Find Academic Supervisors

Lorenzo RosascoView profile

Professor

• Machine Learning
• Artificial Intelligence
• Robotics
• +5 more

Lorenzo Rosasco is a Full Professor at the Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS) at the University of Genoa. He holds roles including University contact for the thematic area "Artificial Intelligence" and membership on the Department Board. His research focuses on machine learning theory, kernel methods, optimization, robotics, and their applications in control systems, domain adaptation, and interdisciplinary fields like biomedical engineering. His educational background includes advanced expertise in mathematical foundations of machine learning, though specific academic credentials are not detailed here. Teaching responsibilities include courses such as MACHINE LEARNING, SEQUENTIAL PREDICTION AND REINFORCEMENT LEARNING, and INFORMATION THEORY AND INFERENCE at both undergraduate and graduate levels. Research interests revolve around developing efficient algorithms for large-scale machine learning, including iterative regularization, transfer learning, and control systems using Koopman operator theory. Notable contributions include work on fast kernel methods, optimization in neural networks, and applications in robotics for tasks like humanoid locomotion and tactile sensing. Recent publications explore topics such as double descent phenomena, sim-to-real transfer learning, and adaptive control strategies. His work bridges theoretical insights with practical applications, emphasizing computational efficiency and interdisciplinary impact.

Spyros AlexakisView profile

Professor

• General Relativity
• Geometric Analysis
• Inverse Problems
• +3 more

Spyros Alexakis is a Professor at the Department of Mathematics, University of Toronto. His research focuses on General Relativity, Geometric Analysis, and Inverse Problems. He has organized significant academic events, including a 2015 thematic program on Einstein's equations at the Fields Institute, featuring conferences and public lectures. His work includes groundbreaking contributions to black hole stability, Penrose inequalities, and conformal invariants. Current students include Gregorios Fournodavlos, Tracey Balehowsky, and Ali Feiz. Collaborators include leading mathematicians such as Arick Shao and Sergiu Klainerman. His research extends to geometric scattering theory, nonlinear wave equations, and boundary regularity of minimal surfaces. Education: Not explicitly stated in the provided text. Affiliations: University of Toronto (Department of Mathematics), Fields Institute (Collaborative programs). Research Interests: General Relativity: Black hole dynamics, Penrose inequalities, uniqueness theorems. Geometric Analysis: Ricci flow, minimal surfaces, conformal invariants. Inverse Problems: Uniqueness theorems in wave equations, boundary rigidity. Recent Article Trends: Focus on black hole stability, geometric invariants, and nonlinear wave behavior. Recent works address the Penrose inequality, Bondi energy bounds, and singular Ricci solitons. Grants & Awards: No explicit awards listed, but significant research grants inferred from thematic program organization and publication activity. Advising & Collaborations: Active in mentoring graduate students and collaborating with international researchers. Notable collaborations include projects on black hole rigidity and geometric uniqueness theorems. Labs/Teams: Engaged in interdisciplinary research through the Fields Institute and the University of Toronto's Analysis/Applied Math Seminar.

Erik LundbergView profile

Professor

• Complex Analysis
• Topology of Random Manifolds
• Partial Differential Equations
• +2 more

Erik Lundberg is Professor in the Department of Mathematics and Statistics at Florida Atlantic University. His core research spans complex analysis, PDE, potential theory, and asymptotics, with applications to gravitational lensing and random topology. He has published over 30 papers in top-tier mathematics journals, with recent works focusing on probabilistic methods in complex analysis and geometric structures. Research explores complex dynamics, random manifolds, harmonic mappings, and gravitational lensing models. Articles demonstrate consistent focus on asymptotic methods, probabilistic approaches to geometric problems, and interdisciplinary connections to physics. Scholarly activities include co-organizing FAU's Analysis and Applications Seminar, advising the Math Club, and authoring a monograph on linear holomorphic PDEs and potential theory. He has delivered 60+ international talks and serves as reviewer for multiple mathematics journals.

Matthias SeissView profile

Researcher

• Differential Equations
• Differential Algebra
• Algebraic Geometry
• +6 more

Matthias Seiß is a Researcher at the University of Kassel's Faculty of Mathematics and Computer Science, Department of Algebra and Differential Equations. His research focuses on differential equations, algebraic geometry, and their applications in mathematical biology and systems theory. He has collaborated on topics such as singular initial value problems, chemical reaction network reduction, and differential Galois theory. Seiß has taught courses like Mathematical Biology, Linear Algebra, and Differential Algebra, leading exercise sessions and seminars. His work bridges algebraic methods with computational and applied mathematics, addressing challenges in modeling biological systems and analyzing complex dynamical behaviors. Key contributions include studies on Markov chain models, conservation laws in chemical networks, and numerical methods for singular equations. He is involved in projects like the Symbiont initiative, advancing symbolic computation in differential equations. His research has been published in journals such as SIAM Journal on Applied Dynamical Systems and Communications in Algebra.

Krishnan SureshView profile

Professor

• Topology Optimization
• Additive Manufacturing
• Computational Mechanics
• +5 more

Prof. Krishnan Suresh is a Professor in Mechanical Engineering at the University of Wisconsin-Madison, affiliated with the College of Engineering. He directs the Engineering Representations and Simulation Laboratory (ERSL) and co-leads the Wisconsin Applied Computing Center. His research focuses on topology optimization, additive manufacturing, computational mechanics, and quantum computing applications. He holds prestigious awards including the Vilas Associate (2023), Mead Witter Professorship (2022), and Woodburn Teaching Award (2018). Education: PhD 1998, Cornell University MS 1995, Cornell University MS 1992, UCLA B.Tech 1990, IIT Madras Research Interests: Dr. Suresh pioneers methods for efficient finite element analysis over tangled meshes, integrates machine learning into topology optimization, and develops quantum computing solutions for engineering problems. His work bridges computational mechanics with advanced manufacturing, emphasizing eco-friendly design and multiscale material systems. Publications: Recent work explores surrogate neural networks for topology optimization, quantum annealing for linear systems, and AI-driven detector design for physics experiments. His methods address challenges in handling inverted elements and improving additive manufacturing support structures. Awards: 2023 Vilas Associate 2022 Mead Witter Professorship 2018 Woodburn Teaching Award 2017 ASME Fellow Advising & Grants: He advises on NSF/DOE-funded projects and collaborates with Sandia National Labs/Autodesk. Courses taught include design optimization, CAD engineering, and graduate research supervision. Labs & Teams: ERSL develops simulation tools for complex systems, integrating high-performance computing and AI for next-gen engineering solutions.

Talles Batista Rattis SantosView profile

Assistant Professor

• Electrical Impedance Tomography (EIT)
• Biomedical Engineering
• Medical Imaging
• +4 more

Talles Batista Rattis Santos is a Teaching Assistant Professor affiliated with the CU-CMU PARTNERSHIP PROGRAM, contributing to interdisciplinary research at the intersection of biomedical engineering and mathematical modeling. He previously held postdoctoral and research roles at Colorado State University, the Polytechnic School of the University of São Paulo, and the EIT Laboratory at Colorado State University. Education : B.Sc. in Electrical Engineering, Federal University of Minas Gerais (2012) M.Sc. and Ph.D. in Control Engineering and Mechanical Automation, University of São Paulo (2015, 2019) His research focuses on Electrical Impedance Tomography (EIT), particularly in developing advanced algorithms for medical imaging, such as D-bar methods with statistical and anatomical priors, hybrid imaging modalities, and machine learning-driven post-processing. He has contributed to applications in respiratory diagnostics, fluid volume estimation, and hydroponic systems. The 15 most recent publications highlight his expertise in computational imaging, with a trajectory from foundational algorithm development (e.g., D-bar methods, Schur complement integration) to applied innovations in clinical settings and agricultural engineering. Keywords span inverse problems, medical imaging, machine learning, and physiological modeling. His career includes collaborations with multidisciplinary teams across institutions, with a focus on improving EIT resolution and robustness through mathematical rigor and cross-domain integration.

• Mathematical Biology
• Computational Fluid Dynamics
• Scientific Computing
• +3 more

Sarah D. Olson is the William Steur Professor and Department Head of Mathematical Sciences at Worcester Polytechnic Institute (WPI). She holds joint appointments in Bioinformatics and Computational Biology and Biomedical Engineering. Her research focuses on Mathematical Biology, Computational Fluid Dynamics, Scientific Computing, and Tissue Engineering. She teaches applied mathematics, scientific computing, and modeling courses at WPI. Education: B.A., Providence College, 2003 M.S., University of Rhode Island, 2005 Ph.D., North Carolina State University, 2008 Research Interests: Mathematical modeling of biological systems Fluid-structure interactions in biofluids Agent-based models for cell dynamics Cartilage regeneration and tissue engineering Swimming dynamics of microorganisms Parameter estimation in biological systems Her work combines mathematical modeling, numerical simulations, and experimental data to understand complex biological processes such as cell movement, mitosis, and tissue repair. Professional Highlights: Interim Head of Mathematical Sciences at WPI (2021) Recipient of a grant to develop computational models for cell division Promoted to Full Professor and Department Head Current Projects: Micro-swimmer models in biofluids Centrosome movement mechanisms during mitosis Agent-based models in heterogeneous environments Parameter estimation frameworks in biological systems Labs/Teams: Leads a research group focused on computational biology and biofluid dynamics, collaborating with experimentalists to bridge theory and application in biological systems.

Karen MeagherView profile

Professor

• Combinatorics
• Algebraic Graph Theory
• Erdős-Ko-Rado Theorems
• +5 more

Karen Meagher is a Professor and Chair of the Graduate Studies Committee in the Department of Mathematics and Statistics at the University of Regina's Faculty of Science. Her research focuses on combinatorics and algebraic graph theory, with particular emphasis on Erdős-Ko-Rado theorems and their extensions. Her primary research interests include: Combinatorics and algebraic graph theory Erdős-Ko-Rado theorem generalizations Design theory and covering arrays Extremal set theory applied to partitions and permutations Zero-forcing sets and metric dimension of graphs Applications of algebraic methods to combinatorial problems Analysis of her recent publications reveals a consistent focus on extending Erdős-Ko-Rado theory to various combinatorial structures including graphs, permutation groups, and set partitions. Her work frequently employs algebraic graph theory techniques and has significant applications in design theory and extremal combinatorics. Meagher leads the Discrete Math Research Group at the University of Regina, which includes faculty members Shaun Fallat and Allen Herman, multiple post-doctoral fellows, and graduate students. She has supervised three PhD students to completion: Alison Purdy (2014), Fatemeh Alinaghipour (2013), and Bahman Ahmadi (2013), with Alison Purdy also completing a Master's under her supervision in 2010. Outside of mathematics, Meagher maintains a 'super-nerdy hobby' of mathematical quilting, creating designs based on combinatorial structures like Greco-Latin squares.

Robert HildebrandView profile

Assistant Professor

• Mixed Integer Nonlinear Programming
• Integer Programming
• Operations Research
• +6 more

Robert Hildebrand is an Assistant Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He holds a Ph.D. in Applied Mathematics from the University of California, Davis (2013). His research focuses on Mixed-Integer Nonlinear Programming, Integer Programming, Complexity Theory, and Redistricting Analytics, with applications in Operations Research and Discrete Geometry. He has received grants from the Air Force Office of Scientific Research and the Office of Naval Research, and the 2019 Sporn Teaching Award. Education: Ph.D., Applied Mathematics (UC Davis, 2013); B.Sc., Mathematics (University of Puget Sound, 2008). Professional history includes postdoctoral roles at ETH Zurich (2013–2015), IBM Watson Research (2015–2017), and a Simons Institute Fellowship (2017). Current roles include Associate Editor for Discrete Optimization and service on INFORMS committees. Research interests emphasize theoretical and computational aspects of optimization, with recent work on gerrymandering analysis, robotic assembly optimization, and algorithmic complexity bounds. Notable publications include advancements in integer programming formulations and scheduling algorithms for autonomous systems. Grants include Virginia Tech's Whole Health Consortium (2024) for veterans' healthcare optimization and an ICTAS Seed Grant for autonomous fleet algorithms (2023). He advises students on topics like rectangle packing and robotic trajectory planning, with former students advancing to doctoral and industry roles. Labs/Teams: Active in Virginia Tech's FASER Lab (robotics optimization) and collaborates with interdisciplinary groups on redistricting analytics and space exploration technologies.

Maurino BautistaView profile

Professor

• Inverse Problems
• Acoustic Scattering
• Partial Differential Equations
• +2 more

Maurino Bautista is a Professor in the School of Mathematics and Statistics at the Rochester Institute of Technology (RIT), part of the College of Science. He holds a BS in Mathematics from Ateneo de Manila University (1975), MS and PhD in Mathematics from Purdue University (1980 and 1984, respectively), supported by a Fulbright Fellowship (1978-81). His research focuses on inverse problems, acoustic scattering, partial differential equations, computational methods, and mathematical modeling, with notable work in biophysical systems and social network analysis. He currently teaches Multivariable Calculus, Linear Algebra, and Boundary Value Problems. His scholarly contributions include groundbreaking studies on light scattering mechanisms in biological systems and applications of graph theory to social networks. Key publications span interdisciplinary fields such as biophysics and applied mathematics. He is also an author of textbooks on differential equations and boundary value problems, emphasizing computational tools like Maple. Dr. Bautista’s awards include the Fulbright Fellowship, and he maintains an active research profile with ongoing projects in mathematical modeling and computational methods. His teaching responsibilities reflect his expertise in foundational and advanced mathematical disciplines.

• Machine Learning
• Operations Research
• Optimization
• +3 more

Silvia Villa is an Associate Professor in the Department of Mathematics (DIMA) at the University of Genoa (UniGe). Her teaching responsibilities include courses such as Game Theory, Machine Learning, Optimization and Operations Research, and Operations Research at both undergraduate and master's levels. Her research focuses on optimization theory and its applications in machine learning, inverse problems, and regularization methods. She has contributed to advancements in iterative regularization techniques, stochastic optimization algorithms, and convex optimization frameworks. Her work often bridges theoretical foundations with practical applications in signal processing and data science. Her research interests emphasize structured optimization problems, including low-complexity regularizers, bilevel optimization, and sparse recovery. Key methodologies in her studies include primal-dual dynamics, proximal algorithms, and variance reduction techniques. Recent trends in her publications highlight advancements in unrolled deep networks for sparse signal restoration, adaptive optimization strategies, and convergence analysis of stochastic methods. Villa's collaborative efforts span interdisciplinary projects involving applied mathematics, computer science, and engineering. While no specific awards are listed, her prolific publication record reflects her active role in the optimization and machine learning communities. She has advised multiple research projects but no formal student names are provided in the available data.

Jim W. PortegiesView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Applied Mathematics
• +5 more

Jim W. Portegies is an Assistant Professor in the Applied Analysis group at the Eindhoven University of Technology (TU/e). He holds additional roles as EAISI Foundational Assistant Professor and EAISI Mobility Assistant Professor within the TU/e's interdisciplinary initiatives. His research focuses on applying mathematical techniques from analysis, geometry, and measure theory to problems in machine learning and artificial intelligence, with a particular interest in designing algorithms that emulate human learning mechanisms. He obtained his MSc in Industrial and Applied Mathematics and Applied Physics from TU/e before completing his PhD in Mathematics at the Courant Institute of Mathematical Sciences, New York. After postdoctoral work at the Max Planck Institute in Leipzig, he returned to TU/e in 2016. Education: MSc in Industrial and Applied Mathematics & Applied Physics (TU/e, 2009) PhD in Mathematics (Courant Institute, NYU, 2014) Research Interests: Jim's work bridges pure mathematics and applied AI. Key areas include: Geometric and spectral methods for machine learning Universal learning algorithms inspired by human cognition Analysis of dimensionality reduction techniques Stochastic processes and control systems His recent work explores neural stochastic differential equations and interpretable AI mechanisms. Awards: Member of TU/e Young Academy of Engineering (2016–present) Collaborations & Grants: Jim collaborates extensively in interdisciplinary projects, including EU-funded initiatives on AI mobility systems. His research has been supported by grants from NWO (Netherlands Organization for Scientific Research) and industry partnerships with semiconductor and control systems companies. Labs & Teams: He contributes to the TU/e's Center for Analysis, Scientific Computing and Applications (CASA) and leads the Applied Analysis & AI research group, focusing on theoretical foundations of AI and their practical implementations.

Maarten SchoukensView profile

Associate Professor

• Nonlinear System Identification
• Machine Learning for Control Systems
• Data-Driven Modeling
• +5 more

Maarten Schoukens is an Associate Professor in the Control Systems group at the Department of Electrical Engineering, Eindhoven University of Technology. He holds a PhD from Vrije Universiteit Brussel (2015) and has held postdoctoral positions at both VUB and TU/e. His research focuses on nonlinear system identification, machine learning applications in control, and data-driven modeling of complex systems. He has received prestigious awards including an ERC Starting Grant (2022) and an EU Marie Skłodowska-Curie Fellowship (2018). He leads projects like DAMOCLES (2021–2025) and CELLSYSTEMICS (2022–2026), advancing constitutive law modeling and human measurement systems. Active in editorial roles for IEEE and ECC conferences, he also co-organizes the nonlinearbenchmark.org initiative. Education: MSc and PhD in Electrical Engineering from VUB (2010–2015). Postdoctoral Researcher at TU/e (2017–2018) before becoming Assistant Professor (2018) and later Associate Professor (2023). Research emphasizes nonlinear dynamical systems, with contributions to system identification, neural network architectures for control, and frequency-domain methods. Key projects include developing LPV control strategies and physics-guided learning frameworks. His work bridges classical control theory with modern machine learning, addressing challenges in autonomous systems and high-tech engineering. Awards include FWO PhD Fellowship (2011), Marie Skłodowska-Curie Fellowship (2018), and ERC Starting Grant (2022). Supervised over 15 graduate students and led multidisciplinary teams in EU-funded initiatives. Labs include the Autonomous Motion Control Lab and High Tech Systems Center, fostering collaborations in robotics, aerospace, and biomedical engineering.

Shahla NasserasrView profile

Assistant Professor

• Combinatorial Matrix Theory
• Spectral Graph Theory
• Totally Positive Matrices
• +3 more

Shahla Nasserasr is an Assistant Professor in the School of Mathematics and Statistics at the Rochester Institute of Technology (RIT), within the College of Science. Her research focuses on combinatorial matrix theory, spectral graph theory, and the inverse eigenvalue problem in graphs, with applications to totally positive matrices and graph theory. She actively contributes to the mathematics community, currently serving as Chair of the Outreach and Membership Committee for the International Linear Algebra Society (ILAS). Teaching responsibilities include courses such as Discrete Mathematics for Computing, Linear Algebra, and Undergraduate Research in Mathematical Sciences. Her work bridges theoretical foundations with practical applications, emphasizing spectral properties of graphs and eigenvalue problems. Over 20+ published articles span topics from graph colorings and matrix completions to quantum-inspired graph diagonalization techniques. Research trends highlight her contributions to understanding graph spectra, eigenvalue multiplicity constraints, and innovative applications like q-analogues of zero forcing. While no awards are explicitly listed, her leadership in ILAS underscores her dedication to advancing linear algebra education and research globally. Her advising and mentoring extend through RIT’s undergraduate research programs, fostering student engagement in mathematical sciences. Collaborative work includes exploring sparsity properties in graphs and the structural implications of spectral arbitrariness. Current research directions aim to deepen insights into graph eigenvalue problems and their computational applications.

Ahmad MousaviView profile

Part-Time Lecturer

• Machine Learning
• Sparse Optimization
• Portfolio Optimization
• +3 more

Ahmad Mousavi serves as an Industrial Associate at the University of Florida's Informatics Institute and a part-time lecturer in American University's Data Science Program within the College of Arts and Sciences, teaching graduate courses including Advanced Machine Learning and Statistical Machine Learning through Fall 2025. His academic foundation spans applied mathematics with specialized expertise in computational optimization. His educational background includes: Ph.D. in Applied Mathematics, University of Maryland, Baltimore County (2013-2019) M.S. in Applied Mathematics, Sharif University of Technology (2008-2011) B.S. in Applied Mathematics, University of Guilan (2004-2008) Dr. Mousavi's research centers on machine learning and optimization theory, with significant contributions to sparse recovery algorithms, portfolio optimization, and support vector machine development. His work bridges theoretical mathematics with practical financial and data science applications, particularly in developing constrained optimization frameworks for real-world problems requiring sparsity and volatility control. Analysis of his publication record reveals a consistent focus on optimization techniques applied to machine learning, with recurring themes in sparse modeling for financial portfolios, kernel methods for classification, and theoretical foundations of compressive sensing. His 2020 survey paper demonstrates expertise in synthesizing complex technical domains. His professional development includes postdoctoral research at the University of Florida's Informatics Institute and the University of Minnesota's Institute for Mathematics and its Applications, where he collaborated on interdisciplinary projects connecting mathematical theory with data-intensive applications.