Ewain Gwynne is a Professor of Mathematics at the University of Chicago, affiliated with the Committee on Computational and Applied Mathematics (CCAM) and the Statistics Department. He previously held postdoctoral positions at the University of Cambridge and earned his Ph.D. from MIT in 2018 under Scott Sheffield. His research focuses on probability theory, particularly random geometric structures in statistical mechanics, including Schramm-Loewner evolution (SLE), Liouville quantum gravity (LQG), and random planar maps. Education: Ph.D. in Mathematics, MIT (2018); M.Sc., MIT (2015); B.Sc., Northwestern University (2013). Research Interests: Random geometric objects in statistical mechanics Liouville quantum gravity and its metric properties Random planar maps and their scaling limits SLE and its relationship with LQG Random walks on random planar maps Percolation and permutons His recent articles explore topics such as supercritical LQG, Gaussian curvature on random maps, and harmonic balls in LQG. He has advised multiple Ph.D. students and serves as an associate editor for Probability and Mathematical Physics . His work bridges probability theory, geometry, and mathematical physics, with applications to understanding critical phenomena in random systems.
Bradley D. Olsen is a full professor in the Department of Chemical Engineering at the Massachusetts Institute of Technology (MIT), where he leads research at the intersection of polymer science, soft matter physics, and bioengineering. His work focuses on designing materials for critical applications in biotechnology, hemostasis, and sustainable polymer development while advancing fundamental understanding of polymer network mechanics and self-assembly. Education: Ph.D. in Chemical Engineering, University of California Berkeley (2007) S.B. in Chemical Engineering, Massachusetts Institute of Technology (2003) Olsen's research spans protein-based materials, block copolymer phase behavior, and mechanochemical hydrogels. He has pioneered methods for quantifying polymer network topology, developing hemostatic nanoparticles, and creating bio-inspired materials for selective biomolecular transport and medical applications. His recent publications emphasize data-driven approaches to polymer characterization and educational outreach in materials science. Scientific Awards: American Physical Society (APS) Fellow (2023) Fulbright Amazonia Scholar (2023) Alexander and I. Michael Kasser Chair in Chemical Engineering (2021) ACS Macro Letters Young Investigator Award (2021) MIT Committed to Caring Honor (2019) AIChE Owens Corning Early Career Award (2019) APS Dillon Medal (2018) Kavli Emerging Leader in Chemistry (2017) ACS Polymer Division Fellow (2016) Camille Dreyfus-Teacher Scholar (2015) Alfred P. Sloan Research Fellow (2014) NSF Career Grant (2013) NIH Postdoctoral Fellowship (2008-2009) Hertz Fellow (2003-2007) Barry M. Goldwater Scholarship (2002) Olsen has received significant grant support including NSF Career (2013) and AFOSR (2012) awards. His teaching activities include innovative international outreach like the 2025 soccer-themed science camp in Brazil. The Olsen Group at MIT explores advanced materials with applications ranging from trauma care to sustainable polymers.
Roberto A. Chica is a Full Professor in the Department of Chemistry and Biomolecular Sciences at the University of Ottawa, Faculty of Science. His research focuses on computational and experimental protein engineering, particularly in designing novel enzymes and fluorescent proteins for biotechnological applications. He develops advanced algorithms for protein design and investigates enzyme dynamics using molecular modeling and structural biology approaches. Key research interests include biocatalysis, structural biology, and the application of computational methods to engineer proteins with tailored functions. His lab integrates experimental protein chemistry with computational simulations to understand catalytic mechanisms and design proteins for industrial and biomedical uses. Recent work emphasizes ensemble-based computational enzyme design, exploring how conformational landscapes influence catalytic efficiency. His articles highlight advancements in artificial enzyme creation, substrate specificity modulation, and fluorescent protein optimization. Chica’s contributions bridge fundamental biochemistry with applied innovations in protein engineering. Notable achievements include the design of bright red fluorescent proteins via computational approaches and the development of biosensors for protein expression monitoring. His research has implications for drug discovery, biocatalytic synthesis, and personalized medicine.
Alan Hammond is a Professor in the Department of Statistics at the University of California, Berkeley. His research focuses on rigorous mathematical probability techniques applied to problems in statistical mechanics, including percolation theory, polymer models, and random growth processes. He has contributed to understanding critical phenomena, phase transitions, and universality classes in stochastic systems. Hammond's work spans topics such as KPZ universality, Brownian motion, and the geometry of random media. He has investigated models like last passage percolation, self-avoiding walks, and tug-of-war games, often uncovering deep connections between stochastic processes and nonlinear PDEs. His teaching includes courses on stochastic processes and statistical theory at both graduate and undergraduate levels. Notable research highlights include studies on fractal properties of Airy processes, stability in dynamical last passage percolation, and the behavior of geodesics in random environments. His contributions bridge probability theory with applications in physics and combinatorics.
Michael Feig serves as Professor in the Department of Biochemistry & Molecular Biology at Michigan State University, leading the Feig Lab within the BioMolecular Science Gateway initiative. His research bridges computational modeling and molecular biology to investigate protein behavior in cellular contexts, with particular emphasis on molecular dynamics simulations and machine learning applications. His academic background includes: Ph.D. (1999) from the University of Houston M.S. (1994) from Technical University of Berlin Feig's research program focuses on computational biophysics of protein systems, specializing in molecular dynamics simulations of crowded cellular environments, bacterial microcompartments, and intrinsically disordered proteins. His lab develops advanced modeling techniques including coarse-grained approaches (COCOMO2) and machine learning frameworks to predict protein properties and conformational landscapes. Current work explores temperature-dependent structural ensembles, enzyme cargo loading mechanisms in engineered microcompartments, and biomolecular condensate physics under shear flow. Analysis of his 15 most recent publications (2024-2025) reveals three dominant research thrusts: (1) integration of deep learning with molecular dynamics for protein structure prediction, (2) engineering of bacterial microcompartments for synthetic biology applications, and (3) fundamental studies of macromolecular crowding effects on diffusion and phase separation. His work consistently emphasizes methodological innovation with biological relevance, notably through enhancements to the CHARMM simulation platform. His scientific recognition includes: Alfred P. Sloan Fellowship (2005) As principal investigator of the Feig Lab, he directs research teams in computational biophysics projects supported by active funding mechanisms. While specific grant details aren't provided, his continuous publication pipeline and lab infrastructure indicate sustained research support. His mentorship spans graduate students in the Cell & Molecular Biology Program, with recent work involving multi-institutional collaborations on bacterial microcompartment engineering and protein phase separation. The Feig Lab operates at the intersection of high-performance computing and molecular biology, maintaining strong connections with experimental groups for method validation. Current initiatives include developing generative models for temperature-dependent protein conformations and investigating cytoplasmic protein capture mechanisms in microcompartments, with potential applications in metabolic engineering and nanobiotechnology.
David S. Cafiso is a Professor in the Department of Molecular Physiology and Biological Physics at the University of Virginia. His research focuses on the molecular mechanisms of membrane transport and cell signaling, utilizing advanced techniques such as EPR spectroscopy, high-resolution NMR, and solid-state NMR. He has made significant contributions to understanding membrane protein structure and function, particularly in relation to synaptic vesicle exocytosis and bacterial nutrient transport. Education: AB, PhD in Biophysics from the University of California, Berkeley; Postdoctoral training at UC Berkeley and Stanford University His research interests span Biochemistry, Biophysics, Structural Biology, Neuroscience, and Microbiology. Recent work highlights conformational dynamics in membrane proteins, lipid-protein interactions, and the role of electrostatics in signaling. Publications emphasize PIP2 regulation, C2 domain function, and TonB-dependent transport systems, with applications in both bacterial physiology and neurosecretion. Professor Cafiso's laboratory investigates two primary areas: (1) Membrane protein attachment mechanisms critical for cell signaling, and (2) Solute transport across lipid bilayers in gram-negative bacteria. His studies often integrate biochemical, structural, and biophysical approaches to probe dynamic processes in membrane biology.
Jason P. Miller is a Professor in the Statistics Laboratory at the Department of Pure Mathematics and Mathematical Statistics (DPMMS), University of Cambridge, and a Fellow of Trinity College, Cambridge. He previously held the Poincaré Chair at IHP in the 2015-2016 academic year and was a post-doctoral researcher at MIT and Microsoft Research. Miller's research focuses on probability theory, particularly stochastic interface models, random surfaces, Schramm-Loewner evolutions (SLE), Liouville quantum gravity, and random planar maps. His work bridges mathematical physics and probability, exploring deep connections between random geometry, conformal field theory, and statistical mechanics. He has made fundamental contributions to understanding the relationship between Liouville quantum gravity and the Brownian map, and has extensively studied the properties of Schramm-Loewner evolutions in various contexts. Miller's publication record shows a consistent focus on the intersection of probability theory and mathematical physics, with a particular emphasis on scaling limits of discrete models to continuum objects. His work often involves collaborations with prominent researchers like Scott Sheffield and Ewain Gwynne, and demonstrates a progression from foundational work on SLE and the Gaussian free field to more recent breakthroughs in Liouville quantum gravity and its connections to random planar maps. Scientific Awards Rollo Davidson Prize, 2015 Poincaré Chair, 2015-2016 academic year Whitehead Prize, 2016 Clay Research Award, 2017 ICM invited speaker (probability and statistics), 2018 Doeblin Prize, 2018 Eisenbud Prize, 2023 Fermat Prize, 2023 Miller has supervised numerous PhD students (though specific names aren't listed in the provided text) and has been involved in significant research grants supporting his work in random geometry and probability theory. His editorial service includes positions on the boards of Probability Theory and Related Fields and Bernoulli journals. While specific laboratory details aren't provided, Miller's research appears to be theoretical in nature, focusing on mathematical analysis of random geometric structures. His work has significant implications for theoretical physics, particularly in understanding quantum gravity and critical phenomena in statistical mechanics.
Slava Rychkov is a Permanent Professor of Theoretical Physics at the Institut des Hautes Études Scientifiques (IHES), a position he has held since 2017. He specializes in strongly coupled quantum and conformal field theories, with applications across high energy physics, statistical mechanics, and condensed matter physics. His current research focuses on the conformal bootstrap and renormalization group techniques, including both perturbative and nonperturbative methods like tensor network renormalization. Education: Ph.D. in Physics, Princeton University (2002) Master of Science, Moscow Institute of Physics and Technology (1996) Recent research highlights include a groundbreaking connection between Deligne categories and symmetries of probabilistic loop ensembles in statistical physics, and a novel method for analytic continuation of Euclidean CFTs to Lorentzian signature. His work on the 2+ϵ expansion challenges established assumptions about critical exponents in 3D systems. Publications span topics from tensor renormalization group methods to rigorous mathematical approaches in the conformal bootstrap program. Scientific Awards: Jacques Solvay International Chair in Physics (2025) Grand Prix Mergier-Bourdeix, French Academy of Sciences (2019) New Horizons in Physics Prize (2014) As Deputy Director of the Simons Collaboration on the Nonperturbative Bootstrap, Rychkov leads efforts to rigorously analyze conformal field theories. His former advisees include prominent researchers at institutions like EPFL, Princeton, and Università di Genova. Current projects focus on resolving fundamental questions about critical phenomena and phase transitions using advanced mathematical physics tools.
Thomas Cheatham III is a Professor of Medicinal Chemistry in the College of Pharmacy and Adjunct Professor of Biomedical Engineering at the University of Utah, specializing in computational biomolecular simulation methodologies. His work bridges theoretical chemistry and biological applications through advanced molecular dynamics techniques. Education: B.A., Middlebury College Ph.D., University of California, San Francisco Research Focus: Dr. Cheatham pioneers molecular dynamics and free energy simulation methods (AMBER/CHARMM) for proteins, nucleic acids, and lipids. His group addresses critical challenges in environmental dependence of nucleic acid structure (ion/hydration effects on DNA), conformational transition pathways (e.g., B-DNA/Z-DNA junctions), and macromolecular flexibility beyond static experimental structures. Recent innovations target force field refinement for modified nucleic acids and polarizable models. Publication Trends: Analysis of his 2023-2025 publications reveals three dominant themes: (1) Nucleic acid force field optimization (60% of recent work), particularly RNA/DNA parameterization; (2) Development of simulation infrastructure including FAIR data principles and AmberTools; (3) Application-driven studies of therapeutic targets like Bcr-Abl inhibitors. His work increasingly integrates polarizable force fields and high-performance computing. Research Infrastructure: He leads the AMBER biomolecular simulation software development effort and maintains an active laboratory focused on methodological innovation. His group collaborates extensively with experimentalists to validate computational predictions and provides open-source tools (PTRAJ/CPPTRAJ) used globally. Current initiatives emphasize reproducibility through standardized simulation protocols and data sharing frameworks.
Sandeep Gupta is a Professor and Director of the School of Computing and Augmented Intelligence at Arizona State University's Ira A. Fulton Schools of Engineering. He also serves as a Senior Global Futures Scientist. His work bridges computer science, engineering, and healthcare applications with a focus on creating reliable cyber-physical systems that interact safely with humans. Education: Ph.D. from The Ohio State University (1995) Research Interests: Professor Gupta's research spans cyber-physical systems, green and sustainable computing, mobile and pervasive computing, and parallel and distributed computing. His work increasingly focuses on human-in-the-loop systems where AI and humans collaborate safely, particularly in healthcare contexts. Recent research integrates large language models with cyber-physical systems to enhance safety and operational effectiveness in critical applications like medical monitoring and industrial automation. Research Trends: Analysis of Professor Gupta's recent publications reveals a strong focus on operational safety in human-AI collaborative systems, particularly in healthcare applications. His work combines physics-guided models with machine learning to detect "unknown-unknowns" in safety-critical systems, develops LLM-based approaches for medical image classification, and creates frameworks for ethical human-AI collaboration. The research increasingly addresses real-world challenges in diabetes management, epilepsy diagnosis, and industrial automation. Professional Service: IEEE Communication Letters Editorial Board, Area Editor (2008-Present) IEEE Journal on Special Areas in Communications - Issue on Body Area Network, Co-editor (2006-Present) Elsevier COMNET - Computer Network Journal, Reviewer (2008-Present) Technical Advisory Committee Chair for Networks (2005-Present) Advising and Grants: Professor Gupta has secured numerous research grants from NSF, NIH, Intel, Raytheon, and other organizations, totaling millions of dollars. His research portfolio includes projects on smart stadiums, mobile ECG sensing, power-aware scheduling, medical device verification, and sustainable data center management. He actively advises PhD and Master's students through thesis courses and research supervision, focusing on cyber-physical systems and healthcare applications. Labs and Research Groups: Professor Gupta leads research in cyber-physical systems with applications in healthcare, sustainable computing, and mobile networks. His work involves interdisciplinary collaboration across engineering, computer science, and medical domains, particularly through ASU's Global Futures initiatives.
Jason Miller is a Reader in Probability at the Department of Pure Mathematics and Mathematical Statistics (DPMMS) within the Faculty of Mathematics at the University of Cambridge. His research focuses on advanced topics in probability theory and mathematical physics, particularly exploring the deep connections between random geometry, conformal invariance, and quantum gravity. Miller's research interests span a sophisticated range of topics in modern probability, with particular emphasis on Schramm-Loewner evolution (SLE), Gaussian free field, Liouville quantum gravity, random planar maps, and random walks. His work sits at the intersection of probability theory, complex analysis, and mathematical physics, developing rigorous mathematical frameworks for understanding two-dimensional random structures that arise in statistical mechanics and quantum gravity. He has made significant contributions to establishing the connections between discrete random structures and their continuum limits, particularly in the context of Liouville quantum gravity and the Brownian map. Analysis of Miller's recent publications reveals a consistent research program focused on establishing the deep connections between various mathematical objects in two-dimensional random geometry. His work demonstrates how Liouville quantum gravity serves as a universal scaling limit for random planar maps, how Schramm-Loewner evolution describes the continuum limits of critical interfaces, and how these objects relate to the Brownian map through various characterization theorems. The mathematical techniques employed span conformal field theory, metric geometry, stochastic analysis, and complex analysis. As a member of the Statistical Laboratory research group within DPMMS, Miller collaborates extensively with leading researchers in probability theory, including Scott Sheffield, Ewain Gwynne, and Wendelin Werner. His work has been published in the most prestigious mathematics journals including Acta Mathematica, Inventiones Mathematicae, and the Annals of Probability, reflecting the significance and rigor of his contributions to the field.
Guo-Cheng Yuan is a Senior Faculty member in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. His research focuses on genomics, epigenetics, and computational biology , with particular emphasis on single-cell analysis, chromatin regulation, and cancer immunology. Institution: Icahn School of Medicine at Mount Sinai School: Graduate School of Biomedical Sciences Department: Genetics and Genomic Sciences Research Interests Dr. Yuan's work spans multiple domains including: Single-cell RNA sequencing applications in brain development and cancer Chromatin structure analysis in differentiation processes Spatial transcriptomics for tissue microenvironment characterization Computational methods for multiomic data integration Enhancer-promoter interaction dynamics Development of robust bioinformatics pipelines Scientific Trends Analysis His recent publications (2021-2025) demonstrate a focus on single-cell and spatial omics methodologies applied to diverse biological contexts ranging from cancer immunology to neurodevelopment . Notable trends include: Development of trajectory analysis algorithms Multiomic integration for regulatory network mapping Epigenetic mechanisms in cell differentiation Computational approaches for data robustness Three-dimensional genome organization studies Applications in both developmental biology and oncology
Silvina Matysiak is an Associate Professor in the Fischell Department of Bioengineering at the University of Maryland's A. James Clark School of Engineering. Her research integrates computational modeling with biophysical principles to investigate fundamental biomolecular processes relevant to neurodegenerative diseases and biomaterial design. Education: Ph.D. in Bioengineering, Rice University (2007) B.S. in Bioengineering, Instituto Tecnologico de Buenos Aires Research Focus: Professor Matysiak's work centers on protein folding landscapes, misfolding mechanisms in neurodegenerative disorders , and multiscale simulation techniques . Her group employs advanced molecular modeling to study how water mediates protein stability, how membranes influence amyloid aggregation, and the molecular basis of Huntington's and Alzheimer's diseases. Key innovations include coarse-grained models that bridge computational gaps across time and length scales. Publication Trends: Analysis of her recent work (2022-2024) reveals three dominant themes: (1) Environmental modulation of amyloid aggregation by lipids, ionic liquids, and sugars; (2) Allosteric mechanisms in protein signaling networks; (3) Development of computational frameworks like ProMPT for multiscale biomolecular simulation. These studies consistently link molecular dynamics to disease pathology and therapeutic design. Awards: National Science Foundation CAREER Award ($650,000, 5-year grant for neurodegenerative disease research) Advising and Funding: Professor Matysiak currently mentors 4 Ph.D. students and has guided 6 doctoral graduates to successful careers in academia and industry (Google, Schrödinger, Flatiron Institute). Her lab receives primary support through her NSF CAREER award, with additional funding from collaborative grants focused on computational biomaterials and neurodegenerative mechanisms. She actively participates in interdisciplinary initiatives within Maryland's Biophysics and Chemical Physics Programs. Research Group: The Biomolecular Modeling Group operates within the Fischell Department, maintaining strong ties to the university's Biophysics and Chemical Physics Programs. The lab develops open-source simulation tools (including ProMPT) and maintains active collaborations with experimental groups studying protein aggregation, membrane biophysics, and biomaterials. Current projects focus on curvature-sensing peptides, metalloprotein allostery, and chitosan-based hydrogels for biomedical applications.
Dapeng Zhan is a Professor in the Department of Mathematics at Michigan State University (MSU), where he has held this position since 2017. He earned his Ph.D. in Mathematics from the California Institute of Technology in 2004. His research focuses on Schramm-Loewner evolution (SLE), a stochastic process describing random fractal curves in two-dimensional spaces, with applications to critical phenomena in statistical physics models like percolation and the Ising model. Zhan has held prior academic positions, including Gibbs Assistant Professor at Yale University (2007–2009) and Morrey Assistant Professor at UC Berkeley (2004–2007). His research interests include Probability Theory, SLE, and statistical lattice models. He has received prestigious awards such as the Salem Prize (2012), Sloan Research Fellowship (2011–2015), and Simons Fellowship (2016). His work frequently appears in top journals like *Annals of Probability* and *Inventiones Mathematicae*. Zhan teaches advanced courses in probability and analysis, including *Brownian Motion and Stochastic Analysis* and *Analysis I*. He has advised numerous research projects and holds grants from the NSF, including a CAREER award (2011–2018). His contributions span theoretical developments in SLE's reversibility, duality, and boundary behavior, with recent work exploring multi-force-point SLE and boundary Green’s functions.
Todd Lillian is a Senior Lecturer at the West Lafayette School of Mechanical Engineering, Purdue University, located at 585 Purdue Mall, West Lafayette, IN. His research focuses on the intersection of biophysics, molecular mechanics, and computational modeling, particularly in studying DNA dynamics, supercoiling phenomena, and their implications in biological systems. He has contributed to understanding the mechanics of DNA in confined environments, such as bacteriophage packaging, and the role of thermal fluctuations in molecular processes. His work integrates advanced computational methods like Brownian dynamics simulations and elastic rod models to explore topics ranging from electric sail modal analysis to DNA supercoil relaxation mechanisms. Notable research areas include topoisomerase function, DNA cyclization, and the structural ensemble of toroidal DNA shapes within viral capsids. Lillian's academic contributions also extend to space propulsion systems, as evidenced by studies on multi-tether electric sails and their dynamics in plasma environments. His articles reflect a sustained focus on molecular-level mechanics, with a strong emphasis on bridging theoretical models with experimental observations in both biological and aerospace engineering contexts. Despite no explicitly listed awards or grants in the provided text, his extensive publication record underscores his dedication to advancing interdisciplinary research at the micro- and nanoscales.
