Yizhe Zhu is an Assistant Professor of Mathematics at the University of Southern California , specializing in theoretical and applied aspects of high-dimensional data analysis. His research bridges mathematics, computer science, and statistics, with a focus on random matrix theory, sparse data structures, and algorithmic analysis for machine learning and privacy-preserving data methods. Research Interests Yizhe Zhu’s work addresses fundamental questions in: Random Matrix Theory : Spectra of sparse and structured matrices, including outlier detection and universality. Graph and Hypergraph Analysis : Community detection, spectral properties, and non-backtracking algorithms for complex networks. Privacy and Data Synthesis : Theoretical frameworks for differentially private synthetic data generation. Tensor Completion : Efficient algorithms for recovering low-rank tensors from sparse observations. Publications Trends His recent research (2024–2025) emphasizes spectral analysis of random structures, optimization in non-convex settings, and privacy-preserving machine learning. Key themes include the interplay between sparsity, spectral theory, and algorithmic robustness in high-dimensional regimes.
Ryan Williams is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Department of Electrical Engineering and Computer Science. Previously, he held a faculty position at Stanford University from 2011 to 2016. He obtained his PhD in Computer Science from Carnegie Mellon University under Manuel Blum and completed his undergraduate studies at Cornell University. His research focuses on computational complexity theory, exploring the boundaries of efficient computation and connections between algorithm design and complexity lower bounds. He teaches advanced courses such as Automata, Computability, and Complexity Theory at MIT. Education: PhD in Computer Science, Carnegie Mellon University (Advisor: Manuel Blum) Bachelor's Degree in Computer Science, Cornell University Research Interests: His work addresses fundamental questions in theoretical computer science, including the P vs. PSPACE problem, circuit lower bounds, and the development of algorithms with provable efficiency. He investigates connections between algorithmic techniques and complexity-theoretic limitations, aiming to establish barriers to solving computational problems efficiently. Publications and Trends: Ryan Williams' recent work spans topics like space-bounded computation, probabilistic polynomial sparsity, circuit lower bounds, and algorithms for compression and graph problems. His research often bridges theoretical insights with practical algorithm design, emphasizing the interplay between computational models and their limitations. Advising and Grants: Current advisees include Rahul Ilango, Ce Jin, and Ted Pyne. He has mentored numerous PhD students who have contributed to areas like fine-grained complexity and circuit analysis. While specific grants are not detailed, his research aligns with foundational studies in theoretical computer science. Labs and Teams: Williams is affiliated with MIT CSAIL, where he collaborates on projects exploring computational complexity and algorithmic foundations.
Jad Beyhum is an Associate Professor in Economics at KU Leuven, affiliated with the Faculty of Economics and Business and its Economics Research Group. He specializes in econometrics and statistics, focusing on high-dimensional data analysis, instrumental variable methods, and survival analysis techniques. Current research themes include high-dimensional econometrics (e.g., LASSO-type procedures, factor models) and instrumental variables techniques (e.g., dynamic treatments, censored data). His methodological contributions target econometric challenges in nonlinear panel data, competing risks models, and duration outcomes. Key applications span corporate bankruptcy prediction, program evaluation, and macroeconomic forecasting. Recent publications highlight innovations in instrumental variable estimation under censoring, testing treatment effect homogeneity, and combining machine learning with traditional econometric frameworks. He actively supervises projects on topics like "Beyond Instrumental Variables" and "New methods to control unobserved heterogeneity" . Selected project leadership roles: "Predicting bankruptcy with machine learning methods" (Promotor, 2023-2027) "Partial identification methods for survival analysis" (Co-promotor, 2023-2027)
Peng Zhao is a Quantitative Researcher affiliated with the University of California, Berkeley's Department of Statistics within the College of Letters and Science. He completed his PhD in Statistics in 2006 under the advisement of Bin Yu, focusing on regularization techniques in high-dimensional data analysis. His research interests center on statistical learning , with emphasis on sparsity, structured regularization, and computational methods for modern data analysis. These areas align with broader trends in machine learning and high-dimensional statistics.
Li Feng, PhD, is an Associate Professor in the Department of Radiology at NYU Grossman School of Medicine, New York University, where he also serves as Director of Rapid Imaging. He earned his PhD from New York University, specializing in advanced medical imaging techniques. His research focuses on accelerating and optimizing Magnetic Resonance Imaging (MRI) through novel computational methods. Key areas include: Rapid imaging protocols for abdominal and liver diagnostics Deep learning-based reconstruction of dynamic MRI data Quantitative mapping techniques for tissue characterization Motion-robust acquisition methods for clinical applications Recent publications demonstrate his leadership in developing GPU-accelerated reconstruction algorithms, non-contrast-enhanced vascular imaging, and AI-driven quantitative MRI techniques applied to neurology, oncology, and metabolic disorders. His work consistently bridges technical innovation with clinical translation. Dr. Feng leads multiple clinical trials including: 3D Free-Breathing Fat and Iron Corrected T1 Mapping Rapid Motion-Robust DCE-MRI for Liver Perfusion Quantification Rapid Structure-Function MRI of the Lung for Post-COVID-19 Management
Dr. Jia Liu is a Professor and Chair of the Department of Mathematics and Statistics at the University of West Florida, within the Hal Marcus College of Science and Engineering. She has been a key academic figure at UWF since 2006, leading both research and departmental initiatives in computational and applied mathematics. Her educational background includes a Ph.D. in Mathematics from Emory University, funded by the National Science Foundation, focusing on preconditioned Krylov subspace methods for incompressible flow problems. She earned her M.S. and B.A. in Mathematics from Central China Normal University, where her bachelor's thesis received the highest honor. Dr. Liu's research lies at the intersection of numerical linear algebra, scientific computing, and interdisciplinary applications. Her primary interests include: Numerical solvers for large sparse linear systems Krylov subspace iterative methods Preconditioning techniques Applications to Navier-Stokes and optimization problems Geometric and topological analysis of ellipsoids Complex networks and community detection via spectral clustering Machine learning for disease prediction and biological modeling The trends in her publications reflect a consistent focus on robust numerical algorithms with applications across fluid dynamics, network science, and biomedical modeling. Her work emphasizes both theoretical development and practical implementation in high-performance computing environments. Dr. Liu has served as an editor and editorial board member for several peer-reviewed journals and regularly reviews submissions. While specific awards are not listed, her sustained publication record in prestigious venues such as SIAM Journal on Scientific Computing and Journal of Biological Dynamics underscores her scholarly impact. As department chair and professor, she plays a central role in academic advising, curriculum development, and research mentorship. She teaches core courses including Differential Equations, Numerical Analysis, and Real Analysis, contributing significantly to both undergraduate and graduate education. Her leadership extends to managing research grants and fostering collaborations across disciplines. Though no formal lab name is mentioned, her research activities suggest involvement with computational modeling groups, likely associated with applied mathematics and data science initiatives at UWF. Her ongoing work continues to advance numerical methods for complex systems in science and engineering.
Maryamolsadat Samavaki is a Researcher affiliated with the Computing Sciences Mathematics Research Centre , focusing on computational modeling of cerebrovascular dynamics and neuroelectromagnetism. Her work bridges biomedical engineering and applied mathematics through advanced numerical methods. Key research areas include EEG source localization , transcranial stimulation , and multi-compartment head modeling . Develops spatiotemporal hemodynamic models to analyze microcirculation and blood volume fractions. Applies L1-norm optimization and metaheuristic algorithms for neurostimulation montage design. Recent publications explore cerebral circulation's impact on electrical conductivity and in silico imaging techniques using anatomical atlases . Collaborations emphasize boundary-fitted meshing for subcortical structures in EEG modeling.
Wojciech Czerwiński is an Associate Professor at the Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw. His research lies at the intersection of automata theory, logic, and computational complexity, with a focus on infinite-state systems such as vector addition systems (VASS) and Petri nets. His research interests include: Automata and logic Infinite-state systems Reachability and separability problems Complexity of computational models Formal verification and concurrency theory His recent work has significantly advanced the understanding of the complexity of reachability in VASS, proving it to be Ackermann-complete. This line of research has been published in top venues including FOCS, STOC, LICS, and CONCUR, where several papers received Best Paper Awards. His publications reflect a strong trend toward resolving long-standing open problems in decidability, complexity, and logical definability in infinite-state models. Scientific awards include: Best Paper Award, CONCUR 2022 Best Paper Award, STOC 2019 He leads an ERC-funded project and is actively recruiting PhD students and post-docs. He organizes the Friday Afternoon Seminar and co-organized the Infinite Automata Workshop (2024). He has also contributed to public understanding of science as an editor and author for the journal Delta from 2013 to 2022. His work involves collaboration with leading researchers such as Sławomir Lasota, Jérôme Leroux, Georg Zetzsche, and others. He has made foundational contributions to problems like bisimilarity, language equivalence, and regular separability in various automata models.
Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.
Dr. Binod Bhattarai is a Lecturer (equivalent to Assistant Professor in the US) in the School of Natural and Computing Sciences at the University of Aberdeen, UK. He is also an Honorary Lecturer at University College London and a Co-founder and Adjunct Research Scientist at NAAMII, Nepal. Dr. Bhattarai heads the Multimodal Learning Lab, a cross-border initiative between the University of Aberdeen and NAAMII, Nepal. His educational background includes a PhD in Computer Science from Universite de Caen, France, and previous work experience as a Senior Research Fellow at University College London, a Postdoctoral Research Associate at Imperial College London, and a Data Scientist at Telenor Group, Norway. Dr. Bhattarai's research focuses on developing robust and interpretable machine learning algorithms that can reason across complex, heterogeneous data. His work spans multiple domains including surgical videos, medical imaging, and low-resource languages, with applications in healthcare, energy, and global agriculture. He follows a core philosophy of building AI that is not only powerful but also trustworthy and explainable, with a belief that true intelligence lies in the ability to seamlessly integrate diverse data sources. His publications demonstrate strong trends in multimodal learning, particularly in medical applications. A significant portion of his recent work focuses on gastrointestinal image analysis, out-of-distribution detection in medical contexts, and federated learning approaches for healthcare data. His research often bridges computer vision, natural language processing, and medical imaging to create practical AI solutions for healthcare challenges. Best Paper Award Finalist, MIUA 2025 Runner-up, ARCADE Challenge, MICCAI 2023 Google Cloud Research Innovator, 2022 Outstanding Reviewer Award, BMVC, 2021 Winner FetReg Endoscopic Vision Challenge at MICCAI 2021 Outstanding Reviewer Award, BMVC, 2019 Best Student Paper Award of Image, Video and Multidimensional Signal Processing, ICASSP, 2016 Best Paper Award Runner up, ACM ICVGIP, 2016 DAAD Postdoc Net-AI-Fellow, 2020 (top 22 out of 192) Dr. Bhattarai actively mentors PhD students and research assistants through the Multimodal Learning Lab. Current PhD students include Jardin Ruari (Assessing AI algorithms for Capsule Endoscopy) and Krit Duangprom (Surgical Tool and Hand Pose Estimation). His lab has successfully guided numerous researchers who have gone on to PhD programs at prestigious institutions including MILA, Dartmouth College, University of Utah, and RIT. He has secured multiple research grants including a Co-PI role for "Non-constrast CT Head Image Analysis" funded by The Ronald Sutton Academic Trust (30.8K GBP, 2024-27), and a PI role for "Frontiers Seed Funding" by the Royal Academy of Engineering (20K GBP, 2023-2024). The Multimodal Learning Lab, which Dr. Bhattarai heads, is a cross-border initiative between the University of Aberdeen and NAAMII, Nepal. The lab focuses on developing robust and interpretable machine learning algorithms that can reason across complex, heterogeneous data. Current research projects include explainable anomaly detection in GI endoscopy, surgical vision world models, multimodal federated learning, surgical data science, and synthetic data generation. The lab operates with a global research pipeline that fosters talent and innovation across borders.
Dr. Tai-Sing Lee is a Professor in the Computer Science Department at Carnegie Mellon University, with affiliations in the Center for the Neural Basis of Cognition (CNBC) and the Machine Learning Department. He holds adjunct roles at the University of Pittsburgh's Neuroscience Department. His research focuses on computational neuroscience, visual perception, and the intersection of biological and machine intelligence. Lee earned his S.B. from Harvard (1986), and dual Ph.D.s from Harvard (1993) and MIT (1993), followed by postdoctoral training at Harvard and MIT. He leads the Lee Lab for Biological and Machine Intelligence Research and directs programs such as the Peking University-CMU and Tsinghua-CMU summer programs in Computer Science. His research investigates computational principles of visual perception, leveraging neurophysiological, mathematical, and machine learning approaches. Key areas include statistical modeling of neural codes, learning/adaptation mechanisms in neural systems, and Bayesian inference frameworks. Lee has developed novel machine learning techniques for analyzing neural data, including studies on V1/V2/V4 neural coding and hierarchical perceptual inference. Lee has advised numerous Ph.D., M.S., and undergraduate students, many of whom pursued academic or tech careers. His honors include the NSF CAREER Award (2000) and ICCV Helmholtz Prize (2013). His lab’s projects include the MICrONS initiative to reverse-engineer brain algorithms and collaborations on predictive coding models in vision and music perception.
Wilker Ferreira Aziz is an Assistant Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning group. His primary affiliation is with the Natural Language Processing & Digital Humanities research unit. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Key areas of interest include language modeling, machine translation, syntactic parsing, text classification, and question answering. He develops techniques for probabilistic inference, gradient estimation, and uncertainty quantification in neural language models. Dr. Aziz's recent publications demonstrate a strong focus on uncertainty in natural language generation, with multiple papers at top-tier conferences like EACL, EMNLP, and ICLR. His work examines how language models represent uncertainty compared to humans, calibration issues when humans disagree on labels, and methods for more robust decision-making in text generation. Best Paper Award at Coling 2020 He actively supervises both PhD and MSc students, with several ongoing PhD projects focusing on uncertainty in language models and neural text generation. Dr. Aziz serves on program committees for major ML and NLP conferences including ACL, EMNLP, NeurIPS, and ICLR, and has acted as area chair for several of these venues. His research has been supported through positions at the Mercury Machine Learning Lab, a collaboration between Booking.com, TU Delft, and the University of Amsterdam.
Jose Perea is an Associate Professor at Northeastern University, jointly appointed in the Department of Mathematics and the Khoury College of Computer Sciences. He holds a Ph.D. from Stanford University and a B.Sc. (Summa cum Laude and Valedictorian) from Universidad del Valle. His research focuses on Topological Data Analysis (TDA), with applications to machine learning, signal analysis, and computational topology. He has developed algorithms like DREiMac and FibeRed for dimensionality reduction, and SW1PerS for periodicity detection in time series and video data. His academic journey includes postdoctoral work at Duke University and a position at Michigan State University. He has received grants from NSF, DARPA, and the NSF CAREER Award. Current projects include topological approaches to neuroscience, medical diagnostics, and geometric data analysis. Perea leads the development of open-source TDA tools and collaborates on applications in engineering and healthcare. Awards: NSF CAREER Award (2020). Research spans theoretical TDA, algorithm design, and interdisciplinary applications. He advises students in computational mathematics and computer science.
Monique Laurent is a Tilburg University professor and senior researcher at CWI (Centrum Wiskunde & Informatica), focusing on discrete mathematics and optimization . Her work bridges algebra, geometry, and computer science to solve complex combinatorial and polynomial optimization problems. Part-time full professor at Tilburg University (since 2009) Group leader of Networks and Optimization at CWI (2005-2016) Current member of CWI Management Team Research Focus: Semidefinite programming hierarchies, noncommutative polynomial optimization, quantum information theory, and matrix factorization. Her recent work explores applications in quantum entanglement , graph parameters , and combinatorial data analysis . Key Publications: 15+ articles from 2017-2024 address topics like copositive matrices , sum-of-squares convergence , and hypergraph optimization . Collaborations span institutions in the Netherlands, France, Germany, and the U.S. Awards: 2023 Khachiyan Prize SIAM Fellow (2017) KNAW member (2018) EUROPT Fellow (2021) Grants & Projects: Leads EU-funded initiatives TENORS (2024) and POEMA (2019), with prior NWO and Marie Curie grants. Organizes international workshops on polynomial optimization and quantum information.
Min Xu is an Assistant Professor in the Department of Statistics at Rutgers University – New Brunswick. He is affiliated with the School of Arts and Sciences and focuses his research on theoretical and methodological aspects of machine learning and high-dimensional statistics, with applications in network analysis and nonparametric estimation. Education: Ph.D. in Machine Learning, Carnegie Mellon University (2015) B.S. in Electrical Engineering and Computer Science (with minor in Mathematics), UC Berkeley Research Interests: Min Xu’s research lies at the intersection of machine learning , high-dimensional statistics , and network science . He develops computationally scalable methods with strong theoretical guarantees for complex data structures, particularly in nonparametric estimation , network analysis , and large-scale inference . His work addresses fundamental challenges in estimating high-dimensional distributions and understanding the structure of evolving networks, with applications in economics and social sciences. Grants & Funding: NSF Grant DMS-2113671 NSF Grant DMS-2311299 Research Trends: Across his publications, a consistent theme is the development of statistically rigorous methods for high-dimensional and network data. His work spans optimal estimation in stochastic block models, convex M-estimation, and inference on dynamic network structures, with a strong emphasis on theoretical guarantees and practical scalability. Affiliations: Previously, Min Xu served as a departmental postdoctoral researcher in the Statistics Department at the Wharton School, University of Pennsylvania. He is currently based at Hill Center, Rutgers University.
