffing levels.

Sep 21 • 5:00 AM EDT • Health • mprnews.org

The Surprising Reason Some Children Struggle With Math, According to Science

Language and reasoning skills help predict math performance, offering clues to the learning difficulties that a test score alone may miss. Why do some students grasp mathematical concepts with ease while others struggle, even after repeated instruction? The answer may lie in abilities that extend far beyond working with numbers. Mathematics requires students to recognize

Oct 17 • 9:48 PM EDT • Science • scitechdaily.com

A Powerful New ‘QR Code’ Untangles Math’s Knottiest Knots

With a newly discovered mathematical tool, researchers are hoping to gain unprecedented insight into the structure of complex knots.

Apr 22 • 10:49 AM EDT • Technology • quantamagazine.org

Scientists say love follows hidden mathematical rules

Could math help explain why some relationships survive life’s inevitable stresses while others slowly fall apart? Researchers have spent decades developing mathematical models that treat romantic feelings as changing systems, revealing patterns in attraction, emotional reactions, conflict, and recovery.

Sep 6 • 8:00 PM EDT • Science • sciencedaily.com

World's largest collection of Olympiad-level math problems now available to everyone

Every year, the countries competing in the International Mathematical Olympiad arrive with a booklet of their best, most original problems. Those booklets get shared among delegations, then quietly disappear. ...

Apr 20 • 12:40 PM EDT • Technology • phys.org

German Health Minister Nina Warken and WHO Hub in Berlin highlight 2025 achievements

Germany’s Federal Minister of Health, Nina Warken, visited the WHO Hub for Pandemic and Epidemic Intelligence in Berlin today to review progress and launch its 2025 Annual Report. The report highlights advances in using artificial intelligence, data science and pathogen genomics to strengthen global preparedness for future pandemics.In 2025, the WHO Hub in Berlin worked with more than 160 Member States and over 190 partners to help countries and organizations develop new systems and tools to detect and understand new public health threats as quickly as possible, whenever and wherever they occur.BMG to confirm whether they wish to contribute a quote. “Detecting outbreaks early is one of the most effective ways to save lives and prevent epidemics from becoming global crises,” said Federal Minister of Health Nina Warken. “Germany is proud to host and support the WHO Hub for Pandemic and Epidemic Intelligence. Its work strengthens our safety at home and helps protect people worldwide.”Artificial intelligence is increasingly transforming the detection of outbreaks. In 2025, the Hub launched an upgraded version of the Epidemic Intelligence from Open Sources (EIOS) system, which uses enhanced AI functions to scan global online information in real time and help experts quickly identify unusual health events. The system is now used by 120 countries.To better anticipate health threats, countries need information beyond traditional health data. The Hub supports countries in implementing Collaborative Surveillance, which integrates data on climate, animals, travel, and population movement to provide a more comprehensive and real-time understanding of health threats.The Hub also expanded global access to pathogen genomics, which helps scientists detect new pathogens, such as viruses and bacteria, understand how they spread and how dangerous they may become. Through the International Pathogen Surveillance Network (IPSN), the WHO Hub in Berlin worked with 309 partners in 101 countries to expand access to these technologies.“We are in a new era of global health threats, and we need new tools to face tomorrow’s challenges,” said Dr Chikwe Ihekweazu, Executive Director of the WHO Health Emergencies Programme. “The WHO Hub in Berlin helps countries use data and technology more effectively so they can identify risks earlier and respond faster. No country can address these threats alone. Strong cooperation remains our best defence.”“Berlin offers an ideal environment for our work. We collaborate closely with world-leading institutions such as the Robert Koch Institute and the Charité Center for Global Health,” said Dr Oliver Morgan, Head of the WHO Hub for Pandemic and Epidemic Intelligence. “Germany has supported the WHO Hub since its establishment, and we are pleased to welcome Federal Minister Nina Warken to the Hub today. This strong partnership helps us advance innovation in pandemic and epidemic intelligence and better support countries to prepare for future health threats.”The Hub is expanding the use of advanced analytics and mathematical modelling to forecast how outbreaks may develop and to assess which response measures are most effective. Through its Collaboratory, the Hub connects modelers and analysts with governments to ensure their work addresses real public health needs. In 2025, the community grew to 1300 members from 50 countries and 200 institutions. An AI-powered pandemic simulator, currently in the prototyping phase, will help decision-makers compare outbreak scenarios and help identify effective disease control measures.

Mar 19 • 8:00 AM EDT • Health • who.int

Engineers solved an airflow mystery hidden nearly a mile underground

Engineers at a deep underground research facility noticed something strange during major rainstorms: airflow underground sometimes reversed direction. Using new sensors and mathematical modeling, they found that water rushing down a shaft was effectively pushing air through the tunnels like a giant piston. The breakthrough explains a long-standing mystery and could help underground operations better predict and manage ventilation during storms and emergencies.

Jul 6 • 8:00 AM EDT • Science • sciencedaily.com

Mathematical Business: Louis Rusconi

Louis Rusconi said he wasn’t even supposed to go to Wake Forest. Originally committed to Syracuse University, Rusconi received a call from Wake Forest on July 13, 2022, that would change his life forever. “I remember getting that call,” Rusconi said. “It was one of the best things that ever happened to me, honestly, and...

May 3 • 7:57 PM EDT • Business • wfuogb.com

AI Agents Are Poised to Hit a Mathematical Wall, Study Finds

LLMs have their limits.

Jan 23 • 2:55 PM EST • Technology • gizmodo.com

Moody Center announces exhibition exploring math, science and tech rooted in Indigenous knowledge systems

Through a framing of North, Central and South America as interconnected regions, “Radiant Geometries: Vectors of Knowledge from the Indigenous Americas” brings together contemporary Indigenous and Latin American artists whose practices illuminate knowledge as a living technology, through which the mathematical, metaphysical and artistic realms converge.

Jun 25 • 8:00 AM EDT • Science • news.rice.edu

Jays/Orioles Series Preview

Tonight the Jays start their second-to-last series of the season. Six games remain, with a mathematical chance of making the playoffs, but math occasionally lies. But a series sweep would make things a little more interesting. Anyway, they have three games in Baltimore: Trey’s last rehab start wasn’t exactly something to build our confidence, but […]

Sep 21 • 12:29 PM EDT • Sports • sports.yahoo.com

Powerhouse UCSB team awarded big grant to investigate mathematical foundations of AI

Researchers Nina Miolane, Haewon Jeong, Sanjuka Krishnagopal and Yao Qin win $1 million from NSF for their collaborative investigation

Oct 20 • 12:22 PM EDT • Science • news.ucsb.edu

Narwhal tusks are twisty, magical mathematical marvels

Their single long tooth twists in opposite directions.

Aug 18 • 11:00 AM EDT • Science • popsci.com

Scientists uncover Feynman’s formula for finding best holiday restaurant

Late physicist turned issue of when to stop searching for a better place to eat into mathematical problem

Jun 1 • 3:00 PM EDT • Science • theguardian.com

Three questions: On the future of AI and the mathematical and physical sciences

Curiosity-driven research has long sparked technological transformations. A century ago, curiosity about atoms led to quantum mechanics, and eventually the transistor at the heart of modern computing. ...

Mar 15 • 3:00 PM EDT • Science • techxplore.com

MIT scientists build the world’s largest collection of Olympiad-level math problems, and open it to everyone

MIT CSAIL scientists have compiled the largest high-quality dataset of proof-based math problems ever created. It can help researchers test AI models’ mathematical reasoning, while capturing the full range of mathematical perspectives and problem-solving traditions within the global math community.

Apr 24 • 1:00 PM EDT • Science • news.mit.edu

This strange “spacetime crystal” can suddenly become a black hole

Physicists have mathematically captured a bizarre “spacetime crystal” that can either dissolve or collapse into a microscopic black hole after only a tiny change in energy. Their breakthrough came from an unusual trick involving infinitely many dimensions, potentially giving researchers a new tool for studying primordial and microscopic black holes.

Aug 30 • 8:00 AM EDT • Science • sciencedaily.com

Scientists Finally Figured Out the Math Behind How You See Color

The team behind the study created what might be the most accurate mathematical representation of photoreception ever.

Mar 26 • 9:30 AM EDT • Science • yahoo.com

Simple math can predict when AI is about to go rogue, physicists say

A new mathematical formula tracks an AI's internal "compass needle" to flag undesirable outputs before they happen.

Oct 14 • 12:34 PM EDT • Technology • livescience.com

Audit spots some errors, but gives Wyoming Business Council passing grade

Though there were some instances of incomplete, misclassified reporting and mathematical errors, an audit of the Wyoming Business Council shows the agency generally operates within a complex set of state

Jul 5 • 7:00 AM EDT • Business • wyomingnews.com

The Math on AI Agents Doesn’t Add Up

A research paper suggests AI agents are mathematically doomed to fail. The industry doesn’t agree.

Jan 23 • 11:00 AM EST • Technology • wired.com

Orioles news: Brewers sweep, nearing playoff elimination

The mathematical end could come as soon as tomorrow.

Sep 21 • 7:00 AM EDT • Sports • sports.yahoo.com

For the 2014 film Interstellar, director Christopher Nolan hired Caltech physicist Kip Thorne as his science consultant — and the visual simulations Thorne helped design for the film's black hole were so mathematically accurate that they generated two peer-reviewed scientific papers and became the model for how astrophysicists visualize black holes today

In late 2012, when the American film director Christopher Nolan formally took over the production of a Warner Brothers science-fiction project that had been drifting through Hollywood development since approximately 2005 (originally intended for Steven Spielberg, based on a screenplay concept the film producer Lynda Obst had developed jointly with the Caltech theoretical physicist Kip […]

Jul 5 • 4:45 AM EDT • Science • spacedaily.com

Scientists Finally Figured Out the Math Behind How You See Color

The team behind the study created what might be the most accurate mathematical representation of photoreception ever.

Mar 26 • 9:30 AM EDT • Science • popularmechanics.com

AI method tackles one of science's hardest math problems

Penn Engineers have developed a new way to use AI to solve inverse partial differential equations (PDEs), a particularly challenging class of mathematical problems with broad implications for understanding the natural world. The advance, which the researchers call “Mollifier Layers,” could benefit fields as varied as genetics and weather forecasting, because inverse PDEs help scientists work backward from observable patterns to infer the hidden dynamics that produced them.

May 1 • 8:00 AM EDT • Science • eurekalert.org

Audit spots some errors, but gives Wyoming Business Council passing grade

Though there were some instances of incomplete, misclassified reporting and mathematical errors, an audit of the Wyoming Business Council shows the agency generally operates within a complex set of state

Jul 3 • 5:11 PM EDT • Business • wyomingnews.com

Can math stay human in the age of AI?

As AI churns out mathematical proofs, a DeepMind study raises a bigger question: What do we lose when we focus only on the answers?

Oct 14 • 1:00 PM EDT • Science • sciencenews.org

Astronomy, Materials Science Awards Plummet at NSF

Physics grants saw a smaller drop in grants awarded this year compared to other fields within NSF’s Mathematical and Physical Sciences Directorate.

Sep 9 • 5:13 PM EDT • Science • aip.org

Quantum supremacy just ran into an unexpected rival: An ordinary laptop armed with new math

Using a conventional computer and cutting-edge mathematical tools and code, physicists at the Center for Computational Quantum Physics (CCQ) at the Simons Foundation's Flatiron Institute and collaborators ...

May 21 • 2:00 PM EDT • Technology • phys.org

Demystifying Anthropic's J-Space: A Mathematical Primer

Clarifying the math behind Anthropic’s representation workspace

Sep 11 • 7:00 AM EDT • Science • towardsdatascience.com

The science behind B.C. Ferries weather cancellations

The process behind deciding to cancel a ferry sailing because of weather is a high stakes calculation that involves a high-tech nerve center, a 28-year Navy veteran and a strict mathematical matrix designed to survive the volatile waters of the Salish Sea.

May 13 • 9:00 AM EDT • Science • cbc.ca

Meet Rachel Chen, the 18-year-old Los Angeles student who expanded a 1997 quantum-math idea to describe entire particle systems with simple diagrams; she won $100,000

At just 18, Rachel Chen has taken a decades-old mathematical idea and expanded it into a visual framework for understanding complex quantum systems, earning her a $100,000 award in the 2026 Regeneron Science Talent Search.

Sep 8 • 1:52 PM EDT • Science • timesofindia.indiatimes.com

Audit spots some errors, but gives Wyoming Business Council passing grade

by Dustin Bleizeffer, WyoFile Though there were some instances of incomplete, misclassified reporting and mathematical errors, an audit of the Wyoming

Jul 4 • 12:07 PM EDT • Business • oilcity.news

The shape behind the Einstein problem just revealed strange new physics

A mathematical shape famous for covering a surface without ever repeating has revealed an unexpected ability to twist light into unusual chiral patterns. The discovery could lead to new ways of controlling light, polarization, and advanced optical devices.

Sep 15 • 8:00 AM EDT • Science • sciencedaily.com

The disunity of science is a feature, not a bug

Mathematical models wield miraculous power, but mistaking them for reality has trapped science in a destructive quest for a “Theory of Everything.” Physicist and philosopher Ragner Fjelland argues that this leads to bloated, Frankenstein theories kept alive by clumsy, ad-hoc amendments, which reduce rather than boost science’s power. Fjelland calls for a radical shift in our attitude: we must reorientate towards creating entirely new theories, celebrating rather than lamenting disunity and diversity in science. Science, from the scientific revolution in the seventeenth century to the present day, has more or less developed in a “reductionist” way. The fundamental assumption is that all sciences may be ordered in a hierarchy that can be represented as an upside-down pyramid. At the top we find the human sciences, and at the bottom physics. Strong (or “ontological”) reductionism maintains that all theories at one level can be completely reduced to the lower level, all the way down to theories of atoms and elementary particles, or whatever is “at the bottom.” In the end, all diversity in the world can be unified in one fundamental theory. Hence it is called a final theory, or a Theory of Everything.One of the central figures in developing the idea of a Theory of Everything is the Dutch theoretical physicist Gerard 't Hooft, who received a Nobel Prize in physics in 1999 for his work. He emphasizes the non-empirical status of the theory:There exists no closely resembling alternative theory. This means that any slight change brought about in the rules would make the theory unlikely or inelegant. The theory will be a "package deal": take it, or leave it. This should hold both for the local laws and for the boundary conditions.This endeavor is not new. On the contrary, it goes all the way back to the birth of Western philosophy 2,500 years ago. The Babylonians made regular astronomical observations during a period of 600 years, from 747 BCE until 150 ACE. They developed mathematical techniques to predict astronomical phenomena, but they made no attempts at explaining them. However, the Greeks developed models, both material and theoretical, to explain the observed phenomena.The best example is Plato’s late dialogue Timaeus. Here he introduces a divine craftsman, the Demiurge, who has constructed the universe according to an original plan. Plato seems to have thought that the original plan is what is actually real, while the material world is merely an imperfect realization of it. He therefore does not, in the Timaeus, reconstruct the material universe, but the original plan, which can then be used to explain the material universe.Plato’s aim in producing a theoretical model is to reconstruct the original plan. That is what he carries out in Timaeus—and some modern physicists have embarked on the same project. For example, Einstein’s basic attitude is reflected in the title of Abraham Pais’ biography: “Subtle is the Lord...,” echoing Einstein’s comment, “Subtle is the Lord, but malicious He is not.” In an interview Einstein compared our situation to a child who enters a huge library. The child knows that someone has written the books, but he does not know the languages in which they are written. He suspects that there is a mysterious order, but he does not know what it is.___“We have all been working in what Husserl called the Galilean style; that is, we have all been making abstract mathematical models of the universe to which at least the physicists give a higher degree of reality than they accord the ordinary world of sensation.”Steven Weinberg, Nobel Prize in Physics (1979)___This Platonist view is contrary to a widespread impression of science, symbolized by the picture of the “father” of modern science, Galileo Galilei, with his telescope. According to this impression, Galileo liberated himself from the dominating metaphysics of his day—basically Aristotelian philosophy—and constructed a telescope that he directed towards the sky, showing that Jupiter has four moons. He also performed experiments with bronze balls rolling down inclined planes and found the law of freely falling bodies.However, in other cases Galileo emphasized the unreliability of our senses. It is easy to see why. He defended Copernicus’ heliocentric hypothesis. According to this hypothesis, we are not at rest at the center of the universe, but moving at a tremendous speed through space. Why don’t we observe this motion? Galileo praises Copernicus because “with reason as his guide he resolutely continued to affirm what sensible experience seemed to contradict.” In other places he directly refers to Plato. SUGGESTED VIEWING The quest for a final theory With Michio Kaku Galileo’s Platonism goes back to his early writings. In The Assayer, published as early as 1623, Galileo compared the universe to a grand book that is written in the language of mathematics, and “its characters are triangles, circles, and other geometric figures without which it is humanly impossible to understand a single word of it.” Therefore, things that cannot be described by mathematics—for example, colors, smells and odors—are not real. They are merely subjective.This division into subjective and objective, advocated by Galileo and his younger contemporary René Descartes, is normally associated with a mechanistic world view, which is regarded as the opposite of Platonism. When we reduce something to, for example, molecules and atoms, we move “downwards.” In contrast, the Platonist moves “upwards,” to abstract entities. However, it only shows that when we address fundamental questions, we should be careful in applying everyday spatial metaphors like “up” and “down.”___Nature is complex, and we cannot in general construct mathematical models that adequately describe the world.___The endeavor of finding a Theory of Everything has been heavily criticized. In 2006, the theoretical physicist Lee Smolin published “The Crisis in Fundamental Physics.” He argued that one of the most fundamental principles of science is that we should only regard theories as scientific if we have the possibility to show that they are false by experiments. Although he did not refer to Karl Popper, other critics have referred to Popper and argued that a theory is not scientific if it cannot be falsified (see for example Ellis & Silk 2014).These were not the first physicists to be anti-reductionists. In 1972, the journal Science published an article by the physicist Philip Anderson with the title “More Is Different.” This is the kind of article a scientist would normally write only after he has won a Nobel Prize. However, Anderson won the Nobel Prize for physics five years later, in 1977. The article is amazing, not primarily because it argues in favor of anti-reductionism in general, but because it argues for anti-reductionism within physics itself. Anderson’s conclusion was:At each stage entirely new laws, concepts, and generalizations are necessary, requiring inspiration and creativity to just as great a degree as in the previous one. Psychology is not applied biology, nor is biology applied chemistry.We may say that Plato started the “unity of science movement,” based on Euclidean geometry as the model of all knowledge. A distinguishing mark of geometry is that all possible constructions can be reduced to a few fundamental principles. We have to acknowledge that this approach has limitations. In general, we can easily model technological systems. In addition, we may construct simplified and idealized models that capture important aspects of systems, from various perspectives and for various purposes. But nature is complex, and we cannot in general construct mathematical models that adequately describe the world. In a complex system, each level has its distinct, emergent, properties that are characteristic of this level. Therefore, a complex system should be investigated at different levels and from different perspectives. Instead of exclusively pursuing a scientific ideal based on the unity of science, we should acknowledge the diversity—or even the disunity—of science.

Dec 23, 2025 • 7:00 AM EST • Science • iai.tv

Terry Winters review – flashes of magic in patterns science has yet to explain

Modern Art, LondonThe mathematically named new works of Along the River are disorienting, illusive and seem to offer a flash of the secret sequences that underpin the physical world

Jun 5 • 2:57 PM EDT • Science • theguardian.com

How Inter can mathematically win Serie A title next weekend

Inter can win the 2025-26 Serie A title next weekend, but they must beat Torino away and hope for favourable results from Napoli and Milan.Inter’s 3-0 win over Cagliari on Friday allowed the Nerazzu...

Apr 18 • 5:05 AM EDT • Sports • sports.yahoo.com

Orioles Sweep Blue Jays To Keep Faint Playoff Hopes Alive

The Orioles did their part to keep the slight mathematical hopes alive as they closed out a miserable home campaign in rare fashion

Sep 23 • 9:21 PM EDT • Sports • sports.yahoo.com

Scientists find a way to slash computer memory energy use by orders of magnitude

Scientists have devised a new way to switch magnetic computer memory while using far less energy than today's leading technologies. By mathematically optimizing the pulses used to flip digital bits, the method could reduce energy consumption by several orders of magnitude. Simulations suggest it may bring future memory devices surprisingly close to the fundamental physical limit for processing information. The same idea could eventually work with electrical currents or ultrafast lasers.

Sep 6 • 8:00 AM EDT • Technology • sciencedaily.com

Crypto billionaire pardoned by Trump gives £20m to science

The London Institute for Mathematical Sciences has secured funding from the British entrepreneur Ben Delo — a major gift for a non-Oxbridge body

Mar 3 • 2:45 PM EST • Science • thetimes.com

Saturday afternoon Orioles game thread: vs. Brewers, 4:05

Where the Birds keep coming close to mathematical elimination from the playoffs, and Félix Bautista is reinstated.

Sep 19 • 3:35 PM EDT • Sports • sports.yahoo.com

Scientists uncover the secret timing behind glowing fireflies

Researchers have discovered the mathematical pattern that allows thousands of synchronized fireflies light up the night.

Mar 16 • 11:51 AM EDT • Science • earth.com

UC Berkeley welcomes rising stars in math and physics

UC Berkeley is welcoming a powerhouse lineup of trailblazing scientists to advance its research enterprise.The Division of Mathematical

Sep 14 • 11:45 AM EDT • Science • ls.berkeley.edu

Scientists build a “periodic table” for AI

Choosing the right method for multimodal AI—systems that combine text, images, and more—has long been trial and error. Emory physicists created a unifying mathematical framework that shows many AI techniques rely on the same core idea: compress data while preserving what’s most predictive. Their “control knob” approach helps researchers design better algorithms, use less data, and avoid wasted computing power. The team believes it could pave the way for more accurate, efficient, and environmentally friendly AI.

Mar 4 • 7:00 AM EST • Science • sciencedaily.com

Mathematical 'Code' Found In Great Pyramid May Reveal Ancient Secret, Researcher Claims

A researcher claims the Great Pyramid of Giza contains a hidden mathematical code linked to the Moon, stars and planets, but the theory remains unproven.

Sep 11 • 9:57 PM EDT • Science • ndtv.com

Assistant Professor of Computer Science

The Department of Mathematical Sciences at Morningside University invites applications for a tenure-track Assistant Professor of Computer Science. This is a nine-month appointment beginning August 10, 2026, with a May Term teaching assignment every three years. This position reflects Morningside University’s continued investment in a modern, applied, and student-centered approach to computer science education. We […]

Jan 22 • 7:00 PM EST • Science • morningside.edu

Shaky political science: using "simulated" elections over real-world elections

[Dear DSOS readers – this article is the latest in our “Shaky Political Science” series. In our 38 page report, “Shaky political ‘science’ misses mark on ranked choice voting,” released in early December, we examined over 40 studies focused on ranked choice voting. We found that many of these studies suffered from puzzling research methodologies, poorly constructed surveys, unrealistic simulated elections, cherry picked data, and faulty analyses that often were contradicted by results from real-world elections (a link to our full paper is here). Here are links to the first three articles in our series: the first one summarizing the overall results of our report, the second one analyzing a study co-authored by University of Minnesota academic Larry Jacobs, which was one of the most error-prone of all the research we reviewed in our report, and a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research]. In this fourth article in our “Shaky Political Science” series, we shine a spotlight on seven studies that were web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group (the ERRG is an ad hoc committee headed by New America’s Political Reform Program and joined by the American Enterprise Institute, the Unite America Institute and Stanford University’s Center on Democracy, Development, and the Rule of Law). This research consortium has been led by Lee Drutman, senior fellow with New America’s Political Reform Program. These seven studies are simply not credible from any objective standard about how political “science” is supposed to illuminate our understanding of either ranked choice voting or politics in general. The feature that six of these seven studies have in common is that they all used surveys – artificial, simulated elections -- to attempt to say something cogent or interesting about ranked choice voting (RCV) elections. None of them used real world data from actual RCV elections – either the 1000+ elections in the US or the many thousands of elections from places like Australia, Ireland and elsewhere where RCV has been used for 100 years. While a few of these studies conclude with some mixed positives about RCV, our analysis here is content neutral. The idea that, with a political reform like RCV, you can ask respondents in a mock, artificial election to vote with that method, with only the barest of explanation about how RCV works or its likely impacts (i.e. no spoiler candidates, majority winners, voters liberated to pick their favorite candidates, coalition-building), and then ask them their thoughts/reactions about voting in that system, with fake candidates and no real campaigning, no real exposure to an actual election or actual candidates, rests on some very puzzling assumptions. The findings of these seven studies actually are contradicted by the findings from many other studies based on real-world RCV elections. These New America studies also failed to list the real-world RCV studies in their own citations, instead including those studies that reinforced their conclusions. The seven studies featured in this article were chosen because they form a cohesive cohort which precisely illustrates one of the main points of our research paper, namely that using surveys, i.e. artificial elections, or mathematical/theoretical models, instead of real-world data, is in fact “shaky political science.” Below are summaries of our analysis of five of the seven New America studies. You can read our complete analysis of all seven studies at our paper, “Shaky political ‘science’ misses mark on ranked choice voting.” Choosing to “Vote As Usual” by Andre Blais, Carolina Plescia, and Semra Sevi. New America. (Blais et al., 2021). This study, web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group, used two surveys in which respondents completed ballots for simulated elections using plurality, approval, RCV and point (Score) voting. In both cases the authors provided the respondents with only minimal explanation of each method. For example, the entire explanation for RCV was “For this vote, rank the candidates from your first to your last choice (you do not need to rank them all). This is called the ranked vote.” That’s it. The study did not provide any context or explanation to survey respondents about why she/he might want to rank multiple candidates. Participants were not even told that ballots would be tabulated using an “instant” runoff, not to mention any description of RCVs ability to eliminate the spoiler effect or liberate voters to rank their favorite candidates without fear of helping the “greater evil” candidate to win. That’s like taking an iPhone back in time to 1970 and asking someone if they like it better than their rotary phone without explaining how it works. Their first survey targeted states that held 2020 Super Tuesday Democratic primaries and used the real candidates’ names in the survey; the second survey used fictitious candidates. In other words, they surveyed respondents who had only ever participated in plurality elections, had no history or background with using RCV or any of the other methods, had no clue about its likely impacts, and did not communicate how the alternative methods would be counted, and then asked them: “How satisfied are you with using each system?” Predictably, the winner was plurality elections, the system used for president, Congress, gubernatorial and state legislative elections – in other words, the method they have been using, year after year, all their voting lives. With so little explanation of RCV and its impacts, this study is simply not credible in discovering anything useful about real-world RCV elections. It merely shows that a basic voter education campaign is needed when adopting a new election system. Additionally, the result contradicts many other post-election surveys following real RCV elections, asking opinions from voters who had just participated in a RCV election, that showed a great deal of satisfaction with RCV (see details on that in Studies 22-25 in our paper, as well as the four studies cited in the review for Study 28). Share Other studies on “satisfaction” and “understanding” make similar mistakes, in terms of basing their surveys on inadequate explanation to respondents and lack of education about context. How could any survey ever hope to measure accurately, in essence simulate a real-world election, without actual campaigning candidates, the media writing about those candidates, organizations endorsing candidates and voters being inundated with crucial information? More than anything, what these “studies” based on surveys show is that if you ask voters about something they don’t know very much about, and fail to adequately educate them, their responses will unsurprisingly reflect their confusion. Equally puzzling is that New America’s Electoral Reform Research Group, headed by political scientist Lee Drutman, found this to be a credible enough study that they not only published it but highlighted it in Drutman’s own discussion of research related to ranked choice voting. This pattern was repeated in study after study featured by New America’s Electoral Reform Research Group. For example in Ranked-Choice Voting and Political Expression: Voter Guides Narrow the Gap between Informed and Uninformed Citizens by Cheryl Boudreau, Jonathan Colner, and Scott MacKenzie, the researchers based their analysis on a survey which found that respondents who were asked to vote in a simulated RCV election, with minimal explanation of RCV or how it works, displayed less evidence of being informed about how to use RCV effectively, with the least informed voters ranking fewer candidates. As pointed out under the study above, in a real election voters are informed by a wide variety of methods, including public education by the election administration, by interested political groups, the media, and the candidates themselves who benefit from voters being aware of how to vote in the new system. In Support for Ranked-Choice Voting across Race and Partisanship by Joseph Anthony, David C. Kimball, Jack Santucci and Jamil Scott, the researchers used respondents from different parties and races voting in simulated plurality and RCV elections, who indicated their satisfaction with each voting method. Instead of using real world election results, the authors fell back on the easy, armchair and less reliable academic research methodology of making up mock elections. As with the other studies, little explanation was given of how RCV works or what its implications might be (electing majority winners or eliminating spoilers, for example). Unsurprisingly, this study found that respondents were not very satisfied with RCV. However, when the authors added an explanation that RCV has a tendency to elect more women and people of color, they found that this greater explanation increased the preference for RCV. The more voters were familiar with it, and the more they understood potential positive impacts – surprise! -- the more they were open to switching away from the status quo. Of note, the results from this study are thoroughly contradicted by a number of other studies whose data was obtained from real RCV elections with real voters. Those studies show a great deal of satisfaction with RCV. More “fake election” research An interesting wrinkle on this “research by fake elections” approach, which only served to illustrate how unreliable such “research” can become, was taken in Ranked-Choice Voting, Runoff, and Democracy: Insights from Maine and Other U.S. States by Joseph Cerrone and Cynthia McClintock_,_ also web-hosted and funded by New America’s ERRG. It gets high marks for creativity, but an eye-roll for thinking this could possibly add anything meaningful to our understanding of real world RCV elections. This study tried to determine whether RCV, plurality or two-round runoff elections provided greater voter satisfaction and acceptance of election results. To determine that, the authors conducted a survey asking if voters were satisfied with a fictional newspaper report of an election, with three different versions given to respondents – plurality, runoff and RCV, with variations of Republican/Democrat winners and variations of “come-from-behind” winners,” i.e. candidates not initially in first place but who win the “runoff.” Again, a minimal explanation of RCV or how it works was given, and the survey showed that respondents had the greatest unfamiliarity with RCV than the other methods. The unsurprising result? Respondents were much more satisfied with “come-from-behind” victories in a traditional two-round runoff election than in RCV, even though RCV is also a runoff system but with a single round of counting. Interestingly, the dissatisfaction with RCV disappeared for those who were more familiar with RCV. In fact, among those respondents who were “very familiar” with RCV, they were equally satisfied with RCV, runoff and plurality. Surprise, surprise. Again, what this study revealed, more than anything, is not only the inadequacy of the explanation for RCV given by the authors of this study, but the failure of the research format itself, thinking that such an artificial mock election could somehow simulate what actually happens in the real world in a real RCV election. Those four studies reviewed above are fitting examples of inadequate and shaky political “science.” Other studies reviewed in our paper from the New America/Electoral Reform Research Group cohort which also used mock elections included: Ranked-Choice Voting is an Acquired Taste by Joseph Anthony and David C. Kimball. New America. (Anthony et al., 2021). Ranked-Choice Voting is No Refuge for Extreme Candidates by Melissa Baker. New America. (Baker, 2021). Also included in this New America cohort is Does Ranked-Choice Voting Reduce Racial Polarization? by Yuki Atsusaka and Theodore Landsman, which actually did use real-world election results data but applied an odd and unconventional methodology to the data which relied on shaky assumptions and non-real-world scenarios that were too divorced from reality. To determine levels of “racially polarized voting,” the researchers imagined “ethnic parties,” by which they mean political parties for each ethnic group, which was especially odd since the election data they were using came from nonpartisan RCV races. But then they made an even odder assumption, namely that since race and ethnic characteristics of studied voters were not available, the authors tried to infer that information based on who voters selected and the racial make-up of voters in each district. This amounted to little more than vague and ambiguous guesswork dressed up with a bunch of mathematical equations used to give it a veneer of scientific appearance. Indeed, it seemed to assume racial polarization in order to prove racial polarization. Based on this questionable methodology, the authors concluded that RCV doesn’t reduce racial polarization. But the premise of this study missed an obvious reality: since RCV incentivizes more candidates to run and can result in more diverse fields of candidates, one would expect more racially-polarized voting in voters’ top rankings, not less, because more voters would have someone on that ballot who represents their racial or ethnic identity and more freedom to vote for their real first choice. By encouraging more diverse candidates and eliminating the spoiler effect, RCV facilitates voting for a favorite candidate of your own race and then selecting a second choice with the best likelihood of winning. The real measure is what voters do with their additional rankings – do they consider other candidates and vote “for the best of the rest”? This study didn’t tell us anything useful about that. But readily available data shows this is common in real RCV elections, and candidates act on this knowledge by engaging with voters outside their racial group. So the authors of this study failed to consider or account for what actually happens in real-world elections, and their conclusions are tainted by this failure. New America has contributed a great deal over the past 2.5 decades to our understanding of many policy areas, including politics and political reform (full disclosure: Steven Hill, one of the authors of this article and of the “Shaky Political Science” research paper and series, is a former senior fellow and former director of New America’s political reform program, 2005-2010). So it was especially disappointing that New America funded and published such a large batch of dubious studies about ranked choice voting. The stated praiseworthy goal of its Electoral Reform Research Group was to inspire new academic research about ranked choice voting that would shed light and help the public better understand the impacts of RCV. Instead, the ERRG research did just the opposite. It produced dubious research that created more confusion and misinformation. In so doing, New America’s political reform program contributed to “shaky political science.” Steven Hill and Paul Haughey Share Upgrade to $5 subscription Thanks for reading DemocracySOS, a reader-supported digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe

Apr 21 • 9:31 AM EDT • Science • democracysos.substack.com

ChatGPT can now create interactive visuals to help you understand math and science concepts

On Tuesday, OpenAI introduced dynamic visual explanations, a new ChatGPT feature that allows users to see how formulas, variables, and mathematical

Mar 10 • 1:51 PM EDT • Science • techcrunch.com

$500K National Science Foundation Grant funds interdisciplinary mathematical biology research at Elon

The National Science Foundation (NSF) has awarded Associate Professor of Mathematics Hwayeon Ryu and Assistant Professor of Biology Efra Rivera-Serrano a three-year, $500,138 grant to study viral...

Aug 7 • 8:00 AM EDT • Science • elon.edu

PhD Student Locks Spot as Rising Cybersecurity Researcher

By Kim Horner | Computer science professor Dr. Kevin Hamlen is on a quest to train students in a combination of high-demand computer-security skills: the mathematical reasoning needed to prove software is secure and the programming expertise needed to uncover real-world vulnerabilities that could lead to cyberattacks. The University of Texas at Dallas cybersecurity expert […]

Aug 6 • 12:35 PM EDT • Science • news.utdallas.edu

Scientists find an elusive vibration — a Higgs mode

Argonne scientists discovered a Higgs mode — a rare, complex vibration and mathematical analog to the Higgs boson — that changed the symmetry of a semiconductor crystal. The results could help researchers control material properties using light.

Jun 4 • 8:00 AM EDT • Science • anl.gov

Clemson Data Science Students Help Ohio Energy Company Tackle Environmental Risk and Workforce Gaps

The need aligned with the applied learning mission of Clemson’s M.S. DSA program. The program, housed in the Department of Management within the Wilbur O. and Ann Powers College of Business, is an online degree developed cooperatively by Clemson’s School of Mathematical and Statistical Sciences and Department of Management.

Feb 24 • 8:34 AM EST • Science • news.clemson.edu

An Old Paper Really Did Suggest A Mathematical "Doomsday" On 13 November 2026, But There's No Need To Worry

The paper, written in 1960, suggests "doomsday" will occur on 13 November 2026. But unless there are billions of secret pregnancies out there, you shouldn't be all that concerned.

Sep 28 • 1:02 PM EDT • Science • iflscience.com