Find Academic Supervisors

• Artificial Intelligence
• Supply Chain
• Sustainability
• +6 more

Evelyn Xiaoyue Gong is an Assistant Professor of Operations Management at the Tepper School of Business , Carnegie Mellon University . She holds a PhD in Operations Research from MIT (2023) and a BS in Honors Mathematics and Interactive Media Arts from New York University (2017) . Research Interests : Developing artificial intelligence solutions for supply chains and sustainability with provable performance guarantees. Online assortment optimization, pure exploration in reinforcement learning, and data-driven decision-making . Recent Work Trends : Her publications focus on applying reinforcement learning and optimization algorithms to supply chain sustainability, server deployment under uncertainty, and resource management. Key themes include AI-driven environmental impact reduction and theoretical advancements in online inventory models . Scientific Recognition : Best Dissertation Prize , Supply Chain Conference (2023) Accenture Fellowship (2022-2023) Bayer Women in Operations Research Scholarship (2021) Professional Service : Reviewer for Management Science , NeurIPS 2025 , and IPCO 2025 . Committee member for INFORMS Public Sector Operations Research Best Paper Award (2025).

• Social Physics
• Graph Convolutional Neural Networks
• Large Language Models (LLMs)
• +4 more

Dr. Alexander Mantzaris is an Associate Professor in the Department of Statistics & Data Science at the University of Central Florida, College of Sciences. His research bridges physics and sociology through Social Physics frameworks, focusing on statistical mechanics and thermodynamic analogies to model social phenomena. Current research explores criticality points in social systems Developing computational tools for NLP and big data Former work on Graph Convolutional Networks in social analysis Specializes in entropy-based modeling of polarization and segregation His publications emphasize interdisciplinary approaches combining network science, computational modeling, and sociological dynamics. Recent articles address thermodynamic formulations of political cycles, energy states in Schelling models, and memory-efficient data processing algorithms. Dr. Mantzaris teaches graduate courses in big data analytics and statistical learning theory. He maintains active research in computational social science with applications to political dynamics, media influence, and complex systems analysis.

James SaundersonView profile

Senior Lecturer

• Convex Optimization
• Semidefinite Programming
• Polynomial Optimization
• +4 more

Dr. James Saunderson is a Senior Lecturer and Director of Education in the Department of Electrical and Computer Systems Engineering at Monash University. He holds a PhD in Electrical Engineering and Computer Science from MIT and has held postdoctoral roles at Caltech and the University of Washington. His expertise spans convex optimization, semidefinite programming, and quantum information theory. Education : PhD in EECS, MIT (2015) MS in EECS, MIT (2011) Bachelor of Engineering (Honours) and Bachelor of Science (Honours), University of Melbourne (2008) Research Interests : Convex optimization, quantum information theory, signal processing, and algorithm design. Focuses on algebraic and geometric aspects of optimization, with applications in engineering and quantum systems. Recent Projects : Exploiting duality in quantum relative entropy optimization Hyperbolic programming and conic optimization Applications in nanotechnology and bioinformatics Teaching : Courses include Control System Design, Signals and Systems, and Optimization for Engineers. Awards : SIAM Optimization Best Paper Prize (2020) Grants and Collaborations : Australian Research Council Discovery Early-Career Research Fellow (2020–2024) Leading projects in quantum optimization and bioengineering applications.

Ryan MurrayView profile

Assistant Professor

• Calculus of variations
• Partial Differential Equations
• Machine Learning
• +5 more

Ryan Murray is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State). His research focuses on developing mathematical tools to address problems in applied analysis, including calculus of variations, partial differential equations (PDEs), and their applications to machine learning, fluid dynamics, and control theory. He holds a PhD in Mathematics from Carnegie Mellon University (2016). His expertise spans regularization methods for machine learning, singular perturbations in materials science, algorithms for distributed optimization, and singularity formation in fluid dynamics. His work is supported by the National Science Foundation (NSF) and the Simons Foundation. He actively collaborates with researchers in data science, PDE analysis, and optimization. Key research areas include adversarial training in classification, geometric data analysis via statistical depths, and the analysis of vortex sheet singularities. His teaching experience includes courses on partial differential equations, optimal control theory, and linear control systems. Ryan has published extensively in journals such as SIAM Journal on Mathematics of Data Science , Archive for Rational Mechanics and Analysis , and Journal of Machine Learning Research . His articles explore topics ranging from graph-based learning to fluid dynamics instabilities.

• Geometric Measure Theory
• Optimal Transport
• Calculus of Variations
• +3 more

Luigi Ambrosio is a Full Professor at the Scuola Normale Superiore di Pisa (SNS), specializing in geometric measure theory, optimal transport, and partial differential equations. His research focuses on the interplay between geometric analysis, functional analysis, and calculus of variations, with applications to metric measure spaces and stochastic processes. He has organized numerous conferences and schools on optimal transport and geometric analysis, including the 2025 'XXXV Convegno Nazionale di Calcolo delle Variazioni.' Key research interests include the theory of currents, regularity of flows, and the application of optimal transport to problems in probability and geometry. Notable contributions include foundational work on metric Sobolev spaces, RCD spaces, and the analysis of geometric flows. Ambrosio frequently collaborates with leading institutions and has supervised numerous seminars on topics ranging from gradient flows to non-smooth geometric structures. His publications span over 150 papers, addressing topics such as the regularity of vector fields, entropy flows in Carnot groups, and the stability of action functionals. Recent works (2021–2025) explore superposition principles for currents, sharp PDE estimates for random matching, and embedding theorems for metric spaces. Ambrosio is also active in academic leadership, contributing to editorial boards and international research networks.

• Optimal Transport
• Geometric Analysis
• Ricci Curvature
• +3 more

Fabio Cavalletti is a Full Professor at the University of Milan, specializing in geometric analysis, optimal transport theory, and synthetic curvature bounds. His work bridges differential geometry, mathematical physics, and functional inequalities, with particular focus on Lorentzian spaces and Ricci curvature. He has organized international workshops and conferences on optimal transport and geometric analysis. Research interests include optimal transport in Lorentzian and Riemannian settings, synthetic Ricci curvature conditions, isoperimetric inequalities, and geometric flows. Recent work explores timelike Ricci curvature bounds, null hypersurfaces in general relativity, and applications to quantum information geometry. Key contributions include foundational results on displacement convexity, quantitative Obata's theorem, and optimal transport in singular spaces. He has collaborated extensively with researchers like Andrea Mondino and Nicola Gigli, producing over 35 peer-reviewed publications. Notable activities include organizing the 2024 School and Conference on Metric Measure Spaces, 2025 Workshop on Optimal Transport and Metric Geometry, and mentoring junior researchers in geometric analysis. Current projects address synthetic timelike curvature in Lorentzian spaces and applications to general relativity.

• Biomechanics
• Gerontology
• Chronic Obstructive Pulmonary Disease (COPD)
• +5 more

Dr. Jenna Yentes is an Associate Professor in the Department of Kinesiology and Sport Management at Texas A&M University, affiliated with the College of Education and Human Development. Her research focuses on functional resiliency in aging populations, biomechanics of chronic obstructive pulmonary disease (COPD), and nonlinear analysis of human movement. She leads studies on gait stability, respiratory-gait coupling, and exoskeleton-assisted walking. Notable contributions include quantifying locomotor reserve and exploring firefighter performance in protective gear. Education: Ph.D. in Biomechanics (University of Nebraska, 2013), M.S. in Kinesiology (California State University Fullerton, 2006), B.A. in Kinesiology (University of Northern Colorado, 2000). Research Interests: Reserve capacity in older adults' mobility and cognition Biomechanical adaptations in COPD patients Methodological rigor in nonlinear data analysis (e.g., entropy metrics) Firefighter physical performance under protective gear Recent Articles Trends: Focus on entropy-based gait analysis, COPD biomechanics, dual-task interference in aging, and exoskeleton effects on interlimb coordination. Methodological rigor in parameter selection for nonlinear algorithms is a recurring theme. Awards: 2023 Faculty Climate Award (Texas A&M), 2019 Promising Scientist Award (International Society of Posture and Gait Research), and 2019 Chancellor's Commission on the Status of Women Award (University of Nebraska). Advising & Grants: Mentors graduate students (noted in publications) and collaborates with TEEX Fire Academy and multiple Texas A&M research centers including the Huffines Institute for Sports Medicine and the Center for Population Health and Aging. Research supported by institutional and federal grants. Labs/Teams: Active in Texas A&M's Human Movement and Aging Lab, collaborating with interdisciplinary teams in sports medicine, rehabilitation engineering, and pulmonary research.

• Auctions and Mechanism Design
• Microeconomic Theory
• Information Design
• +3 more

Benny Moldovanu is Professor of Economics at the University of Bonn, where he holds the Chair of Microeconomics. He is a principal investigator in the Collaborative Research Center TR 224 (CRC TR 224) and a member of the Cluster of Excellence ECONtribute: Markets & Public Policy, as well as the Hausdorff Center for Mathematics and the Bonn Graduate School of Economics (BGSE). Education: Detailed curriculum vitae available at CV link . Research Interests: His research focuses on auctions and mechanism design , microeconomic theory , information design , voting theory , and dynamic allocation problems . He has made significant contributions to understanding strategic behavior in auctions, optimal voting rules, and the design of mechanisms under incomplete information. Recent Publications Trend: Recent work (2020–2025) explores entropy-regularized optimal transport in information design, optimal security design for risk-averse investors, order independence in sequential voting, and insurance design under adverse selection. These studies combine sophisticated theoretical tools with applications in finance, political economy, and market design. Scientific Awards and Honors: Winner of the first AEA Best Paper Award for the American Economic Journal: Microeconomics (2009) for “Dynamic Revenue Maximization with Heterogeneous Objects: A Mechanism Design Approach”. Advising and Doctoral Training: Professor Moldovanu has supervised a large cohort of doctoral students and postdocs, many of whom now hold faculty positions worldwide (e.g., Philipp Strack—Clark Medal recipient, Alex Gershkov, Andreas Kleiner, Xianwen Shi, Thomas Kittsteiner). He teaches Microeconomics II (summer 2025) and offers thesis supervision to BGSE students. Labs and Research Groups: He leads the Microeconomics Research Group at Bonn and coordinates research activities in the CRC TR 224 project B01, focusing on market design and information economics.

Max FathiView profile

Professor

• Optimal Transport
• Probability Theory
• Statistical Physics
• +9 more

Max Fathi is a Professor of Mathematics at Université Paris Cité, affiliated with the Laboratoire Jacques-Louis Lions (LJLL) and Laboratoire de Probabilités, Statistique et Modélisation (LPSM). He concurrently holds a part-time teaching position at the Department of Mathematics and Applications (DMA) at École Normale Supérieure (ENS). Since 2023, he has been a member of the Institut Universitaire de France (IUF), a prestigious national research fellowship in France. He completed his PhD in 2013 at Université Pierre et Marie Curie under Cédric Villani, followed by a postdoctoral position at the University of California, Berkeley with Lawrence C. Evans and Fraydoun Rezakhanlou. Previously, he was a CNRS researcher at the Institut de Mathématiques de Toulouse before joining Université Paris Cité. His habilitation thesis (2019) focuses on optimal transport applications in analysis and probability. Fathi's research centers on optimal transport theory, particularly its applications to analysis, probability, and statistical physics. Key topics include interacting particle systems, functional inequalities (e.g., Poincaré, log-Sobolev), high-dimensional phenomena, Ricci curvature in discrete/continuous spaces, Stein's method, concentration of measure, and numerical methods for stochastic dynamics. His work is supported by the ANR project 'Conviviality.' He has delivered courses on functional analysis at ENS and participated in summer schools, including an MSRI course on functional inequalities and localization techniques. His teaching materials include lecture notes on optimal transport and stochastic processes. His contributions have been recognized through awards such as the IUF membership. Notable research collaborations include work with Thomas Courtade, Matthias Erbar, and Gabriel Stoltz on topics ranging from stability estimates of inequalities to hypocoercivity and numerical analysis of stochastic systems.

Iñaki EsnaolaView profile

Senior Lecturer

• Information Theory
• Machine Learning
• Cybersecurity
• +6 more

Dr. Iñaki Esnaola is a Senior Lecturer at the Department of Automatic Control and Systems Engineering, University of Sheffield, and a Visiting Research Collaborator at Princeton University. He holds a MSc from the University of Navarra (2006) and a PhD from the University of Delaware (2011). His research focuses on information theory, machine learning, and cybersecurity, particularly in cyberphysical systems like smart grids. His work addresses data integrity, privacy, robust estimation, and optimal sensor placement. Research interests include: Information theory and data science, machine learning and high-dimensional statistics, cybersecurity (especially data injection attacks), privacy, robust estimation, and sensor placement optimization. Recent projects involve empirical risk minimization with regularization, stealth attacks on control systems, and compressive sensing for environmental monitoring. Key publications include studies on relative entropy in machine learning, sensor placement for sewer networks, and stealth attacks in smart grids. He leads a research group with ongoing projects in resilient cyberphysical systems and received a UKRI grant for advanced manufacturing. His work bridges theoretical foundations with real-world applications in energy systems and environmental monitoring.

Zhou ZhouView profile

Senior Lecturer

• Stochastic Control
• Mathematical Finance
• Optimal Stopping Problems
• +3 more

Zhou Zhou is a Senior Lecturer in the School of Mathematics and Statistics at the University of Sydney. His academic roles include Senior Lecturer (2022–present) and Lecturer (2018–2021) at the University of Sydney, as well as postdoctoral positions at the University of Michigan and University of Minnesota. He holds a Ph.D. in Applied & Interdisciplinary Mathematics from the University of Michigan (2015) and a B.S. in Mathematics from Nankai University (2010). His research focuses on stochastic control, mathematical finance, and game theory, with particular emphasis on time-inconsistent problems, optimal stopping, and equilibrium strategies. Key areas include applications in financial mathematics, stochastic processes, and dynamic optimization. His work has been published in journals such as Mathematical Finance, SIAM Journal on Control and Optimization, and Finance and Stochastics. Zhou has secured grants including the 2023 Faculty of Science Startup Scheme for time-inconsistent control research and the 2022 Australian Research Council grant on green investment impacts. He teaches courses like Arbitrage Pricing in Continuous Time and supervises research students in financial mathematics. His academic contributions span over 50 publications, with notable work on binomial-tree approximations for stopping problems, equilibrium strategies in mean-field games, and policy iteration for stochastic control. Presentations include talks at international conferences and universities worldwide, emphasizing interdisciplinary applications of stochastic analysis.

Ziquan LiuView profile

Lecturer

• Reliable Machine Learning
• Uncertainty Quantification
• Computer Vision
• +4 more

Dr. Ziquan Liu is a Lecturer (Teaching & Research) at Queen Mary University of London's School of Electronic Engineering and Computer Science, affiliated with the Centre for Multimodal AI. He holds a PhD from City University of Hong Kong (2023) and dual B.Sc./B.Eng. degrees from Beihang University (2017). His research focuses on trustworthy machine learning, adversarial robustness, and uncertainty quantification in foundation models. He has served as a reviewer for top conferences like NeurIPS, ICLR, and CVPR, earning an Outstanding Reviewer Award in 2021. His teaching includes modules on machine learning for visual data analysis and principles of machine learning. He supervises PhD students in AI safety and reliability, with notable work on conformal prediction, adversarial attacks, and multimodal learning. His research outputs span top venues such as ICML, CVPR, and NeurIPS, addressing challenges in algorithmic fairness, model certification, and cross-modal alignment.

Andrew NgView profile

Adjunct Professor

• Artificial Intelligence
• Machine Learning
• Robotics
• +4 more

Andrew Ng is an Adjunct Professor at Stanford University's Computer Science Department and a globally recognized leader in AI. He is the Founder of DeepLearning.AI, Executive Chairman of LandingAI, General Partner at AI Fund, and Co-Founder of Coursera. His work has revolutionized machine learning and online education, with over 200 research papers in AI, robotics, and related fields. He was named to the 2023 Time100 AI list of most influential figures in AI. Ng's research focuses on machine learning, deep learning, reinforcement learning, and their applications in robotics and education. He pioneered the development of massive open online courses (MOOCs), notably through Stanford's early experiments in 2011 that attracted hundreds of thousands of learners. His contributions include foundational work in algorithms like Latent Dirichlet Allocation (LDA) for text analysis and advancements in spectral clustering and inverse reinforcement learning. His publications span topics from robotic hand design to scalable deep learning systems, emphasizing practical and scalable solutions. Ng's educational initiatives, such as the Machine Learning and Deep Learning Specializations, have educated millions worldwide. He advocates for accessible AI education and ethical AI development, emphasizing collaboration between academia and industry.

• Machine Learning
• Control Systems
• Robotics
• +3 more

Sebastian Trimpe is a Full Professor and Head of the Institute for Data Science in Mechanical Engineering at RWTH Aachen University, concurrently serving as Co-Executive Director of the RWTH Center for Artificial Intelligence since 2023. Previously, he led a Max Planck Research Group at the Max Planck Institute for Intelligent Systems from 2018 to 2022. His educational background includes: Ph.D. in Dynamic Systems and Control from ETH Zurich (2013) Dipl.-Ing. (M.Sc.) in Electrical Engineering from TU Hamburg (2007) MBA in Technology Management from TU Hamburg (2007) B.Sc. in General Engineering from TU Hamburg (2005) Professor Trimpe's research integrates machine learning with control theory to address safety and efficiency challenges in autonomous systems. His work spans theoretical frameworks for robust decision-making under uncertainty and practical implementations in robotics, with particular emphasis on event-triggered control, distributed systems, and data-efficient learning methodologies. Key contributions include novel approaches to safe reinforcement learning and model predictive control with guaranteed stability. Analysis of his recent publications reveals a pronounced focus on bridging machine learning with control engineering, especially in safety-critical robotics applications. Common themes include distribution-aware learning for medical diagnostics, diffusion-based control approximation, and hardware-in-the-loop validation of theoretical frameworks, demonstrating strong alignment between algorithmic innovation and real-world deployment. His scientific achievements have been recognized with prestigious honors: IFAC World Congress Interactive Paper Prize (2011) Klaus Tschira Award for public understanding of science (2014) Best Paper Award at International Conference on Cyber-Physical Systems (2019) Future Prize by Ewald Marquardt Stiftung (2020) As institutional leader, he directs the Institute for Data Science in Mechanical Engineering and co-leads the RWTH AI Center, overseeing strategic research initiatives and industry collaborations. His academic service includes editorial roles for IEEE Control Systems Society conferences and participation in the Cluster of Excellence 'Internet of Production'. The Institute for Data Science in Mechanical Engineering operates as a multidisciplinary hub where fundamental research in learning-based control meets industrial applications. Current projects focus on drone swarm coordination, deformable object manipulation, and medical diagnostics systems, leveraging both simulation environments and physical testbeds like the Mini Wheelbot platform.

Klemens FellnerView profile

Professor

• Partial Differential Equations
• Reaction-Diffusion Systems
• Kinetic Equations
• +6 more

Klemens Fellner is a Professor of Mathematics/Computational Sciences and Group Leader of the Applied Analysis Group at the Institute of Mathematics and Scientific Computing, University of Graz. His research focuses on the analysis of partial differential equations and mathematical modeling across physics, chemistry, and biology. Research Interests: Prof. Fellner's work spans theoretical analysis of nonlinear PDEs (reaction-diffusion, kinetic, and non-local equations) using entropy/duality methods, with applications to: Biological systems (lipolysis, protein localization, stem-cell division) Physical processes (organic photovoltaics, semiconductor modeling) Collective behavior (swarming micro-organisms, aggregation dynamics) Interdisciplinary Mathematics-Arts collaborations Publication Trends: His recent articles (2018-2021) demonstrate strong focus on: Global existence and regularity for reaction-diffusion systems Convergence to equilibrium via entropy methods Drift-diffusion models in semiconductor physics Mathematical biology applications (prion dynamics, lipolysis) Novel approaches for non-local aggregation and hysteresis phenomena Research Leadership: Currently leads the Applied Analysis Group with members including postdocs and PhD students. Key projects: Doctoral School IGDK (International Graduate School) SFB Lipid Hydrolysis (Special Research Program) Mathematics and Arts collaborations Colibri research platform Supervises PhD candidate Reymart Lagunero studying generalized reaction-diffusion systems.