Find Academic Supervisors

• Probability Theory
• Stochastic Analysis
• Machine Learning
• +5 more

Xiong Wang is a J.J. Sylvester Assistant Professor in the Department of Mathematics at Johns Hopkins University (non-tenure track). His research focuses on probability theory, stochastic analysis, and their applications to machine learning and complex systems. He holds a Ph.D. from the University of Alberta (2022), where he received the Faculty of Science Dissertation Award. Education: Ph.D. in Mathematics, University of Alberta (2022) Advisor: Professor Yaozhong Hu Research Interests: Probability Theory & Stochastic Processes Machine Learning Algorithms Partial Differential Equations (PDEs) Interacting Particle Systems Inverse Problems in Stochastic Dynamics Mathematical Foundations of Data Science Teaching: Instructor for advanced courses at Johns Hopkins, including Stochastic Differential Equations, Partial Differential Equations, and Real Analysis Prior teaching roles at University of Alberta (Calculus, Linear Algebra) Recent Activities: Speaker at over 30 international conferences/seminars since 2021 Active in SIAM, CMS, and ICERM events Collaborations with institutions globally (e.g., Peking University, Brown University)

Lingzhou XueView profile

Professor

• High-Dimensional Statistics
• Nonparametric Statistics
• Statistical Learning
• +9 more

Lingzhou Xue is a Professor of Statistics at The Pennsylvania State University, affiliated with the Eberly College of Science. He holds dual roles as a faculty member and the Associate Director of the National Institute of Statistical Sciences (NISS). His research focuses on high-dimensional statistics, nonparametric methods, statistical learning, and optimization, with applications in biomedical, environmental, and social sciences. He leads the SLDM (Statistical Learning and Data Mining) Lab and MDS (Microbiome Data Science) Lab. Education: B.Sc. in Statistics from Peking University (2008), Ph.D. in Statistics from the University of Minnesota (2012), postdoctoral training at Princeton University (2012–2013). Professional roles include Associate Editorships at the Journal of the American Statistical Association, Annals of Applied Statistics, and ACM Transactions on Probabilistic Machine Learning. Research Interests: Federated learning, causal inference, graphical models, reinforcement learning, optimal transport, and large-scale optimization. Recent work emphasizes theoretical guarantees for sparse PCA and federated Q-learning algorithms. Awards: IMS Fellow (2024), ASA Fellow (2023), COPSS Emerging Leader Award (2021), and Bernoulli Society New Researcher Award (2019). He has mentored 17 Ph.D. students and 3 postdocs, with four students securing tenure-track faculty positions. Service: Organized multiple NISS writing workshops, co-chaired the Ingram Olkin Statistics Serving Society Forum on Gun Violence, and contributed to the ASA whitepaper 'Discovery with Data' (2014).

Shahin TavakoliView profile

Senior Lecturer

• Statistics
• Functional Data Analysis
• Neuro-imaging
• +4 more

Shahin Tavakoli is a Senior Lecturer in the Research Institute for Statistics and Information Science at the Geneva School of Economics and Management (University of Geneva). He holds a PhD in Mathematical Statistics from EPFL and has held positions as a University Research Fellow at the University of Cambridge and a tenure-track Assistant Professor at the University of Warwick. His research focuses on functional data analysis with applications in neuroimaging, phonetics, biophysics, econometrics, and genomics. Education: BSc/MSc in Mathematics (EPFL), PhD in Mathematical Statistics (EPFL). Key roles include Associate Editor for the Journal of Statistical Planning and Inference and proposer for a JRSS B discussion paper. Teaching includes Applied Bayesian Statistics, Multivariate Analysis, and Mathematics courses at the University of Geneva. Research interests emphasize statistical methodologies for complex data structures, including high-dimensional functional time series and spatial modeling of linguistic data. Notable recent publications address phonetic analysis, brain imaging, and econometric factor models. Collaborations span institutions like the University of Cambridge, University of Warwick, and LMU Munich. Advising includes PhD students Marco Palma and Beatrice Matteo, with contributions to projects such as functional regression clustering and normative brain mapping. His work bridges theoretical statistics with applied domains, reflecting interdisciplinary impact across natural and social sciences.

• High-dimensional Data Analysis
• Survival Analysis
• Machine Learning
• +7 more

Dr. Tong Tong Wu is a Professor of Biostatistics and Computational Biology at the University of Rochester, serving as Director of Master's Programs in her department. She holds a Ph.D. from UCLA (2006). Her research focuses on high-dimensional data analysis, machine learning, survival analysis, and computational biology, with applications in cancer, HIV, epidemiology, dental health, and medical engineering. Education: Ph.D. in Biostatistics, University of California, Los Angeles (2006) Affiliations: UR Medicine, Department of Biostatistics and Computational Biology Roles: Director of Master's Programs, Faculty Member, and Researcher Her research interests emphasize statistical methodologies for complex biomedical data, including variable selection, clustering, and longitudinal trajectory analysis. Recent work explores oral microbiome dynamics in child-mother dyads, antifungal susceptibility, and machine learning for caries prediction. Dr. Wu collaborates across disciplines, contributing to studies on neurological disorders (e.g., Charcot-Marie-Tooth disease) and clinical outcomes in hemodialysis patients. Publications highlight innovations in penalized empirical likelihood, high-dimensional inference, and statistical modeling for biomedical applications. She advises students on topics ranging from longitudinal hemodynamic responses to physical activity clustering in young females. Her work bridges theoretical statistics and applied health research, addressing critical questions in public health and precision medicine.

Konrad Paul KordingView profile

Professor

• Neuroscience
• Machine Learning
• Computational Neuroscience
• +6 more

Konrad Paul Kording is the Nathan Francis Mossell University Professor at the University of Pennsylvania's School of Engineering and Applied Science, holding primary appointments in Bioengineering and Neuroscience, and a secondary appointment in Computer and Information Science. His research focuses on computational neuroscience, machine learning, and their applications to understanding neural systems and human behavior. He leads a research group located in Room 404 Richards and maintains an active research website and personal academic page. His work explores topics such as neural decoding, causal inference, and the alignment of artificial neural networks with biological systems. He has pioneered methods for analyzing neural activity in macaques, developing large-scale facial analysis resources like PrimateFace. His interdisciplinary approach integrates computer vision, medical informatics, and robotics to address challenges in neurology and developmental disorders. Recent research highlights include advancing techniques for early prediction of cerebral palsy using motion tracking and automated segmentation of synchrotron-scanned fossils. His contributions to causal discovery methodologies and neural network interpretability have been influential in both neuroscience and machine learning communities.

Hai ShuView profile

Assistant Professor

• High-dimensional statistics
• Machine learning
• Deep learning
• +5 more

Dr. Hai Shu is an Assistant Professor in the Department of Biostatistics at NYU's School of Global Public Health. He earned his Ph.D. in Biostatistics from the University of Michigan and B.S. from Harbin Institute of Technology. Previously, he was a Postdoctoral Fellow at MD Anderson Cancer Center. Education: Ph.D. in Biostatistics - University of Michigan B.S. in Information and Computational Science - Harbin Institute of Technology His research focuses on high-dimensional data analysis, machine/deep learning, and medical image applications in neurodegenerative diseases and oncology. He develops statistical methods for analyzing complex biomedical data from neuroimaging and genomics. His publications demonstrate consistent focus on developing novel statistical methods for medical imaging data, with increasing emphasis on deep learning approaches and multi-modal data integration in recent years. Awards: NYU GPH Goddard Award (2023) He mentors graduate students and serves as associate editor for Statistica Sinica and The American Statistician. His NIH-funded research includes studies on neuroimaging analysis and AI applications in healthcare. Leads research in medical image analysis and statistical learning, collaborating with neuroscience and oncology teams to develop computational tools for disease diagnosis and progression tracking.

• Numerical Solution of Partial Differential Equations
• Spectral Element Methods
• Reduced Basis Methods
• +3 more

Einar Malvin Rønquist is a Professor and Head of the Department of Mathematical Sciences at NTNU since August 2013. He holds a MSc from NTNU (1980) and a PhD from MIT (1988). His research focuses on numerical solutions of partial differential equations, spectral element methods, reduced basis methods, and computational fluid dynamics. He has been a leader in several research initiatives, including the Computational Science and Visualization program at NTNU (2003–2011). Rønquist is a member of prestigious academies: NTVA (since 2005) and DNKVS (since 2010). He has supervised 8 PhD students and 25 MSc students. His work spans computational science, with notable contributions to parametric modeling, parallel computing, and fluid dynamics simulation. His administrative roles include Vice President of R&D at Nektonics, Inc. (1991–1999) and Deputy Head of NTNU’s Department of Mathematical Sciences (Fall 2012). His publications highlight advancements in numerical methods for PDEs, including spectral element techniques, reduced basis approaches, and high-order approximations for complex geometries.

Anna SeigalView profile

Assistant Professor

• Applied Algebraic Geometry
• Algebraic Statistics
• Tensors and Multilinear Algebra
• +5 more

Anna Seigal is an Assistant Professor of Applied Mathematics at Harvard University's School of Engineering and Applied Sciences (SEAS), with an affiliation in the Department of Statistics. Her research focuses on applied algebraic geometry, tensors, multilinear algebra, and algebraic statistics, particularly in the context of data science. She explores algebraic approaches to data analysis, including matrix/tensor factorizations, parameter estimation, causal inference, and optimization, with applications to physical and biological systems. Her work is supported by the Sloan Foundation and Harvard's Dean’s Competitive Fund. Research Interests: Algebraic statistics, tensors and multilinear algebra, applied algebraic geometry, and the mathematics of data science. She investigates group symmetries in models, dimensionality reduction techniques, and machine learning algorithms. Current projects include causal disentanglement via cumulants and invariant theory applications to maximum likelihood estimation. Her academic contributions span theoretical advancements and interdisciplinary applications, such as genomic template analysis and COVID-19 molecular phenotyping. She collaborates with postdocs and students on research projects and teaches courses like Applied Math 210. Awards and Funding: Supported by the Alfred P. Sloan Foundation and Harvard's internal grants. No explicit named awards listed, but her research is institutionally recognized. Labs/Teams: Leads a research group focused on applied algebra and geometry in data science. Collaborates across departments in SEAS and the Statistics Department.

• Information Theory
• Coding Theory
• Network Coding
• +7 more

Sidharth Jaggi is a Professor in the School of Mathematics at the University of Bristol, specializing in Information Theory and Coding Theory with applications in secure data systems. His work provides unconditional information-theoretic security guarantees for communication/storage systems under malicious attacks. Education: B.Tech M.Phil PhD Research Focus: Professor Jaggi develops theoretical frameworks for adversarial communication, covert channels, and sparse signal estimation. His CAN-DO-IT research team (Codes, Algorithms, Networks – Design and Optimization for Information Theory) bridges abstract mathematics with real-world applications in distributed data storage, secure computing, and epidemic testing via group-testing methodologies. Key innovations include fundamental limits for stealthy communication and robust coding against jamming adversaries. Publication Trends: Recent works analyze density-dependent group testing structures, causality benefits in security against limited-view adversaries, and hybrid coding strategies for jamming channels. These publications demonstrate convergence of information theory, high-dimensional geometry, and optimization for next-generation secure data systems. Research Leadership: Principal Investigator for "Information Theory for Interactive Distributed AI" (2024-2029) Research Group: Leads the CAN-DO-IT team focusing on theoretical foundations for secure, robust information systems with tangible industrial applications in data processing infrastructure.

• Uncertainty Quantification
• Predictive Modeling
• Computational Mathematics
• +5 more

Nicholas Zabaras is a Professor of Uncertainty Quantification and the Director of the Warwick Centre for Predictive Modelling at the University of Warwick. He is a Hans Fischer Senior Fellow at the TUM Institute for Advanced Study (TUM-IAS), hosted by Phaedon-Stelios Koutsourelakis. His research focuses on advancing computational methods for uncertainty quantification, predictive modeling, and multiscale/multiphysics systems. Key areas include Bayesian methods, stochastic modeling, and data-driven approaches for complex materials systems. Education: He holds a diploma in Mechanical Engineering from the National Technical University of Athens (1982), an M.Sc. in Materials Science and Engineering from the University of Rochester (1983), and a Ph.D. in Theoretical and Applied Mechanics from Cornell University (1987). He has held academic positions at the University of Minnesota, Cornell University, and the University of Warwick, where he now leads the Warwick Centre for Predictive Modelling. Research Interests: His work integrates computational mathematics, statistics, and scientific computing to address challenges in materials science, computational physics, and engineering systems. Specific themes include Bayesian uncertainty quantification, high-dimensional modeling, information-theoretic coarse graining, and stochastic model reduction. Awards: He has received the Royal Society Wolfson Research Merit Award (2014), the Michael Tien’72 College of Engineering Teaching Award (2009), and is a Fellow of the American Society of Mechanical Engineers (2006). Labs/Teams: Director of the Warwick Centre for Predictive Modelling, and leader of the Scientific Computing and Artificial Intelligence (SCAI) Laboratory at the University of Notre Dame, focusing on interdisciplinary research in AI-driven predictive modeling and uncertainty quantification.

• Statistics
• Econometrics
• Machine Learning
• +7 more

Marcel Scharth is a Lecturer in Business Analytics at the University of Sydney Business School and affiliated with the Centre for Translational Data Science. He holds a Ph.D. from VU University Amsterdam (2012) and has conducted postdoctoral research at the University of New South Wales. His expertise spans Bayesian methods, computational statistics, machine learning, and financial econometrics. Research focuses include high-dimensional stochastic volatility models, Monte Carlo methods, and Bayesian machine learning applied to time series and longitudinal data. He teaches courses like QBUS2810 (Statistical Modelling), QBUS2820 (Predictive Analytics), and QBUS5001 (Quantitative Methods for Business), emphasizing statistical reasoning and computational skills. His work has been published in top journals such as the Journal of Econometrics and the Review of Economics and Statistics. He actively engages in AI discourse, contributing to media articles on prompt engineering for generative AI and the limitations of models like ChatGPT. Professional activities include Effective Altruism collaborations and open-source contributions via GitHub repositories like forecasting and Machine Learning for Business .

• Algorithmic Sequential Decision-Making
• Robust AI
• Safe AI
• +11 more

Ilija Bogunovic is an Assistant Professor (UK Lecturer) in the Department of Electronic and Electrical Engineering at University College London (UCL), Faculty of Engineering Sciences. His research focuses on algorithmic sequential decision-making for robust, reliable, and safe artificial intelligence, with applications in large language model alignment, reinforcement learning, and human feedback integration. His research interests lie at the intersection of machine learning, optimization, and decision theory. He investigates robustness in Bayesian optimization, bandits, and reinforcement learning under adversarial attacks, model misspecification, and distributional shifts. His work spans both theoretical foundations and real-world applications in energy systems, mobility, and AI safety. A central theme is developing algorithms that are provably robust and efficient in uncertain and potentially corrupted environments. The recent publications highlight a strong trend in robust decision-making, particularly in reinforcement learning and preference optimization. Key themes include adversarial robustness in large models, distributional robustness in RL, safe multi-agent systems, and robust Bayesian optimization. The work frequently involves theoretical analysis of regret and sample complexity, combined with practical algorithm design for high-impact applications. Scientific Awards: Google Research Scholar Program Award (Machine Learning & Data Mining), 2023 EPSRC New Investigator Award, 2023 Ilija Bogunovic actively supervises PhD and MSc students, with several of his advisees leading papers accepted to top venues like NeurIPS and ICLR. He has secured competitive research grants, including the EPSRC New Investigator Award, to support his work on robust decision-making. He is building a research team focused on robust and safe AI. He leads a research team and has initiated an online reading group on modern adaptive experimental design and active learning, fostering a collaborative research environment.

Milica TodorovicView profile

Visiting Professor

• Machine Learning
• Materials Science
• Quantum Mechanics
• +6 more

Milica Todorovic is a Visiting Professor at Aalto University's Department of Applied Physics , leading machine learning research within the Computational Electronic Structure Theory (CEST) group. Her work bridges quantum mechanical simulations and machine learning algorithms to optimize material functionality, particularly for solar cell components and organic-inorganic interfaces . Research Interests include: Data-driven materials science Active learning for molecular datasets Bayesian optimization in atomic structure prediction Quantum simulations of surfaces and adsorbates Thermodynamic property modeling for atmospheric molecules Article Trends (2017-2025) show focus on machine learning applied to materials science , with subfields spanning bayesian optimization , conformational analysis , and density functional theory . Key collaborations include Patrick Rinke and Hanna Vehkamäki . Activities include organizing workshops like the Young Researcher’s Workshop on Machine Learning for Materials Science (2019) and International Workshop on Machine Learning for Materials Science (2018).

Selin AslanView profile

Assistant Professor

• Applied Mathematics
• Computational Mathematics
• Imaging Science
• +2 more

Selin Aslan serves as an Assistant Professor in the Department of Mathematics at Koç University, Istanbul, Turkey, where she conducts research at the intersection of computational mathematics and imaging science. Her academic appointments and research activities are centered within the university's mathematics department, contributing to both undergraduate and graduate education in mathematical sciences. Her educational qualifications include: PhD in Mathematics from Virginia Polytechnic Institute and State University (2018) Master's in Mathematics from Rochester Institute of Technology (2013) B.A. in Mathematics from Ege University (2010) Dr. Aslan's research program focuses on developing advanced computational methods for solving inverse problems in imaging, with particular expertise in phase retrieval, tomographic reconstruction, and ptychography. Her work bridges theoretical mathematics with practical applications in medical imaging, microscopy, and materials science, emphasizing algorithmic innovation and computational efficiency. She integrates techniques from deep learning, optimization theory, and high-performance computing to address challenges in image reconstruction under physical constraints. Analysis of her publication record reveals a consistent trajectory toward solving complex imaging problems through hybrid approaches that combine physics-based models with data-driven techniques. Her recent work demonstrates increasing emphasis on scalability for large datasets, robustness in photon-limited scenarios, and real-time processing capabilities, with applications spanning biomedical imaging to advanced microscopy. No scientific awards were documented in the available sources. Information regarding student advising and research grant activities was not specified in the provided materials, though her publication record suggests active research collaboration. Her computational focus implies engagement with high-performance computing resources for large-scale image reconstruction tasks. While specific laboratory infrastructure details were unavailable, her research on multi-GPU implementations and distributed computing indicates utilization of advanced computational facilities for handling large-scale imaging datasets.

Linbo WangView profile

Associate Professor

• Causal Inference
• Biostatistics
• Data Science
• +4 more

Linbo Wang is an Associate Professor at the Department of Statistical Sciences , University of Toronto, with cross-appointments to the Department of Computer and Mathematical Sciences at U of T Scarborough and the Department of Computer Science at U of T. He is also an Adjunct Associate Professor at the University of Washington and a Canada Research Chair in Causal Machine Learning. B.Stat. from Peking University (2011) PhD in Biostatistics from the University of Washington (2016) Postdoctoral Fellowship at Harvard T.H. Chan School of Public Health His research focuses on causal inference , machine learning , and biostatistics , particularly causal modeling, graphical models, and robust inference for high-stakes domains like healthcare and justice. Recent work includes causal mediation analysis, instrumental variable methods, and trustworthy AI frameworks emphasizing fairness , explainability , and stability . His publications span causal inference for longitudinal studies, missing data, high-dimensional models, and biomedical applications. Awards include the NSERC Discovery Accelerator Supplement , Ontario Early Researcher Award , and recognition as a Canada Research Chair . He is affiliated with the Vector Institute and the Data Sciences Institute , co-organizing conferences like the 23rd Meeting of New Researchers in Statistics and Probability and serving as tutorial co-chair for the 39th Conference on Uncertainty in Artificial Intelligence . Currently, he is accepting students and postdocs for research in causal inference and machine learning.