Find Academic Supervisors

• Discrepancy Theory
• Optimization
• Combinatorics
• +3 more

François Clément is a Researcher at the University of Washington , Department of Mathematics, mentored by Stefan Steinerberger. He completed his PhD at Sorbonne Université with co-supervision from Carola Doerr and Luís Paquete (University of Coimbra), focusing on discrepancy theory and optimization for constructing uniformly distributed point sets . Educational Background: Classe Préparatoire (MPSI-MP*) at Lycée Hoche, Versailles Diplôme d’Ingénieur de l’École Polytechnique (theoretical computer science, optimization) Master in Computer Science from ETH Zürich (Theoretical Computer Science, 2022) Research Interests: François Clément's research lies at the intersection of discrepancy theory , combinatorics , and numerical optimization . He investigates practical constructions of low-discrepancy point sets for finite N and d , develops greedy algorithms for discrepancy minimization, and explores connections between Fourier analysis , linear algebra , and combinatorial geometry in diverse applications. Publications & Preprints: His work spans theoretical advancements in star discrepancy optimization , novel permutation-based point set constructions , and practical applications in gradient descent for discrepancy minimization. Collaborations include prominent researchers in mathematics and computer science. Teaching & Supervision: Clément has mentored bachelor and master students at multiple institutions, including a WXML undergraduate research project at UW. His teaching experience ranges from introductory Python programming to advanced topics in discrete mathematics and algorithmic design .

Richard PengView profile

Associate Professor

• Algorithms
• Theoretical Computer Science
• Graph Theory
• +4 more

Richard Peng is an Associate Professor in the Computer Science Department at Carnegie Mellon University, part of the School of Computer Science. He specializes in designing efficient algorithms for fundamental computational problems, particularly in graph algorithms, dynamic algorithms, and linear algebraic computations. Prior to joining CMU in 2023, he earned his BMath from the University of Waterloo, a PhD from CMU under Gary L. Miller, and completed a postdoc at MIT's Applied Math department. His research focuses on advancing algorithmic efficiency, including work on sparse linear systems, graph convolutions, and flow optimization. He advises PhD students Hoai-An Nguyen, Alicia Stepin, and Junzhao Yang. His teaching includes courses such as 15495 and 15151, reflecting his engagement in both research and education. Peng’s articles span topics like approximate spanning tree counting, dynamic graph algorithms, and Laplacian solvers, emphasizing practical scalability and theoretical guarantees. His contributions bridge theoretical computer science with applied challenges in network analysis and numerical computation.

Xin TongView profile

Associate Professor

• Asymmetric Statistical Learning
• Neyman-Pearson Classification
• High-dimensional Statistics
• +5 more

Xin Tong is an Associate Professor in the Department of Data Sciences and Operations at the Marshall School of Business, University of Southern California. His research focuses on asymmetric statistical learning, Neyman-Pearson classification, high-dimensional statistics, and network analysis. He holds a PhD in Operations Research from Princeton University (2012) and a B.S. in Mathematics from the University of Toronto (2007). Key contributions include developing Neyman-Pearson classification frameworks for imbalanced datasets and advancing spectral clustering techniques. His work has been supported by NSF grants and NIH funding. Tong serves as an Associate Editor for the Journal of the American Statistical Association and Journal of Business and Economic Statistics. Recent research highlights include studies on label-noise-adjusted classification algorithms, citation network analysis, and fairness-adjusted machine learning. He teaches courses on modern statistical learning and GenAI/ML strategies for business transformation. Notable awards include the Zellner Thesis Award (2013) and multiple NSF grants. His lab's work bridges statistical theory with applications in genomics, social networks, and healthcare risk assessment.

• Quantum Computing
• Quantum Walks
• Quantum Simulation
• +4 more

Jingbo Wang is Professor of Physics at the University of Western Australia, where she heads the Physics department and directs the QUISA Research Hub for Quantum Information, Simulation, and Algorithms. Her research pioneers quantum walk applications in complex systems analysis and quantum compiler development. After completing her PhD at the University of Adelaide, she established a leading quantum computation group at UWA. Her work bridges quantum dynamics, algorithm design, and complex network theory. Recent publications demonstrate strong focus on quantum machine learning, optimization algorithms, and experimental quantum computing implementations. Her team consistently develops efficient quantum circuits for practical problem-solving in finance, biology, and material science. Fellow of Australian Institute of Physics (2021) Vice-Chancellor’s Award in Research Mentorship (2022) School Research Mentorship Award (2022)

Keith LevinView profile

Assistant Professor

• Network analysis
• Dimension reduction
• Concentration inequalities
• +5 more

Keith Levin is an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison, part of the School of Computer, Data & Information Sciences. He holds a Ph.D. in Computer Science from Johns Hopkins University (2017) and completed a postdoctoral fellowship at the University of Michigan. His research focuses on network analysis, dimension reduction, and statistical inference in complex systems, with applications to neuroscience and speech processing. Levin teaches courses on computational tools for data science and has developed methodologies for network bootstrapping, latent space modeling, and spectral graph analysis. His work bridges theoretical statistics with practical machine learning challenges, particularly in high-dimensional data settings. Research Interests: - Network analysis and community detection - Dimensionality reduction techniques - Latent space models and graph embeddings - Applications in neuroscience and speech processing - Statistical methods for large-scale data Selected Contributions: - Developed bootstrap methods for network data analysis - Advanced spectral embedding techniques for graph inference - Authored tools for clustering and motif detection in stochastic blockmodels

Yiqiao ZhongView profile

Assistant Professor

• Generative AI
• Large Language Models
• Deep Learning Theory
• +5 more

Yiqiao Zhong is an Assistant Professor of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. He holds a PhD from Princeton University (2019) and a B.S. in Mathematics from Peking University (2014). His research focuses on the theoretical foundations of generative AI, Large Language Models (LLMs), and deep learning, with emphasis on model generalization, interpretability, and safety. Recent work includes analyzing OOD generalization in LLMs through compositional latent structures and revealing geometric properties of Transformer models. Key research contributions include studies on out-of-distribution generalization, hidden neural network geometry, and reliability of visualization techniques like t-SNE/UMAP. He has received recognition for his work on phase synchronization, including the SIAM best student paper prize (2018). Zhong teaches advanced courses such as STAT 709 (Mathematical Statistics) and STAT 453 (Introduction to Deep Learning), and actively collaborates on projects related to statistical theory and AI ethics. His lab explores intersections between theory and practice in generative models, with ongoing work on model editing, toxicity reduction in LLMs, and robust statistical methods for high-dimensional data. Current efforts aim to bridge gaps between empirical AI advancements and rigorous scientific understanding through interdisciplinary approaches.

Michał DerezińskiView profile

Assistant Professor

• Theoretical Computer Science
• Randomized Numerical Linear Algebra (RandNLA)
• Stochastic Optimization
• +3 more

Michał Dereziński is an Assistant Professor of Computer Science and Engineering at the University of Michigan, affiliated with the College of Engineering. His research focuses on the theoretical foundations of randomized algorithms for numerical linear algebra, machine learning, optimization, and data science. Previously, he held postdoctoral positions at UC Berkeley (Department of Statistics) and the Simons Institute for the Theory of Computing, and completed his Ph.D. in Computer Science at UC Santa Cruz under Manfred Warmuth, alongside Master's degrees in Mathematics and Computer Science from the University of Warsaw. He teaches courses such as Foundations of Computer Science (EECS 376), Algorithms for Data Science (EECS 474), and specialized RandNLA courses. His work emphasizes bridging theory and practice in RandNLA, with applications to ML and optimization. Key achievements include an NSF CAREER Award and a NeurIPS 2020 Best Paper Award for work on column subset selection. Current advisees include Sachin Garg, Jiaming Yang, and Shabarish Chenakkod. His research explores topics like sketching algorithms, stochastic optimization, and random matrix theory, with publications in top venues such as NeurIPS, COLT, and SIAM journals. He actively contributes to the RandNLA community through tutorials and software development efforts like RandBLAS/RandLAPACK initiatives.

Eric DarveView profile

Professor

• Machine Learning
• Numerical Linear Algebra
• High-Performance Computing
• +5 more

Eric Darve is a Professor of Mechanical Engineering and Director of the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning for science/engineering, numerical linear algebra, high-performance computing, and GPU-accelerated algorithms. He holds a PhD in Applied Mathematics from Pierre et Marie Curie University (2009), and completed postdoctoral work at Stanford/NASA Ames before joining Stanford’s faculty in 2001. Education: PhD, Applied Mathematics, Paris VI University (1999) MS, Applied Mathematics, Paris IX University (1994) BS, Mathematics & Physics, Paris VI University (1993) Research interests include: Machine learning for scientific applications Surrogate/Reduced Order Modeling Anomaly detection in engineering systems Parallel computing and numerical algorithms Physics-informed learning machines Awards include: H. Julian Allen Award (NASA, 2010) Habilitation à Diriger des Recherches (France, 2007) Leslie Fox Prize in Numerical Analysis (2001) James H. Clark Faculty Scholar (2001) Led development of scalable linear solvers and hybrid physics-data approaches. Active in computational engineering education and interdisciplinary research initiatives through ICME.

Madeleine UdellView profile

Assistant Professor

• Optimization
• Machine Learning
• Data Analysis
• +4 more

Madeleine Udell is an Assistant Professor of Management Science and Engineering at Stanford University, with affiliations at the Institute for Computational and Mathematical Engineering (ICME) and a courtesy appointment in Electrical Engineering. She holds a PhD from Stanford University in Computational and Mathematical Engineering and completed a postdoc at Caltech. Her research focuses on accelerating optimization and machine learning, with applications in healthcare, finance, and engineering. Udell's work bridges algorithmic efficiency, interpretability, and practical deployment in data-driven systems. Education: BS in Mathematics and Physics, Yale University (2009) PhD in Computational and Mathematical Engineering, Stanford University (2015) Research Interests: Her research emphasizes optimization algorithms, scalable machine learning techniques, and their applications in real-world domains. Key areas include low-rank matrix methods, automated machine learning (AutoML), and interpretable models for healthcare. She develops tools like OptiMUS for natural language-based optimization modeling and explores second-order methods for improving deep learning efficiency. Awards: Kavli Fellowship (2023) Alfred P. Sloan Research Fellowship (2021) NSF CAREER Award (2020) ONR Young Investigator Award (2020) Grants & Advising: Udell leads projects funded by ONR and NSF, focusing on randomized numerical linear algebra and automated machine learning. She advises PhD students in optimization modeling, deep learning, and causal inference. Her group collaborates on tools like PINNs for scientific computing and OpenReviewer for bias-aware academic reviews. Labs & Teams: She directs the Udell Research Group at Stanford, emphasizing interdisciplinary work in optimization, machine learning, and data science. Collaborations span academia and industry, including work with Aurora Solar on photovoltaic array design and Google Brain on scalable AutoML.

Dustin MixonView profile

Associate Professor

• Applied Harmonic Analysis
• Mathematical Signal Processing
• Compressed Sensing

Dustin Mixon is Associate Professor of Mathematics at Ohio State University, specializing in applied harmonic analysis, compressed sensing, and frame theory. His research develops mathematical foundations for signal processing, including equiangular tight frames, optimal line packings, and phase retrieval guarantees. Recent work addresses max filtering injectivity, Euclidean distortion of orbit spaces, and clustering algorithms via semidefinite programming. Mixon has contributed to the theory of numerically erasure-robust frames (NERFs) and deterministic matrix constructions with the restricted isometry property. He co-authored papers on group-invariant packings and phase transitions in phase retrieval, solving open problems in combinatorial geometry. His blog 'Short, Fat Matrices' disseminates research on frame theory and compressed sensing.

Joshua GrochowView profile

Associate Professor

• Theoretical Computer Science
• Algebraic Complexity
• Group Theory
• +3 more

Joshua Grochow is an Associate Professor in Computer Science at the University of Colorado Boulder, with joint appointments in Mathematics. His research spans theoretical computer science, algebraic complexity, and complex systems. Key research areas: Algebraic approaches to matrix multiplication complexity Isomorphism problems for groups/tensors/polynomials Geometric complexity theory and circuit lower bounds Network analysis and complex systems modeling Recent publications feature breakthroughs in tensor isomorphism completeness and parallel algorithms for group isomorphism. Research combines representation theory, combinatorics, and complexity theory. Awards include Omidyar and Dean's Fellowships. Leads research groups in complexity theory and complex networks. Organizes workshops on wildness in computer science.

Xinfeng LiuView profile

Professor

• Computational Biology
• Mathematical Modeling of Cancer Stem Cells
• Signal Transduction
• +4 more

Xinfeng Liu serves as Professor and Undergraduate Director in the Department of Mathematics at the University of South Carolina. His academic journey includes progression from Assistant Professor (2009-2013) to Associate Professor (2013-2017) and finally to full Professor (2018-present), following a Visiting Assistant Professor position at UC Irvine (2006-2009). His research spans Computational Biology with focus on cancer stem cell modeling and signal transduction mechanisms, alongside Numerical PDEs specializing in front tracking methods and integration factor techniques. Liu's work bridges mathematical theory with biological applications, particularly in morphogen gradient formation and MAPK cascade regulation. His computational approaches address complex phenomena like Rayleigh-Taylor instability in fluid dynamics. Liu's recent publications demonstrate strong trends in developing numerical methods for free boundary problems in biological systems, with significant contributions to cancer stem cell modeling. His work increasingly integrates stochastic elements and time-delay systems in therapeutic response modeling. NSF Mathematical Biology Division grant DMS1853365 (2019-2022) NSF Mathematical Biology Division grant DMS1308948 (2013-2017) NSF Mathematical Biology Division grant DMS1019544 (2010-2014) University of South Carolina ASPIRE I grant (2013-2014) South Carolina EPSCoR/IDeA GEAR program (2011-2013) Liu has mentored three doctoral students to completion, all securing competitive postdoctoral positions at Los Alamos National Lab, University of Waterloo, and University of North Texas. His teaching portfolio spans computational mathematics, differential equations, and advanced analysis courses since 2009, including honors sections and specialized topics like Modeling of Complex Biological Systems.

Aanjaneya MridulView profile

Associate Professor

• Computer Graphics
• Scientific Computing
• Programming Languages
• +3 more

Aanjaneya Mridul is an Associate Professor in the Department of Computer Science at Rutgers University, where he joined in 2017. He is also a member of the Computational Biomedicine Imaging and Modeling Center (CBIM). His research spans computer graphics, scientific computing, programming languages, and robotics, with a focus on developing numerical methods in computational physics that leverage modern computing power. Dr. Aanjaneya completed his doctoral studies in Computer Science from Stanford University in 2013 under Ronald Fedkiw, followed by postdoctoral studies at the University of Wisconsin-Madison with Eftychios Sifakis. He received his undergraduate degree in Computer Science and Engineering from the Indian Institute of Technology Kharagpur in 2008. His research interests include: Computer graphics and visual simulation Scientific computing and numerical methods Programming languages for computational physics Robotics and differentiable physics engines Computational physics for interdisciplinary applications Dr. Aanjaneya's recent work has focused on applying numerical methods to robotics for learning unknown physical parameters, and designing correctly-rounded implementations of elementary functions for new floating-point variants. His long-term goal is to enable next-generation algorithms that facilitate interdisciplinary collaboration with researchers in engineering and medicine. He has received several prestigious awards including the Ralph E. Powe Junior Faculty Enhancement Award (2019) and the NSF CAREER Award (2023). Ralph E. Powe Junior Faculty Enhancement Award (2019), sponsored by Oak Ridge Associated Universities (ORAU) NSF CAREER Award (2023) At Rutgers, Dr. Aanjaneya directs the Laboratory for Interactive Virtual Environments (LIVE), where he mentors PhD students and postdoctoral researchers. He has secured multiple NSF grants including the CAREER award, an NSF NRI grant with Professors Boularias and Yu, and grants with Professor Santosh Nagarakatte for projects on formally certified low-dimensional linear programs and correctly rounded math libraries. Outside of his academic work, Dr. Aanjaneya enjoys running, rock climbing, hiking, watching movies, and playing piano.

Ahana ChakrabortyView profile

Assistant Professor

• Condensed Matter Physics
• Quantum Information Science
• Non-Equilibrium Dynamics
• +5 more

Dr. Ahana Chakraborty is an Assistant Professor of Physics at Louisiana State University's Department of Physics & Astronomy (since 2024). Her research focuses on theoretical condensed matter physics and quantum information science, particularly non-equilibrium dynamics of open quantum systems, light-matter interactions, and disordered systems. She holds a Ph.D. from the Tata Institute of Fundamental Research, Mumbai, India (2019), and completed postdoctoral work at Rutgers University (2021–2024) and the Max Planck Institute for Physics of Complex Systems (2019–2021). Research interests include entanglement dynamics in quantum circuits, light-induced phenomena in quantum materials (e.g., superconductivity and ferroelectricity), and many-body localization. Techniques employed include non-equilibrium field theory, real-time quantum circuit simulations, and exact diagonalization. Her work addresses fundamental questions like thermalization in quantum systems and practical challenges such as robustness of entanglement phase transitions to decoherence. Current projects involve collaborations to realize measurement-induced entanglement transitions in experimental platforms like multimode circuit QED systems. No scientific awards are explicitly listed in the provided materials. She advises graduate, undergraduate, and postdoctoral researchers and collaborates with experimental groups on quantum materials and quantum information technologies. Her lab, the Quantum Dynamics and Information Group, emphasizes interdisciplinary approaches to non-equilibrium quantum many-body systems, with a focus on theoretical and computational tools for driven, dissipative, and disordered systems.

Jeffrey HoganView profile

Associate Professor

• Harmonic Analysis
• Signal Processing
• Wavelet Analysis
• +5 more

Jeffrey Hogan is an Associate Professor in the School of Information and Physical Sciences (Mathematics), University of Newcastle. He holds academic appointments including Head of Mathematics Discipline and former Deputy Head of the School of Mathematical and Physical Sciences. His research focuses on harmonic analysis, wavelet theory, and applications in signal processing, including bandlimited signals, prolate spheroidal wave functions, and quaternion-valued wavelets. Hogan earned his BSc (Hons) and PhD from the University of New South Wales, followed by postdoctoral roles at institutions like the University of Texas and University of Arkansas before joining Newcastle in 2009. Education: Bachelor of Science, University of New South Wales PhD (Mathematics), University of New South Wales Research Interests: Jeffrey Hogan explores pure, applied, and computational harmonic analysis, including singular integrals, wavelet frames, Clifford-Fourier theory, and applications in mineral fractionation. His work emphasizes spatio-spectral limiting, multidimensional wavelet construction, and quaternionic signal processing. Key projects involve developing algorithms for EEG analysis, optimizing wavelet design via projection methods, and analyzing signals on graphs and hypercubes. Grant Funding: A$546,913: Enhanced Fractionation of Mineral Particles According to Density (ARC Discovery Project, 2020–2022) A$593,621: Relaxed Reflection Methods for Feasibility and Matrix Completion Problems (ARC Discovery Project, 2016–2018) Collaborations & Teams: Hogan collaborates with experts like Prof. Kevin Galvin (University of Newcastle), Prof. Joseph Lakey (New Mexico State University), and Dr. Matthew Tam (University of Göttingen). His work integrates interdisciplinary approaches, combining pure mathematics with applications in engineering and mineral processing. His lab focuses on computational harmonic analysis and optimization-driven wavelet design.