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Ricky Ini LiuView profile

Associate Professor

• Algebraic Combinatorics
• Algebraic Geometry
• Combinatorial Geometry
• +5 more

Ricky Ini Liu is an Associate Professor in the Department of Mathematics at the University of Washington. Previously, he held positions at North Carolina State University, the University of Michigan, and the University of Minnesota. He earned his Ph.D. in Mathematics from MIT in 2010 under Alexander Postnikov. His research focuses on algebraic combinatorics, particularly its intersections with algebraic geometry, combinatorial geometry, and representation theory. Key interests include Schubert polynomials, polytopes, Hopf algebras, and Kronecker coefficients. He has contributed to foundational work on birational rowmotion, Gelfand-Tsetlin polytopes, and Fomin-Kirillov algebras. Liu has taught a wide range of courses at UW, including special topics in dynamical algebraic combinatorics, combinatorial theory, and problem-solving. He has also been a key instructor at the Mathematical Olympiad Summer Program since 2007 and mentored undergraduates in research programs at the University of Minnesota, Duluth. His publications span high-impact journals like Selecta Mathematica and Journal of Combinatorial Theory , with recent work addressing topics such as determinantal formulas for Schubert polynomials and applications of flow polytopes to diagonal harmonics. Though no specific awards are listed, his extensive publication record and academic roles reflect significant contributions to combinatorial mathematics.

Gianluca IaccarinoView profile

Professor

• Scientific Computing
• Uncertainty Quantification
• Turbulence Modeling
• +6 more

Gianluca Iaccarino is a Professor of Mechanical Engineering at Stanford University and the Robert Bosch Chairholder. He serves as Director of the PSAAP Center and leads large-scale computational research initiatives in uncertainty quantification, exascale computing, and multiphysics simulations. His academic journey includes a PhD in Mechanical Engineering from Politecnico di Bari (2005), postdoctoral work at Stanford's Center for Turbulence Research, and progression from Research Engineer to full Professor. Education : PhD (Politecnico di Bari), MS/BS in Aeronautical Engineering (University of Naples) Research : Computational engineering, turbulence modeling, uncertainty quantification, biomedical fluid dynamics, and exascale-ready algorithms Publications : 15+ recent articles focus on turbulence modeling, data-driven simulations, and uncertainty quantification across diverse applications in aerospace, biomedical, and energy systems Awards : PECASE (2010), APS Fellow (2019), multiple best paper awards (AIAA, ASME), Terman Fellow (2007) Students : Advises doctoral and master's students in mechanical engineering and computational methods Leadership : Director of PSAAP Center (2014-present), Chair of Mechanical Engineering Department (2024-present)

Brian LawrenceView profile

Assistant Professor

• Arithmetic Geometry
• Number Theory
• Diophantine Geometry
• +2 more

Brian Lawrence is an Assistant Professor in the Department of Mathematics at the University of Wisconsin–Madison, currently on leave as of 2025. Previously, he held positions at UCLA, the University of Chicago, and Columbia University, following doctoral studies at Stanford University under Akshay Venkatesh. His educational background includes: PhD in Mathematics from Stanford University (advisor: Akshay Venkatesh) Lawrence's research centers on arithmetic geometry, specializing in Diophantine problems through p-adic methods. His work develops innovative approaches to Mordell's conjecture, Shafarevich-type results, and rational point distribution using p-adic Hodge theory, étale cohomology, and period mappings. He bridges theoretical number theory with computational frameworks, particularly in algorithmic solutions for Diophantine equations. His publication trends reveal sustained focus on foundational Diophantine geometry problems, with recent work emphasizing conditional algorithms for the Mordell problem and sparsity phenomena in integral points. Collaborations with leading mathematicians like Venkatesh and Sawin demonstrate interdisciplinary engagement across number theory and algebraic geometry. Lawrence actively mentors undergraduate researchers, supervising projects on resultants, Hodge theory, and symmetric polynomials by students including Pramana Saldin, Yuchen Chen, Anuj Sakarda, and Spencer Dembner. His organizational roles include founding the Crystalline Cohomology seminar at Columbia and co-organizing the University of Chicago Number Theory Seminar, fostering collaborative research environments. He contributes extensively through expository notes on schemes, polynomials on lattices, and Fibonacci numbers modulo p, reflecting commitment to mathematical education and knowledge dissemination across multiple institutions.

Ram VasudevanView profile

Associate Professor

• Optimization and control of nonlinear systems
• Legged robot locomotion
• Autonomous systems safety
• +4 more

Ram Vasudevan is an Associate Professor and Associate Chair of Graduate Studies in the Department of Robotics at the University of Michigan. His research focuses on developing tools for safe and robust deployment of robotic systems, emphasizing optimization, nonlinear control, and real-world applications. Key areas include legged robot locomotion, shared control systems, and safety-critical autonomous systems. Research Interests: Optimization and control of nonlinear systems, locomotion of legged robots, shared control active safety systems, and automation of diagnostic/rehabilitative tasks. His ROAHM Lab prioritizes mathematical guarantees for robotic performance, with applications in medical robotics, autonomous vehicles, and soft robotics. Recent work emphasizes trajectory optimization, sensor fusion, and safety-aware control strategies. He has contributed to benchmarks for autonomous vehicle perception and novel methods in thermal image restoration using neural radiance fields. Awards: None explicitly listed in provided text. Labs/Teams: Directs the ROAHM Lab, collaborating on projects like robotic tail mechanics, real-time motion planning, and sensor data analysis. Active in academic conferences including RSS and ICRA.

Lijie ChenView profile

Assistant Professor

• Theoretical Computer Science
• Computational Complexity Theory
• Quantum Complexity Theory
• +4 more

Lijie Chen is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley, where he is part of the Berkeley Theory Group. Previously, he was a Miller Research Fellow at UC Berkeley, hosted by Avishay Tal and Umesh Vazirani, and earned his Ph.D. from MIT under Ryan Williams. His research focuses on theoretical computer science, particularly computational complexity theory, with applications to quantum physics and AI safety. Education: Ph.D. in Computer Science from MIT (2022), B.Sc. from Yao Class at Tsinghua University. Research Interests: Complexity theory, quantum complexity, derandomization, circuit lower bounds, and foundational aspects of AI safety. Chen has made significant contributions to understanding fundamental questions in complexity theory, including circuit lower bounds and the connections between randomness and computation efficiency. His work often bridges theoretical insights with practical implications in quantum computing and algorithm design. Awards and Honors: Machtey Award for Best Student Paper (2019). Danny Lewin Best Student Paper Award (2019). Invited to SICOMP Special Issues for FOCS and STOC papers. He has organized workshops on complexity theory and derandomization, and his research has been recognized in venues like STOC, FOCS, and the Journal of the ACM.

Olga Sorkine HornungView profile

Professor

• Computer Graphics
• Geometric Modeling
• Geometry Processing
• +3 more

Prof. Olga Sorkine Hornung is a Full Professor of Computer Science at ETH Zürich, leading the Interactive Geometry Lab. She holds a BSc and PhD from Tel Aviv University (2000 and 2006) and conducted postdoctoral research at Technical University Berlin. Her research focuses on computer graphics, geometric modeling, and geometry processing, with applications in shape editing, digital fabrication, and animation. She has received numerous accolades, including the ACM Fellowship (2020), ERC Consolidator Grant (2020), and the Golden Owl Teaching Award (2021). Her work bridges theoretical foundations and practical algorithms, addressing challenges in parameterization, surface compression, and interactive design tools. Her research interests span: Computer Graphics & Visualization Geometric Modeling & Processing 3D Content Creation & Digital Fabrication Garment Design & Simulation Human Motion Analysis & Animation Awards and grants include: 2024: Best Paper Honorable Mention (EUROGRAPHICS) 2023: Member of Swiss Academy of Engineering Sciences (SATW) 2020: ERC Consolidator Grant 2017: Rössler Prize (ETH Zurich) Her lab focuses on developing novel methods for interactive geometry processing, with recent advancements in garment modeling (e.g., AIpparel, Rags2Riches) and motion retargeting systems like WalkTheDog. She actively collaborates on interdisciplinary projects, including biomedical applications and sustainable fashion technology.

Jacob FoxView profile

Professor

• Combinatorics
• Probability
• Graph Theory
• +3 more

Jacob Fox is a Professor at Stanford University, specializing in Combinatorics and Probability. His research focuses on extremal combinatorics, Ramsey theory, graph theory, and additive combinatorics. He advises students like Maya Sankar. His work explores structural and enumerative aspects of graphs, hypergraphs, and combinatorial configurations. Recent studies include advancements in Ramsey numbers, sumset theory, and probabilistic methods in discrete mathematics. Key research areas include Ramsey numbers for sparse structures, hypergraph properties, and applications of combinatorial geometry. His publications often bridge theoretical insights with algorithmic applications. No scientific awards are listed in the provided text. His advising includes Maya Sankar, with research aligned to combinatorial problems. Collaborative projects involve extremal graph theory and probabilistic combinatorics. No labs or dedicated research groups are explicitly mentioned.

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +6 more

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

Giuseppe AtenieseView profile

Professor

• Cybersecurity
• Blockchain Technology
• Applied Cryptography
• +4 more

Giuseppe Ateniese is a Professor and Eminent Scholar in Cybersecurity at George Mason University, affiliated with the Department of Computer Science and Cyber Security Engineering. Formerly, he held the Farber Endowed Chair and chaired the Department of Computer Science at Stevens Institute of Technology. His research focuses on cloud security, applied cryptography, blockchain technology, and AI-driven cybersecurity solutions. He has received prestigious awards including the NSF CAREER Award and Google/IBM Faculty Awards. AIFurther, he teaches courses on blockchains, cryptography fundamentals, and cryptofinance. His work includes pioneering contributions to rewritable blockchains, password cracking via AI (e.g., PassGAN), and secure decentralized systems. Education: PhD in Computer Science, University of Genoa (2000) Laurea (M.Sc.), University of Salerno (1995) Master of Engineering (Honoris Causa), Stevens Institute of Technology (2020) Research Interests: Ateniese’s work bridges theoretical and applied cryptography, with emphasis on blockchain scalability, privacy-preserving machine learning, and secure data storage. He has developed foundational techniques like proxy re-cryptography and accountable storage systems. His recent efforts address AI’s dual role as both a cybersecurity tool and threat vector, including AI-based password cracking and defense strategies against LLM-driven attacks. Awards: NSF CAREER Award (for privacy/security research) Google Faculty Research Award (cloud security) IBM Faculty Award IEEE CISTC Technical Recognition Award Teaching & Labs: He teaches advanced courses on blockchains (CS695), cryptography fundamentals (CYSE/ECE476), and introductory cryptography (CS487/587). His research team focuses on cryptographic protocols, decentralized systems, and AI-security intersections. Collaborations include projects with Accenture on editable blockchain prototypes and NSF-funded work on password security.

Fatma Kılınç-KarzanView profile

Associate Professor

• Mathematical Optimization
• Convex Optimization
• Non-convex Optimization
• +7 more

Fatma Kılınç-Karzan is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business, with a courtesy appointment as Associate Professor of Computer Science. She is also affiliated with the Algorithms Combinatorics and Optimization (ACO) PhD Program and was a Visiting Scientist at Berkeley's Simons Institute for the Theory of Computing during Fall 2017. Her educational background includes a PhD from Georgia Institute of Technology's H. Milton Stewart School of Industrial & Systems Engineering with a minor in Mathematics, supervised by Prof. Arkadi Nemirovski. She earned her B.S. and M.S. degrees from the Industrial Engineering Department of Middle East Technical University with a minor in Information Systems. Dr. Kılınç-Karzan's research spans mathematical optimization with emphasis on convex and non-convex optimization theory, algorithms, and applications. Her work bridges theoretical foundations with practical implementations in optimization under uncertainty (robust optimization, chance constraints, distributionally robust optimization), machine learning (preference learning from limited data), and business analytics. She develops foundational theory for large-scale optimization problems with applications in decision making under uncertainty and high-dimensional statistical inference. Analysis of her recent publications reveals a strong focus on convex hull characterizations, semidefinite programming relaxations, distributionally robust optimization, and online convex optimization frameworks. Her work demonstrates increasing integration of optimization theory with machine learning applications, particularly in developing data-driven approaches for decision making under uncertainty. NSF CAREER Award (2015) INFORMS Optimization Society Young Researcher Prize (2015) INFORMS Junior Faculty Interest Group (JFIG) Best Paper Award (2014) BP Junior Faculty Chair (2014-2015) Faculty Giving Chair (2012-2013) Wimmer Fellowship (2012-2013) Dr. Kılınç-Karzan has successfully mentored numerous PhD students who have received prestigious awards, including the 2021 INFORMS Optimization Society Best Student Paper (1st prize) and multiple honorable mentions. Her research has been supported by significant grants including an NSF CAREER Award, an ONR grant (with S. Küçükyavuz), and an AFOSR grant. She serves on editorial boards for Mathematical Programming, Operations Research, Mathematics of Operations Research, and other leading journals, and has held leadership positions in professional societies including the Mathematical Optimization Society and INFORMS Computing Society. Through her affiliations with CMU's Tepper School, Computer Science Department, and ACO Program, she collaborates across disciplines to advance optimization theory and its applications. Her professional service includes committee chair roles for major INFORMS competitions and program committee leadership for international optimization conferences.

Tom A.E. OomenView profile

Professor

• Motion Control
• Control Systems Technology
• Data-Driven Modeling
• +4 more

Tom A.E. Oomen is a Full Professor in the Department of Control Systems Technology at the Eindhoven University of Technology (TU/e). He holds affiliations with the Mechanical Engineering School and the EAISI High Tech Systems institute. His research focuses on data-driven control, motion control of mechatronic systems, and system identification, with applications in industries like automotive, medical, and energy systems. Academically, he earned his MSc and PhD from TU/e, and held visiting positions at KTH (Sweden) and the University of Newcastle (Australia). He has received notable grants (NWO Veni/Vidi) and awards, including IEEE and Mechatronics Paper Prize recognition. Oomen is a Senior Member of the IEEE and serves as an Associate Editor for IFAC Mechatronics and IEEE Control Systems Letters. His work bridges fundamental research and industry collaboration, emphasizing advanced motion control and learning algorithms. Projects include PROACTHIS (projection-based control) and ML4CONTROL (AI-driven motion control). He has supervised 99+ works and contributed to over 500 research outputs.

Andrew ChildsView profile

Professor

• Quantum Algorithms
• Quantum Information Processing
• Quantum Simulation
• +2 more

Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.

• Quantum-safe cryptography
• Lattice-Based Cryptography
• Post-Quantum Cryptography
• +4 more

Ron Steinfeld is an Associate Professor in the Department of Software Systems & Cybersecurity at Monash University, Australia. He holds editorial roles in Designs Codes and Cryptography (since 2017) and has served on technical committees for top-tier conferences like ASIACRYPT, CRYPTO, and EUROCRYPT. His research focuses on quantum-safe cryptography, lattice-based cryptography, and blockchain security, with over 80 refereed publications and AUD$4M+ in research funding. Education: BSc Mathematics and Physics (Monash University, 1998) BE (Hons., First Class) Electrical and Computer Systems (Monash University, 2000) PhD Computer Science (Monash University, 2003) Research Interests: Design and analysis of cryptographic algorithms, quantum-safe protocols, lattice-based security foundations, and blockchain applications. His work underpins NIST standard algorithms like Kyber and Dilithium through structured lattice problem research. Recent Articles Trends: Focus on privacy-preserving blockchain protocols, post-quantum signature schemes, encrypted data search, and cryptographic applications in adversarial AI. Recent work includes fair Bitcoin watchtower schemes and genomic database privacy solutions. Awards: ASIACRYPT 2015 Best Paper Award Advising & Grants: Supervises PhD projects on quantum-safe cryptography and blockchain security. Leads initiatives like the AUD$4M ARC-funded Quantum Information Technology project. Collaborates with industry partners including CSIRO/Data61. Labs/Teams: Key contributor to Monash’s cybersecurity and cryptography research groups, involving interdisciplinary projects on materials discovery ( Æinstein initiative) and post-quantum blockchain security.

Joan BrunaView profile

Professor

• Machine Learning
• Deep Learning
• Signal Processing
• +5 more

Joan Bruna is a Full Professor of Computer Science, Data Science, and affiliated Mathematics at New York University's Courant Institute and Center for Data Science. He leads the CILVR group and co-founded the MaD group. His research focuses on mathematical foundations of machine learning, deep learning, signal processing, and their applications in computational science, climate modeling, and geophysics. He holds a Ph.D. in Applied Mathematics from École Polytechnique and has held roles at UC Berkeley, the Institute for Advanced Study, and the Flatiron Institute. Education: Ph.D. (2013) and M.Sc. (2005) in Applied Mathematics, École Polytechnique and ENS Cachan; dual B.Sc./M.Sc. in Telecommunications and Mathematics from UPC Barcelona (2002–2004). Awards include the NSF CAREER Award (2019) and Alfred P. Sloan Fellowship (2018). Research interests span theoretical aspects of deep learning, optimization, and statistical methods, with applications to climate science and inverse problems. He has advised numerous PhD students and postdocs, contributing to seminal work on scattering networks, geometric deep learning, and neural ODEs. Professional service includes editorial roles at TMLR, JMLR, and TPAMI, plus program chairs for major conferences. His work bridges mathematics and machine learning, emphasizing rigorous analysis and real-world impact.

Marc PollefeysView profile

Professor

• Computer Vision
• 3D Reconstruction
• Robotics
• +12 more

Marc Pollefeys is a Full Professor at the Department of Computer Science, ETH Zurich, and Director of the Microsoft Mixed Reality and AI Lab. His work focuses on advanced perception systems for HoloLens, 3D computer vision, robotics, and machine learning. Key contributions include automated 3D modeling from video, real-time reconstruction pipelines, and vision-based autonomous systems. Education: PhD from KU Leuven (1999) Previous Affiliation: Professor at UNC Chapel Hill Research interests span 3D reconstruction , computer vision , robotics , SLAM , augmented reality , and privacy-preserving mapping . His work often integrates geometric modeling , feature matching , and deep learning . Recent projects emphasize implicit 3D representations , open-vocabulary scene understanding , and robust estimation using neural-guided algorithms. Recent publications highlight advancements in neural implicit fields , line-based correspondence , and vision-language integration . Trends include hybrid point-line methods, differentiable RANSAC, and privacy-aware localization frameworks. Scientific recognition includes: IEEE Fellow (2012) David Marr Prize (ICCV 1998) DAGM Best Paper Award (1999) Advisees include current and alumni PhD students such as Yagız Aksoy, Federico Camposeco, and Sudipta Sinha. Collaborations span institutions like UNC Chapel Hill, ETH Zurich, and Microsoft Zurich. Research sponsors include Microsoft, Google, and European research initiatives.