Find Academic Supervisors

Márton KarsaiView profile

Associate Professor

• Network Science
• Computational Social Science
• Data Science
• +5 more

Márton Karsai is an Associate Professor and Head of the Department of Network and Data Science at Central European University (CEU). He also serves as a Research Professor at the Rényi Institute of Mathematics (Hungary) and Editor-in-Chief of Advances in Complex Systems . His work focuses on computational social science, human dynamics, and data-driven modeling of socioeconomic systems. Karsai holds advanced degrees including a DSc from the Hungarian Academy of Sciences and an HDR (Habilitation) in Computer Science from École Normale Supérieure de Lyon. His research integrates temporal networks, human mobility, and social contagion phenomena, often using large-scale datasets from digital platforms and wearable sensors. Notable projects include studies on evacuation behavior during disasters, vaccination hesitancy, and urban socioeconomic stratification. Karsai leads interdisciplinary initiatives like the DyLNet project, which examines social interactions and language development in preschool environments through sensor technology. Recent publications highlight innovations in network clustering algorithms (PASCO), epidemic modeling with generalized contact matrices, and the application of machine learning to infer socioeconomic status from satellite imagery. His work bridges computational methods with real-world challenges in public health, urban planning, and humanitarian development.

• Neuroimaging
• Developmental Neuroscience
• Biomedical Engineering
• +3 more

Weili Lin is the Dixie Lee Boney Soo Professor of Biomedical Engineering and Radiology at the University of North Carolina. Her research focuses on advancing neuroimaging techniques and understanding developmental brain processes, particularly in pediatrics. She leads projects integrating machine learning with medical imaging to study neurocognitive development, prenatal exposures, and functional connectivity. Key research interests include MRI technology innovations, longitudinal brain mapping, and AI-driven analysis of neurodevelopmental data. She collaborates on large-scale initiatives like the UNC/UMN Baby Connectome Project, investigating infant brain maturation and environmental impacts. Her work spans clinical and computational domains, addressing challenges in fetal imaging, motion correction, and harmonization of multi-site datasets. Recent studies explore the effects of phthalates and opioids on infant neurodevelopment, leveraging GANs, transformers, and federated learning for predictive modeling. Lin’s contributions include developing pipelines like iBEAT V2.0 for infant cortical surface reconstruction and frameworks for functional parcellation. She emphasizes translational research bridging imaging technology and clinical insights.

Yingxue ZhangView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Meta-Learning
• +5 more

Yingxue Zhang is an Assistant Professor in the Computer Science Department at Binghamton University's School of Computing. She holds a PhD in Data Science from Worcester Polytechnic Institute (2022), an MS in Financial Engineering from Stevens Institute of Technology (2018), and a BS in Computer Science from Shanghai Jiao Tong University (2016). Her research focuses on deep learning, meta-learning, imitation learning, and spatial-temporal data mining, with applications to smart cities and public safety. She has advised PhD students Xinbo Zhao and Yuhang Liu, and an MS student Junjie Xu. Her work includes developing generative adversarial networks (GANs) for urban traffic prediction and spatial-temporal data analysis, as well as large-scale solar energy forecasting. She serves on technical program committees for top conferences like KDD, NeurIPS, and ICLR. She teaches courses in data mining, machine learning, and information systems. Education PhD in Data Science, Worcester Polytechnic Institute (2022) MS in Financial Engineering, Stevens Institute of Technology (2018) BS in Computer Science, Shanghai Jiao Tong University (2016) Research Interests Deep learning and AI techniques for spatial-temporal big data Urban dynamics prediction using large language models Generative adversarial networks (GANs) for traffic estimation Human behavior analysis and decision-making modeling Meta-learning and multi-task reinforcement learning Recent Publications Her research highlights include UrbanMind (KDD 2025), C3-GAN+ (TKDD 2024), and SolarCube (NeurIPS 2024), which demonstrate innovations in spatio-temporal modeling, urban systems analysis, and energy forecasting. Her work often bridges theory and real-world applications in smart cities. Collaborations & Leadership Dr. Zhang actively participates in organizing top-tier conferences, including serving as a TPC member for KDD 2025, NeurIPS 2025, ICLR 2025, and others. She leads a research group at Binghamton focused on advancing AI-driven solutions for urban and environmental challenges.

Evgeny KharlamovView profile

Associate Professor

• Artificial Intelligence
• Knowledge Representation
• Machine Learning
• +3 more

Evgeny Kharlamov is an Associate Professor at the University of Oslo and Senior Research Expert at Bosch AI. His research focuses on Neuro-Symbolic AI, Knowledge Representation (knowledge graphs, rules, ontologies), data integration, and applications in manufacturing, energy, and corporate intelligence. He has published over 190 papers in venues like IJCAI, AAAI, WWW, and holds an h-index of 41 with ~6K citations. Key awards include ranking 7th in the AI 2000 Knowledge Engineering list (2024) and top 111 German AI researchers (2023). **Education & Previous Roles**: Previously held positions at the Universities of Oxford and Bozen-Bolzano, INRIA Saclay, and Telecom Paris. His expertise spans individual research to managing teams at Bosch AI and coordinating a research center at the University of Oslo. He raised ~6.7M EUR in EU/UK research funding and led multiple large-scale EU projects. **Research Interests**: Combines theoretical advances in AI with industrial applications, emphasizing explainable and scalable solutions. Key areas include knowledge graphs, semantic web technologies, and LLM-centered systems like RAG. **Awards**: Notable recognitions include the 2020 Best Research Paper Award at ESWC, multiple ISWC awards (2015–2017), and IBM’s Best Master’s Thesis Award (2008). His PhD thesis was honored by AI*IA (2012). **Grants & Projects**: Co-secured ~6.7M EUR in funding. Led projects in semantic data integration (e.g., Siemens streaming data), digital twins, and predictive quality monitoring in manufacturing. **Teaching**: Taught courses on Digital Ecosystems (2024), Semantic Web (2023), Knowledge Graphs (2023), and Foundations of Databases (2007). **Labs/Teams**: Active in Bosch AI’s research initiatives and the University of Oslo’s data science teams, focusing on industrial AI applications.

Shizhe ChenView profile

Assistant Professor

• Statistics
• High-Dimensional Data Analysis
• Point Processes
• +3 more

Shizhe Chen is an Assistant Professor in the Department of Statistics at the University of California, Davis. His research focuses on statistical methodology for high-dimensional data, point processes, network reconstruction, and machine learning applications in neuroscience and biostatistics. He holds a Ph.D. and teaches advanced statistical courses such as STA 290. His work emphasizes flexible modeling approaches for complex systems and nonparametric methods for independence testing. Key research interests include: High-dimensional statistical inference and graphical models Point process modeling with applications to neuroscience and social networks Development of assumption-light screening techniques for Hawkes processes Network reconstruction using ordinary differential equations Recent work explores temporal dynamics in hippocampal sequences and distribution-free statistical tests for modern data structures. His methods bridge theoretical advancements with practical applications in computational biology and econometrics.

Henning S. MortveitView profile

Associate Professor

• Graph Dynamical Systems
• Simulation Models
• Network Theory
• +3 more

Henning S. Mortveit is an Associate Professor at the University of Virginia, affiliated with the Department of Systems and Information Engineering and the Network Systems Science and Advanced Computing division within the UVA Biocomplexity Institute. He holds a Ph.D. in Mathematics from the Norwegian University of Science and Technology (2000) and previously worked at Los Alamos National Laboratory and Virginia Tech. His research focuses on graph dynamical systems , including their combinatorial and algebraic structures, stability, resiliency, and applications in large-scale simulation models. His work bridges discrete and continuous dynamical systems, emphasizing structure-to-function theories. Mortveit's recent publications explore topics such as attractor stability in biological systems, cycle equivalence in GDS, and hardware-software co-design for supercomputing. His methodological contributions span from foundational mathematical frameworks to applied crisis response modeling. He is based at the UVA Biocomplexity Institute, where he develops simulation frameworks and advanced computing solutions for complex systems analysis.

Rob WorleyView profile

Research Fellow

• Robotics
• Autonomous Systems
• Sensor Networks
• +3 more

Dr. Rob Worley is a Research Fellow at the University of Sheffield’s School of Electrical and Electronic Engineering since 2022, following his PhD studies initiated in 2018. His research focuses on robot navigation and localization within buried pipe networks, with a particular emphasis on acoustic and visual sensing technologies. He leads the Pipebot Patrol project, advancing autonomous inspection systems for water and sewer infrastructure. His work integrates state estimation, artificial intelligence, and robotics to address challenges in feature-sparse underground environments. Worley’s research interests span robot localization algorithms, SLAM (Simultaneous Localization and Mapping), and sensor fusion, with applications to cylindrical pipe networks. His projects leverage cylindrical regularity and hybrid metric-topological models to enhance navigation accuracy. Notable contributions include acoustic echo localization using microphone arrays and visual SLAM techniques for sewer networks. He has published extensively on topics like error detection in particle filters and condition monitoring in underground pipes. Advising and grants: While no specific advisees are listed, his projects likely involve collaborative research teams. He has contributed to the Pipebots initiative since its inception, focusing on localization and sensing advancements. Labs/Teams: Active member of the University of Sheffield’s robotics research group, contributing to the development of autonomous inspection technologies for infrastructure maintenance.

Frank van HarmelenView profile

Professor

• Artificial Intelligence
• Semantic Web
• Knowledge Representation
• +3 more

Prof. Frank van Harmelen is a Full Professor at Vrije Universiteit Amsterdam, holding positions in the Faculty of Science (Artificial Intelligence), Network Institute, and Knowledge Representation and Reasoning department. He also serves as an Adjunct Professor at Wuhan University of Science and Technology and Wuhan University Faculty of Information Sciences since 2014 and 2015, respectively. He earned his PhD in Artificial Intelligence from the University of Edinburgh (1985–1989), focusing on meta-level inference efficiency. His research spans Semantic Web, Knowledge Representation, Ontology Engineering, and neurosymbolic AI, with contributions to UN Sustainable Development Goals related to technological innovation. His recent work emphasizes knowledge graphs (e.g., KG4NH for nutrition/health, OfficeGraph for IoT), neurosymbolic integration, and applications in healthcare, sustainability, and education. He has led projects like the HumanE AI Network and contributed to datasets such as MetaLink and equivalence class analyses in LOD. Awards: Fellow of European AI Society (2007), Member of Royal Dutch Academy of Sciences (2017), Ten Year Impact Award (2012) Activities: Steering roles in international conferences, member of prestigious academies, and involvement in EU/US Agent Markup initiatives. Teaching: Courses on AI in Health and Introduction to AI. Grants/Projects: Principal Investigator for HumanE AI Network (2020–2024), contributed to IoT and smart grid solutions. His 292+ publications include foundational texts like the Semantic Web Primer and recent advancements in knowledge graph completion and guideline-informed reinforcement learning.

John FernleyView profile

Research Fellow

• Probability Theory
• Statistical Mechanics
• Random Graphs
• +5 more

John Fernley is a Research Fellow at the University of Warwick's Centre for Research in Statistical Methodology (CRiSM). His research focuses on particle systems on large random graphs, including opinion dynamics, infection spread, branching processes, and dynamic queueing systems. He holds a PhD from the University of Bath and has held postdoctoral positions at ENS de Lyon and the Alfréd Rényi Institute of Mathematics. His educational background includes a PhD in Mathematics from the University of Bath (details unspecified), followed by postdoctoral research in France and Hungary. His work bridges probability theory and network science, with particular emphasis on dynamic systems evolving on complex networks. Research interests center on phase transitions in contact processes, voter models on scale-free networks, and adaptive network structures. His recent talks include analyses of immunization strategies on power-law trees and cutoff phenomena in multitype networks. He maintains active engagement through ORCiD and Google Scholar. No grants or awards are listed, though his publications indicate active contributions to stochastic processes and network dynamics. He is affiliated with CRiSM, part of Warwick's Department of Statistics, and collaborates internationally.

Benoit BouletView profile

Professor

• Systems and Control
• Reinforcement Learning
• Robotics
• +5 more

Professor Benoit Boulet is a Full Professor in the Department of Electrical & Computer Engineering at McGill University and serves as Director of the McGill Engine Centre for Technological Innovation and Entrepreneurship. His research focuses on systems and control, with applications in robotics, automation, smart grids, and electric vehicles. He is affiliated with the Systems and Control Unit and the CIM Research Group. His work spans reinforcement learning, time series forecasting, anomaly detection, and traffic signal control. Notable contributions include advancements in electric vehicle transmission systems, autonomous driving trajectory prediction, and energy management systems for smart grids. His research emphasizes practical applications in transportation, renewable energy, and industrial automation. Key technical areas include: Reinforcement learning frameworks for control systems Multi-agent systems and meta-learning Electric vehicle powertrain design Graph-based trajectory prediction Energy-efficient building systems His recent publications (2023-2025) highlight innovations in causal discovery algorithms, fault detection methodologies, and adaptive control strategies. Current initiatives focus on bridging AI advancements with real-world control engineering challenges.

Sungho ShinView profile

Assistant Professor

• Mathematical optimization
• Control theory
• Machine learning
• +3 more

Sungho Shin is an Assistant Professor in the Department of Chemical Engineering at the Massachusetts Institute of Technology (MIT) . He leads the Shin Group, focusing on mathematical optimization, control theory, machine learning, and high-performance computing for applications in process systems engineering and energy systems. Education : PhD in Chemical Engineering (University of Wisconsin-Madison), B.S. in Mathematics and Chemical Engineering (Seoul National University) Postdoc : Argonne National Laboratory (Mathematics and Computer Science Division) His research addresses scalable algorithms for decision-making in complex systems, GPU computing for nonlinear optimization, data-driven control methods, and decarbonization of energy systems through modeling, optimization, and software development. His group has produced award-winning publications, including the 2022 Outstanding Paper Award from Mathematical Programming Computation. Key software contributions include MadNLP.jl (GPU-accelerated nonlinear programming solver) and ExaModels.jl (graph-structured optimization modeling). His team maintains local computing resources for GPU development, including NVIDIA GV100 and AMD Radeon VII systems. Scientific Awards : W. David Smith, Jr. Graduate Publication Award AIChE CAST Directors’ Student Presentation Award IFAC ADCHEM Young Author Award IFAC NMPC Young Author Award Korea Presidential Science Fellowship Kwanjeong Fellowship Grainger Wisconsin Distinguished Graduate Fellowship

Johan UganderView profile

Associate Professor

• Social Networks
• Social Systems
• Causal Inference
• +5 more

Johan Ugander is an Associate Professor of Management Science & Engineering (MS&E) at Stanford University, affiliated with the School of Engineering. He is also a member of the Social Algorithms Lab (SOAL), the Institute for Computational & Mathematical Engineering (ICME), and the Center for Computational Social Science. Prior to Stanford, he held a postdoctoral position at Microsoft Research and was affiliated with Facebook's Data Science team. He received his Ph.D. in Applied Mathematics from Cornell University (2014), with additional degrees from the University of Cambridge, Lund University, and Deep Springs College. His research focuses on algorithmic and statistical frameworks for analyzing social networks, causal inference, and large-scale data. Key areas include graph theory, machine learning, and network experimentation. He has developed methodologies for seed set expansion, causal inference under network interference, and scalable computational tools for social systems analysis. Ugander teaches courses such as Networks (MS&E 135), Social Algorithms (MS&E 231), and Data Privacy & Ethics (MS&E 234). He has advised numerous Ph.D. students and postdocs in areas spanning computational social science, network science, and machine learning. His work has been supported by NSF, ARO, Cisco, and the Hellman Foundation. Notable contributions include foundational studies on social contagion, graph partitioning, and the analysis of Facebook's social graph structure. He is on sabbatical at Yale University for 2024–25.

• Bioinformatics
• Genomics
• Sequence Analysis
• +3 more

Antoine Limasset is a Researcher (Chargé de recherche) at CNRS, affiliated with the CRIStAL research center and the BONSAI team at Université de Lille, France. His work focuses on computational methods and data structures in sequence bioinformatics, particularly addressing challenges in genome assembly, metagenomics, and third-generation sequencing data analysis. He holds a Ph.D. in Computer Science from Université de Rennes 1 (2014-2017), supervised by Pierre Peterlongo and Dominique Lavenier, followed by a postdoctoral position at Université Libre de Bruxelles (2017-2018). Key research areas include de Bruijn graph algorithms, k-mer indexing, and scalable genomic data processing. Limasset has developed tools like BLight for efficient k-mer management, STRONG for metagenomic strain resolution, and ELECTOR for evaluating long-read correction methods. He actively contributes to conference committees, including SPIRE, RECOMB, and ACM-BCB, and leads the ANR JCJC grant on graph structures for third-generation sequencing exploration. He advises two Ph.D. students: Coralie Rohmer (on multiple alignment algorithms for third-gen sequencing) and Léa Vandamme (on indexing third-gen sequencing datasets). His publications span high-impact venues like ISMB, WABI, and Genome Biology, with a focus on optimizing algorithms for genomic data scalability and accuracy.

Pascal MaillardView profile

Professor

• Probability Theory
• Mathematical Physics
• Statistical Mechanics
• +3 more

Pascal Maillard is a Professor at the Department of Mathematics at Université Toulouse III - Paul Sabatier, affiliated with the Institut de Mathématiques de Toulouse (CNRS UMR5219). He has been a Junior member of the Institut Universitaire de France since October 2021. His research focuses on probability theory, particularly branching random walks, multiplicative cascades, and random energy models, with applications in statistical mechanics and mathematical physics. Maillard has coordinated the ANR-DFG funded project REMECO (2021-2024), investigating extreme value distributions, partition functions at complex temperatures, and optimization algorithms in random energy models. He has also co-organized the annual 'Les probabilités de demain' conference (2016–2019) to support early-career researchers in probability. His teaching spans advanced modules in stochastic modeling, probability theory, and mathematical statistics at both undergraduate and graduate levels. He is currently developing lecture notes on branching random walks and multiplicative cascades based on his Master's course at Université Paris-Sud. Research Interests: Extremal processes in branching systems, random energy landscapes, stochastic optimization, and applications to statistical physics. Awards: Junior member of Institut Universitaire de France (2021–present). Advising: Supervised three PhD students and multiple research projects in probability theory, including studies on SLE, Markov chain mixing times, and Erdős-Rényi graphs. Labs/Teams: Co-lead of REMECO project with Lisa Hartung, involving institutions in Toulouse and Mainz.

Laura I. TomaView profile

Professor

• Computer Science
• Geographic Information Systems (GIS)
• Algorithms
• +4 more

Laura I. Toma is a Professor of Computer Science at Bowdoin College. Her research focuses on cache-efficient algorithms for large geospatial data, particularly applications in GIS such as terrain analysis, visibility, flooding, and least-cost-path surfaces. She emphasizes resource-efficient approaches backed by theoretical guarantees and practical implementation, with a goal of contributing to open-source GIS software. Education: PhD (2003), MS (2001) from Duke University; BS & MS (1997) from University Politehnica of Bucharest, Romania. Affiliations: Department of Computer Science, Bowdoin College; previously supported by NSF Award 0728780 (2007–2013). Teaching: Courses include Algorithms, GIS Algorithms, Data Structures, and High-Performance Computing. Research Interests: Computational geometry, I/O-efficient algorithms, parallel computing, geospatial data processing, and algorithm optimization. Her work bridges theory and practice, addressing challenges in massive terrain datasets. Key Contributions: Developed algorithms for viewshed computation (SIGSPATIAL 2018), Terracost for least-cost paths (ACM GIS 2006), and I/O-efficient flow modeling (GeoInformatica 2003). Awards: NSF funding (2007–2013), Best Paper Award at ACM SIGSPATIAL GIS 2009 (collaborative work). Students: Advised over 20 graduate and undergraduate researchers, including honors theses on viewsheds, grid simplification, and algorithmic self-efficacy studies. Labs/Teams: Collaborates on Bowdoin's High-Performance Computing (HPC) initiatives and contributes to open-source GIS projects like GRASS.