Find Academic Supervisors

Peizhong JuView profile

Assistant Professor

• Machine Learning
• Smart Grid
• Optimization
• +5 more

Dr. Peizhong Ju is an Assistant Professor in the Department of Computer Science , Stanley and Karen Pigman College of Engineering at the University of Kentucky since 2024. He earned his PhD in Electrical and Computer Engineering from Purdue University (2021) and a BSc in Electrical Engineering from Peking University (2016). His research bridges mathematical rigor with practical applications in machine learning, smart grid, and wireless communication systems. Education Purdue University (PhD, Electrical and Computer Engineering, 2021) Peking University (BSc, Electrical Engineering, 2016) Dr. Ju’s research focuses on theoretical foundations of machine learning , including generalization performance analysis, federated learning, continual learning, and optimization frameworks. His work combines probability theory, optimization, game theory , and random matrix theory to study complex systems under uncertainty. Prominent publication trends include edge computing (2024-2025), multi-agent reinforcement learning (2023-2024), diffusion models (2025), and multi-objective optimization (2024-2025). Earlier works (2018-2022) concentrated on neural tangent kernels , full-duplex communication , and power distribution networks . Scientific Awards Best Paper Award, ACM e-Energy 2022 Bilsland Dissertation Fellowship 2021 Best Paper Finalist, ACM e-Energy 2018 National and corporate scholarships in China (2013-2016) He has received funding through the NSF AI Institute on Future Edge Networks and Distributed Intelligence (AI-EDGE) and has participated in workshops such as the NeTS Early Career Workshop (2025). His teaching includes advanced courses on generative AI and artificial intelligence .

• Stochastic Geometry
• Spatial Statistics
• Image Analysis
• +3 more

Claudia Redenbach is a Professor and Dean of Mathematics at RPTU Kaiserslautern, Germany. She is affiliated with the Department of Mathematics and leads the Statistics Working Group (AG Statistik), with additional connections to the Department of Statistics and the Graduate School 'Mathematics as a Key Technology.' Position: Dean of Mathematics Institution: RPTU Kaiserslautern Location: Building 48, Room 534, Gottlieb-Daimler-Straße, 67663 Kaiserslautern Contact: claudia.redenbach@rptu.de | +49 (0)631 205 3620 Professor Redenbach's research focuses on the intersection of stochastic geometry, spatial statistics, and image analysis with applications in materials science. Her work bridges theoretical mathematics with practical engineering applications, particularly in analyzing microstructures of concrete, foams, composites, and other materials. She has developed innovative methods for analyzing spatial point patterns, directional data, and 3D image data from various microscopy techniques including CT, FIB-SEM, and light-sheet microscopy. Her recent publications (2024-2025) reveal a strong emphasis on computational methods for materials analysis, including crack detection in concrete, fiber orientation analysis, artifact removal in imaging, and the development of mathematical morphology techniques for directional data. She frequently collaborates with materials scientists and engineers, with K. Schladitz appearing as a consistent co-author across numerous publications. Her work demonstrates a consistent focus on developing mathematical tools that solve practical problems in materials characterization and analysis. Professor Redenbach has made significant contributions to spatial statistics, particularly in anisotropy analysis of point patterns, and to the application of stochastic geometry models for material microstructure analysis. Her research spans both theoretical developments in statistical methods and their practical implementation for real-world materials science problems.

Alan ZameView profile

Professor

• Mathematics
• Statistics
• Probability Theory
• +4 more

Alan Zame is a Professor in the Department of Mathematics at the University of Miami , College of Arts and Sciences. His research spans diverse areas in mathematics, statistics, and probability theory. Education & Training: (Details not explicitly provided in the text) Research Interests: Dr. Zame's work includes combinatorics , probability theory , game theory , and number theory . He has contributed to understanding stochastic processes, tournament planning, urn schemes, and subgroup chains. Publication Trends: His publications from 1984–2010 focus on probabilistic modeling, strategic games, and mathematical structures. Key topics include random sequences, gambling strategies, string matching, and combinatorial fairness in tournaments. Advising & Grants: No specific students or grants are listed in the provided text.

Alistair SinclairView profile

Professor

• Randomized Algorithms
• Markov Chain Monte Carlo
• Statistical Physics
• +3 more

Alistair Sinclair is the Kikuo Ogawa and Kaoru Ogawa Professor of Computer Science in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. He received his BA in Mathematics from the University of Cambridge (1982) and PhD in Computer Science from the University of Edinburgh (1988). After briefly serving on faculty at Edinburgh, he joined UC Berkeley in 1994. Sinclair has held visiting positions at DIMACS, Princeton University, Rutgers University, Microsoft Research, École Polytechnique, University of Paris-Orsay, and University of Rome III. His research explores: Randomized algorithms and Markov chain Monte Carlo methods Phase transitions in statistical physics Algorithmic applications of stochastic processes Nonlinear dynamical systems Combinatorial optimization Analysis of Sinclair's recent publications (2017-2025) reveals strong emphasis on statistical physics models (especially Ising and random-cluster systems), Markov chain dynamics, phase transitions, and algorithmic solutions for combinatorial problems. Key methodologies include spatial mixing analysis, entropy decay measurements, and deterministic approximation techniques. Scientific Awards: 1996 ACM-EATCS Gödel Prize 2006 Fulkerson Prize 2017 SIGACT Distinguished Service Prize Sinclair has advised 17+ PhD students including notable researchers in theoretical computer science and mathematics. He served as Founding Associate Director (2012-2017) of the Simons Institute for the Theory of Computing, receiving recognition for developing its research programs on probability, geometry, and computational complexity.

Viresh PatelView profile

Lecturer

• Extremal combinatorics
• Probabilistic combinatorics
• Graph polynomials / partition functions
• +5 more

Dr. Viresh Patel is a Lecturer in Optimisation at the School of Mathematical Sciences, Queen Mary University of London. His research focuses on extremal and probabilistic combinatorics, graph polynomials, phase transitions, and approximation algorithms. University: Queen Mary University of London School: School of Mathematical Sciences Email: viresh.patel@qmul.ac.uk Research Interests: Patel’s work bridges combinatorics, graph theory, and statistical physics. Key areas include Hamiltonian cycles in dense graphs, algorithmic approaches to graph polynomials, and complexity analysis of physical models. Publication Trends: Recent articles address structural graph theory (cycle/path decomposition), computational complexity (Potts/Ising models), and approximation algorithms using probabilistic and Taylor series methods. Collaborations: Regular co-authorship with researchers like Guus Regts, Allan Lo, and Matthew Jenssen indicates strong interdisciplinary engagement.

• Condensed Matter Physics
• Materials Science
• Molecular Dynamics
• +6 more

Dr. Alston Misquitta is a Lecturer in Condensed Matter and Materials Physics at the School of Physics and Astronomy, Queen Mary, University of London . His research focuses on intermolecular forces, symmetry-adapted perturbation theory (SAPT), and computational modeling of molecular and solid-state systems. Research Interests Development of ab initio and non-empirical force fields Intermolecular interaction energy decomposition Electronic structure theory applied to molecular and nanoscale systems Crystal structure prediction and validation High-pressure materials science Publication Trends Dr. Misquitta’s publications span computational chemistry, condensed matter physics, and materials science, with a strong emphasis on SAPT-based methods, polarizable force fields, and molecular dynamics simulations for organic and inorganic systems. His work addresses challenges in modeling hydrogen bonding, dispersion interactions, and excited-state phenomena. Contact Information Email: a.j.misquitta@qmul.ac.uk Phone: 020 7882 3427 Room: G Jones 216 Address: 327 Mile End Road, London, E1 4NS

Alexander BarvinokView profile

Professor

• Computational Complexity
• Algorithms
• Algebra
• +6 more

Alexander Barvinok is a Professor in the Department of Mathematics at the University of Michigan, Ann Arbor. His office is located in East Hall (4066 East Hall), where he has been conducting research and teaching advanced courses in computational mathematics since receiving his Ph.D. from Leningrad State University in 1988. Professor Barvinok's research focuses on computational complexity and algorithms in algebra, geometry and combinatorics. He is particularly interested in connections between various notions of phase transition in statistical physics, analytical properties of partition functions and computational complexity. His work bridges theoretical mathematics with practical computational approaches, exploring how physical phenomena can inform algorithmic design and analysis. His research spans convex geometry, combinatorial optimization, and the computational aspects of polynomial systems. His recent publications (2016-2024) demonstrate a consistent focus on partition functions, computational aspects of convex bodies, and approximation algorithms for counting problems. He has made significant contributions to understanding the zeros of partition functions in statistical physics models, developing efficient volume estimation algorithms for polyhedra, and creating polynomial-time approximation schemes for problems previously thought to be computationally intractable. His work frequently connects algebraic properties of polynomials with computational feasibility. Professor Barvinok has authored several influential textbooks including "A Course in Convexity" (AMS Graduate Studies in Mathematics, 2002), "Integer Points in Polyhedra" (Zurich Lectures in Advanced Mathematics, 2008), and "Combinatorics and Complexity of Partition Functions" (Springer, 2016). He regularly teaches advanced graduate courses such as Math 669 on specialized topics including "Combinatorics, Geometry and Complexity of Integer Points" and "Topics in Convexity," with his lecture notes often evolving into significant research contributions.

• Combinatorial Geometry
• Multivariate Linear Models
• Nonlinear Analysis
• +7 more

Dr. Magdalena Łysakowska is a researcher at the University of Zielona Góra, affiliated with the Department of Computer Science Applications. She teaches courses in linear algebra, logic and set theory, combinatorial analysis, differential geometry, and general algebra. Research focuses on combinatorial geometry (cube partitions, Keller's hypothesis) Nonlinear analysis (Lipschitz maps, fixed point theorems) Stochastic inclusions and multivalued equations Iterative algorithms (Gaussian and Archimedes-Borchardt) Applications of computer science to secure data transmission and software development She participates in Erasmus teaching collaborations, delivering courses at the University of Debrecen (Hungary) and Ilmenau University of Technology (Germany). Her work spans theoretical and applied mathematics, emphasizing geometric structures, functional equations, and computational methods.

Niv BuchbinderView profile

Professor

• Combinatorial Optimization
• Online Algorithms
• Algorithmic Game Theory
• +2 more

Prof. Niv Buchbinder is a faculty member in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University. His research centers on algorithmic solutions for combinatorial optimization in offline and online contexts, with significant contributions to primal-dual methodologies and algorithmic game theory. His academic background includes a Ph.D. in Computer Science from the Technion (2008) under Prof. Seffi Naor and an M.Sc. in Computer Science from the Technion (2003) under Prof. Erez Petrank. Key research areas encompass Combinatorial Optimization, Online Algorithms, Algorithmic Game Theory, Primal-Dual Methods, and Submodular Optimization, focusing on competitive analysis for problems like set cover, ad-auctions, and caching. Recent publications (2012-2015) reveal a concentrated effort in submodular optimization and online decision-making, with applications in advertising, resource allocation, and machine learning. These works consistently employ primal-dual frameworks to achieve strong competitive ratios in adversarial settings. Scientific recognition includes: Best Paper Award at ESA 2007 for “Online Primal-Dual Algorithms for Maximizing Ad-Auctions Revenue” Best Paper Award at FOCS 2011 for “A Polylogarithmic Competitive Algorithm for the k-Server Problem” No information is available regarding student advising or research grants. Similarly, details about laboratory facilities, research teams, or future projects are not provided in the source materials.

Yaiza CanzaniView profile

Professor

• Partial Differential Equ
• Semiclassical Analysis
• Mathematical Physics
• +7 more

Yaiza Canzani is a Professor in the Department of Mathematics at the University of North Carolina at Chapel Hill, where she serves as Director of Graduate Studies. Her research explores the behavior of Laplace eigenfunctions in high-energy regimes, utilizing techniques from harmonic analysis, spectral theory, geometric analysis, and mathematical physics. B.S., Universidad de la República, Uruguay (2008) Ph.D., McGill University (2013) Canzani’s work bridges quantum mechanics and geometry, analyzing how eigenfunctions concentrate and interact with underlying manifolds. Her recent publications focus on spectral partitions, Zoll manifolds, and asymptotic analysis, reflecting interdisciplinary applications in physics and differential geometry. Her research trends include geometric spectral theory, microlocalization, and probabilistic methods in eigenfunction studies. She co-organized conferences like the Geometric Spectral Theory and Applications Program Conference (2026) and contributed to collaborative projects in spectral minimal partitions. Board of Governor’s University Teaching Award (2025) Benjamin Peirce Fellowship (Harvard, 2013–2016) As co-PI on a $2.5-million grant, Canzani has developed new undergraduate mathematics courses and resources. She advises doctoral researchers like Andrew Lyons and collaborates on projects with colleagues across institutions.

Kristóf BércziView profile

Researcher

• Approximation Algorithms
• Combinatorial Optimization
• Matroid Theory
• +3 more

Kristóf Bérczi is a researcher at the Department of Operations Research, Eötvös Loránd University, Hungary. His work intersects computer science, mathematics, and operations research, focusing on theoretical and applied optimization problems. MSc and PhD in Operations Research, ELTE Postdoc at Hungarian Academy of Sciences Research Interests: Approximation algorithms Combinatorial optimization Matroid theory Submodular functions Algorithmic game theory Graph theory Recent Publication Trends: His 2024-2025 work explores matroid-constrained partitioning, inverse optimization algorithms, rainbow subgraph problems, and envy-free pricing models, often combining matroid theory with practical algorithm design. Scientific Contributions: Active in program committees (APPROX 2019, IPCO 2024, WALCOM 2025) Organized workshops on matroid optimization and matching theory Editorial board member of SIAM Journal on Discrete Mathematics Mentorship: Supervises PhD students Áron Jánosik, András Imolay, and others, with graduates now in postdoc/lecturer positions at institutions like CMM Chile and LSE UK.

John Morgan SampsonView profile

Associate Professor

• Energy Efficient Computing
• Energy Harvesting
• Dark Silicon
• +6 more

John Morgan Sampson is an Associate Professor in the Department of Computer Science and Engineering. His research focuses on energy-efficient computing, neuromorphic systems, and memory safety. Active in energy harvesting systems and in-memory computing Specializes in dark silicon, non-volatile memory, and deep neural networks Collaborates on projects related to distributed sensor networks and secure architectures Research Trends: Recent articles highlight advancements in energy-efficient computer architecture , memory safety validation , and neuromorphic computing . Key subfields include spiking neural networks , edge-cloud partitioning , and heap memory protection .

Rakesh VenkatView profile

Assistant Professor

• Theoretical Computer Science
• Approximation Algorithms
• Hardness of Approximation
• +1 more

Rakesh Venkat is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad. His research focuses on Theoretical Computer Science, including approximation algorithms, hardness of approximation, and communication complexity. Education : Ph.D., Tata Institute of Fundamental Research (TIFR), Mumbai. Research Trends : His work addresses fundamental challenges in algorithm design, such as optimizing cache misses, improving clustering algorithms, analyzing graph expansion, and exploring embedding techniques. Publications span top-tier conferences like APPROX, FSTTCS, ICALP, and ITCS, with collaborations at institutions including HUJI, TIFR, and IIT-Bombay. Teaching : Courses taught include Approximation Algorithms, Advanced Data Structures, Discrete Mathematics, and Spectral Graph Theory.

James Emmett HerndonView profile

Professor

• Cancer Clinical Trials
• Survival Analysis
• Longitudinal Data Analysis
• +3 more

James Emmett Herndon is a Professor of Biostatistics & Bioinformatics at Duke University and a Member of the Duke Cancer Institute, specializing in statistical methodologies for cancer clinical trials with emphasis on neuro-oncology applications. Education Ph.D., University of North Carolina, Chapel Hill (1988) Research Interests Herndon pioneers biostatistical approaches for cancer trials, focusing on time-dependent covariates in survival models, phase II trial designs minimizing logistical barriers, and longitudinal analysis of quality-of-life data in advanced cancer. His neuro-oncology work addresses critical challenges in brain tumor research through innovative statistical frameworks. Publication Trends With 589 publications, Herndon's recent work (2024-2025) demonstrates dominance in neuro-oncology, integrating machine learning for brain tumor classification, analyzing clinical trial disparities, and developing novel therapeutic strategies for gliomas. Key themes include financial toxicity in brain tumor care, immune-based therapies, and prognostic tool validation. Grants Inflammasomes - a driver of sexual dimorphism to glioma therapies (NIH, 2025-2030) McCain/Bayh Glioblastoma Consortium (DoD, 2025-2029) ATRX mutations, innate immune activation and therapeutic vulnerability in malignant gliomas (NCI, 2022-2027) Enhancing efficacy of Radiation Therapy for brainstem glioma (NCI, 2022-2027) Randomized Phase 2 trial of Lerapolturev for recurrent glioblastoma (2024-2027) Targeting tumor-neural interactions in lung cancer brain metastasis (NIH, 2021-2026) ABL-regulated networks in breast cancer metastases (DoD, 2022-2025) Synergistic microglial activation for GBM response (NINDS, 2021-2025) Genomic analysis of oligodendroglioma (Doris Duke, 2022-2025) Reviving cancer immune surveillance with CD4 T cell help (NCI, 2022-2025) Collaborative Leadership As senior faculty, Herndon mentors junior researchers across Duke Cancer Institute and leads multi-institutional consortia including the McCain/Bayh Glioblastoma Consortium. His grant portfolio demonstrates exceptional collaboration with neurosurgeons, oncologists, and immunologists to translate statistical innovations into clinical impact.

Emil Petkov KamenovView profile

Assistant Professor

• Probability
• Operations Research
• Statistics
• +3 more

Emil Petkov Kamenov serves as an Assistant Professor in the Department of Probability, Operations Research and Statistics at the Faculty of Mathematics and Informatics, Sofia University 'St. Kliment Ohridski', where he maintains an office in room FMI-510 and can be contacted via phone at +359 2 8161-543. His research program centers on combinatorial probability and number-theoretic structures, with deep specialization in integer partition theory, plane partitions, and asymptotic enumeration methods. Dr. Kamenov investigates probabilistic behaviors of partition components, structural properties of random partitions, and asymptotic distributions in discrete combinatorial systems, bridging theoretical probability with analytic number theory. Analysis of his publications reveals a consistent focus on asymptotic methodologies applied to partition combinatorics, particularly examining large distinct parts in random integer partitions (2001) and enumeration of plane partitions through asymptotic formulas (2006). These works demonstrate rigorous application of analytic combinatorics to solve complex enumeration problems in discrete mathematics. While no scientific awards or student advisement records appear in the source material, his scholarly contributions reflect sustained engagement with fundamental problems in combinatorial mathematics through collaborative research, as evidenced by his co-authored publication with L. Mutafchiev.