Find Academic Supervisors

Fernando Granha JeronimoView profile

Assistant Professor

• Complexity Theory
• Pseudorandomness
• Quantum Computing
• +6 more

Fernando Granha Jeronimo is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign. His research explores theoretical computer science with emphases on coding theory, expander graphs, quantum computing, and optimization. He holds a PhD from the University of Chicago, M.Sc./B.Sc. degrees from Unicamp (Brazil), and an engineering degree from Telecom Paris. His work investigates interactions between complexity theory, pseudorandomness, quantum algorithms, and high-dimensional expanders. Recent studies focus on explicit code constructions near information-theoretic bounds, quantum-classical complexity separations, and efficient decoding algorithms leveraging expander properties. Publications demonstrate consistent focus on coding theory (explicit codes, list decoding), quantum complexity (unentangled proofs, pseudoentanglement), and optimization (LP/SDP hierarchies). A trend toward quantum applications is evident in recent works on quantum LDPC codes and property testing. Awards & Fellowships: Simons-Berkeley Fellow Google Research Fellow TA Prize, University of Chicago (awarded twice) Advising & Grants: Actively recruits graduate students for his research group. Previously supported by Simons Institute and Google Research Fellowship during postdoctoral work at IAS. Current courses include quantum computing (CS 498) and advanced topics in codes/optimization (CS 598). Leads the Local-to-Global TCS Mentorship Program and research groups focused on coding theory, quantum complexity, and expander applications.

Michael DabkowskiView profile

Visiting Professor

• Analysis
• Applied Mathematics
• Differential Equations
• +1 more

Michael Dabkowski is a Visiting Professor in the Department of Mathematics at the University of Michigan. He holds a B.Sc. from the University of Michigan-Dearborn (2004) and a Ph.D. from the University of Wisconsin-Madison (2011). His research focuses on Analysis, Applied Mathematics, Differential Equations, and Mathematics, with particular emphasis on topics like inequalities, PDEs, and mathematical physics. His work explores global asymptotic stability in diffusive models, properties of Green's functions, spectral theory of operators, and geometric analysis on manifolds. He has contributed to understanding Schrödinger operators on compact manifolds, curvature relations in differential geometry, and active scalar equations in fluid dynamics. Key research trends include studying long-time behavior of solutions to nonlinear PDEs, investigating geometric curvature properties, and analyzing operator-theoretic frameworks. His publications span diverse subfields, reflecting interdisciplinary approaches bridging pure and applied mathematics. Contact: mgdabkow@umich.edu | Office: East Hall 5858, Ann Arbor, MI 48109-1043

Richard S LaugesenView profile

Professor

• Differential Equations
• Mathematical Physics
• Complex Analysis
• +3 more

Richard S Laugesen is a Professor in the Department of Mathematics at the University of Illinois at Urbana–Champaign, serving as Associate Chair for Faculty. He specializes in differential equations, mathematical physics, and complex analysis with a focus on extremal problems. His roles include Director of Graduate Studies in Mathematics (2012–2017) and leadership in interdisciplinary programs like the Illinois Sloan University Center of Exemplary Mentoring (co-PI, 2015–2018). Education: PhD in Mathematics from Washington University in St. Louis (1993). Research interests span spectral theory, geometric optimization, and partial differential equations. His work emphasizes eigenvalue optimization, capacity theory, and applications to physics and engineering. Recent studies investigate Riesz capacity, Neumann/Robin eigenvalues, and spectral shape optimization. Notable awards include the Campus Award for Excellence in Graduate Teaching (2017), Distinguished Teaching Award (2016), and multiple teaching honors from 2003. He has secured grants from the Sloan Foundation, NSF, and Simons Foundation, supporting initiatives in STEM education and mathematical sciences. Advising and mentorship form a key part of his career, with contributions to internship networks and graduate training programs. His research group actively explores interdisciplinary problems at the intersection of analysis, geometry, and applied mathematics.

Giulia FerrandiView profile

Researcher

• Statistics
• Linear Algebra
• Optimization
• +3 more

Giulia Ferrandi is a Researcher and Guest Researcher at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). She completed her PhD under the supervision of Prof. Michiel Hochstenbach (TU/e) and Prof. Rosário Oliveira (IST, Lisbon) as part of the European BIGMATH project. Her research focuses on Statistics, Linear Algebra, and Optimization applied to statistical problems, with contributions to gradient methods, trace ratio problems, and Markov chain analysis. She holds a master’s degree from Università degli Studi di Milano (Italy), specializing in Probability and Statistics. Prior to her academic roles, she briefly taught in high school and worked in industry. Research Interests : Her work bridges numerical linear algebra and optimization, with emphasis on: Development and analysis of gradient methods (e.g., limited memory, harmonic frameworks) Applications of Rayleigh quotients in optimization and eigenvalue problems Robust multigroup classification via trace ratio techniques Statistical analysis of Markov chains for farmland transitions and non-stationary processes Publications : Her recent work demonstrates contributions to: Subspace methods for large-scale trace ratio problems Advancements in unconstrained optimization via limited-memory gradient approaches Integration of Rayleigh quotients into gradient-based algorithms Harmonic frameworks for optimizing stepsizes in numerical methods Collaborations & Grants : Her PhD was funded by the EU’s BIGMATH project. Collaborators include Prof. Michiel Hochstenbach (TU/e) and Prof. Nataša Krejić (University of Novi Sad). No explicit advising roles or grants are listed beyond her doctoral funding. Technical Expertise : Expertise in numerical analysis, statistical modeling, and linear algebra applications.

• Applied Mathematics
• Computational Science
• Plasma Physics
• +7 more

Giovanni Samaey is a Professor of Applied Mathematics and Mathematical Engineering at KU Leuven's Faculty of Engineering Science. He leads research in computational and multiscale methods, focusing on plasma edge modeling for nuclear fusion reactors, Bayesian inversion, and Monte Carlo algorithms. Appointed in 2011, he currently supervises ten PhD students and has held a five-year membership in the Young Academy. His work bridges academic research with societal impact, co-founding Platform Wiskunde Vlaanderen to strengthen mathematics in Flanders. Education: Graduated in Computer Science (specializing in applied mathematics) from KU Leuven (1996), completed a PhD in 2001 under Prof. Dirk Roose, supported by an NFWO fellowship. He transitioned from engineering studies due to a passion for mathematics' societal impact, initially avoiding academia but ultimately embracing teaching and research. Research Interests: Development of numerical methods for multiscale phenomena, including micro-macro acceleration algorithms, multilevel Monte Carlo techniques, and hybrid fluid-kinetic models. His work addresses challenges in plasma physics, fusion energy systems, and inverse problems. Key contributions include the X-Factor book (with Joos Vandewalle) promoting mathematics outreach and advancing computational tools for plasma edge simulations. Awards/Honors: Member of the Young Academy (2016-2021), NFWO Aspirant Fellowship (2001-2002). His efforts in STEM advocacy and mathematics promotion through Platform Wiskunde Vlaanderen highlight his dedication to education and public engagement. Advising & Leadership: Supervises a team of PhD students in applied mathematics and computational science. Active in curriculum development and interdisciplinary collaborations, particularly in fusion energy modeling. His research group contributes to codes like EMC3-EIRENE for plasma edge simulations. Labs/Teams: Leads projects in multiscale numerical methods and plasma simulation within KU Leuven's engineering faculty. Collaborates internationally on fusion reactor modeling and Monte Carlo algorithm design, emphasizing computational efficiency and scalability.

• Stochastic Models
• Applied Probability
• Algorithms in Stochastic Models
• +3 more

Professor Zhang Hanqin is a faculty member at the Department of Analytics & Operations, National University of Singapore (NUS), affiliated with the Institute of Operations Research and Analytics (IORA), part of NUS’s Smart Nation Research Cluster. His research focuses on stochastic models, applied probability, algorithms in stochastic systems, supply chain management, and queueing theory. He has contributed extensively to optimization in inventory systems, manufacturing systems, and service operations. His work spans theoretical advancements and practical applications, including optimal staffing for queues, assemble-to-order systems, and quantity flexible contracts. Recent studies address time-varying queues, minimal phase-type distributions, and impulse control strategies. His research bridges stochastic processes, operations research, and applied mathematics with real-world systems. Key achievements include foundational contributions to diffusion approximations, Coxian representations, and policy design for complex systems. His articles often explore multi-source inventory systems, tandem queues, and expediting strategies, reflecting a deep engagement with both theoretical rigor and industrial relevance.

Daniel SternView profile

Assistant Professor

• Differential Geometry
• Partial Differential Equ as needed for Daniel Stern. The full list would include the most recent 15 articles, but the user's input appears to have fewer distinct articles. Please provide additional publications or confirm if the provided list is complete.
• Calculus of Variations
• +6 more

Daniel Stern is an Assistant Professor at the Department of Mathematics, Cornell University, within the College of Arts and Sciences. He earned his Ph.D. in 2019 from Princeton University, advised by Fernando Codá Marques. His research bridges geometric analysis with mathematical physics, focusing on partial differential equations and variational problems. Research Interests: Differential Geometry, Partial Differential Equations, Calculus of Variations, Geometric Analysis, Minimal Submanifolds, Harmonic Maps, Gauge Theory, Spectral Geometry, Ginzburg-Landau Equations Teaching: Courses include Math 2210 (Linear Algebra), Math 6160 (Partial Differential Equations), and Math 3210 (Manifolds and Differential Forms) at Cornell, alongside previous teaching at the University of Chicago and University of Toronto.

Laura GrigoriView profile

Professor

• Numerical Linear Algebra
• High Performance Computing
• Deterministic and Randomized Algorithms
• +4 more

Laura Grigori is a Full Professor and Chair of High Performance Numerical Algorithms and Simulations at EPFL's School of Basic Sciences (SB) Department of Mathematics (MATH). Her research focuses on numerical linear algebra, high performance computing, and tensor computations, with applications in astrophysics and molecular simulations. She leads the HPNalgs lab and teaches courses in numerical analysis and HPC. Her awards include the SIAM Supercomputing Career Prize (2024) and SIAM Fellow distinction (2020). She advises four PhD students and has authored numerous papers on communication-avoiding algorithms, randomized methods, and parallel linear algebra techniques. Her work addresses scalability challenges in scientific computing and large-scale data analysis. Labs/Teams: HPNalgs Lab (https://www.epfl.ch/labs/hpnalgs/) Grants: ERC Synergy Grant (2019) for Extreme-scale Computational Chemistry

Mike KrebsView profile

Professor

• Graph Theory
• Algebraic Combinatorics
• Discrete Mathematics
• +6 more

Mike Krebs is a Professor in the Department of Mathematics at California State University, Los Angeles (Cal State LA). He is affiliated with the College of Natural & Social Sciences. His research focuses on algebraic combinatorics, graph theory, and discrete mathematics, with notable work on Cayley graphs, expander families, and combinatorial trace methods. Krebs has collaborated extensively with undergraduate and graduate students, mentoring over 20 theses and co-authoring numerous papers with them. His academic background includes a Ph.D. from Johns Hopkins University (2005), advised by Dr. Richard Wentworth. Krebs has published widely in journals like Discrete Mathematics , Linear Algebra and its Applications , and Journal of Algebraic Combinatorics , with a focus on topics such as chromatic numbers of Cayley graphs, Kazhdan constants, and expander graph constructions. Research trends in his articles emphasize interdisciplinary applications, including sports analytics (PageRank for baseball teams) and pedagogical innovations in mathematics education. He has authored a textbook, Expander Families and Cayley Graphs: A Beginner's Guide (2011), and contributed to the Handbook of Graph Theory . Krebs’s work bridges pure mathematics with computational tools, as seen in his Mathematica code for chromatic number calculations.

Xuwen ZhuView profile

Assistant Professor

• Differential Geometry
• Mathematical Physics
• Spectral Theory
• +3 more

Xuwen Zhu is an Assistant Professor of Mathematics at Northeastern University, affiliated with the College of Science. His research focuses on geometric analysis, differential geometry, and mathematical physics, with particular emphasis on conical metrics, spectral theory, and gravitational instantons. He has received notable recognition, including the 2023 COS Excellence in Equity, Diversity, Inclusion, and Justice Faculty Award for his contributions to fostering inclusive academic environments. Dr. Zhu’s work bridges geometric analysis and theoretical physics, addressing problems such as the construction of ALF gravitational instantons, spectral properties of singular metrics, and the realization of branched covers in spherical geometries. His research often intersects with moduli spaces, edge manifolds, and supergravity equations. His publications span topics from spherical conical metrics to eleven-dimensional supergravity, demonstrating a deep engagement with both classical and modern geometric challenges. Notable recent work includes studies on degenerating hyperbolic surfaces and spectral gaps, as well as the resolution of canonical fiber metrics in Lefschetz fibrations. Awards: COS Excellence in Equity, Diversity, Inclusion, and Justice Faculty Award (2023) Advising: No current advisees listed Labs/Teams: Collaborates on interdisciplinary projects at the intersection of geometry and physics

• Matrix Analysis
• Discrete Mathematics
• Graph Theory
• +3 more

Margarida Mitjana Riera is a Senior Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Applied Mathematics I and the Faculty of Mathematics and Statistics. She is part of the MAPTHE (Matrix Analysis and Discrete Potential Theory) and OMGRAPH (Optimization Methods on Graphs) research groups. Her work focuses on matrix analysis, graph theory, linear algebra applications in networks, and optimization. She holds a degree in Mathematics and a PhD from UPC. Her research interests include inverse eigenvalue problems, network analysis, resistance distances, and circulant matrices. She has authored over 200 publications, including articles in journals like Linear Algebra and Its Applications and Special Matrices , and contributed to projects on optimization methods on graphs and discrete potential theory. She actively participates in conferences and collaborates on educational initiatives like the Curs 0 preparatory program for engineering students. Her grants include AGRUPS 2024 and prior competitive R+D+I projects. She advises on research in subdivision networks and matrix theory, with contributions to both theoretical and applied mathematics.

• Calculus of Variations
• Optimal Transport
• Density Functional Theory
• +3 more

Guy Bouchitté is a Full Professor at Université du Sud Toulon-Var. His research focuses on Calculus of Variations, Optimal Transport, Density Functional Theory, and Homogenization. He has contributed to theoretical advancements in optimal design, shape optimization, and multi-marginal transport problems. His work bridges mathematical analysis with applications in quantum chemistry, structural mechanics, and material science. Key research areas include asymptotic analysis of variational problems, optimal transport with non-linear costs, and the interplay between geometry and functional inequalities. Recent studies address dissociation limits in Density Functional Theory and quantization effects in many-body systems. He has organized seminars on Geometric Measure Theory and Frontiers of the Calculus of Variations. His contributions span over 35 publications, including works on Monge-Kantorovich duality, compliance sensitivity, and spectral optimization for Schrödinger operators. Professional activities include participation in workshops such as 'Variational Challenges in Materials Science' and 'Calculus of Variations and Applications'.

Aleks JevnikarView profile

Associate Professor

• Partial Differential Equations
• Geometric Analysis
• Mathematical Physics
• +5 more

Associate Professor Aleks Jevnikar is affiliated with the Department of Mathematical, Computer and Physical Sciences at the University of Udine. His research focuses on advanced topics in mathematical analysis, particularly partial differential equations (PDEs), geometric analysis, and mathematical physics. He is active in areas including Liouville-type equations, Toda systems, mean field equations, and curvature prescription on manifolds with singularities. His work often intersects with plasma physics and nonlinear analysis, addressing free boundary problems and blow-up phenomena. Contact him via aleks.jevnikar@uniud.it or visit his website at https://sites.google.com/site/ajevnika/ . Key research interests include: Analysis of nonlinear PDEs with applications in geometry and physics Study of Toda systems and Liouville equations with singularities Curvature prescription problems on surfaces with conical singularities Free boundary problems arising in plasma physics Uniqueness and non-degeneracy of solutions to nonlinear systems Bifurcation theory and global solution diagrams In recent years, his contributions have addressed cutting-edge topics such as sharp estimates for superlinear free boundary problems, eigenvalue analysis of Liouville-type systems, and the interplay between geometric structures and PDE solutions. His articles often explore existence, uniqueness, and qualitative properties of solutions, contributing to both theoretical advancements and applications in mathematical physics. No scientific awards are listed in the provided information. His advising and grant activities are not detailed here, though his research indicates strong engagement in collaborative projects. Further details about his academic contributions can be found on his personal website.

Zhongyuan ChenView profile

Assistant Professor

• Biostatistics
• Cancer Genomics
• Bioinformatics
• +5 more

Zhongyuan Chen, PhD, is an Assistant Professor in the Division of Biostatistics at the Medical College of Wisconsin (MCW). She holds a PhD in Statistics from Rice University and previously served as a Visiting Assistant Professor at Purdue University's Department of Statistics. Her research focuses on integrating statistical methodologies with biosciences, cancer genomics, and computational biology, emphasizing applications in personalized medicine, high-dimensional data analysis, and large-scale scientific computing. Dr. Chen collaborates extensively with cancer researchers at institutions like MCW Cancer Center, MD Anderson Cancer Center, and the IU School of Medicine. Her key research interests include statistical machine learning for treatment recommendation, causal inference, and cancer genomic data analysis. She has developed novel strategies for analyzing somatic mutations and germline variations, as well as improving eigenvalue solution algorithms for numerical computations. Dr. Chen's work bridges statistical theory with practical biomedical applications, particularly in oncology and precision medicine. Notable publications highlight her contributions to understanding mitochondrial genomic variants in transplantation outcomes, Hippo-YAP pathway regulation in bone metastasis, and metabolic subtypes in cancers. She actively seeks motivated students and postdocs for collaborative research projects, emphasizing interdisciplinary approaches to biomedical challenges. Teaching responsibilities include courses in biostatistical computing and statistical quality control.

• Inverse Problems
• Partial Differential Equations
• Integral Equations
• +5 more

Fioralba Cakoni is a Distinguished Professor of Mathematics and serves as the Graduate Vice-Chair in the Department of Mathematics at Rutgers, The State University of New Jersey. She is a tenured faculty member specializing in Applied Analysis, Data Science, Machine Learning, Artificial Intelligence, and Partial Differential Equations, with specific expertise in Inverse Problems, PDEs, Integral Equations, and Inverse Scattering Theory. Her research interests focus on advanced mathematical analysis of wave phenomena and inverse problems. Dr. Cakoni's work bridges theoretical mathematics with practical applications in imaging, nondestructive testing, and electromagnetic scattering. Her research has significant implications for understanding wave propagation, target identification, and material characterization through rigorous mathematical frameworks. Dr. Cakoni's publications demonstrate sustained leadership in inverse scattering theory, particularly in transmission eigenvalues, target signatures, and imaging techniques for complex media structures. Her recent work shows increasing integration of data science and machine learning approaches with traditional mathematical analysis, reflecting the evolving interdisciplinary nature of her field. The publication timeline extending through 2025 indicates ongoing research productivity and continued relevance in cutting-edge mathematical research. Her scholarly contributions include influential works on transmission eigenvalues and inverse scattering theory, with her publication "Inverse Scattering Theory and Transmission Eigenvalues" appearing in a second edition in 2023, indicating its significance as a reference in the field. As Graduate Vice-Chair, Dr. Cakoni plays an important administrative role in the Mathematics Department, overseeing graduate programs and mentoring students. Her office hours (Mondays and Fridays from 2:00-3:00pm in HLL-722 on Busch Campus) demonstrate her commitment to student engagement and academic support within the Rutgers academic community.