Dr Jack Betteridge is an Honorary Research Fellow in the Department of Mathematics, Faculty of Natural Sciences, at Imperial College London. His work bridges computational mathematics with environmental sciences, focusing on numerical methods for atmospheric and oceanic systems. His research interests include: Numerical and Computational Mathematics Atmospheric Sciences Oceanography Physical Geography and Environmental Geoscience Computation Theory and Mathematics Distributed Computing Analysis of his 2019-2024 publications reveals deep engagement with finite element methods, particularly through the Firedrake project for automated PDE solutions. His work emphasizes high-performance computing applications in geophysical fluid dynamics, developing novel preconditioners and solvers for atmospheric modeling while contributing to computational education for mathematicians.
Professor Jared Tanner is Professor of the Mathematics of Information at the University of Oxford's Mathematics Institute and a Fellow of Exeter College. Previously, he held positions at the University of Edinburgh (2007-2012) as Professor, Reader, and Lecturer in Mathematics, University of Utah (2006-2007) as Assistant Professor, and Stanford University (2004-2006) as an NSF Postdoctoral Fellow. His research focuses on extracting models from high-dimensional data to reveal essential information, with specific contributions including sampling theorems in compressed sensing using stochastic geometry, efficient algorithms for matrix completion, and theoretical understanding of deep neural networks. Recent interests include neural network initialization techniques to preserve geometric and information-theoretic properties, as well as network pruning methods. Professor Tanner has supervised numerous doctoral students at Oxford and Edinburgh, including Alireza Naderi, Thiziri Nait Saada, Ilan Price, Giuseppe Ughi, Charles Millard, Michael Murray, Simon Vary, Bernadette Stolz, Bogdan Toader, Rodrigo Mendoza-Smith, Ke Wei, Bubacarr Bah, and Andrew Thompson, many of whom have gone on to prestigious positions in academia and industry. His publication record spans over two decades with significant contributions to compressed sensing, matrix completion, and more recently deep learning theory. His work demonstrates a consistent progression from foundational theoretical work to practical applications in signal processing and machine learning. As an academic leader, Professor Tanner serves as Founding Editor-in-Chief of Information and Inference: A Journal of the IMA and has held editorial positions at several prestigious journals including Applied and Computational Harmonic Analysis and IEEE Signal Processing Letters . He has organized numerous conferences and workshops including Prospects in Mathematics and the FoCM Computational Harmonic Analysis workshop.
Daniel Liberzon , the Richard T. Cheng Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign , is a leading expert in theoretical and applied control systems. He also serves as a professor at the Coordinated Science Laboratory and an affiliate professor in the Department of Mathematics . Ph.D. in Mathematics, Brandeis University (1998) Undergraduate, Mathematics, Moscow State University (1989-1993) His research spans foundational and applied aspects of nonlinear control theory , switched/hybrid systems , and control under limited information . Applications include networked control, power systems synchronization, and robotic coordination. Recent work focuses on topological entropy, almost Lyapunov functions, and robust observer design. Key article trends include: Entropy analysis for interconnected nonlinear systems Stability criteria under switching and disturbances Observer robustness via ISS and qDES frameworks Model reduction techniques for power systems Scientific awards include: 2019 ACM SIGBED HSCC Best Paper Award 2016 IFAC Fellow 2013 IEEE Fellow 2007 AACC Donald P. Eckman Award 2007 UIUC CoE Xerox Award 2002 IFAC Young Author Prize 2002 NSF CAREER Award Liberzon has supervised numerous sponsored projects funded by NSF and AFOSR. He teaches advanced courses in nonlinear control, optimal control, and hybrid systems, appearing on the UIUC List of Teachers Ranked as Excellent multiple times.
Dr. Jan Salmen is a researcher at Ruhr University Bochum's Faculty of Computer Science, affiliated with the Institute of Neuroinformatics (INI). His work focuses on real-time systems, computer vision, and machine learning. Doctoral thesis: Efficient video-based driver assistance systems Salmen's research spans autonomous driving, traffic sign recognition, stereo vision, and sports analytics. He has contributed to benchmarks in traffic sign detection and soccer analysis. Publications highlight his expertise in image processing, pattern recognition, and sensor fusion for autonomous systems. Key trends include optimization of machine learning algorithms for real-time applications. He collaborates with interdisciplinary teams at INI, which integrates experimental psychology, neurophysiology, and robotics into artificial cognitive systems research.
Mehtaab Sawhney is a Clay Research Fellow and a tenure-track assistant professor at Columbia University specializing in combinatorics, probability, analytic number theory, and theoretical computer science. His academic journey began at the University of Pennsylvania where he enrolled in a Bachelor of Engineering in Computer Science (2016-2017), then continued at MIT where he earned a Bachelor of Science in Mathematics with Minor in Computer Science (2017-2020), followed by a Doctor of Philosophy in Mathematics (2020-2024) under the advisorship of Yufei Zhao. His research spans probabilistic combinatorics, random matrix theory, additive number theory, and theoretical computer science. Sawhney's work bridges theoretical mathematics with computational applications, focusing on random structures, additive combinatorics, and spectral properties of discrete objects. His publications demonstrate a strong interdisciplinary approach that connects number theory with probabilistic methods to solve complex combinatorial problems. The analysis of his publication record reveals a consistent focus on foundational mathematical structures with applications across multiple domains. His work on random graphs, additive bases, and arithmetic progressions has established him as a leading researcher in modern combinatorics, often collaborating with prominent mathematicians including Ashwin Sah, Yufei Zhao, and Vishesh Jain. His research output shows remarkable depth and breadth, with contributions to both pure mathematics and theoretical computer science. 2024 Clay Research Fellow 2021 Frank and Brennie Morgan Prize for Outstanding Research in Mathematics by an Undergraduate Student (joint with Ashwin Sah) Churchill Scholar 2020 Best Student Paper STOC 2021 (Joint with Ryan Alweiss, Yang Liu) Best Student Paper ITCS 2022 (Joint with Yang Liu, Ashwin Sah) 2023 Hartley Rogers Jr. Prize 2022 Charles W. and Jennifer C. Johnson Prize (joint with Ashwin Sah) NSF Graduate Fellowship Sawhney has established a robust research program with significant contributions across multiple mathematical disciplines. His frequent collaborations with top researchers worldwide indicate an active and influential research network. While specific advisees aren't listed in available information, his extensive publication record with numerous co-authors suggests active mentorship of junior researchers through collaborative projects.
Univ.-Prof. Dr. Norbert Schuch is a Professor of Physics and Mathematics at the University of Vienna, leading the Quantum Information and Quantum Many-Body Physics group. His research bridges Quantum Information Theory, Quantum Computing, and the study of complex quantum many-body systems, with a focus on Tensor Networks, Topological Order, and Symmetry Breaking. He has offices at both the Faculty of Physics (Boltzmanngasse 9) and Faculty of Mathematics (Oskar-Morgenstern-Platz 1). Research Interests : Quantum Information at the interface of Many-Body Physics, including Entanglement Theory, Topological Quantum Computation, Tensor Network algorithms, and Symmetry-Protected Topological (SPT) phases. His work develops numerical and analytical frameworks to study entanglement order parameters and prepare/experiment with topological states in quantum simulators. Teaching : Courses on Quantum Information, Quantum Computing, Theoretical Physics, and seminars on quantum many-body topics. Prior to Vienna, he was a tenured group leader at Max-Planck-Institute of Quantum Optics and a lecturer at Technical University Munich. Scientific Awards : ERC Consolidator Grant SEQUAM (2020–2025) FWF ESPRIT Programme ESP 306 FWF SFB BeyondC FWF Entanglement Order Parameters Research Trends : His recent publications explore Quantum Algorithms, Tensor Networks for Topological Phase Transitions, Entanglement Spectra, and Symmetry-Protected Phases. Key subfields include Non-Abelian Anyons, Chiral Spin Liquids, and Computational Complexity in Many-Body Systems. Group Members : Current team includes postdocs like Dr. Ilya Kull and Dr. András Molnár, with historical alumni spanning PhDs, Masters, and BSc students now at institutions like Xanadu, MIT, and Quantinuum.
Associate Professor Pierre Le Clech is a leading expert in membrane science and chemical engineering at the University of New South Wales , affiliated with the UNESCO Centre for Membrane Science & Technology. His work focuses on optimizing membrane processes for water and wastewater treatment, particularly addressing fouling mechanisms caused by biopolymeric materials and algal blooms. Current research explores fouling characterization, energy-efficient desalination, and membrane regeneration strategies. He contributes to Desalination and Water Treatment as Associate Editor, and serves on the editorial boards of Membrane Water Treatment and Process Safety and Environmental Protection . Research Trends : Recent publications highlight advancements in algal fouling analysis, graphene oxide membrane applications, and computational modeling of fouling dynamics. His work bridges chemical engineering with environmental technology , emphasizing sustainable solutions for global water challenges. Scientific Awards : Associate Editor, Desalination and Water Treatment Editorial Board Member, Membrane Water Treatment and Process Safety and Environmental Protection Supervision & Collaboration : He actively supervises projects in municipal wastewater treatment, potable water systems, and membrane fouling mitigation. His team collaborates with industry partners on innovations like solar-integrated desalination and biofouling prevention.
Jianlin Xia is a Professor of Mathematics at Purdue University, with a courtesy appointment in the Department of Computer Science. He joined the university in 2014. Xia holds a Ph.D. in Applied Mathematics from the University of California, Berkeley (2006). His research focuses on numerical linear algebra, fast algorithms for structured matrices, and their applications in computational science and engineering. His work addresses challenges in solving large-scale linear systems, eigenvalue problems, and partial differential equations (PDEs) using innovative methods like fast multipole techniques, hierarchical structures, and randomized algorithms. Key areas of research include: Design and analysis of fast algorithms for structured matrices (e.g., hierarchical, semiseparable, Cauchy matrices) Efficient direct and iterative solvers for PDEs, especially Helmholtz equations in seismic modeling Stability and robustness of numerical methods in high-performance computing Applications in wave propagation, inverse problems, and machine learning Xia’s contributions include advancements in low-rank approximations, divide-and-conquer eigenvalue decomposition, and scalable preconditioning techniques. His work emphasizes both theoretical analysis and practical implementation, often leveraging parallel computing architectures. Contact: xiaj@purdue.edu .
Professor Aida X El-Khadra is a leading theoretical physicist at the University of Illinois Urbana-Champaign, affiliated with the Department of Physics within the Grainger College of Engineering. She holds the rank of Professor since 2008, following roles as Associate and Assistant Professor. Her research focuses on precision calculations in lattice QCD, particularly in the context of the muon's anomalous magnetic moment (g-2) and hadronic vacuum polarization. She chairs the Muon g-2 Theory Initiative and is a key contributor to the Particle Data Group and Snowmass process. Education: PhD from UCLA (1989), Diplom in Physics from Freie Universität Berlin (1984). Research Highlights: Lattice QCD applications to Standard Model precision tests, CKM matrix determinations, and quantum simulations for high-energy physics. Her work addresses discrepancies between experimental muon g-2 results and theoretical predictions, with contributions to resolving these via lattice computations and data-driven analyses. Awards include the Simons Fellowship, AAAS Fellowship, and Fermilab Distinguished Scholar appointment.
Youssef Marzouk is a Professor of Aeronautics and Astronautics at MIT, serving as co-director of the MIT Center for Computational Engineering and director of the Aerospace Computational Design Laboratory. His research focuses on integrating physical modeling with statistical inference, emphasizing Bayesian computation, uncertainty quantification, and optimal experimental design. He holds a SB, SM, and PhD from MIT and has been recognized with prestigious awards including the DOE Early Career Award and the Junior Bose Teaching Prize. Education: PhD in Aeronautics and Astronautics, MIT SM in Aeronautics and Astronautics, MIT SB in Aeronautics and Astronautics, MIT Research Interests: Uncertainty Quantification techniques for complex systems Bayesian computational methods and inverse problem solutions Optimal experimental design strategies Interdisciplinary applications in geophysics, environmental science, and engineering Awards: 2022: Report to the President, Center for Computational Science and Engineering 2021: Bayesian Inference Software Framework (hIPPYlib-MUQ) 2012: MIT School of Engineering Junior Bose Award 2010: DOE Early Career Research Award Labs & Leadership: Aerospace Computational Design Laboratory (Director) MIT Center for Computational Engineering (Co-Director) Editorial Board roles: SIAM Journal on Scientific Computing, Advances in Computational Mathematics
Rahul Jain is a Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. He was promoted to full Professor from January 2020, having previously served as Associate Professor (July 2013-July 2013) and Assistant Professor (November 2008-July 2013). He is also a Principal Investigator at the Centre for Quantum Technologies (CQT), Singapore since November 2008. Dr. Jain earned his Ph.D. in Computer Science from Tata Institute of Fundamental Research, Mumbai (2003) and B.Tech. in Electrical & Electronics Engineering from Indian Institute of Technology, Mumbai (1997). Prior to joining NUS, he conducted postdoctoral research at UC Berkeley (2004-2006) and at the Institute for Quantum Computing, University of Waterloo, Canada (2006-2008). His research spans quantum computation, information theory, complexity theory, communication complexity, and cryptography. Dr. Jain has made significant contributions to quantum information theory, particularly in quantum communication complexity, quantum key distribution, and quantum algorithms. His work bridges theoretical computer science with quantum information processing, exploring fundamental limits of quantum computation and communication. His research demonstrates strong expertise in both theoretical proofs and practical applications of quantum information principles. Analysis of Dr. Jain's recent publications (2022-2025) reveals a consistent focus on quantum cryptography foundations, quantum communication protocols, and quantum information theory. His work frequently appears in top theoretical computer science venues including FOCS, STOC, and QIP, as well as leading journals like IEEE Transactions on Information Theory. Key themes include non-malleable quantum codes, quantum state redistribution, quantum communication complexity, and quantum cryptographic protocols with rigorous security proofs. Award under the VISITING ADVANCED JOINT RESEARCH FACULTY SCHEME (VAJRA) 2017-18 by Department of Science and Technology, Government of India BEST of 2016 by ACM Computing Reviews Young Researcher Award, National University of Singapore, 2012 Best paper award at STOC 2010 IBM Distinguished Dissertation Award, 2005 TAA-Sasken Best Thesis Award, 2005-2006 Dr. Jain has supervised numerous graduate students who have secured positions at Harvard University, IBM, JPMorgan Chase, University of Waterloo, and other prestigious institutions. His research is supported by significant grants including the VAJRA Faculty Scheme award. He serves as Associate Editor for the Journal of Computer and System Sciences and on program committees for major conferences including ITCS 2025, FOCS 2022, and QIP 2022-2014. At CQT, he leads research in quantum information theory and quantum algorithms, contributing to Singapore's position as a regional hub for quantum computing research.
Alexander Bastounis is a Lecturer in Applied Mathematics at King's College London, affiliated with the Department of Mathematics and the King’s Institute for Artificial Intelligence. His research focuses on computational mathematics, optimization, and the trustworthiness of AI systems. He holds a PhD from the University of Cambridge and has held academic roles at institutions including Leicester University, City University of Hong Kong, and TU Berlin. Education: PhD in Applied Mathematics from DAMTP, University of Cambridge (2018). Earlier academic qualifications not specified. Research interests include foundational aspects of computational mathematics, AI limits and robustness, adversarial attacks, and inverse problems. His work explores computational barriers in estimation and learning, with recent attention on stealth attacks in AI models and feature selection reliability. Received the Leslie Fox Prize (2019) for work on inverse problems Contributed to SIAM News articles on compressed sensing and AI challenges Advising and grants: Currently supervises the EPSRC-funded project '50:50 Haleon/EPSRC DLA Studentship' (2025–2029). No listed students. Labs/teams: Active in King’s Institute for Artificial Intelligence and collaborates on interdisciplinary projects across computational mathematics and AI security.
Dr. HanQin Cai is the Paul N. Somerville Endowed Assistant Professor in the Department of Statistics and Data Science at the University of Central Florida (UCF), also serving as Director of the Data Science Lab. He holds a joint appointment with the Department of Computer Science. His research focuses on theoretical and algorithmic foundations of mathematical optimization, data science, and machine learning, with emphasis on non-convex algorithms, adversarial attacks, signal/image processing, and deep learning integration. He has secured NSF grants totaling over $2.6M, including a $121K single-PI grant and a $2.49M co-PI grant. His work has been recognized with the UCF OSCaR Award (2025) and IEEE Senior Member status (2024). Education: PhD in Applied Mathematical and Computational Sciences from University of Iowa (2018), with M.S. in Mathematics (2014) and M.C.S. in Computer Science (2017). Previously served as a Postdoc at UCLA Mathematics Department under Dr. Wotao Yin. Research highlights include: query-efficient zeroth-order optimization, robust signal processing with corrupted data, adversarial attacks on neural networks, and tensor-based methods for high-dimensional data analysis. His recent publications explore advanced techniques in matrix recovery, tensor decompositions, and explainable AI. Grants & Awards: NSF DMS-2304489 (2022–2025), NSF DUE-2321986 (2024–2029), UCF OSCaR Award, IEEE Senior Membership. Labs & Teams: Directs UCF's Data Science Lab, collaborates across disciplines in statistics, computer science, and engineering.
Cory Simon serves as Associate Professor in the Department of Chemical, Biological, and Environmental Engineering within Oregon State University's College of Engineering. His research integrates machine learning, optimization, and chemical engineering to advance materials discovery and environmental sensing systems. His academic foundation includes a Ph.D. in Chemical Engineering from the University of California, Berkeley and a B.S. in Chemical Engineering from The University of Akron. Simon's work centers on Bayesian methodologies for scientific challenges, featuring: Bayesian optimization for adaptive materials synthesis Statistical inversion of physical systems with uncertainty quantification Computational design of nanoporous sensor arrays Stochastic algorithms for robotic environmental monitoring Recent publications demonstrate accelerating focus on multi-fidelity optimization for molecular design and atmospheric water harvesting, bridging chemical engineering with computational science through data-driven approaches. Leading The Simon Ensemble research group, Simon champions a versatile 'buffet-style' research philosophy—drawing from mathematics, statistical mechanics, and machine learning to address interdisciplinary problems across chemistry, materials science, and environmental engineering.
Guifang Li is a Professor of Optics and Electrical & Computer Engineering at the University of Central Florida (UCF), affiliated with CREOL, The College of Optics and Photonics. He holds the position of Editor-in-Chief of Advances in Optics and Photonics . His academic journey includes a Ph.D. from the University of Wisconsin-Madison and leadership roles such as Director of the NSF IGERT program in Optical Communications and Networking at UCF. Dr. Li's research focuses on optical communication and networking , RF photonics , and all-optical signal processing . His innovations include pioneering work on photonic computing architectures and high-capacity optical communication systems. He co-founded Optium, UCF's first venture startup, which became a public company (OPTM) in 2006 and later part of II-VI. His scientific contributions are recognized through prestigious awards, including the NSF CAREER Award, Office of Naval Research Young Investigator Award, and fellowships from IEEE, OSA, SPIE, and the National Academy of Inventors. He has advised over 20 Ph.D. students and leads a multidisciplinary research group involving postdoctoral scholars and graduate students. Recent research trends in his publications emphasize photonic computing (e.g., photonic matrix processors, floating-point arithmetic) and advanced optical systems (e.g., quantum cascade lasers, MPLC-based demultiplexers). His work bridges fundamental optics with practical applications in telecommunications and sensing. Labs/Teams: His research team specializes in optical communication systems, photonic integrated circuits, and computational optics.
