Find Academic Supervisors

Hannah HogansonView profile

Research Fellow

• Geometric Group Theory
• Low Dimensional Topology
• Mapping Class Groups
• +4 more

Hannah Hoganson is an NSF Postdoctoral Fellow in the Department of Mathematics at the University of Maryland, mentored by Christian Rosendal after previously holding a Brin Postdoctoral Fellowship under Lei Chen. Her research bridges geometric group theory, low-dimensional topology, and descriptive set theory, with a focus on mapping class groups of infinite-type surfaces and topological groups. Her educational background includes a PhD from the University of Utah (2022) advised by Ken Bromberg, and prior graduate studies at Miami University where she investigated Thompson's groups. She has taught multiple calculus courses at UMD and the University of Utah, receiving exceptional student evaluations for her clarity and supportive teaching style. Hoganson's research explores the coarse geometry of mapping class groups, connections between topological groups and descriptive set theory, and geometric structures on infinite-type surfaces. Her work often combines algebraic, geometric, and topological methods to address fundamental questions about group actions and classification problems in low-dimensional topology. Her recent publications demonstrate a strong trend toward interdisciplinary approaches, integrating geometric group theory with descriptive set theory to analyze infinite-type mapping class groups and Polish groups. Key themes include geometric finiteness, coarse boundedness, and the interplay between algebraic structures and topological dynamics in infinite settings. NSF Postdoctoral Fellowship (DMS-2303365) Brin Postdoctoral Fellowship Hoganson has advised an undergraduate reading course in geometric group theory (Spring 2024) and served as a mentor for REU students at SUMSRI. Her current research is supported by NSF grant DMS-2303365, which funds her postdoctoral work on geometric and topological aspects of infinite-type surfaces and groups. She actively collaborates with researchers including George Domat, Sanghoon Kwak, and Robbie Lyman across multiple projects. She co-organizes the University of Maryland Geometry and Topology Seminar and has co-led specialized workshops including the Big Mapping Class Groups log cabin workshop in Young, AZ (2024) and the AWM special session on Women in Groups, Geometry and Dynamics (2023), fostering collaborative research environments in geometric topology.

• Geometry
• Discrete Mathematics
• Linear Algebra
• +3 more

Maria Rita Casali is a Full Professor at the Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia. Her research focuses on geometry, topology, and mathematical structures, particularly in relation to PL-manifolds and colored tensor models. Teaching: Courses in Geometry, Discrete Mathematics, and Linear Algebra for Engineering and Strategic Sciences degrees. Research: Investigates combinatorial invariants (regular genus, G-degree, gem-complexity) for compact 4-manifolds, linking them to quantum gravity and tensor models. Publications: Recent works include studies on trisections of 4-manifolds, classifications via colored graphs, and combinatorial properties of the G-degree. Her contributions to crystallization theory and PL-manifold representation have advanced the understanding of geometric topology and its applications in theoretical physics.

Edouard OyallonView profile

Researcher

• Machine Learning
• Deep Learning
• Distributed Systems
• +5 more

Edouard Oyallon is a CNRS Researcher at Sorbonne University's MLIA team within the Institute of Intelligent Systems and Robotics (ISIR). His research focuses on machine learning foundations, particularly the symmetries of deep neural networks, and large-scale distributed/decentralized training algorithms. He has contributed to frameworks like Kymatio for wavelet scattering transforms and collaborates on projects such as SHARP (Frugal Learning) and ADONIS (ANR-funded). He advises multiple PhD and postdoctoral researchers and teaches advanced deep learning courses at Institut Polytechnique de Paris (IPP). Grants include the ADONIS project (ANR/Sorbonne) and participation in VHS and CoCa4AI initiatives. His work spans theoretical and applied aspects, with recent emphasis on optimizing LLM training at exascale. He maintains active roles in academic service, including organizing workshops on federated learning and graph machine learning.

Amit SingerView profile

Professor

• Computational Mathematics
• Structural Biology
• Cryo-Electron Microscopy
• +3 more

Amit Singer is a Professor of Mathematics at Princeton University, specializing in computational methods for structural biology and cryo-electron microscopy (cryo-EM). His work focuses on developing mathematical frameworks and algorithms for analyzing large-scale microscopy datasets, particularly in 3D reconstruction and heterogeneity analysis of molecular structures. He leads research in manifold learning, optimal transport, and harmonic analysis, with applications to cryo-EM, signal processing, and inverse problems. Research interests include: (1) Mathematical methods for cryo-EM, including particle alignment, density map analysis, and subspace-based reconstruction techniques; (2) Development of rotation-invariant representations for imaging problems; (3) Application of machine learning and optimization to biomedical imaging challenges. His contributions bridge pure mathematics (e.g., harmonic analysis, manifold theory) with applied computational techniques for real-world microscopy data. Key trends in his recent articles (2023–2025) include advancements in multi-reference alignment methods, Wasserstein distance-based image registration, and overcoming particle detection limitations in cryo-EM. He also explores sparsity constraints, autocorrelation analysis, and novel algorithms for handling heterogeneous datasets. These methods improve resolution and reduce computational costs in analyzing molecular structures at atomic scales. Notable contributions include the ASPiRE software package for steerable PCA, and foundational work on synchronization problems in cryo-EM orientation estimation. His research often addresses algorithmic scalability and robustness to noise in experimental setups.

Jiao LichengView profile

Professor

• Artificial Intelligence
• Machine Learning
• Image Understanding
• +6 more

Jiao Licheng is a Distinguished Professor and Doctoral Supervisor at Xidian University, leading the School of Artificial Intelligence and the Department of Computer Science and Technology. He holds prominent roles such as Director of the Key Laboratory of Intelligent Perception and Image Understanding (Ministry of Education) and the International Joint Research Center for Intelligent Perception and Computing. His research focuses on Artificial Intelligence, Deep Learning, Evolutionary Computation, and Remote Sensing, with significant contributions to image understanding and brain-inspired computing. Education: B.E. (1982) from Shanghai Jiao Tong University, M.E. (1984) and Ph.D. (1990) from Xi'an Jiaotong University. Postdoctoral research at Xidian University (1990–1992). Research Interests include AI, Machine Learning, Image Processing, and Big Data Analysis. His work bridges theoretical advancements and practical applications, such as medical imaging, SAR image analysis, and autonomous systems. Recent articles emphasize innovations in remote sensing, deep learning architectures, and evolutionary algorithms. Awards include IEEE Fellow, IET Fellow, and the Wu Wenjun Artificial Intelligence Outstanding Contribution Award. Labs/Teams: Key Lab of Intelligent Perception, International Joint Research Center, and leadership in national innovation bases. Active in academic societies, including editorial roles in IEEE Transactions on Cybernetics and Geoscience and Remote Sensing.

• Self-organized criticality
• Neuronal network dynamics
• Excitation/Inhibition balance
• +5 more

Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .

Aaron SmithView profile

Associate Professor

• Applied Probability
• Computational Statistics
• Monte Carlo Methods
• +3 more

Aaron Smith is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds a PhD from Stanford University. His research focuses on applied probability, computational statistics, Monte Carlo methods, and Markov chains, with an emphasis on advancing theoretical understanding and practical applications of these methodologies. Dr. Smith's work includes contributions to community detection algorithms, Markov chain mixing times, and synthetic health data generation. His recent publications explore topics such as nonstandard Dirichlet form representations, perturbation analysis of MCMC algorithms, and sparse Bayesian multidimensional scaling. He advises students in applied probability and has supervised postdoctoral researchers in related fields. His research interests span a wide range of topics, including stochastic processes, statistical inference, and algorithm design. He is particularly known for his analysis of convergence rates in Markov chains and the development of efficient sampling techniques for complex models. His interdisciplinary work bridges theoretical mathematics and practical computational challenges in data science and healthcare. Dr. Smith collaborates on projects involving synthetic data frameworks for privacy-preserving applications and has contributed to foundational work on mixing times and perturbation effects in stochastic systems.

Chris De SaView profile

Associate Professor

• Machine Learning
• Distributed Systems
• Optimization Algorithms
• +5 more

Chris De Sa is an Associate Professor in the Department of Computer Science at Cornell University, affiliated with the Cornell Machine Learning Group and leading the Relax ML Lab. His research focuses on algorithmic, software, and hardware techniques for high-performance machine learning, particularly relaxed-consistency stochastic algorithms like asynchronous and low-precision stochastic gradient descent (SGD). He earned his Ph.D. from Stanford University under advisors Kunle Olukotun and Chris Ré. His work emphasizes constructing efficient, parallel, and distributed machine learning frameworks for deep learning and data analytics. Education: Ph.D. in Computer Science, Stanford University (2017) Research Interests: Algorithmic techniques for scalable ML, quantization, distributed optimization, hyperbolic geometry in ML, and reliable measurement of ML systems. His group develops frameworks for efficient inference/training and explores the intersection of ML with domains like agriculture and plant science through courses like PLSCI 7202. Recent Highlights: DARPA YFA Grant (2024), NSF CAREER Award, Google Research Scholar Award, and multiple best paper recognitions. Key contributions include QuIP quantization methods, Coneheads attention mechanisms, and theoretical advances in decentralized training. Awards: NSF CAREER Award DARPA YFA Grant (2024) Google Research Scholar Award Mr. & Mrs. Richard F. Tucker Teaching Award Grants & Advising: Advises 8 Ph.D. students (including Ruqi Zhang, Yucheng Lu, A. Feder Cooper) and holds leadership roles in MLSys conferences. Active in grant-funded research (e.g., NSF Robust Intelligence). Labs/Teams: Leads the Relax ML Lab and participates in Cornell’s Institute for Digital Agriculture (CIDA).

• VLSI Design
• Computer-Aided Design
• Spectral Graph Theory
• +4 more

Zhuo Feng is Professor of Electrical and Computer Engineering at Stevens Institute of Technology, directing the HUDSON Lab and holding a Ph.D. from Texas A&M University. His research develops spectral graph methods for VLSI design, including circuit simulation, power grid verification, and machine learning applications. Funded by NSF CAREER and multiple grants, his work has produced award-winning algorithms like GRASS for graph sparsification. Recent publications focus on spectral methods for circuit stability analysis, physics-informed neural networks, and explainable AI frameworks. He teaches graduate courses in VLSI design and GPU programming while co-founding LeapLinear Solutions. NSF CAREER Award (2014) ACM/IEEE DAC Best Paper Award (2013) Multiple Best Paper Nominations (ICCAD 2008, 2006)

Melanie WeberView profile

Assistant Professor

• Geometric Machine Learning
• Optimization on Manifolds
• Non-Euclidean Machine Learning
• +3 more

Melanie Weber is an Assistant Professor of Applied Mathematics and Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS), leading the Geometric Machine Learning Group. Her research focuses on leveraging geometric structures in data for designing efficient machine learning and optimization algorithms with theoretical guarantees. She holds a PhD from Princeton University (2021) and has held fellowships at the Mathematical Institute of Oxford, Brasenose College, and the Simons Institute. Her work bridges geometry, optimization, and machine learning, with funding from NSF, Sloan Foundation, and Harvard initiatives. Education : PhD in Applied Mathematics, Princeton University (2021) BSc/MSc in Mathematics and Physics, University of Leipzig (2016) Research Interests : Dr. Weber's research integrates geometric principles into machine learning and optimization, focusing on non-Euclidean spaces, graph structures, and manifold-based methods. Key areas include optimization on Riemannian manifolds, curvature-based analysis (e.g., Ricci curvature), and developing algorithms resilient to data geometry challenges like over-smoothing in graph neural networks. Her work emphasizes theoretical foundations while addressing practical scalability in high-dimensional data. Awards & Recognition : 2024 Sloan Research Fellowship 2023 Leslie Fox Prize in Numerical Analysis 2023 NSF Grant for Geometric Optimization Grants & Funding : Supported by National Science Foundation (NSF), Alfred P. Sloan Foundation, Aramont Foundation, Harvard Dean’s Fund, and Harvard Data Science Initiative. Labs & Collaborations : Leads the Geometric Machine Learning Group at SEAS, collaborating with institutions like MIT, Max Planck Institute, and industry labs (Facebook, Google, Microsoft). Active in organizing workshops on geometric methods and curvature analysis.

Rayan SaabView profile

Assistant Professor

• Compressed Sensing
• Signal Processing
• Applied and Computational Harmonic Analysis
• +5 more

Rayan Saab is an Assistant Professor in the Department of Mathematics at the University of California, San Diego. His research focuses on the mathematics of information, data science, and signal processing, with an emphasis on quantization, compressed sensing, and machine learning algorithms. Education: Ph.D. in Electrical and Computer Engineering from the University of British Columbia (2010). His work bridges theoretical mathematics and practical applications in data acquisition, digitization, and processing. Recent publications analyze quantization methods for neural networks and compressed sensing systems. He has taught graduate and undergraduate courses in numerical analysis, optimization, and mathematical methods in data science. Research trends include: Developing quantization algorithms with provable guarantees. Applications in high-dimensional data and machine learning. Stochastic frameworks for neural network compression. Scientific awards: Banting Postdoctoral Fellowship (2011-2013). Hellman Fellowship. Advising and grants: Advised students in thesis projects related to signal processing and data science. Received funding for research in mathematical data acquisition. Contact: rsaab@ucsd.edu | Office: AP&M 5157.

Behnaam AazhangView profile

Professor

• Signal and Data Processing
• Information Theory
• Neuroengineering
• +4 more

Behnaam Aazhang is the J.S. Abercrombie Professor of Electrical and Computer Engineering at Rice University and Director of the Rice Neuroengineering Initiative (NEI). He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign. His roles include leading the multi-university Rice Neuroengineering Initiative and directing the Center for Neuroengineering. He has held an Academy of Finland Distinguished Visiting Professorship (FiDiPro) at the University of Oulu (2006-2014) and received an Honorary Doctorate from the University of Oulu in 2017. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (1986) M.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1983) B.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1981) Research Interests: Dr. Aazhang’s work focuses on signal/data processing, information theory, and neuroengineering applications. Key areas include: Neuronal circuit connectivity and learning impacts Real-time closed-loop neuromodulation for neurological disorders (epilepsy, Parkinson’s, depression) Patient-specific cardiac pacing systems Cybersecurity in cloud computing Awards & Honors: 2022 Rice Outstanding Doctoral Thesis Advisor Award 2019 SIGMOBILE Test of Time Award 2017 Honorary Doctorate (University of Oulu) 2013 IEEE Communication Society Advances in Communication Award AAAS and IEEE Fellowships (2012 and 1999) Grants & Advising: His research is supported by multi-university collaborations and grants. He has advised numerous graduate students in electrical engineering and neuroengineering, though specific names are not listed here. Labs & Teams: Leads the Aazhang Lab and the Rice Neuroengineering Initiative, focusing on translational technologies for neurological and cardiac disorders, including non-invasive neuromodulation and cloud security systems.

Timothy RileyView profile

Professor

• Geometric Group Theory
• Geometric Topology
• Algorithmic Complexity
• +3 more

Timothy Riley is a Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. His research focuses on geometric group theory, studying infinite discrete groups through geometric lenses such as Riemannian manifolds, graphs, and cell complexes. Key interests include curvature, geometric features of group presentations, and algorithmic complexity. He holds a D.Phil. from Oxford University (2002). Research spans geometric aspects of the word problem, hyperbolic groups, Dehn functions, and applications to cryptography and topology. His work integrates algebraic, topological, and computational perspectives. Notable collaborations include studies on hydra groups, Cayley graphs, and asymptotic cones. Publications emphasize geometric topology and group theory, with contributions to understanding filling invariants and geometric algorithmic properties. His office is 403 Malott Hall, and he maintains an active personal web page. No explicit awards are listed in the provided text, though he was part of news items related to teaching honors in 2024.

So, Man Cho, AnthonyView profile

Professor

• Optimization
• Algorithm Design
• Data Analytics
• +7 more

Anthony Man-Cho So is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong (CUHK). He currently serves as Dean of the Graduate School and Deputy Master of Morningside College . With a BSE from Princeton University and a PhD in Computer Science from Stanford University, his career at CUHK began in 2007. Academic Leadership: Dean, Graduate School (2023–present); Deputy Master, Morningside College (2019–present) Education: BSE (Princeton), MSc/PhD (Stanford) His research focuses on optimization theory and its interdisciplinary applications in computational geometry, machine learning, signal processing, and statistics. Key projects include non-convex optimization for wireless networks, robust graph learning, and decentralized learning algorithms. His publications span high-impact journals like Mathematical Programming , SIAM Journal on Optimization , and conferences such as NeurIPS and ICML . Recent work emphasizes dynamic regret analysis , low-rank matrix recovery , and stochastic beamforming . He has authored over 50 refereed papers and a monograph on semidefinite programming. Awards include IEEE Fellow (2023), CUHK Research Excellence Award (2016–17), and multiple IEEE/INFORMS best paper and teaching accolades. He has served on editorial boards of journals like Mathematical Programming and SIAM Journal on Optimization , and as Lead Guest Editor for IEEE Signal Processing Magazine . Teaching roles include courses on optimization, discrete mathematics, and machine learning. Scientific Awards IEEE Fellow (2023) CUHK Outstanding Fellow (2019) Multiple IEEE/INFORMS Best Paper Awards (2010–2022) IEEE/UGC Teaching Awards (2008–2022) His methodology integrates theoretical rigor with practical applications, particularly in wireless communication systems, sensor networks, and financial engineering. Collaborations span institutions in Hong Kong, mainland China, and the U.S., reflecting a global academic influence.

Dmitri KrioukovView profile

Associate Professor

• Network Science
• Complex Networks
• Mathematical Physics
• +7 more

Prof. Dmitri Krioukov is an Associate Professor in the Department of Physics at Northeastern University and holds an affiliated faculty position in Electrical and Computer Engineering. He directs the DK-Lab at the Network Science Institute, focusing on theoretical aspects of complex networks, including latent network geometry, random geometric graphs, and navigation in networks. His work bridges mathematical physics and applied network science, with applications to real-world data such as the Internet's structure. Research interests revolve around the interplay between network topology and geometry, including studies of causal sets, graph curvature, and dynamics in complex systems. He has pioneered frameworks linking network growth to hyperbolic geometry, enabling efficient routing algorithms. Notable contributions include the discovery of latent geometric structures underlying real-world networks and their implications for navigation and scalability. He has been recognized for high-impact publications, including multiple Stanford University Annual Assessments placing him among the top 2% most-cited scientists in his field (2024, 2023, 2022). His lab's interdisciplinary approach integrates principles from physics, mathematics, and computer science to address fundamental questions in network science.