Find Academic Supervisors

Jan VogesView profile

Researcher

• Genomic Data Compression
• DNA Data Storage
• Bioinformatics
• +3 more

Dr. Jan Voges is a Research Group Leader at the Institute for Information Processing and the L3S Research Center at Leibniz University Hannover, where he leads the BioAI Lab. He has held previous research leadership positions at the same institution (2022-2024), CIMA University of Navarra (2024-2025), and maintains a visiting position at the Peter L. Reichertz Institute for Medical Informatics. Dr. Voges' research spans the intersection of computer science, genomics, and medical informatics, with a focus on: Developing AI methods for multi-omics data analysis DNA sequencing data compression and storage 3D genome reconstruction Single-cell RNA sequencing data modeling International standardization of genomic data His recent work demonstrates a strategic shift toward integrating artificial intelligence with genomic data analysis, particularly for medical applications. The development of Genie, the first open-source ISO/IEC encoder for genomic data, represents a significant contribution to genomic data standards. His research shows strong translational potential, bridging computational methods with clinical applications as evidenced by his P4D Dashboard project for depression cohort monitoring. Dr. Voges has received recognition for his work, most notably earning his Dr.-Ing. degree with distinction in 2022. His active participation in ISO/IEC standardization committees and collaborations with international institutions demonstrates the impact of his work beyond traditional academic boundaries. As a supervisor, Dr. Voges maintains a focused lab structure with 3 PhD students, emphasizing quality mentorship and opportunities for students to contribute to high-impact projects with real-world applications. His lab provides unique opportunities at the cutting edge of genomic data science, with exposure to international standardization processes and interdisciplinary collaborations.

Zeyu GuoView profile

Assistant Professor

• Theoretical Computer Science
• Computational Complexity
• Pseudorandomness
• +3 more

Zeyu Guo is an Assistant Professor in the Department of Computer Science and Engineering at The Ohio State University , based in Dreese Laboratories, Columbus, Ohio. He earned his Ph.D. in Computer Science from the California Institute of Technology in 2017 under the supervision of Chris Umans, followed by postdoctoral appointments at the University of Texas at Austin, the University of Haifa (Israel), and the Indian Institute of Technology Kanpur (India). Research Interests Theoretical Computer Science Computational Complexity Pseudorandomness and Derandomization Coding Theory Algebraic Complexity Theory Algebraic Algorithms His work explores deep interplay between algebra and computation, designing efficient algorithms, constructing pseudorandom objects, and establishing fundamental limits in error-correcting codes. Recent Publication Landscape Across 2021-2025, Guo’s papers predominantly target list decoding capacity for Reed–Solomon and Gabidulin codes, polynomial identity testing for restricted circuit classes, and fast algebraic algorithms such as multivariate multipoint evaluation. A recurring theme is leveraging algebraic geometry and additive combinatorics to derandomize constructions and achieve optimal parameters. Awards & Funding NSF CAREER Award (current support) Advising & Mentoring PhD student: Zihan Zhang Postdoc mentee: Ashish Dwivedi (2023–2024) Teaching & Course Development Guo regularly teaches core graduate and undergraduate courses including Algorithms, Computability and Complexity, and specialized topics in algebraic complexity theory and error-correcting codes.

Imola WilhelmView profile

Research Fellow

• Neurovascular unit
• Blood-brain barrier
• Cerebral circulation
• +4 more

Imola Wilhelm serves as a senior research associate at Hungary's Biological Research Centre in Szeged, where she leads pioneering investigations into the neurovascular unit, blood-brain barrier dynamics, and cerebral circulation. Her work specifically examines pericyte function in pathological contexts including brain metastasis, neuroinflammation, and aging processes. As a leading scholar in neurovascular biology, Wilhelm's research integrates molecular and physiological approaches to unravel blood-brain barrier mechanisms under disease conditions. Her dual expertise spans both neurovascular pathology and advanced photonics, with significant contributions to silicon nitride waveguide technologies for biosensing applications. Her 15 most recent publications (2021-2025) reveal a strategic convergence of machine learning and photonic engineering, featuring optimization of silicon nitride platforms, loss-minimization techniques, and biosensor development. This work demonstrates consistent methodological innovation across fabrication, characterization, and application domains. Wilhelm's exceptional contributions have earned prestigious recognition: Bolyai János Research Fellowship from the Hungarian Academy of Sciences L’Oreal-UNESCO Women in Science national scholarship Junior Award for early-career research excellence She actively mentors BSc, MSc, MD, and PhD students while directing multiple scientific grants as principal investigator. Since 2020, she has served as a Szent-Györgyi mentor for the National Academy of Scientist Education and currently holds an executive board position at the Hungarian Young Academy (joined 2021). Her science communication outreach includes Researchers’ Night lectures since 2014 and Brain Awareness Week participation since 2018. Wilhelm leads a multidisciplinary research team at the Biological Research Centre that bridges neurovascular biology with photonic sensor development, maintaining active collaborations across both domains while supervising graduate trainees and postdoctoral researchers.

Steve ButlerView profile

Professor

• Spectral Graph Theory
• Enumerative Combinatorics
• Mathematics of Juggling
• +3 more

Steve Butler is a Professor and the Barbara J. Janson Professor in the Department of Mathematics at Iowa State University. His research focuses on spectral graph theory, combinatorics, and the mathematics of juggling. He earned his Ph.D. from UC San Diego (2008) under Fan Chung and completed an NSF Postdoc at UCLA with Benny Sudakov. Research Interests: Spectral Graph Theory: Studies eigenvalues of graph matrices, equitable partitions, and Laplacian properties. Recreational Mathematics: Explores mathematical patterns in juggling, card shuffling, and performance. Combinatorics: Investigates Eulerian numbers, distance matrices, and graph automorphisms. Scientific Awards: Barbara J. Janson Professorship (2017) LAS Early Achievement in Teaching Award (2014) Cassling Family Faculty Award (2014) MAA Iowa Section Award for Distinguished Teaching (2015) NSF Postdoctoral Fellowship (2008-2011) Advising and Outreach: Butler actively collaborates with students, producing educational videos and organizing undergraduate research initiatives. He served as Iowa State's calculus coordinator (2018-2020) and contributes to outreach programs for young STEM enthusiasts.

• Applied Probability
• Financial Mathematics
• Numerical Analysis
• +4 more

Jean-François CHASSAGNEUX is a Full Professor in Finance at Université Paris, holding a permanent position at CREST (Center for Research in Economics and Statistics). His academic career focuses on the intersection of probability theory, numerical analysis, and financial mathematics, with particular expertise in backward stochastic differential equations and their applications to financial modeling. His research interests span Applied Probability, Financial Mathematics, Numerical Analysis, Stochastic Analysis, Backward Stochastic Differential Equations (BSDE), Large Population Stochastic Control, Non-linear pricing methods, and Markets with imperfections . His work bridges theoretical mathematics with practical financial applications, particularly in derivative pricing, risk management, and sustainable finance. CHASSAGNEUX has developed novel numerical methods for solving complex financial models, including probabilistic approaches to non-linear partial differential equations and advanced techniques for hedging problems. His publication record demonstrates consistent contributions to top journals in mathematics and finance, with a recent focus on sustainable finance applications including carbon markets and impact investing. His collaborative work with researchers like D. Crisan, G. Pagès, and A. Richou has significantly advanced the field of probabilistic numerical methods for financial engineering. As an educator, CHASSAGNEUX teaches advanced courses in Financial Mathematics (APM_4FI02_AE) and Numerical Methods in Financial Engineering (APM_5FI10_AE), training the next generation of quantitative finance professionals. His teaching combines theoretical foundations with practical computational techniques essential for modern financial engineering.

Andreas GalanisView profile

Associate Professor

• Algorithms
• Combinatorics
• Statistical Physics
• +3 more

Dr. Andreas Galanis is an Associate Professor in Computer Science at the University of Oxford, where he serves as a Tutorial Fellow at Hertford College. He earned his PhD from Georgia Tech University in 2015 with a focus on Algorithms, Combinatorics, and Optimization. Research Interests: His work bridges approximate counting/sampling problems in computer science and phase transitions in statistical physics, with a strong emphasis on the computational complexity of partition functions, stochastic processes, and spin systems. Recent Publications: His research spans approximate counting algorithms ( ICALP 2019 , APPROX/RANDOM 2019 ), hardness results in Potts and random-cluster models ( Electronic Journal of Probability 2018 , APPROX/RANDOM 2018 ), and theoretical insights into correlation decay and spatial mixing ( SIAM Journal on Computing 2019 ). Scientific Awards: Best Paper Award, ICALP 2016 Collaborations: He frequently collaborates with researchers like Leslie A. Goldberg and Eric Vigoda on topics involving Markov chains, graph theory, and computational complexity.

Madhu SudanView profile

Professor

• Theories of Computation and Communication
• Coding Theory
• Property Testing
• +5 more

Madhu Sudan is the Gordon McKay Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS), where he also serves as Director of Graduate Studies. He holds a B.Tech. from IIT Delhi (1987) and a Ph.D. from UC Berkeley (1992). Prior to Harvard, he was at MIT (1997–2015), IBM Research (1992–1997), and Microsoft Research (2009–2015). His research focuses on the theories of computation and communication, with notable contributions to probabilistically checkable proofs (PCP theorem), list-decoding algorithms for error-correcting codes, and sublinear-time algorithms. Education: B.Tech., Indian Institute of Technology Delhi, 1987 Ph.D., University of California, Berkeley, 1992 Research Interests: Communication and computing under errors Coding theory and its applications Property testing and sublinear algorithms Algebraic methods in computation Awards and Honors: Nevanlinna Prize (2002) IEEE Hamming Medal (2016) Member, National Academy of Sciences (2017) Infosys Prize in Mathematical Sciences (2018) Fellow of ACM, IEEE, and AMS Teaching and Service: Current courses: Algebra and Computation , Coding Theory Directed Graduate Studies in Computer Science at Harvard Editorial roles: Foundations and Trends in Theoretical Computer Science , Theory of Computing Research Groups: Member of Harvard's Theory of Computation group, collaborating on projects in coding theory, complexity theory, and distributed computing.

Yi-Chun AkchenView profile

Assistant Professor

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

Yi-Chun Akchen is an Assistant Professor at the UCL School of Management, University College London. His research focuses on customer choice modeling, leveraging machine learning and optimization techniques. He holds a PhD from UCLA Anderson School of Management, an M.S. from Stanford University, and a B.S. from National Taiwan University. Education: PhD in Management, UCLA Anderson School of Management M.S. in Computational & Applied Math, Stanford University B.S. in Physics, National Taiwan University His research explores data-driven decision-making, with a focus on irrational choice modeling, assortment optimization, and consideration set dynamics. Key contributions include the Decision Forest model and applications in retail analytics. Publications span operations research, machine learning, and marketing, addressing topics like POMDPs, Bayesian networks, and multi-agent systems. Notable awards include the Nicholson Student Paper Prize and Decision Analysis Award. Awards: Finalist, Service Science Best Cluster Paper Competition (2024) Honorable Mention, George Nicholson Student Paper Prize (2020) 1st Place, Decision Analysis Student Paper Award (2019) His work integrates theoretical rigor with practical applications, evidenced by contributions to open-source code for the Decision Forest model on GitHub.

Gianluca BaioView profile

Professor

• Health Economics
• Bayesian Statistics
• Causal Inference
• +5 more

Gianluca Baio is a Professor of Statistics and Health Economics at University College London (UCL), Department of Statistical Science. He holds a PhD in Applied Statistics from the University of Florence and has worked extensively in Bayesian statistical modeling for healthcare applications, including cost-effectiveness analysis and causal inference. His research focuses on hierarchical models, missing data methods, and health economic evaluation, with notable contributions to HPV vaccination cost-effectiveness and policy impact assessments. Education: PhD in Applied Statistics, University of Florence (Italy) Master's in Statistics and Economics, University of Florence Visiting Researcher, MIT Sloan School of Management (USA) Research Interests: Bayesian methods in health economics Missing data imputation in clinical trials Cost-effectiveness analysis Interrupted time series for policy evaluation Value of Information analysis Healthcare resource allocation Key Grants: Wellcome Trust-funded study on Bayesian hierarchical frameworks for policy evaluation (2021–2025) MRC grant on regression discontinuity design for drug evaluation (2013–2015) NIHR grants on stepped-wedge trial methodologies Awards & Recognition: None explicitly listed, but recognized for contributions to Bayesian HTA methodologies. Professional Activities: Lead of UCL's Statistics for Health Economic Evaluation Group Author of R packages BCEA and survHE Editorial board member of Medical Decision Making

Radu HerbeiView profile

Professor

• Statistical Inference for Stochastic Processes
• Monte Carlo and MCMC Methods
• Inverse Problems
• +3 more

Radu Herbei is a Professor of Statistics at The Ohio State University, affiliated with the Department of Statistics. He joined the faculty in 2006 and has been funded by NSF and ONR. His research focuses on statistical inference for stochastic processes, including stochastic differential equations and stochastic partial differential equations, with an emphasis on 'exact' inference methods that avoid user-selected grids or approximations. He develops exact Markov chain Monte Carlo (MCMC) techniques using approximations of intractable probability density functions and explores high-performance GPU computing to address computational challenges. His education includes a PhD in Statistics from Florida State University (2006). Research areas include Bayesian statistics, Monte Carlo methods, inverse problems, and uncertainty quantification. Notable contributions include work on the Bernoulli factory algorithm for exact Bernoulli random variates and applications in phylogenetics and environmental modeling. Collaborations span computational biology, oceanography, and ecological systems. Key publications include developments in taxicab MCMC samplers for discrete spaces, Bayesian function registration, and statistical inference for stochastic differential equations. His work bridges theoretical advancements with computational tools, addressing complex modeling challenges in diverse scientific domains.

Leo DuanView profile

Associate Professor

• Statistical Methodology
• Bayesian Statistics
• Combinatorial Optimization
• +3 more

Leo Duan is an Associate Professor with Tenure in the Department of Statistics at the University of Florida since 2018. His research focuses on developing statistical methods, computational tools, and Bayesian frameworks to address challenges in neuroscience, engineering, and transportation science. He specializes in combinatorial structures like tree graphs, clustering, and signal pathways, integrating optimization and probabilistic techniques. Dr. Duan’s recent work includes advancements in Bayesian inference using optimization, such as bridged posteriors and gradient-based methods. He co-teaches a short course on optimization in Bayesian inference at JSM 2025. His funded projects include NSF-ATD grants for geospatial modeling and hurricane risk mitigation, alongside other institutional awards. His advising spans doctoral students in optimization and diffusion models. Past trainees include Edric Tam (Stanford Postdoc) and Maoran Xu (Indiana University faculty). Current students Zeyu Yuwen and Yu Zheng are nearing graduation. Dr. Duan’s lab actively recruits new graduate researchers. Key achievements include the 2018 NeurIPS Bayesian Non-parametrics Award and a series of high-impact publications in JASA, JMLR, and Biometrika. His research bridges theory and application, with contributions to spanning trees, graphical models, and Bayesian vector autoregression.

Alexander SemenovView profile

Assistant Professor

• Network Science
• Social Media Analytics
• Algorithm Design
• +7 more

Alexander Semenov is an Assistant Professor in the Department of Industrial & Systems Engineering at the University of Florida and serves as Assistant Director of the Center for Applied Optimization (CAO). He holds a Ph.D. in Computer Science from the University of Jyväskylä (2013). His research focuses span network science, social media analytics, algorithm design, large dataset analysis, optimization, and machine learning applications. University: University of Florida Department: Industrial & Systems Engineering Lab: Center for Applied Optimization (CAO) Academic Rank: Assistant Professor Email: asemenov@ufl.edu Office: REEF 132 Phone: (850) 833-9350 [Ext: 239] His recent publications demonstrate interdisciplinary applications of network science and machine learning in healthcare (AI-assisted EHR standardization, drug discontinuation detection), financial markets (P/E ratio forecasting, high-frequency trading impact analysis), art economics (color palette effects on painting prices, authorship analysis), and cybersecurity (key management in WSN). Current methodological work involves novel approaches to influence propagation modeling, change-point detection via MIP, and SAR image navigation systems. Research themes emphasize solving real-world problems through graph-based methodologies, deep learning, and optimization frameworks. His editorial roles include Associate Editor positions at the Journal of Combinatorial Optimization and IET Blockchain journal. Contact details available at REEF 132, University of Florida.

Jaehee KimView profile

Assistant Professor

• Population Genetics
• Statistical Genetics
• Mathematical Phylogenetics
• +3 more

Jaehee Kim is an Assistant Professor in the Department of Computational Biology at Cornell University's College of Agriculture and Life Sciences (CALS). Her research integrates mathematical, statistical, and computational approaches to address fundamental questions in population genetics and evolutionary biology, with applications in epidemiology, forensic genetics, and ecological modeling. Education: Ph.D. in Physics, Stanford University B.A. in Physics, Columbia University B.A. in Mathematics, Columbia University Research Focus: Kim's work examines population-genetic dynamical systems, including evolutionary processes that generate genetic/phenotypic variation. She develops models to study dormancy in pathogens like tuberculosis, gene drive dynamics in plants, and methods for analyzing multi-omic data in cancer genomics. Her lab combines statistics, machine learning, and physics to bridge theoretical population genetics with real-world biomedical and ecological challenges. Recent Publications: Her recent studies explore Bayesian phylodynamic inference of dormancy in population dynamics, network-based frameworks for genetic monitoring of fragmented habitats, and multi-omic analyses of cancers like malignant pleural mesothelioma. These works highlight her interdisciplinary approach to linking evolutionary theory with practical applications.

Alan FriezeView profile

Professor

• Combinatorics
• Computer Science
• Random Graphs
• +6 more

Alan Frieze is a University Professor at Carnegie Mellon University , affiliated with the Department of Mathematical Sciences. His research spans combinatorics, random graphs, algorithms, and probabilistic methods. Academic Rank: Professor Department: Mathematical Sciences Emails: frieze@cmu.edu, af1p@andrew.cmu.edu, alan@random.math.cmu.edu Research Interests: Frieze's work focuses on combinatorics, random graph theory, algorithm design, and probabilistic models. His recent publications address Hamiltonian cycles, rainbow subgraphs, and stochastic processes in graph theory. Scientific Awards: He has received prestigious honors including the Fulkerson Prize (1991), Guggenheim Fellowship (1997), and fellowships from the AMS and SIAM. Teaching Activities: Frieze has taught courses such as Random Graphs, Combinatorics, and Doctoral Thesis Research at Carnegie Mellon University.

James A. MingoView profile

Professor

• Free probability
• Random matrices
• Operator algebras
• +5 more

James A. Mingo is a Professor in the Department of Mathematics and Statistics at Queen's University. His research focuses on free probability, random matrices, operator algebras, and related areas. He holds a Ph.D. from Dalhousie University. Education: Ph.D. (Dalhousie University) Research Interests: Mingo's work explores the interplay between free probability and random matrices, with applications to operator algebras and mathematical physics. Key topics include infinitesimal freeness, asymptotic distributions of random matrices, and combinatorial structures in non-commutative probability. His contributions bridge abstract algebraic frameworks with probabilistic methods, addressing questions in both theoretical and applied contexts. Grants and Collaborations: Mingo collaborates widely, as evidenced by his co-authored papers with researchers like Roland Speicher, Mihai Popa, and Daniel Munoz George. His research has been supported by grants from institutions like the Simons Foundation. Teaching and Service: Mingo has taught courses such as STAT/MTHE 353 and MATH 892 at Queen's University, reflecting his commitment to both undergraduate and graduate education. He also organizes research seminars and workshops in free probability and random matrices.