Find Academic Supervisors

• Algebraic Geometry
• Singularities of Algebraic Varieties
• Hodge Theory
• +4 more

Mircea Mustata is a Professor in the Department of Mathematics at the University of Michigan. His research focuses on singularities of algebraic varieties and their role in higher-dimensional geometry, with significant work on invariants like log canonical thresholds, minimal log discrepancies, multiplier ideals, and Hodge ideals derived from Saito's mixed Hodge modules. He collaborates extensively, notably with Mihnea Popa on Hodge ideals and with other researchers on topics such as square-free polynomials and F-thresholds. Contact: 3064 East Hall, mmustata@umich.edu Education: Ph.D. from UC Berkeley (2001) Research Interests: Mustata's work bridges algebraic geometry and singularity theory, utilizing tools from resolutions of singularities, jet schemes, D-modules, and positive characteristic methods. His long-term collaboration with Popa explores Hodge ideals, while other projects address Du Bois complexes, stable rationality, and cohomological dimension. Teaching: Courses include Algebraic Geometry I/II, D-modules, commutative algebra, and advanced linear algebra. Lecture notes for topics like toric varieties, rationality, and cohomology are publicly available. Editorial & Organizational Roles: Managing editor of the Michigan Mathematical Journal and member of editorial boards for Compositio Mathematica, Journal de l'École polytechnique-Mathématiques, and Journal of Singularities. Organized events like the Spring School in Ann Arbor and conferences on algebraic geometry.

Johnny GuzmánView profile

Professor

• Numerical Analysis
• Partial Differential Equations
• Scientific Computing
• +5 more

Johnny Guzmán is a Professor of Applied Mathematics at Brown University, specializing in numerical analysis of partial differential equations and scientific computing. He holds a Ph.D. in Applied Mathematics from Cornell University (2005) and a B.S. in Mathematics from California State University, Long Beach (1999). His research focuses on numerical methods for PDEs, including discontinuous Galerkin methods, mixed finite element methods, and fluid-structure interaction problems. Key contributions include work on hybridizable and mixed finite element methods, discontinuous Galerkin discretizations, and stability analysis of numerical schemes. He has been funded by multiple NSF grants, including a Postdoctoral Fellowship (2005–2008) and awards totaling over $1M in research support. Notable recognitions include the Comfort and Urry Family Fund Prize (2013). Guzmán collaborates with institutions globally and serves on editorial boards for journals like Journal of Numerical Mathematics and Calcolo . His teaching spans computational linear algebra, numerical methods for differential equations, and finite element analysis.

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.

Alex WongView profile

Assistant Professor

• Sensor fusion
• Unsupervised learning
• 3D vision
• +2 more

Alex Wong is an Assistant Professor of Computer Science at Yale University, specializing in computer vision, robotics, and medical imaging. His research focuses on sensor fusion, unsupervised learning, 3D vision, robust perception under adverse conditions, and medical image analysis. He holds degrees from the University of California, Los Angeles (UCLA), including a B.S., M.S., and Ph.D. in Computer Science. Wong’s work bridges theoretical advances with practical applications, particularly in depth estimation, autonomous systems, and medical diagnostics. He has received prestigious awards such as the NeurIPS Outstanding Student Paper Award (2011) and the ICRA Best Paper Award in Robot Vision (2019). His research often addresses challenges in unstructured environments, emphasizing robustness and adaptability. Recent projects include developing novel frameworks for unsupervised depth completion, adversarial robustness in vision systems, and multimodal fusion techniques. His contributions span conferences like CVPR, ICCV, and ICRA, with a strong focus on advancing AI for real-world applications in healthcare and robotics. Education: B.S., Computer Science, UCLA M.S., Computer Science, UCLA Ph.D., Computer Science, UCLA Awards: NeurIPS Outstanding Student Paper Award (2011) ICRA Best Paper Award in Robot Vision (2019) His lab at Yale Engineering focuses on AI-driven solutions for perception challenges, collaborating across disciplines to advance medical imaging and autonomous systems. Current efforts explore generative models, continual learning, and vision-language integration for robust scene understanding.

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.

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.

Catherine LaiView profile

Associate Professor

• Speech and Language Processing
• Computational Linguistics
• Prosody
• +17 more

Catherine Lai is a Reader (~Associate Professor) in the Department of Linguistics and English Language at the University of Edinburgh, with strong affiliations to the Centre for Speech Technology Research (CSTR) and the Institute for Language, Cognition and Computation (ILCC) in the School of Informatics. She is based in the School of Philosophy, Psychology and Language Sciences and is actively involved in research, teaching, and academic service. Department: Department of Linguistics and English Language School: School of Philosophy, Psychology and Language Sciences Research Institutes: Centre for Speech Technology Research, Institute for Language, Cognition and Computation Email: C.Lai@ed.ac.uk Her research centers on the role of prosody—non-lexical aspects of speech—in spoken communication. She investigates how prosody contributes to discourse structure, information structure, and affect in dialogue, using interdisciplinary methods from linguistics and machine learning. Her work bridges theoretical linguistics and practical speech technology, aiming to improve spoken language understanding and synthesis systems. She is particularly interested in how prosody shapes listener expectations and how affect and topic are expressed and perceived in conversation. Her recent publications reflect a strong focus on self-supervised learning in speech models, emotion recognition, ASR error correction using large language models, cognitive state classification, and ethical considerations in language technology. She explores topics such as the uncanny valley in synthetic speech, gender expression through voice, and community-centered development of language technologies. Prize from Scopus Profile Catherine Lai has supervised several PhD students, including Leimin Tian and Yuanchao Li, and has been involved in significant research projects, such as a Toyota-funded initiative on spoken dialogue for robot companions. She has secured multiple grants and leads a research agenda that integrates theoretical inquiry with real-world applications in assistive technologies and social science. Her academic service includes organizing major conferences like Interspeech and UK and Ireland Speech. She is a key member of research teams at CSTR and ILCC, collaborating across disciplines to advance the understanding of spoken communication and the development of robust, ethical speech technologies.

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.

Mark D. HaimanView profile

Professor

• Algebra
• Combinatorics
• Algebraic Geometry
• +12 more

Mark D. Haiman is a Professor at the University of California, Berkeley, Department of Mathematics, with research interests spanning algebra, combinatorics, and algebraic geometry. His work connects symmetric function theory with geometric objects like Hilbert schemes and algebraic structures such as Cherednik algebras and Hecke algebras. Appointed: 2001 Contact: mhaiman@math.berkeley.edu Teaching: Math 256B—Algebraic Geometry (Spring 2025), Math 249—Algebraic Combinatorics (Spring 2024), and others in calculus and Lie groups. Research Interests : Haiman's research focuses on Macdonald polynomials, LLT polynomials, Hilbert schemes of points in the plane, and their combinatorial and geometric implications. His work includes resolving the Macdonald positivity conjecture and the n! conjecture through algebraic geometry. Publications : Haiman has contributed to foundational papers in combinatorial and algebraic structures, including generalizations of the shuffle theorem and positivity results for LLT polynomials. His articles often bridge representation theory, symmetric functions, and geometric methods. Students : He has supervised numerous PhD students, including Magda Hlavacek (2023), Foster Tom (2022), Jeremy Meza (2021), Maryam Farahmand-Asil (2018), Maria Monks Gillespie (2016), and others working on combinatorial algebraic geometry and related fields.

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.

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.

• Algebraic Geometry
• Mathematical Physics
• Mirror Symmetry
• +7 more

Maxim Kontsevich has been a permanent professor at the Institut des Hautes Études Scientifiques (IHÉS) since 1995, holding the AXA-IHES Chair for Mathematics . He is also Professor at Rutgers University (one month per year since 1997) and was Professor at University of California, Berkeley (1993–1995). Born 25 August 1964 in Khimki, former USSR, he became a French citizen in 1999. Education: Ph.D. in Mathematics, University of Bonn, March 1992. Research Interests: Prof. Kontsevich’s work lies at the intersection of algebraic geometry , mathematical physics , and quantum theory . He introduced revolutionary ideas in mirror symmetry , non-commutative geometry , and deformation quantization , creating new algebraic structures that have found applications far beyond their original contexts. His current ERC Synergy Grant “ReNew Quantum” (2019-2025) explores recursive and exact new quantum theory . Publications & Trends: Across more than 50 influential papers, Kontsevich has consistently pushed the boundaries of enumerative geometry , motivic integration , and topological field theory . His recent work delves into symplectic aspects of homological algebra , motivic Donaldson-Thomas invariants , non-archimedean geometry , and integrable systems , revealing deep connections between geometry, algebra, and physics. Scientific Awards & Honors: Fields Medal (1998) Crafoord Prize (2008) Shaw Prize in Mathematical Sciences (2012) Breakthrough Prize in Fundamental Physics (2012) Breakthrough Prize in Mathematics (2014) ERC Synergy Grant “ReNew Quantum” (2019) AMS Moore Prize (2025) Doctor Honoris Causa: Aarhus University (2014), Universität Wien (2015), Syddansk Universitet (2023) Member of Académie des Sciences, Institut de France, Academia Europaea, National Academy of Sciences (foreign), London Mathematical Society (honorary) Grants & Leadership: As Principal Investigator of the ERC Synergy Grant “ReNew Quantum” (€10 million, 2019-2025), Kontsevich coordinates an international team advancing exact methods and resurgence in quantum field and string theories. Editorial Service & Community Roles: He serves on the editorial boards of Compositio Mathematica , Publications Mathématiques IHÉS , Journal of Noncommutative Geometry , and several other leading journals, shaping the direction of contemporary mathematical research.

Alexander GorodnikView profile

Professor

• Dynamical Systems
• Analytic Number Theory
• Diophantine Approximation
• +5 more

Alexander Gorodnik is a Professor of Mathematics at the University of Zurich, focusing on the interplay between dynamical systems and number theory. His work bridges ergodic theory, homogeneous dynamics, and Diophantine approximation, with applications to arithmetic counting problems and geometric distribution of lattice orbits. Current lectures include MAT121: Analysis I and MAT221: Analysis III at the University of Zurich Co-author of the book The ergodic theory of lattice subgroups (Princeton University Press, 2010) Editor of the journal Ergodic Theory and Dynamical Systems His research explores Diophantine approximation through dynamical systems, investigating how orbits of group actions distribute in homogeneous spaces. Key topics include mixing properties , central limit theorems , and metric theorems for multiplicative approximation. Recent publications address automorphic density estimates , discrepancy in intrinsic Diophantine approximation , and effective equidistribution of translated measures. His work often employs tools from representation theory and spectral analysis . Current working group members include Zhiyuan Deng , Zouhair Ouaggag , and Yuval Yifrach . He has taught courses at institutions in Zurich, Bristol, Princeton, and Mumbai, with lecture materials covering topics from ergodic theorems to Každan's property (T) .