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Professor Todd Squires is a distinguished faculty member in the Department of Chemical Engineering at the University of California, Santa Barbara, within the Robert Mehrabian College of Engineering. His research focuses on the fundamental principles of transport phenomena as applied to interfaces, membranes, and complex fluids, employing theoretical, computational, and experimental approaches to address significant challenges in micro-scale fluid mechanics. Dr. Squires' educational background includes: BS in Physics, UCLA (1995) BA in Russian Language and Literature, UCLA (1995) PhD in Physics, Harvard University (2002) His research interests span microfluidics and electrokinetics, active and nonlinear microrheology of complex materials, polymer dynamics and sensors, with particular emphasis on non-linear electrokinetic flows, interfacial rheology, and the self-assembly of nanostructured materials. His work bridges fundamental fluid mechanics with practical applications in microfluidic devices, energy storage, and biomedical systems, demonstrating the versatility of this fascinating field. Analysis of Professor Squires' recent publications reveals a consistent focus on interfacial phenomena, with particular attention to the rheological properties of fluid interfaces, particle dynamics in complex fluids, and novel microfluidic techniques for measuring and manipulating these systems. His research demonstrates strong interdisciplinary connections between chemical engineering, physics, and materials science, with applications spanning energy storage, biomedical engineering, and environmental systems. Professor Squires has received numerous prestigious awards and honors: 2018 Robert W. Vaughan Lecture in Chemical Engineering, Caltech 2015 Elected Fellow of the American Physical Society 2013 Mid-Career Award, American Electrophoresis Society 2012 The Dudley Saville Memorial Lecture at Princeton 2010 Pierre Gilles de Gennes Prize 2010 Allan P. Colburn Memorial Lectureship, University of Delaware 2009 Francois Frenkiel Award for Fluid Mechanics 2009 Camille Dreyfus Teacher-Scholar Award 2008 Beckman Young Investigator 2007 NSF CAREER Award 2005 'Rising Star' - Chronicle of Higher Education As principal investigator of the Squires Group, Professor Squires leads a dynamic research team that combines experimental, theoretical, and computational approaches to investigate transport phenomena at interfaces. His work has been supported by major funding agencies including the National Science Foundation, with his CAREER award indicating early recognition of his potential as both researcher and educator. While specific grant details aren't provided in the source material, his extensive publication record and prestigious awards suggest robust and sustained research funding. The Squires Group maintains state-of-the-art laboratory facilities for studying micro-scale fluid mechanics, including specialized equipment for microrheology measurements, microfluidic device fabrication, and interfacial characterization. Their research environment fosters collaboration across disciplines, with connections to materials science, physics, and biomedical engineering researchers at UCSB and beyond.

• Fluid Mechanics
• Transport Science
• Microfluidics
• +7 more

Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.

• Process Systems Engineering
• Energy Systems
• Nonlinear Optimization
• +3 more

John MacFarlane is a Professor of Philosophy at the University of California, Berkeley , with affiliations to the Group in Logic and the Methodology of Science and co-organizer roles in the Meaning Sciences Club and Townsend Center Working Group . His research spans philosophy of language, philosophical logic, metaphysics, epistemology, philosophy of mathematics, history of logic , and ancient philosophy , particularly Aristotle . He has held visiting positions at institutions like École des Hautes Études en Sciences Sociales (EHESS), Paris . Education : Ph.D. in Philosophy (2000, University of Pittsburgh), M.A. in Classics (1997) and Philosophy (1994), A.B. summa cum laude in Philosophy (1991, Harvard). Research Interests focus on semantic relativism , particularly assessment sensitivity as explored in his book Assessment Sensitivity: Relative Truth and Its Applications (Oxford, 2014). He investigates how epistemic modals and vagueness interact with truth evaluation. His work in philosophical logic includes a textbook Philosophical Logic: A Contemporary Introduction (Routledge, 2021). Secondary interests include ancient philosophy (Aristotle, Plato), formal logic , and fiddlosophy . Article Trends reveal contributions to relativism , epistemic modals , vagueness , and logicism . His recent work (2025) examines Plato’s Protagoras and belief utility , while 2022–2020 articles address semantic indecision and probabilistic knowledge . Earlier works (2008–2000) include foundational studies on Frege, Kant, and Aristotle . Scientific Awards include a 2023 Residential Fellowship at Wissenschaftskolleg, Berlin (declined), 2016–17 Fellow at IEA Paris , and 2015 American Academy of Arts and Sciences membership . He has received multiple Humanities Research Fellowships at Berkeley (2016, 2008, 2003) and Research Enabling Grants (2000–2009). Advising includes supervising 15+ PhD students in areas like logic, epistemology, and philosophy of language . Notable advisees include Richard Lawrence (2017) and Sophie Dandelet (2021). He also guided honors theses at Berkeley, such as Benjamin Logan’s work on epistemic modals (2018). Labs and Teams involve co-organizing the Townsend Center Working Group in History and Philosophy of Logic, Mathematics, and Science and contributing to the Group in Logic and Methodology of Science . His interdisciplinary approach integrates philosophy, logic, and computational tools , including creating Pandoc for document conversion.

• Philosophy of Language
• Philosophical Logic
• Metaphysics
• +8 more

Kartik Prasanna is a Professor in the Department of Mathematics at the University of Michigan , affiliated with the College of Literature, Science and the Arts . He received his PhD from Princeton University in 2003 under the supervision of Andrew Wiles. Research Interests: His work lies at the intersection of number theory , algebraic cycles , and automorphic forms , focusing on the Langlands program , L-functions , algebraic cycles , and Iwasawa theory , particularly through the lens of the Bloch-Beilinson and Bloch-Kato conjectures . His recent publications explore periods of automorphic forms and the arithmetic of Shimura varieties. Grants & Awards: Simons Fellowship (2014-15) von Neumann Fellowship at the Institute for Advanced Study (2014-15) Current NSF Grants : DMS 2001293 and RTG DMS 1840234 Previous NSF grants: DMS 1600494, DMS 1160720, DMS 1015173, DMS 0801191, DMS 0854900 Academic Contributions: He has advised or collaborated with numerous postdoctoral researchers including Christopher Lyons , Ruochuan Liu , and Cameron Franc . Prasanna organized the 2011 FRG/RTG Workshop on L-functions, Galois Representations and Iwasawa Theory at the University of Michigan.

• Number Theory
• Automorphic Forms
• Algebraic Cycles
• +6 more

Lauren A. Delisio is an Associate Professor of Special Education at Rider University's Department of Teacher Education. She has taught graduate and undergraduate courses in special education, inclusive practices, and assistive technology since 2015. Ph.D. in Exceptional Student Education (2015, University of Central Florida) Graduate Certificate in Autism Spectrum Disorders (2012, University of Central Florida) M.S. in Teaching (2005, Pace University) B.A. in Communications (2000, Rider University) Her research focuses on universal design for learning (UDL) , assistive technology , and inclusive education practices for students with autism spectrum disorders (ASD), attention deficit hyperactivity disorder (ADHD), and intellectual disabilities. She has explored trauma-informed instruction, STEM accessibility, and evidence-based practices in inclusive classrooms. Her recent publications analyze UDL implementation, video prompting for independence, and pandemic impacts on inclusive education. She has received grants through the Department of Education's Office of Special Education Programs (OSEP) for Project LEAD and served as a doctoral intern with OSEP. Delisio has contributed to STEM research via video game integration for students with disabilities and supported alumni engagement through data collection. She collaborates with service providers, universities, and policymakers to improve educational outcomes for neurodiverse learners.

• Special Education
• Inclusive Education
• Autism Spectrum Disorders
• +5 more

Dr. Yongjia Song is an Associate Professor in the Department of Industrial Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research focuses on optimization under uncertainty, stochastic programming, and network interdiction with applications in disaster logistics, energy systems, and humanitarian operations. BS in Computational Mathematics (2009), Peking University MS in Industrial Engineering (2012), University of Wisconsin-Madison MS in Computer Sciences (2012), University of Wisconsin-Madison PhD in Industrial Engineering (2013), University of Wisconsin-Madison His work addresses complex systems under uncertainty through: Stochastic and robust optimization frameworks Integer programming for discrete decision problems Applications in disaster response and transportation networks Evacuation planning and shelter management Human trafficking disruption modeling Recent publications demonstrate trends in: Multistage stochastic programming for dynamic disaster response Bayesian preference elicitation for complex design problems Network interdiction models for security and trafficking disruption Integration of logistics and evacuation planning under uncertainty Adaptive algorithms for large-scale optimization Professional affiliations include: Institute for Operations Research and the Management Sciences (INFORMS) Mathematical Optimization Society (MOS) Society for Industrial and Applied Mathematics (SIAM) He teaches graduate courses in risk modeling (IE 8090) and actively works on practical implementations of optimization techniques in real-world systems.

• Optimization
• Stochastic Programming
• Integer Programming
• +5 more

Imogen Forbes-Macphail is an Assistant Professor at the University of California, Davis, specializing in the interdisciplinary intersections of nineteenth-century British poetry and mathematical thought. Her current book project, Line and Number: Formal Problems in Nineteenth-Century Poetry and Mathematics , examines how Victorian poets and mathematicians engaged with analogous formal challenges. Victorian Poetry Caucus Representative, North American Victorian Studies Association Co-Editor (with Anna Henchman), Special Issue of Nineteenth-Century Gender Studies on women’s writing and scientific knowledge production (“Longing to Know”) Research Focus: Forbes-Macphail’s work bridges literary formalism and historical scientific methodologies, particularly exploring topics like computational proof, topological poetics, and the mysticism inherent in Victorian-era mathematical and poetic discourse. Her interdisciplinary approach highlights how mathematical concepts influenced poetic structures and vice versa. Scientific Contributions: Her publications span analyses of the Four-Color Theorem, Ada Lovelace’s literary-scientific legacy, and the gendered dynamics of 19th-century knowledge production. Key trends include the aestheticization of mathematical rigor, the role of formalism in shaping Victorian intellectual paradigms, and crossdisciplinary epistemological inquiries. Honorable Mention, “Expanding the Field” Essay Prize, Northeast Victorian Studies Association (2022) Susan Morgan Graduate Student Essay Prize, Interdisciplinary Nineteenth-Century Studies (2019) Sally Mitchell Prize, North American Victorian Studies Association (2018) Honorable Mention, Susan Morgan Graduate Student Essay Prize (2018) Sussex-Samuel Prize, Australasian Universities Language and Literature Association (2013) Forbes-Macphail’s teaching includes courses such as ENL 164: Writing Science Publications , emphasizing the synthesis of scientific and literary communication. She previously held a Junior Fellowship at the Harvard Society of Fellows (2022–25), underscoring her prominence in Victorian studies.

• Nineteenth-Century Poetry
• Mathematics and Literature
• Victorian Studies
• +3 more

Timothy R. Tangherlini serves as the Elizabeth H. and Eugene A. Shurtleff Chair in Undergraduate Education and Professor in the Department of Scandinavian and the School of Information at the University of California, Berkeley. A distinguished folklorist and ethnographer, he has pioneered computational approaches to folklore studies, bridging traditional humanities scholarship with cutting-edge digital methods. His research expertise spans multiple interconnected domains: Digital humanities and computational folkloristics Danish and Scandinavian cultural traditions Network analysis of narrative structures Machine learning applications in cultural analysis Conspiracy theory formation and transmission Korean cultural studies and K-Pop analysis Tangherlini's work focuses on how stories circulate across social networks and how individuals use narratives to negotiate ideology within their social groups. He has been instrumental in developing the field of Culture Analytics, co-directing a three-year program at the NSF's Institute for Pure and Applied Mathematics and leading the NEH's Institute for Advanced Topics in Digital Humanities on Network Analysis for the Humanities. His research combines ethnographic depth with computational sophistication, creating novel methodologies for analyzing large cultural corpora. His scholarly contributions have earned him significant recognition: Fellow of the American Folklore Society Fellow of the Royal Gustav Adolf Academy (one of Sweden's Royal Academies) Elizabeth H. and Eugene A. Shurtleff Chair in Undergraduate Education Tangherlini has secured substantial funding from prestigious organizations including the NEH, NSF, NIH, AFOSR, Mellon Foundation, Nordic Council of Ministers, and Google. His work on conspiracy theories, K-Pop choreography analysis, and historical Danish folklore has garnered media attention, demonstrating the public relevance of his research. With extensive international experience including appointments at the University of Copenhagen, University of Iceland, and Harvard University, he maintains a global scholarly perspective while contributing significantly to undergraduate education at Berkeley.

• folklore
• Danish culture
• Danish cinema
• +9 more

Andrew D. White is an Associate Professor of Chemical Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the University of Washington (2013). His research focuses on automating scientific discovery through AI, particularly leveraging large language models (LLMs) and deep learning techniques in chemistry. His lab develops agents that integrate literature analysis, hypothesis generation, and experimental design to advance fields like molecular dynamics and drug discovery. Education: PhD in Chemical Engineering, University of Washington, 2013 BS/MS (not explicitly stated in text, inferred from career timeline) Research Interests: Large language models for scientific automation Deep learning applications in chemistry and materials science Molecular dynamics simulations Scientific agents and autonomous systems Publications: His work includes groundbreaking studies on closed-loop AI systems for chemistry, federated learning in molecular property prediction, and multi-agent systems for drug discovery. Recent highlights include the Robin system and ChemCrow tools. Awards: Recipient of the NSF Career Award (2018), NIH Outstanding Investigator Award (2020), and the Curtis Teaching Award (2019). He also advises biotech companies and serves on the National Academy of Sciences' Chemical Sciences Roundtable. Grants & Funding: Supported by DOE, NSF (multiple grants including CBET-1751471), NIH (R35GM137966), and LLNL projects. Collaborates with institutions like Argonne National Lab and Qubit Pharmaceuticals. Labs & Teams: Leads the White Lab at Rochester and co-founded FutureHouse, a nonprofit advancing AI-driven scientific discovery. Supervises a multidisciplinary team of PhD students and postdocs in computational chemistry, AI, and biophysics.

• Large language models
• Deep learning in chemistry
• Molecular dynamics
• +5 more

Song Mei is an Assistant Professor in the Department of Statistics and Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She received her Ph.D. from Stanford University in 2020 under Andrea Montanari and maintains active research collaborations with institutions including Amazon (as a 2023 Amazon Research Award recipient) and OpenAI (where she is currently on leave). Her research spans the intersection of statistics, machine learning, information theory, and computer science, with particular emphasis on foundational theories for modern AI systems. Key interests include language models, diffusion models, quantum algorithms, and high-dimensional statistics, often leveraging insights from statistical physics literature. Analysis of her recent publications reveals a strong focus on theoretical underpinnings of generative AI, with significant contributions to understanding contrastive pre-training (CLIP), attention mechanisms in LLMs, and mathematical foundations of diffusion models. Her work demonstrates consistent interdisciplinary connections between statistical theory and practical AI development. Sloan Research Fellowship (2025) Noether Early Career Scholar Award (2025) Google Research Scholar Award (2024) Amazon Research Award (2024) She actively advises graduate students through MA programs and has secured significant research grants including Amazon Research Awards. Her work with AGI Labs at Amazon demonstrates applied impact of theoretical research. Current projects focus on mechanistic interpretability of large language models and mathematical frameworks for generative AI. Professor Mei leads research on the statistical principles behind frontier AI models, with particular focus on developing rigorous theoretical frameworks for understanding emergent behaviors in large-scale systems.

• Language Models
• Diffusion Models
• Theory of Deep Learning
• +6 more

Andrew Ollett is an Associate Professor and Director of Graduate Studies in the Department of South Asian Languages and Civilizations at the University of Chicago , where he has taught since 2019. His research focuses on the literary and intellectual traditions of South and Southeast Asia, particularly through works in Sanskrit, Prakrit, Apabhramsha, and Kannada from the first millennium CE. Co-founder and editor of NESAR (New Explorations in South Asia Research) Director of NEH-funded edition/translation of the Kavirājamārga (earliest Kannada literary manual) Expert in Prakrit and Kannada literary traditions His work explores the 'question of language'—examining how language choice shaped cultural production and change in premodern India. Current projects include a monograph on context-sensitivity in Indian language theories and a study of manuscript technology's impact on early South Asian knowledge communities. He has authored The Mirror of Ornaments (Naples: Unior Press, 2025) and Language of the Snakes (UC Press, 2017), and co-authored Lilavai (Harvard, 2021). Recent publications include interdisciplinary studies on: Sanskrit-Prakrit campū genre reevaluation Pragmatics in Mīmāṁsā interpretive concepts Manuscript literacy's influence on literary forms Historical linguistics of Middle Indic syllable reduction Philosophy of language in Śālikanātha and Kumārila Bhaṭṭa Awarded the NEH 'Scholarly Editions and Translations' grant , he teaches classical South Asian literature courses covering Sanskrit, Prakrit, and Tamil traditions. He maintains digital resources including a Prakrit Digital Texts Project and Mīmāṁsādigdarśinī digital text repository, and actively contributes to open-access scholarship.

• Poetics
• History of Literature
• Intellectual History
• +9 more

Rex Ying is an Assistant Professor in the Department of Computer Science at Yale University's School of Engineering & Applied Science. He leads research in graph neural networks, geometric representation learning, and explainable AI, with applications spanning physical simulations, biology, knowledge graphs, and recommender systems. His lab actively recruits PhD students interested in geometric deep learning, graph neural networks, and trustworthy AI. Dr. Ying received his PhD in Computer Science from Stanford University under Jure Leskovec, with a thesis titled "Towards Expressive and Scalable Deep Representation Learning for Graphs." Prior to that, he graduated from Duke University in 2016 with highest distinction, majoring in Computer Science and Mathematics. His research focuses on three interconnected areas: advancing graph neural network architectures for improved expressiveness, scalability, and interpretability; innovating in geometric representation learning for data with diverse characteristics; and developing real-world applications across scientific domains. He has pioneered influential algorithms including GraphSAGE, PinSAGE, and GNNExplainer, and developed the first billion-scale graph embedding services at Pinterest as well as graph-based anomaly detection algorithms at Amazon. His recent publication trends show a strong focus on hyperbolic geometry for foundation models, non-Euclidean representation learning, and multimodal applications in computational biology. The research demonstrates increasing integration of geometric deep learning with large language models and foundation model architectures. KDD 2022 Dissertation Award 2019 Baidu Scholarship in Artificial Intelligence Dr. Ying actively serves the research community as a committee member for major conferences including AAAI, ICML, NeurIPS, ICLR, KDD, and WebConf for over seven years, and as area chair for LoG 2022. He co-leads the open-source PyTorch Geometric project and has organized numerous workshops on graph learning. His industry collaborations include Pinterest, Amazon, Facebook AI Research, DeepMind, Siemens, SLAC National Accelerator Laboratory, and Saudi Aramco. He teaches "Deep Learning for Graph-Structured Data" at Yale and mentors students in developing cutting-edge graph learning algorithms. His research lab collaborates with both academic institutions and industry partners to advance the state-of-the-art in graph representation learning, with particular emphasis on geometric deep learning and its applications to scientific discovery and real-world systems.

• Graph Neural Networks
• Geometric Representation Learning
• Explainable AI
• +3 more

Danqi Chen is an Associate Professor in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. Their research focuses on advancing large language models (LLMs), with emphasis on model alignment, safety, and long-context reasoning capabilities. Key research areas: LLMs, AI safety, retrieval systems, and model optimization Recent work explores theorem proving, context encoding, and ethical content generation Their 2025 publications highlight innovations in formal verification scaffolding, attention mechanism efficiency, and copyright-aware generation. 2024 studies investigate continual memorization, rule-based chatbot representations, and scientific literature retrieval benchmarks. Current projects demonstrate commitment to improving model robustness, interpretability, and security compliance in multimodal systems.

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +4 more

Igor Kriz is a Professor of Mathematics at the University of Michigan, specializing in algebraic topology. He is affiliated with the Department of Mathematics within the College of Literature, Science, and the Arts (LSA). His research focuses on advanced topics in algebraic topology, particularly stable homotopy theory and related areas. Kriz received his Ph.D. from Charles University in 1988. His academic journey has led him to become a prominent researcher in algebraic topology, with significant contributions to the field over several decades. Professor Kriz's primary research interests lie in algebraic topology , which studies topological spaces through algebraic invariants. He specializes in equivariant stable homotopy theory, Mackey functors, cobordism, and motivic homotopy theory . His work involves calculations of stable homotopy groups and other generalized homology theories, including Morava K-theories of classifying spaces of finite groups. He has made significant contributions to the study of operads and structures up to homotopy, with applications extending to differential geometry and physics, particularly string theory. His research often bridges multiple mathematical disciplines, creating connections between topology, algebra, and geometry. His recent publications (2022-2025) demonstrate a strong focus on equivariant topology and its connections to algebraic structures. Kriz frequently collaborates with researchers like P. Hu, P. Somberg, and others, producing work that explores the intersection of homotopy theory with representation theory and algebraic geometry. His research program shows consistent evolution from foundational work in stable homotopy to more recent applications in motivic contexts and topological Hochschild homology. Professor Kriz teaches both undergraduate and graduate courses at the University of Michigan. His teaching portfolio includes Math 425 (Introduction to Probability), Math 592 (Introduction to Algebraic Topology), and advanced graduate courses Math 695 and Math 696 (Algebraic Topology I and II). His course materials are regularly updated, reflecting his commitment to education in mathematical topology. Based in East Hall (room 3846) at the University of Michigan, Professor Kriz maintains an active research program while contributing to the academic community through teaching and mentorship. His work continues to advance our understanding of complex topological structures and their algebraic representations.

• Algebraic Topology
• Stable Homotopy Theory
• Equivariant Topology
• +5 more

W. Brent Lindquist is a Professor in the Department of Mathematics and Statistics at Texas Tech University, affiliated with the TTU Mathematical Finance Program. His contact details include office location in the Mathematics & Statistics building (Room 104), phone (+1 806 834 2348), and email brent.lindquist@ttu.edu. His research spans computational financial mathematics, porous media flow, neuroscience applications, and quantum electrodynamics. Key contributions include dynamic asset pricing with market microstructure integration, pore-scale flow modeling using 3D micro-tomography, automated neuron morphology identification, and QED computations for electron magnetic moments. Recent work emphasizes ESG factor incorporation into financial models. Analysis of 2023–2025 publications reveals a dominant focus on sustainable finance, particularly ESG-integrated option pricing and portfolio optimization. Methodologies include random forests for market microstructure analysis, skew random walks for volatility modeling, and Lévy processes for Bitcoin dynamics. Cross-cutting themes involve hedonic real estate models with ESG factors and unified asset pricing frameworks bridging classical finance theories.

• Computational Finance
• Financial Mathematics
• Porous Media Flow
• +2 more