Maxim Kontsevich is a permanent professor at the Institut des Hautes Études Scientifiques (IHÉS), holding the AXA Chair for Mathematics since 1995 and a visiting chair at Rutgers University (one month annually since 1997). Born in 1964 in Khimki, USSR, he earned his PhD from Bonn University in 1992. His career includes visiting positions at Harvard, the Institute for Advanced Study, and Berkeley, where he was a professor from 1993 to 1995. His research spans mathematical physics, algebraic geometry, and non-commutative geometry. Notable contributions include deformation quantization, mirror symmetry, and motivic integration. His work bridges algebraic structures with geometric and physical concepts, influencing areas like topological field theories, string theory, and integrable systems. Awardees of Fields Medal (1998), Crafoord Prize (2008), and Breakthrough Prize (2014), he also holds editorial roles at Compositio Mathematica and Publications Mathématiques IHÉS. His over 50 publications explore advanced topics such as quantum cohomology, Hodge theory, and categorical structures in geometry.
Ana Maria Alonso Rodriguez is a Full Professor of Numerical Analysis at the Department of Mathematics, University of Trento. She holds a PhD in Applied Mathematics from Universidad Complutense de Madrid (1993) and has held academic positions across Italy and Spain since 1990. Her research focuses on numerical methods for partial differential equations, computational electromagnetism, finite element methods, and domain decomposition techniques. She has organized international workshops and minisymposia, including the 2022 Oberwolfach workshop on Hilbert Complexes and the 2018 ICOSAHOM conference session on high-order methods. Her work bridges numerical analysis, topology, and applied electromagnetism, with recent contributions to Whitney finite elements and discrete potential theory. Education: PhD in Applied Mathematics, Universidad Complutense de Madrid (1988-1993) Licenciatura en Ciencias Matematicas, same institution (1982-1987) Research emphasizes high-order discretizations for electromagnetic problems, leveraging finite element exterior calculus and graph-based decomposition techniques. Recent work (2024) advances tree-cotree methods for curl operator spectra and Whitney form interpolation. She actively collaborates with international institutions like the CI2MA in Chile and the Laboratoire J. A. Dieudonné in France. Teaching includes courses on numerical PDEs, finite elements, computational electromagnetism, and MATLAB-based numerical analysis at both undergraduate and PhD levels. She has supervised numerous courses in Italy and Spain since 2000, integrating practical software tools like FreeFem and MODULEF into instruction.
Eric Roberts is a Professor of Geology and Geological Engineering and Director of the Potential Gas Agency at the Colorado School of Mines. His research focuses on basin analysis, energy exploration, and sedimentary geology with specializations in Mesozoic-Cenozoic basins of Gondwana and North America. He leads projects in U-Pb geochronology, critical minerals exploration, and paleoenvironmental reconstruction through vertebrate paleontology and stratigraphy studies. Roberts' work integrates advanced geochronological techniques with field-based analyses to address questions in paleobiology, resource exploration, and earth system dynamics. Key research themes include: Geochronology of sedimentary and volcanic deposits Paleoclimate reconstruction using paleosols Stratigraphic frameworks for dinosaur fossil records Resource potential of helium/hydrogen reserves Antarctic and African vertebrate paleontology His exploration of hominin burial practices in the Rising Star Cave System has provided critical insights into early human behavior. Roberts' interdisciplinary approach bridges geological, biological, and environmental sciences to advance understanding of Earth's history and resource management strategies.
Dr. Tyler H. Summers is an Assistant Professor of Mechanical Engineering at the University of Texas at Dallas (UTD), with an affiliate appointment in Electrical Engineering. He holds a PhD in Aerospace Engineering from the University of Texas at Austin (2010) and completed a postdoctoral fellowship at ETH Zurich (2011–2015). His research focuses on feedback control and optimization in complex dynamical networks, including electric power grids and distributed robotics. Key contributions include stochastic optimal power flow methods, distributed formation control algorithms, and robust control design under uncertainty. Education: PhD in Aerospace Engineering (University of Texas at Austin, 2010) M.S. in Aerospace Engineering (University of Texas at Austin, 2007) B.S. in Mechanical Engineering (Texas Christian University, 2004) His research interests emphasize theoretical and computational tools for cyber-physical systems, including power networks and robotic teams. Notable achievements include a NSF CAREER Award ($500K) and an Army YIP grant ($350K). He leads the Control, Optimization, and Networks (COIN) Lab, which develops algorithms for robust control and distributed optimization. Recent work addresses challenges in integrating renewable energy into power systems and enabling safe autonomous robotics in uncertain environments. Grants and projects involve collaborations with institutions like the University of Melbourne and the Australian National University. Grants & Awards: NSF CAREER Award (2021) Army Research Office YIP (2017) Air Force Office of Scientific Research (2019) Lab & Team: The COIN Lab focuses on interdisciplinary projects involving students and postdocs in control theory, robotics, and optimization.
Jeff Urbach is a Professor in the Department of Physics at Georgetown University and Vice Provost for Research. He earned a B.A. in Physics from Amherst College (1985), a Ph.D. from Stanford University (1993), and completed a postdoctoral fellowship at the University of Texas at Austin (1993-1996). He joined Georgetown in 1996, advancing to Professor in 2006, and held leadership roles including Department Chair (2000-01, 2004-07, 2016-20) and Director of the Institute for Soft Matter Synthesis and Metrology (2011-15). Education: B.A. in Physics (Amherst College, 1985); Ph.D. in Physics (Stanford University, 1993) His research focuses on complex dynamics and biophysics , applying statistical physics, nonlinear dynamics, and advanced imaging to systems like granular materials, cytoskeletal proteins, and neuronal migration. Current work emphasizes quantitative modeling of multifaceted, interacting systems through computer simulations and experimental analysis. Scientific awards include the Sloan Foundation Fellowship and the Presidential Early Career Award for Scientists and Engineers . Research funding has been secured from the National Science Foundation, National Institutes of Health, NASA, Air Force Office of Scientific Research, NIST, and other foundations.
Jacob D. Leshno is an Associate Professor of Economics and Robert H. Topel Faculty Scholar at the University of Chicago Booth School of Business. His research employs game theory, applied mathematics, and microeconomic theory to study allocation mechanisms and marketplace design, with applications spanning school choice systems, patient assignments to nursing homes, and decentralized cryptocurrency protocols. Professor Leshno's academic background includes: PhD in Economics from Harvard University, completed under Nobel laureate Alvin Roth M.Sc. in Pure Mathematics from Tel Aviv University B.Sc. in Pure Mathematics from Tel Aviv University His research program centers on market design theory with two primary strands. The first focuses on matching markets, where he developed tractable cutoff characterizations that clarify market structures for college admissions and medical residency matching (NRMP). His work demonstrates how price discovery mechanisms can streamline inefficient processes like college applications and subsidized housing allocation. The second strand examines cryptocurrencies and blockchain technology, investigating how open-source computer code functions as market rules in decentralized systems. This research explores both the economic security of permissionless consensus and fundamental limitations of proof-of-work protocols. Professor Leshno's publications reveal a cohesive research trajectory applying economic theory to increasingly complex market structures. His work consistently bridges theoretical rigor with practical implementation, evolving from traditional matching markets to the frontier of decentralized digital systems. Publications in top journals like American Economic Review and Journal of Political Economy demonstrate both analytical depth and real-world relevance across education, healthcare, and financial technology sectors. Professor Leshno has received significant recognition for his contributions: ACM SIGecom Test of Time Award for foundational work in matching markets INFORMS Frederick W. Lanchester Prize for outstanding contributions to operations research Prior to Chicago Booth, Professor Leshno served as Assistant Professor at Columbia Business School and completed a postdoctoral fellowship at Microsoft Research New England, following industry experience at Yahoo! and IBM. He teaches MBA courses in Competitive Strategy and Market Design, and developed a PhD seminar bridging computer science theory with economic principles for distributed systems. His research continues to influence both academic theory and practical implementations of market mechanisms across multiple sectors. Professor Leshno maintains active collaborations with leading researchers including Itai Ashlagi, Irene Lo, and Gur Huberman, advancing the theoretical foundations of market design while addressing contemporary challenges in digital marketplaces and allocation systems.
Ali Abbas is a Senior Researcher and Methods Fellow at the MRC Epidemiology Unit within the University of Cambridge's School of Clinical Medicine. His academic background includes a PhD in Computer Science from Manchester Metropolitan University, an MSc in Media Informatics from RWTH Aachen University, and a BS in Computer Science from Mohammad Ali Jinnah University. With over two decades of research experience, he specializes in data-driven approaches including exploratory analysis, statistical modelling, and interactive visualization. His core research focuses on developing environmentally sustainable transport systems with positive public health outcomes, particularly through: Agent-based modeling of transport behaviors Cycling infrastructure and active travel interventions Health impact assessment of urban mobility policies Spatial analysis using GIS technologies Complex systems approaches to public health He leads several major research initiatives including the JIBE project (integrating transport and built environment models), GLASST (global health impact assessment), TIGTHAT (integrated global transport-health tool), National Propensity to Cycle Tool, and Impacts of Cycling Tool. His publication portfolio demonstrates consistent focus on transport-health interactions, physical activity epidemiology, and urban health modelling, with recent work emphasizing policy applications and global scalability. As an advocate for open science, he maintains active GitHub repositories of his computational models. He additionally manages junior researchers and contributes to training initiatives within his unit.
Duncan Astle is the Gnodde Goldman Sachs Professor of Neuroinformatics at the Department of Psychiatry, University of Cambridge. He serves as a Programme Leader at the Medical Research Council's Cognition and Brain Sciences Unit (MRC CBU) and is a Fellow of Robinson College. Astle heads the 4D Lab (Development, Dynamics, Disorders, Data Science), which provides a research home for approximately 15 Early Career Researchers working at the intersection of developmental cognitive neuroscience and advanced data science methodologies. Astle's research focuses on understanding childhood development through innovative analytical approaches. His work employs transdiagnostic methods to study children with attention, learning, and memory difficulties, moving beyond traditional diagnostic categories. He investigates how neural systems develop in childhood, how they relate to developmental disorders, and how they respond to intervention. His research integrates network science, machine learning, and generative modeling to capture the complexity of neurodevelopmental diversity, examining how cognitive skills, literacy, numeracy, and mental health interrelate over developmental time. His publication record reveals a strong focus on brain connectivity and organization across development. Recent work explores structural and functional neurodevelopmental trajectories, brain wiring economics, and the impact of environmental factors on neural development. Astle's research frequently employs advanced data science techniques to identify sub-populations of children with different cognitive or brain profiles, regardless of diagnosis, and to map non-linear relationships between brain organization and cognitive difficulties. His work has increasingly focused on transdiagnostic approaches to understanding developmental disorders and the application of computational models to developmental neuroscience. Astle actively supervises PhD students and has built a substantial research group that contributes to major projects including the Centre for Attention Learning and Memory (CALM) and Resilience in Education and Development (RED). His work has been supported by prestigious funding bodies including the Royal Society, the British Academy, the Medical Research Council, and the Economic and Social Research Council, as well as multiple charitable foundations. The 4D Lab, under Astle's leadership, utilizes state-of-the-art facilities at the University of Cambridge, including on-site magnetic resonance imaging and magnetoencephalography scanners. The lab contributes to building specialist cohorts such as CALM (800 children with cognitive difficulties plus 200 comparison children) and RED, which study children's development, resilience, and educational outcomes. Astle's team explores how growing up in adverse environments affects children's brains, behavior, and mental health, with the aim of identifying early markers of risk and resilience.
David L. Henann serves as the James R. Rice Associate Professor of Solid Mechanics in the Department of Engineering at Brown University's School of Engineering. His research focuses on continuum-level constitutive modeling of engineering materials, with particular expertise in granular materials, viscoelastic foams, and bubble dynamics in soft solids. Henann leads an active research group developing computational frameworks for material behavior prediction through numerical simulation. PhD, Massachusetts Institute of Technology (2011) SM, Massachusetts Institute of Technology (2008) BS, State University of New York at Binghamton (2006) Henann's research spans constitutive theory development and computational implementation for complex material systems. His group pioneers nonlocal continuum models for granular flows, large-deformation viscoelastic theories for elastomeric foams, and high-strain-rate characterization of microcavitation phenomena. Current projects include modeling size segregation in granular media, bubble dynamics in viscoelastic hydrogels, and electromechanical instabilities in dielectric elastomers. His publication record reveals consistent focus on material instability phenomena , constitutive model validation , and experimental-computational synergy . Henann frequently collaborates with experimental groups to validate theoretical frameworks, particularly in soft matter mechanics and cavitation dynamics. Eshelby Mechanics Award for Young Faculty (2020) NSF CAREER Award (2016) Pi Tau Sigma Gold Medal (ASME, 2016) Brown University Teaching Awards (2015-2016) Henann maintains an active teaching portfolio covering continuum mechanics, solid mechanics, and plasticity at both undergraduate and graduate levels. His research group operates a computational mechanics laboratory with extensive Fortran-based simulation capabilities, evidenced by multiple open-source repositories on GitHub for granular rheology, foam modeling, and dielectric elastomer analysis. Current work focuses on extending nonlocal granular models to industrial applications and developing predictive frameworks for soft material failure under extreme loading conditions.
Dr Shu-Ling Lu is an Associate Professor at the University of Reading , serving as Director of the MSc Project Management Programme and a Member of Senate . Her research spans Innovation Management , Quality Control , and Net Zero Transitions in construction, alongside Heritage Building Integration and Gender Dynamics in built environment sectors. Her academic journey includes a PhD , MSc in Construction Engineering , and a Diploma in Architectural Engineering , all from institutions in Taiwan and the UK, complemented by a Postgraduate Certificate in Higher Education Practice from the University of Salford. Research Interests : Innovation in construction, quality management, heritage conservation, net-zero transitions, gender equity, and system dynamics applications. Article Trends : Focus on defects analysis , heritage integration , gender dynamics , system dynamics , and net-zero strategies across 15 recent publications. Scientific Awards : Fellow of the Chartered Institute of Building (FCIOB) Fellow of Higher Education Academy (FHEA) Full member, Association for Project Management (MAPM) BSI Committee Participation (Quality Management Standards) Supervision & Grants : Mentored 7 PhD students and led/co-led projects funded by Natural Environment Research Council (NERC) , EPSRC , and COST , with total grants exceeding £500,000.
Ben Seiyon Lee is an Assistant Professor in the Department of Statistics at George Mason University's College of Science. His work bridges computational statistics, climate modeling, and environmental risk assessment. Education: PhD in Statistics, Pennsylvania State University (2020) Lee specializes in computational methods for high-dimensional spatiotemporal data and uncertainty quantification in climate models. His research explores climate change impacts on extreme hydrological events, wildfire emissions, and medical decision-making. Recent publications focus on Bayesian spatiotemporal frameworks for extreme precipitation analysis, zero-inflated spatial models, and multisector uncertainty quantification. His work addresses challenges in flood risk assessment, agricultural yield projections, and healthcare compliance metrics.
Michael Barnes is a Tutorial Fellow in Physics and Professor of Physics at the University of Oxford. He contributes to the Department of Physics through teaching and research, with a focus on plasma behavior in magnetic fields. His work has critical applications in sustainable energy production via fusion and astrophysical systems. Professor Barnes teaches Mathematical Methods for Physicists to undergraduate students at University College and lectures on Complex Numbers and Ordinary Differential Equations . His pedagogical emphasis is on developing mathematical fluency for advanced physics topics. His research explores plasma turbulence suppression by sheared flows, particularly in magnetic confinement fusion. Key projects include the development of the TRINITY multiscale gyrokinetic transport code and studies on tokamak transport barriers. Recent publications highlight advancements in gyrokinetic simulations, collision operators, and beam diagnostics for fusion applications. Notable trends in his publications include multiscale modeling of plasma turbulence, zonal flow dynamics, and experimental comparisons for fusion devices like JET, MAST, and ITER. Subfields span from fundamental kinetic theory to applied fusion engineering.
Dr. Monica P. Colaiacovo is a Professor of Genetics at Harvard Medical School, where she leads research in the Department of Genetics within the Blavatnik Institute. Her laboratory is located in the New Research Building in Boston, Massachusetts. Dr. Colaiacovo's research focuses on the molecular mechanisms of meiosis, chromosome dynamics, and DNA repair in the Caenorhabditis elegans model system. Her work examines how environmental toxicants impact germline function and reproductive health, with particular emphasis on chromosome segregation, recombination, and the synaptonemal complex. Her publications reveal a consistent research trajectory examining critical aspects of meiotic chromosome behavior, including double-strand break formation and repair, crossover designation, and chromosome movement during prophase I. Her laboratory has made significant contributions to understanding how environmental exposures like bisphenol A and phthalates disrupt normal meiotic progression and lead to germline dysfunction. Dr. Colaiacovo's work demonstrates strong interdisciplinary connections between basic chromosome biology, environmental health sciences, and reproductive medicine. Her research group employs advanced genetic, molecular, and imaging techniques to dissect the complex mechanisms ensuring accurate chromosome segregation during gamete formation. Her laboratory actively collaborates with researchers studying aging, DNA repair pathways, and environmental toxicology, as evidenced by publications spanning multiple high-impact journals including Nature, PLoS Genetics, and Genetics.
Jeff Linderoth is the Harvey D. Spangler Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on large-scale numerical optimization, mixed-integer nonlinear programming, and stochastic programming, with applications in energy systems, global routing, and industrial processes. Education: BS in General Engineering (highest honors) from University of Illinois at Urbana-Champaign, MS in Operations Research from Georgia Institute of Technology, PhD in Industrial Engineering from Georgia Institute of Technology. Linderoth's work addresses theoretical and applied challenges in optimization, including developing algorithms for mixed-integer programming, analyzing knapsack polytopes, and creating tools like the Minotaur optimization toolkit. His recent publications explore integer programming techniques for subspace clustering, complementarity constraints, and customized coverage instrumentation. Selected trends in his research include advancements in stochastic programming, orbital branching for symmetric integer programs, and congestion analysis in power systems. His group contributes to optimization software and data-driven libraries like MIPLIB. Scientific Award: Harvey D. Spangler Professor.
Manfred Droste is a Professor at the Institute of Computer Science of the University of Leipzig, where he leads the Research Group on Automata and Formal Languages. He serves as Director of the Graduate Centre Mathematics, Computer Science and Natural Sciences and is Vice-speaker of the DFG-Research Training Group Quantitative Logics and Automata. His academic career spans decades of research and leadership in theoretical computer science and algebra. Prof. Droste's research focuses on theoretical computer science, particularly automata theory, logic, algebraic models for concurrent systems, and domain theory. In algebra, his interests include model theory, automorphism groups, and ordered algebraic structures. His work bridges theoretical foundations with practical applications in formal language theory and quantitative systems. His extensive publication record demonstrates a consistent focus on weighted automata, formal languages, and their logical characterizations. Over the years, his research has evolved to address increasingly complex quantitative models, with recent work focusing on weighted complexity classes, weighted linear dynamic logic, and decidability boundaries for weighted automata. Prof. Droste has received significant recognition including election to Academia Europaea, an honorary doctorate from Immanuel Kant Baltic Federal University, and fellowship in the Asia-Pacific Artificial Intelligence Association. These honors reflect his substantial contributions to theoretical computer science. He has supervised numerous PhD students including Dietrich Kuske, Paolo Boldi, and Karin Quaas, many of whom have become prominent researchers. His extensive grant portfolio includes multiple DFG projects on weighted automata and international collaborations through DAAD funding. Prof. Droste leads a vibrant research team including Andrea Hesse, Karin Quaas, Erik Paul, and others. He has organized the international workshop series "Weighted Automata: Theory and Applications" since 2002, fostering global collaboration in this specialized field.
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