Dr. Gianluca Ceruti is a researcher affiliated with the Center for Intelligent Systems (CIS) at École Polytechnique Fédérale de Lausanne (EPFL). His work focuses on computational efficiency in neural networks through dynamical low-rank approximation methods. He has collaborated with institutions including the Karlsruhe Institute of Technology (KIT), Gran Sasso Science Institute (GSSI), and University of Innsbruck. Research Interests: Low-rank optimization for neural networks Reducing computational/memory footprint of AI models Numerical integration techniques for machine learning His 2023 presentation at EPFL CIS-RIKEN AIP series introduced the DLRT algorithm , which adaptively modifies neural network weight matrix ranks during training to preserve accuracy while lowering resource requirements. This aligns with CIS research pillars like AI for medicine and decentralized edge AI infrastructure.
Robert D. Mawhinney is a Professor of Physics and Dean of Science in the Faculty of Arts and Sciences at Columbia University. He is affiliated with the Computing Systems for Data-Driven Science Committee and specializes in theoretical particle physics with a focus on Quantum Chromodynamics (QCD). He received his B.S. in Physics from the University of South Florida (1980) and his Ph.D. in Physics from Harvard University (1987). His research explores the non-linear dynamics of quarks and QCD, requiring advanced computational methods. He co-designed the QCDSP supercomputer (1 TFlop/s), which won the 1998 Gordon Bell Prize for price-to-performance efficiency, and contributed to the QCDOC system that influenced IBM's BlueGene architecture. Recent publications (2018–2023) focus on lattice QCD techniques, including scale setting for fermion lattices, decay-violation modeling, GPU-accelerated Dirac equation solvers, and collaborative FLAG reviews. His work bridges theoretical particle physics, high-performance computing, and algorithm innovation. Awards: Gordon Bell Prize (1998) He leads collaborations such as RBC-UKQCD and has pioneered specialized supercomputers for QCD simulations at Columbia, Brookhaven National Laboratory, and the University of Edinburgh.
Boaz Barak is a Professor at the Weizmann Institute of Science in the Department of Computer Science, Faculty of Mathematics and Computer Science. With an h-index of 64 and over 17,301 citations, he is a leading researcher in theoretical computer science with significant contributions spanning computational complexity, cryptography, and machine learning theory. His research interests include: Computational Complexity Cryptography Zero-Knowledge Proofs Program Obfuscation Interactive Proofs Privacy-Preserving Computation Machine Learning Theory Barak's publication record reveals a trajectory from foundational work in theoretical computer science to contemporary research at the intersection of theory and practice. His early work established impossibility results for program obfuscation and advanced techniques for zero-knowledge proofs beyond black-box simulation. His influential textbook "Computational Complexity: A Modern Approach" has become a standard reference in the field. More recently, his research has expanded into machine learning phenomena like double descent and scaling laws for language models, demonstrating the evolving nature of his theoretical contributions. His work consistently bridges deep theoretical insights with practical implications for computing. His notable collaborations include extensive work with Sanjeev Arora (77 publications, 6,879 citations), David Steurer (95 publications, 4,945 citations), and Oded Goldreich, among others. Professor Barak leads a research group at the Weizmann Institute focused on theoretical aspects of computer security and complexity theory. His work has been consistently supported by major research funding, enabling significant contributions to the theoretical foundations of computer science. He maintains an active research program with publications spanning over two decades, demonstrating sustained impact in multiple subfields of theoretical computer science.
Jon Lee is the G. Lawton and Louise G. Johnson Professor of Engineering at the University of Michigan's College of Engineering. He previously held faculty positions at Yale University and the University of Kentucky, and served as an adjunct professor at New York University. Before his academic career, he was a Research Staff member at IBM T.J. Watson Research Center where he managed the mathematical programming group. Lee's research focuses on mathematical optimization, particularly combinatorial optimization, integer programming, and maximum-entropy sampling. His work bridges theoretical foundations with practical applications in experimental design, statistical modeling, and computational mathematics. He has made significant contributions to D-optimal design theory, perspective relaxations for nonconvex optimization, and generalized inverse computations. His recent publication trends (2022-2025) show consistent work in maximum-entropy sampling problems, D-optimal design algorithms, convex relaxations for nonconvex optimization, and generalized inverse computations. The articles demonstrate increasing sophistication in handling large-scale optimization problems while maintaining theoretical rigor, with particular emphasis on algorithmic efficiency for real-world applications. Lee has received notable recognition including: INFORMS Computing Society Prize (2010) Fellow of INFORMS (since 2013) As an academic leader, Lee has served as founding Managing Editor of Discrete Optimization (2004-06), currently serves as Co-Editor of Mathematical Programming, and is on editorial boards for Optimization and Engineering and Discrete Applied Mathematics. He chaired the Mathematical Optimization Society (2008-10) and the INFORMS Optimization Society (2010-12). His textbook A First Course in Combinatorial Optimization (Cambridge University Press) and open-source book A First Course in Linear Optimization have become standard references in the field. Lee maintains active research collaborations through his work with the Mathematical Optimization Society and INFORMS, and has participated in significant research programs including the Fall 2017 program on Bridging Continuous and Discrete Optimization at the Simons Institute.
Yuxin Chen is a Professor of Statistics and Data Science and Electrical and Systems Engineering at the University of Pennsylvania (UPenn), where he has been since 2022. Prior to UPenn, he served as an Assistant Professor at Princeton University (2017–2021) and a postdoc at Stanford University (2015–2017). His research spans machine learning theory , diffusion models , reinforcement learning , nonconvex optimization , and high-dimensional statistics . Education : Ph.D. in Electrical Engineering (Stanford, 2015) Awards : SIAM Activity Group on Imaging Science Best Paper Prize (2024), IEEE Transactions on Power Electronics Prize Paper Award (2024), ICCM Best Paper Award (Gold Medal, 2017), Alfred P. Sloan Fellowship His recent work focuses on theoretical foundations of diffusion models , including convergence analysis, generalization behavior, and sampling efficiency. He has developed probability flow distance (PFD) to quantify distributional generalization and explored low-dimensional adaptation in diffusion processes. His research also addresses implicit regularization in nonconvex statistical estimation, with applications to phase retrieval and matrix completion. Key scientific applications of his work include controllable image editing, inverse problems in scientific imaging, and robustness in generative models. Collaborative projects with Prof. Qing Qu and Liyue Shen at ICML 2025 highlight his leadership in bridging theory and practice of generative AI. He has mentored several Ph.D. students who now hold academic positions, including Zihan Zhang (HKUST), Yuchen Zhou (UIUC), Hong Hu (WUSTL), Joshua Agterberg (UIUC), and Yuling Yan (UW-Madison). His grants include Google Research Scholar and Amazon Research Awards, supporting advancements in theoretical foundations of AI.
Professor Wei-Biao Wu is a faculty member in the Department of Statistics at the University of Chicago. His research focuses on advancing statistical theory and methodology for high-dimensional data under dependence, addressing critical problems in covariance matrix estimation, regression, and multiple testing where traditional assumptions of independence or light tails fail. Research Focus Establishing theoretical frameworks for high-dimensional inference with dependent observations Developing tools to account for dependence in statistical models Studying deviation and concentration inequalities for dependent random variables Investigating deep Gaussian approximation problems Awards & Recognition Faculty Award for Excellence in Graduate Teaching (2022) Alexander von Humboldt Foundation Research Award (2019) He maintains an active research program and contributes to graduate education at the University of Chicago.
Michael Kapralov is an Associate Professor in the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he is part of the Theory Group. His research focuses on theoretical computer science with emphasis on the theoretical foundations of big data analysis. Dr. Kapralov completed his PhD at Stanford iCME under the supervision of Ashish Goel. Following his doctoral studies, he spent two years as a postdoc at the Theory of Computation Group at MIT CSAIL working with Piotr Indyk, and then a year at the IBM T. J. Watson Research Center as a Herman Goldstine Postdoctoral Fellow. His research primarily centers on sublinear algorithms, with specific directions including streaming algorithms, sketching techniques, sparse recovery, and Fourier sampling. Kapralov's work addresses fundamental questions in the theoretical computer science of big data, developing algorithms that can process massive datasets efficiently with limited computational resources. His approach often combines deep theoretical insights with practical considerations for real-world applications of theoretical computer science principles. Analysis of Kapralov's recent publications reveals a strong focus on advancing the state of the art in sublinear-time algorithms, particularly for graph problems and kernel methods. His work demonstrates a consistent trajectory toward developing more efficient algorithms for fundamental computational problems while establishing tight theoretical bounds on what's possible in streaming and sublinear settings. There's a notable emphasis on bridging theoretical computer science with practical machine learning applications, particularly through kernel methods and Fourier analysis techniques. ERC Starting Grant SUBLINEAR (2018-2023) Gene H. Golub Dissertation Award Best paper award in CT at Fully3D 2007 Institute of Physics (IoP) Select article Professor Kapralov has advised numerous PhD students and postdocs who have gone on to successful careers in both academia and industry, including several who now hold faculty positions at prestigious institutions worldwide. He has also received significant research funding, most notably the European Research Council Starting Grant that supported his SUBLINEAR project from 2018 to 2023. Within the EPFL academic community, Kapralov is actively involved in several initiatives including organizing the Turing Course for high school students, leading a reading group on the Foundations of Deep Learning, and participating in the EPFL Theory Seminar series. He also contributes to the broader theoretical computer science community through program committee service for major conferences including SOSA 2023 and STOC 2022.
Jongse Park is currently an Associate Professor at the School of Computing (SoC), KAIST , and a core member of the Computer Architecture and Systems Laboratory (CASYS) . He holds co-affiliations with the School of Electrical Engineering , Graduate School of AI Semiconductor , Graduate School of System Architect , and Department of Semiconductor System Engineering at KAIST. Since 2025, he has been serving as a Visiting Associate Professor at Stanford University's Pervasive Parallelism Lab within the EECS department. PhD in Computer Science, Georgia Institute of Technology (2018), advised by Prof. Hadi Esmaeilzadeh MS in Computer Science, KAIST (2012), advised by Prof. Seungryoul Maeng BS in Computer Science and Engineering, Sogang University (2010) His research focuses on accelerating AI serving systems , enabling on-device AI , processing-in-memory (PIM) architectures , and flexible AI compiler frameworks . Recent work explores transformer optimization, heterogeneous AI semiconductors, and efficient video-language processing. Recent publications include MICRO 2025 work on PIM for LLMs, VLDB 2025 research on video-language engines, and ISCA 2025 contributions to LLM quantization. His team's projects have received funding from the K-Cloud Project , NRF Young Researcher Program , and IITP Core Technology Development grants . Teaching Innovation Award Excellence Prize, KAIST (2025) Samsung Humantech Paper Award Gold Prize (2025) IEEE Senior Member (2024) Best Paper & Distinguished Artifact Awards at IISWC (2024) and ISCA (2024) He actively supervises PhD and MS students in AI systems research and serves on program committees for top conferences like ASPLOS , ISCA , and MICRO , including organizing roles as Sponsorship Chair for MICRO 2025.
Oleh Melnyk is a Substitute Professor at the Bavarian AI Chair for Mathematical Foundation of Artificial Intelligence at Ludwig-Maximilians-Universität München (LMU Munich), currently on leave from TU Berlin. His academic journey includes a Ph.D. in Mathematics (2023) from Technical University of Munich/Helmholtz Center Munich, an M.Sc. in Mathematics in Data Science (2018) from TU Munich, and a B.Sc. in Statistics (2016) from Taras Shevchenko National University of Kyiv. His research focuses on Mathematical Imaging , Phase Retrieval , Numerical Analysis , Optimization , and Compressed Sensing , with applications spanning ptychography, optical flow, sparse regression, and inverse problems. Recent work emphasizes algorithm convergence, noise-robust recovery, and high-dimensional data decomposition. Melnyk's publications demonstrate a strong focus on inverse problems and computational mathematics , with recurring themes in ptychographic imaging, phase retrieval algorithms, and sparse modeling. His articles frequently address theoretical convergence guarantees and practical applications in medical imaging and electron microscopy. He collaborates with research groups at LMU Munich, TU Berlin, and Helmholtz Munich, focusing on mathematical foundations of imaging and AI. No students, awards, or grants are mentioned in the source materials.
Venkat Chandrasekaran is the Kiyo and Eiko Tomiyasu Professor at the Computing and Mathematical Sciences and Electrical Engineering divisions within the Division of Engineering and Applied Science at the California Institute of Technology . B.A., Rice University, 2005 B.S., Rice University, 2005 M.S., Massachusetts Institute of Technology, 2007 Ph.D., Massachusetts Institute of Technology, 2011 As an applied mathematician, Chandrasekaran's work focuses on optimization and the information sciences . His group develops mathematical foundations for applications in science and engineering, spanning convex optimization , statistical inference , inverse problems , graphs and combinatorial optimization , and applied algebra and geometry . His publications from 2024–2004 demonstrate expertise in mathematical optimization , statistical modeling , and graphical representation . Themes include convex geometry , high-dimensional data analysis , and environmental systems modeling , with applications in hydrology , machine learning , and signal processing . INFORMS Optimization Society Prize Sloan Research Fellow AFOSR Young Investigator Award NSF CAREER Award Okawa Research Grant Young Researcher Prize Chandrasekaran has advised students and postdocs including Armeen Taeb (University of Washington), Yong Sheng Soh (National University of Singapore), and Kevin Shu (Caltech Postdoc). His courses include Mathematical Optimization, Mathematics of Electrical Engineering, and Great Ideas in Data Science.
Fahad Panolan is a Lecturer in the Algorithms and Complexity group at the School of Computing, University of Leeds, UK, a position he has held since August 2023. Prior to this, he was an Assistant Professor in the Department of Computer Science and Engineering at IIT Hyderabad, India, from July 2019 to August 2023. He conducted postdoctoral research at the Department of Informatics, University of Bergen, Norway, between 2016 and 2019. Education: PhD in Theoretical Computer Science, The Institute of Mathematical Sciences, HBNI, Chennai, India (2012–2015) MSc in Theoretical Computer Science, The Institute of Mathematical Sciences, HBNI, Chennai, India (2010–2012) Master of Computer Applications, National Institute of Technology, Calicut, India (2006–2009) BSc in Physics, DGM MES Mampad College, University of Calicut, India (2002–2005) His research lies at the intersection of theoretical computer science and algorithm design, with core interests in Parameterized Algorithms and Complexity , Graph Theory , Approximation Algorithms , and Streaming Algorithms . He has made significant contributions to kernelization, matroid-based techniques, and the development of subexponential-time algorithms for NP-hard graph problems. His work often bridges structural graph theory with algorithmic efficiency, particularly on sparse and geometric graphs. The recent publications show a strong trend in advancing the frontiers of fixed-parameter tractability, including efficient kernelization, approximation schemes for matrix problems, and reconfiguration algorithms. His work frequently appears in top-tier venues such as STOC, SODA, ICALP, and journals like JACM and Algorithmica. Scientific Service: PC Member: WALCOM 2026, IPEC 2023, ESA 2022, IPEC 2021, AAAI 2021 Scientific Coordinator: Parameterized Complexity 201 Workshop Referee for journals including TALG, SIDMA, TCS, Algorithmica, and conferences like STOC, SODA, ICALP, ESA. Fahad Panolan advises PhD students and interns, including Shubhada Suresh Aute and Seshikanth Varma. He has secured research grants through collaborative projects and has delivered invited talks at international workshops and seminars, including Dagstuhl, DIMAP, and Parameterized Complexity workshops. He has taught courses such as Algorithms, Design and Analysis of Algorithms, and Parameterized Algorithms at both University of Leeds and IIT Hyderabad. He is actively involved in the parameterized complexity and algorithms research community, organizing workshops and contributing to the theoretical foundations of efficient computation on hard problems.
Rodrigo I. Silveira is an Associate Professor in the Department of Mathematics at Universitat Politècnica de Catalunya (UPC), where he conducts research in computational and combinatorial geometry. He is a member of the UPC research group on Discrete, Combinatorial and Computational Geometry and has a strong academic background, having earned his PhD at Utrecht University under Marc van Kreveld and completed prior studies at the Universidad de Buenos Aires. His research focuses on Computational Geometry , particularly problems arising from Geographic Information Science (GIS) , such as map construction, trajectory analysis, and visibility. He also works on Graph Drawing and algorithms for geometric structures. His work combines theoretical algorithm design with practical applications in spatial data. His recent publications reflect a consistent trend in geometric algorithms, including shortest paths in complex environments (e.g., portalgons), Voronoi diagrams with color constraints, robot motion coordination, and map inference from GPS data. These works appear in top-tier journals and conferences such as Algorithmica , Computational Geometry: Theory and Applications , SoCG, WADS, and LATIN. He has received scientific recognition, including a Best Paper Award at AGILE 2009 and another at SpatialGems 2021. He has supervised several PhD students to completion, including Guilermo Esteban (2024) and Pilar Cano (2020), and continues to advise current students. Rodrigo has been deeply involved in the academic community, organizing major events such as the European Workshop on Computational Geometry (EuroCG 2023), Graph Drawing 2018, and the Intensive Research Program on Discrete Geometry (2018). He has served on the program committees of SoCG, WADS, LATIN, and CCCG, and has chaired tracks for EGC and CG:YRF. His work is supported by collaborations with leading researchers such as Kevin Buchin, Maarten Löffler, Prosenjit Bose, and Vera Sacristán, and he contributes to both theoretical advances and practical implementations in geometric computing.
João Carlos Martinho Lopes Dias is a Professor of Mathematics at the School of Economics and Management (ISEG) of the Technical University of Lisbon . He holds a PhD in Mathematics from the University of Cambridge and currently serves as Vice-President of ISEG for Academic Affairs. Previously, he chaired the Mathematics Department and coordinated the CEMAPRE Research Center. Education 2011: Aggregation in Mathematics Applied to Economics and Management, ISEG 2002: PhD in Mathematics, University of Cambridge 1996: Bachelor's in Technological Physics Engineering, Instituto Superior Técnico His research focuses on dynamical systems , particularly in polygonal billiards, hyperbolicity, and Lyapunov exponents, with applications in mathematical physics and nonlinear phenomena. His publications (15 most recent) span journals like Advances in Mathematics , Communications in Mathematical Physics , and Nonlinearity , emphasizing chaotic behavior, SRB measures, and renormalization techniques. He has supervised multiple Master's students, including Martim Alves da Costa and Daniel Alcântara, and held leadership roles in academic governance, such as Vice-Dean and President of CEMAPRE. His work bridges theoretical mathematics with practical applications in economics and finance.
Omri Weinstein is an Assistant Professor in the Department of Computer Science at Columbia University. His research bridges Information Theory, Data Structures, and Optimization, focusing on dynamic data structures and dimensionality-reduction techniques to accelerate optimization and search. He received his PhD from Princeton University and was a Simons Society Junior Fellow at the Courant Institute (NYU). Education: PhD in Computer Science, Princeton University Simons Society Junior Fellowship, Courant Institute (NYU) His work explores the theoretical foundations of data structure lower bounds, communication complexity, and secure computation. Recent research includes advancements in dynamic matrix inversion for linear programming, oblivious near-neighbor search, and the interplay between matrix rigidity and data structure efficiency. Key trends in his publications include: Proving polynomial and super-logarithmic lower bounds for static and dynamic data structures Developing novel techniques in information complexity and protocol compression Applications in parallel algorithms, compressed data structures, and algorithmic game theory Scientific Awards: NSF CAREER Award Simons Society Junior Fellow Best Paper Award at CSR '13 Advising and Grants: Advised PhD students Hengjie Zhang and Shunhua Jiang MsC student Victor Lecomte and postdoc Alexander Golovnev Research funded by NSF CAREER Award on data structure lower bounds Labs and Teams: Omri is affiliated with the Theoretical Computer Science Group at Columbia and leads the Data-Structure Lower Bounds Reading Group.
Professor Rolf Drechsler is affiliated with the Department of Mathematics and Computer Science at the University of Bremen, where he maintains an active research profile in formal verification, hardware design, and quantum computing. His office is located in the Multi-purpose high-rise building (MZH) 4330, and he can be reached at drechsler@uni-bremen.de or drechsler@informatik.uni-bremen.de. Dr. Drechsler's research focuses on formal verification techniques, particularly polynomial formal verification methods, binary decision diagrams (BDDs), in-memory computing architectures, and quantum circuit verification. His work bridges theoretical computer science with practical hardware implementation challenges. Notably, he has recently explored the integration of large language models (LLMs) with hardware verification and design automation, representing an emerging interdisciplinary research direction. An analysis of his 2024-2025 publications reveals a strong emphasis on verification methodologies for emerging computing paradigms. His research spans quantum computing verification (qSAT, quantum circuit debugging), in-memory computing (MAGIC-based architectures, memristive crossbars), and traditional hardware verification enhanced by AI techniques. The publications show a pattern of addressing verification challenges in novel computing architectures while maintaining theoretical rigor in formal methods. Professor Drechsler has made significant contributions to Binary Decision Diagram optimization, formal verification of arithmetic circuits, and hardware security. His work on polynomial formal verification represents a distinctive research thread that has evolved over recent years, addressing verification challenges for sequential circuits, approximate adders, and multi-valued logic circuits.
