Find Academic Supervisors

Laura Smith ChowdhuryView profile

Professor

• Applied Mathematics
• Crime Modeling
• Complex Networks
• +5 more

Laura Smith Chowdhury is a Professor in the Department of Mathematics at California State University, Fullerton (CSUF). She holds a PhD from the University of California, Los Angeles (UCLA), and MS and BS degrees from Western Washington University. Her research focuses on applied mathematics, crime modeling, complex networks, dynamical systems, image processing, and computational science. She has contributed to agent-based models for social dynamics, territorial gang patterns, and public health interventions. Notable works include stochastic models for political conflict forecasting and network partitioning algorithms. Her research spans diverse applications, including crime prevention strategies for resource-limited police departments, social media analysis for controversial topics, and migration patterns in population dynamics. She has developed methodologies like spectral clustering with epidemic diffusion and variational methods for density estimation. Her work integrates computational tools with real-world problems in policy, epidemiology, and urban planning. Prof. Chowdhury’s publications appear in journals like the Bulletin of Mathematical Biology, European Journal of Applied Mathematics, and ACM Transactions on Knowledge Discovery from Data. She co-authored a patent (US 8,938,115 B2) on data fusion mapping techniques. Her research often bridges mathematical theory with practical societal challenges, emphasizing interdisciplinary collaboration.

Jannik WolffView profile

Researcher

• Generative Models
• Multimodal Learning
• Probabilistic Machine Learning
• +6 more

Jannik Wolff is a Researcher and Ph.D. Candidate at TU Berlin's Machine Learning Group, advised by Prof. Klaus-Robert Müller and Shinichi Nakajima. He is also a Research Associate at the Berlin Institute for the Foundations of Learning and Data (BIFOLD). Previously, he worked at SAP AI Research. His research focuses on generative models for multimodal data, probabilistic modeling, and applications in computer vision and inverse problems. He holds a Master’s degree from TU Berlin and a patent for graph-based priors in zero-shot learning. Key research interests include generative models (e.g., diffusion models, transformers), probabilistic ML, and multimodal learning. He has published peer-reviewed work on hierarchical VAEs, mixture-of-experts models, and low-shot learning techniques. Notable contributions include a patent on graph-based priors for image classification and a drone delivery system optimized via hybrid behavior planning. His work on Bayesian deep learning for optimization and emotion recognition using EEG data further highlights his interdisciplinary expertise.

Ulrich KrügerView profile

Research Professor

• Mathematical applications in finance
• macroeconomics
• Credit risk modeling
• +4 more

Ulrich Krüger is a Research Professor at the Research Centre of the Deutsche Bundesbank, focusing on financial stability and systemic risk. His work integrates mathematical models with macroeconomic and regulatory frameworks, emphasizing Basel II/III compliance and interbank market dynamics. He holds a leadership role in the Directorate General Financial Stability, contributing to policy analysis and stress-testing methodologies. Research Interests: Mathematical finance applications Credit risk modeling Capital regulation frameworks Systemic risk contagion mechanisms Recent work highlights include analyzing digital euro impacts on German banking liquidity, strategic interactions among banks, and calibrating capital buffers for systemically important institutions. His presentations at global forums (e.g., ECB, Basel Committee workshops) underscore his role in shaping regulatory practices.

• Combinatorics
• Discrete Mathematics
• Random Graphs
• +4 more

Juan José Rué Perna is an Associate Professor in the Department of Mathematics at Universitat Politècnica de Catalunya (UPC), where he serves in the Faculty of Mathematics and Statistics. He also holds the position of Director of CFIS (Centro de Formación Interdisciplinaria Superior) and maintains strong affiliation with the Centre de Recerca Matemàtica (CRM). His academic career spans prestigious institutions across Europe, including Freie Universität Berlin, CSIC-ICMAT in Madrid, and École Polytechnique in Paris. Licenciado en Matemáticas (5-year degree) Engineering degree in Telecommunications (5-year program) PhD in Applied Mathematics (2009), supervised by Professor Marc Noy Rué's research focuses on combinatorics and discrete mathematics, with expertise in combinatorial structures, random graphs, and enumerative problems. His work bridges theoretical mathematics with applications in computer science, particularly in structural graph theory and probabilistic methods. He has made significant contributions to the understanding of planar graphs, random discrete structures, and additive combinatorics problems. His research combines analytic techniques with combinatorial reasoning to solve challenging enumeration and structural problems. His recent publications demonstrate a consistent trajectory in combinatorial mathematics, with emphasis on planar structures, random discrete objects, and additive problems. The works span theoretical investigations of graph structures, enumeration techniques, and probabilistic approaches to combinatorial problems. His research group GAPCOMB (Geometric, Algebraic and Probabilistic Combinatorics) at UPC continues to produce influential work in these areas. Premi Albert Dou de la SCM Rué has successfully supervised multiple PhD students to completion, including Clément Requilé, Christoph Spiegel, Vasiliki Velona, and Maximilian Wötzel. His research has been supported through numerous competitive grants, including ERC projects, ExploreMaps, and participation in the Berlin Mathematical School. He has organized significant academic events such as EUROCOMB 2021 and various workshops on combinatorics and discrete mathematics. As leader of the GAPCOMB research group at UPC, Rué fosters collaboration between geometric, algebraic, and probabilistic approaches to combinatorial problems. The group maintains strong connections with international research centers including ICMAT in Madrid and institutions across Europe, contributing to the vibrant combinatorial research community in Spain and internationally.

Laura DietzView profile

Associate Professor

• Information Retrieval
• Natural Language Processing
• Knowledge Graphs
• +3 more

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire. She leads the TREMA lab and focuses on text-based machine learning, information retrieval, and environmental data science. Her academic journey includes postdoctoral roles at Mannheim University and the University of Massachusetts, alongside a PhD from the Max Planck Institute for Informatics. Education: PhD in Computer Science (Max Planck Institute, 2011), followed by postdoctoral research at UMass (2012–2015) and Mannheim (2015–2016). Tenure-track appointment at UNH since 2016. Research Interests : IR+NLP: Integrates knowledge graphs, entity retrieval, and machine learning to automatically generate comprehensive articles. This work is supported by an NSF CAREER Award (2019–2023). Watershed Data Science: Analyzes storm event impacts using time-series forecasting, with grants from the Northeast Big Data Hub. Machine Learning: Develops methods for sequential data analysis across domains like social media and environmental science. Grants & Awards : NSF CAREER Award (Utilizing Fine-Grained Knowledge Annotations) Elite Postdoc Program of BW-Stiftung (2016–2019) Best Paper at JCDL 2018 for Entity-Aspect Linking Service & Leadership : Organized HIPstIR Workshop on non-mainstream IR research Coordinated TREC Complex Answer Retrieval Track (2017–2019) Keynote speaker at ECIR, NAACL, and SPIRE Labs & Teams : Head of TREMA Lab at UNH, focusing on query-driven knowledge synthesis and environmental data science. Collaborates with faculty in Natural Resources and the Environment Department.

David TewodroseView profile

Professor

• Geometric Analysis
• Metric Measure Spaces
• Riemannian Manifolds Limits
• +3 more

David Tewodrose is a Junior Professor in the Department of Mathematics and Data Science at the Vrije Universiteit Brussel (Belgium). His research focuses on geometric analysis, particularly metric measure spaces arising as Gromov-Hausdorff limits of Riemannian manifolds. His work involves studying Ricci curvature bounds, Sobolev inequalities, and spectral embeddings with applications in data analysis. Key research areas include geometric flows, stability of manifolds under curvature conditions, and functional inequalities in non-smooth settings. His recent publications explore topics such as Kato bounds on Ricci curvature, asymptotic Laplacian operators, and heat kernel behavior in metric measure spaces. He organizes academic events, such as the 2025 Workshop on Geometric Flows and Quantization of Kähler Metrics. Contact: David.Tewodrose@vub.be | Office: G.6.15.

• Probability Theory
• Mathematical Physics
• Combinatorics
• +3 more

Alexey Bufetov is a Professor at Leipzig University , supported by an ERC Starting Grant 'Integrable Probability' (2022–2027). Previously, he held roles as W2-Professor (Bonn Junior Fellow) at the Hausdorff Center for Mathematics (2018–2021) and CLE Moore Instructor at MIT (2015–2018). He completed his Ph.D. in 2015 under advisor G. Olshanski. His research focuses on Probability Theory , Mathematical Physics , and Combinatorics , with emphasis on stochastic particle systems, integrable probability, random matrix theory, and asymptotic analysis. His work bridges algebraic structures (e.g., representation theory) with probabilistic methods, addressing topics like exclusion processes, vertex models, and limit shape phenomena. Recent articles highlight contributions to ASEP dynamics, Mallows measures, and Yang-Baxter integrability. His work often intersects with statistical mechanics and combinatorial identities. No scientific awards are explicitly listed, though his ERC grant underscores research impact. Advising and grants include ERC support and collaborations with institutions like Bonn University. His research outputs span 20+ years, with a focus on advancing probabilistic frameworks for complex systems.

Jean VaillancourtView profile

Academic

• Probabilities
• Stochastic Equations
• Random Games
• +2 more

Jean Vaillancourt is an Affiliated Professor at the Department of Decision Sciences, HEC Montréal. He holds a PhD in Mathematics from Carleton University. His research focuses on stochastic processes, probability theory, and data mining applications in social media analysis. Key areas include martingale theory, random walks in random environments, and social network dynamics. He has contributed to methodologies for estimating multi-modal histograms and predicting network restructuring following node removals. His work integrates mathematical rigor with computational techniques, appearing in venues like Stochastic Analysis and Applications and Neural, Parallel and Scientific Computations . Active on platforms like Academia.edu and ResearchGate, his research bridges theoretical probability with practical applications in data science and network analysis. Education: PhD in Mathematics, Carleton University Research Interests: Probabilities, stochastic equations, social media analysis, data mining, and network science. His work emphasizes statistical methods for pattern extraction, centrality-based network prediction, and measure-valued processes. Articles Overview: Recent publications explore martingale convergence theories, random walk periodicity detection, and cascading removals in social networks. These studies highlight his dual focus on foundational probability theory and applied data-driven analysis. Grants & Advising: Supervision activities span the last five years, though specific grants or advisees aren't detailed here. His work on road database automation and edge detection showcases interdisciplinary impact. Labs & Teams: Collaborates across disciplines, integrating mathematical modeling with computational tools. Active in HEC's research ecosystem, contributing to formal concept analysis and SAR imagery processing initiatives.

Andrei BRAITORView profile

Associate Professor

• Control Systems
• Power Electronics
• DC Microgrids
• +6 more

Andrei BRAITOR is an Associate Professor at CentraleSupélec, affiliated with the L2S (Laboratoire des Signaux et Systèmes). His research focuses on systems and control engineering, particularly in DC microgrids, power electronics, and stability analysis. Key areas of expertise include voltage stability in aviation power systems, distributed control for multi-agent systems, and cyber-physical components of autonomous systems. Research interests span multiple domains including energy systems (hybrid electric aircraft, Industry 4.0 applications), telecommunications, and signal processing. He leads projects such as COMEDY (Control of Dynamical Systems), MODESTY (Modelling & Estimation), and SYCOMORE (Telecommunications & Networks). His work integrates advanced hierarchical control frameworks and stability analysis techniques. Recent contributions highlight innovations in overvoltage protection mechanisms, decentralized primary control strategies, and adaptive droop control for DC microgrids. He actively participates in international conferences like IFAC World Congress and the American Control Conference, contributing to both theoretical advancements and practical implementations in energy distribution systems. Prof. Braitor also pioneers educational initiatives using animated cartoons (∞ sCaR project) to enhance control engineering pedagogy. His research group collaborates across disciplines, leveraging L2S's interdisciplinary environment in signal processing, inverse problems, and modelling teams.

Bernardo Cuenca GrauView profile

Professor

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Knowledge Graphs
• +6 more

Bernardo Cuenca Grau is a Professor of Computer Science at the University of Oxford and a Tutorial Fellow at Keble College. His research focuses on artificial intelligence, particularly knowledge representation and reasoning, knowledge graphs, computational logic, and semantic technologies. He leads the Information Systems Group and has contributed to high-impact software tools like LogMap, MORe, and PAGOdA. His work bridges theoretical foundations with practical applications, including industry collaborations through his co-founded startup Oxford Semantic Technologies, acquired by Samsung in 2024. He has received prestigious awards such as the Royal Society University Research Fellowship (2009-2017) and the 2025 Vice-Chancellor Award for Innovation. Affiliations: University of Oxford Department of Computer Science, Keble College Professional Roles: Tutorial Fellow, Co-founder of Oxford Semantic Technologies Research interests span declarative data analysis, graph neural networks, temporal reasoning, and ontology-based systems. His recent work explores connections between graph neural networks and logic programming (e.g., Datalog). He advises PhD students on topics like knowledge graph completion and explainable AI models. Key publications include foundational work on limit Datalog, ontology-based data access, and neural network stability. His work on LogMap (ontology matching) earned the Semantic Web Science 10-Year Award (2021). He actively participates in conferences like IJCAI and AAAI, serving as General Chair for the 2020 Description Logics workshop. Awards: Vice-Chancellor Award (2025), Distinguished Paper Award (IJCAI 2017), Royal Society Fellowship (2009-2017) Grants/Funding: Royal Society Fellowship, VC Innovation Award, MPLS Commercial Impact Award Cuenca Grau co-develops tools like SemFacet (faceted search system) and MeTeoR (temporal Datalog reasoner). His research group emphasizes interdisciplinary collaboration, addressing challenges in scalable reasoning, privacy in knowledge graphs, and industrial ontology management.

• Artificial Intelligence
• Cybersecurity
• Computer Science
• +3 more

Michael Morak is a Professor at the Department of Artificial Intelligence and Cybersecurity, Alpen-Adria-Universität Klagenfurt, affiliated with the Faculty of Technical Sciences. He holds the titles Privatdozent, Diplom-Ingenieur, and Doctor. His research focuses on artificial intelligence, cybersecurity, and computer science, with notable contributions in logic programming, answer set programming, and algorithmic methods. He has led and contributed to multiple research projects funded by entities like the Austrian Research Promotion Agency (FFG) and the Austrian Agency for International Cooperation in Education (OeAD), addressing topics such as multi-agent systems, radar simulation, and educational technology. His work spans theoretical advancements, including reversibility in planning and dynamic programming, as well as practical applications like interactive SQL learning tools (aDBenture). He collaborates with industry partners, including Infineon Technologies, and has published extensively in top-tier conferences and journals. Morak’s teaching emphasizes innovative methods in computer science education, leveraging game-based learning and interactive platforms to enhance student engagement. Key areas of research include formal methods in AI, algorithmic complexity, and the integration of logic programming with real-world systems. His projects often bridge academia and industry, aiming to solve complex challenges in cybersecurity, robotics, and educational innovation.

Alexey PokrovskiyView profile

Lecturer

• Discrete Mathematics
• Combinatorics
• Graph Theory
• +10 more

Alexey Pokrovskiy is a Lecturer in the Department of Mathematics at University College London (UCL), part of the Faculty of Mathematical and Physical Sciences. His research focuses on Discrete Mathematics and Combinatorics, with particular emphasis on graph theory, Ramsey theory, and positional games. He holds a PhD from the London School of Economics (LSE), supervised by Professors Jan van den Heuvel and Jozef Skokan. Prior to UCL, he held postdoctoral positions at Freie Universität Berlin (with Prof. Tibor Szabó) and ETH Zurich (with Prof. Benny Sudakov), and was a lecturer at Birkbeck, University of London. His research interests include Latin squares, Ramsey theory, probability, graph partitioning, Hamiltonian cycles, graph limits, directed graphs, regularity lemmas, hypergraph matchings, algorithms, and positional games. He has contributed to numerous conferences and published extensively on topics such as monochromatic cycle partitioning, rainbow trees, Rota's basis conjecture, and extremal graph theory. Key achievements include resolving Ringel's conjecture, counterexamples to Stein's equi-n-square conjecture, and advancements in understanding Ramsey numbers and graph decomposition. His work often bridges combinatorial theory and algorithmic applications, with a focus on structural graph properties and extremal problems.

• Financial Mathematics
• High-Frequency Financial Econometrics
• Stochastic Volatility Modeling
• +2 more

Giacomo Toscano is a Tenured Assistant Professor of Financial Mathematics at the University of Florence, Department of Economics and Management. His research focuses on high-frequency financial econometrics, stochastic volatility modeling, and applications of Fourier methods in economics. He holds a Ph.D. in Financial Mathematics from Scuola Normale Superiore. Research interests include climate risk measurement and econometric methodologies for analyzing financial markets. His work bridges statistical theory with practical applications, such as volatility forecasting using graph attention networks and developing the FMVol MATLAB library for volatility estimation. Recent studies address microstructure noise, Hawkes-driven volatility models, and historical financial systems like 15th-century Barcelona's foreign exchange markets. Publications span 2020–2025, emphasizing volatility dynamics, estimation techniques, and algorithmic trading strategies. His research often combines advanced mathematical tools with empirical validation using S&P500 and other financial datasets. No scientific awards are explicitly listed, though his active publication record suggests potential recognition in econometrics or financial mathematics. Advising and grants details are not provided in the source text.

• Combinatorial Optimization
• Operations Research
• Algorithms
• +5 more

Adam Kurpisz is a Tenure Track Professor at Bern Business School (BFH) and a senior researcher at the Institute for Operations Research (IFOR) at ETH Zürich. He holds a PhD from Wrocław University of Science and Technology and has held postdoctoral and visiting researcher positions at IDSIA and the Max-Planck-Institut für Informatik. His research bridges theoretical computer science and applied optimization, focusing on combinatorial optimization, semi-algebraic proof systems, and robust optimization. He has secured over CHF 600,000 in grants, including leadership in the Ambizione Junior Research Group. His teaching spans convex optimization, polynomial optimization, and discrete mathematics at ETH Zürich and BFH. He co-founded startups Silencions and Deeptale, advancing to international competitions and securing EU grants. Research interests include leveraging algebraic geometry and Fourier analysis to develop algorithms for optimization problems. He has published extensively on sum-of-squares hierarchies, approximation algorithms, and scheduling under uncertainty. His work has been presented at venues like ISSAC, ICALP, and SIAM Conferences.

Frank R. KschischangView profile

Professor

• Digital Communications
• Coding Theory
• Information Theory
• +4 more

Frank R. Kschischang is a Distinguished Professor of Digital Communication in the Department of Electrical and Computer Engineering at the University of Toronto. He holds the Canada Research Chair in Communications Algorithms and has been a faculty member since 1991. His research focuses on coding theory, digital communications, and information theory, with applications in optical, wireless, and fiber-optic systems. He is a Hans Fischer Senior Fellow at the Institute for Advanced Study (TUM-IAS), Technische Universität München, collaborating on advancing fiber-optic communication capabilities. Education: B.A.Sc. (University of British Columbia, 1985), M.A.Sc. and Ph.D. (University of Toronto, 1988 and 1991). He has held visiting roles at MIT (1997–1998) and ETH Zurich (2005). Academic roles include IEEE Information Theory Society President (2010) and editorial roles for the IEEE Transactions on Information Theory. Research emphasizes error-correcting codes, fiber-optic channel modeling, and nonlinear Fourier transform-based communication. Notable contributions include staircase codes for 100 Gb/s optical networks and rank-metric codes for network coding. His work has led to practical advancements in high-speed optical systems and improved channel capacity understanding. Awards include IEEE Fellow (2006), Killam Research Fellowship (2010), and multiple teaching and service awards. He advises numerous graduate students and collaborates internationally on projects like spectral modulation in fiber optics and machine learning for signal processing. Labs and teams: Leads the Coding and Information Theory group at UofT, focusing on cutting-edge research in communications algorithms and hardware implementation. Collaborates with industry partners like the Communications Research Centre Canada and Inphi Corp.