Find Academic Supervisors

Andreas MaggioriView profile

Researcher

• Online Decision Making
• Machine Learning
• Theoretical Computer Science
• +3 more

Dr. Andreas Maggiori is a Postdoctoral Research Scientist at Columbia University's Data Science Institute, collaborating with Professors Will Ma and Eric Balkanski. His research focuses on the intersection of Online Decision Making, Machine Learning, and Theoretical Computer Science, emphasizing learning-augmented algorithms to enhance decision-making processes. He earned a PhD from École Polytechnique Fédérale de Lausanne (EPFL) , advised by Rudiger Urbanke and Ola Svensson. During his PhD, he visited the Simons Institute at UC Berkeley for the Data-Driven Decision Processes program. He holds a bachelor’s degree in Electrical and Computer Engineering from the National Technical University of Athens . His work spans algorithmic fairness, dynamic clustering, and optimization, with applications in online matching and energy-efficient scheduling. He interned at Google Zurich , collaborating with Nikos Parotsidis and Ehsan Kazemi on algorithmic research. Key research directions include: Learning-augmented algorithms for decision-making Sublinear-time dynamic algorithms Algorithmic fairness in clustering and matching Energy-minimization via speed scaling No scientific awards are explicitly mentioned in the provided text. Advising: No formal advisees listed. Grants: No specific grants detailed. His research is supported through institutional and collaborative affiliations. Labs/Teams: Active in the Data Science Institute at Columbia, with interdisciplinary collaborations across academia and industry (e.g., Google, Simons Institute).

• Spectral Shape Analysis
• 3D Computer Vision
• Geometric Deep Learning
• +4 more

Riccardo Marin is a Postdoctoral Researcher at the Technical University of Munich (TUM) within the Computer Vision Group. His research focuses on Spectral Shape Analysis, Geometric Deep Learning, and 3D Human Modeling. He holds affiliations with ELLIS Society and is an alumni of the Alexander von Humboldt Foundation and Marie Skłodowska-Curie Actions. Marin's work bridges computational geometry and deep learning, addressing challenges in 3D human registration (NICP), avatar creation (Human-3Diffusion), and neural surface modeling (NSF). He has contributed to open-source tools for spectral shape analysis and maintains active GitHub repositories showcasing his research implementations. His research interests span 3D reconstruction, neural fields, and implicit representations, with applications in virtual humans and human-object interaction analysis. Marin's recent work emphasizes scalable registration pipelines and diffusion models for 3D generation. Awards: ELLIS Member, Alexander von Humboldt Alumni, Marie Skłodowska-Curie Fellow Key Contributions: NICP, Human-3Diffusion, NSF framework Education: PhD background (implied from postdoc status), involvement in a Spectral Shape Analysis course at University of Verona

• Distributed Computing
• Theoretical Computer Science
• Parallel Algorithms
• +5 more

Hsin-Hao Su is an Assistant Professor in the Department of Computer Science at Boston College. His research focuses on distributed computing through the lens of theoretical computer science, with particular emphasis on parallelism, locality, and communication. He explores combinatorial optimization, symmetry breaking problems, and bio-inspired algorithms. Su holds a Ph.D. from the University of Michigan (2010-2015), advised by Seth Pettie, and completed a postdoctoral stint under Nancy Lynch at MIT (2015–2017). **Education** Ph.D. in Computer Science, University of Michigan (2010–2015) Postdoctoral Associate, MIT (2015–2017) **Research Interests** Distributed Algorithms and Parallel Computing Graph Theory and Combinatorial Optimization Algorithmic Locality and Communication Efficiency Bio-Inspired Algorithms (e.g., Gossip-Based Protocols) **Professional Contributions** PC Member: ESA 2022, DISC 2021, OPODIS 2020, ICDCS 2020, PODC 2019 Workshop Chair: PODC 2021 Student Travel Awards Organizer: PODC 2018 His recent work includes advancements in distributed shortest path algorithms, correlation clustering, and expander-based routing protocols. He also investigates dynamic distributed task allocation and lower bounds in multi-agent systems.

Adrian HorzykView profile

Professor

• Artificial Intelligence
• Neural Networks
• Associative Systems
• +4 more

Prof. Adrian Horzyk is a distinguished academic at AGH University of Science and Technology, where he leads research in Artificial Intelligence , Associative Systems , and Biocybernetics within the Department of Biocybernetics and Biomedical Engineering . His work bridges Neural Networks , Knowledge Graphs , and Biomedical Applications , with a focus on associative computing and computational intelligence . Key Roles: Professor, AGH University of Science and Technology Labs: Biocybernetics, Cognitive Science, and Artificial Intelligence Laboratory Horzyk's research explores associative knowledge graphs (ANAKG/LANAKG), self-optimizing neural networks (ASONN), and graph-based data structures (AGDS). He investigates how neural plasticity , synaptic delays , and semantic memory can enhance AI systems, including applications in medical diagnostics (e.g., Fetal Alcohol Syndrome classification) and emotional state analysis via chatbots. His recent publications span topics like ECG signal compression , stock market forecasting , and associative reasoning in neural networks . He has developed AVB+ trees for data structure optimization and DASNG (Deep Associative-Semantic Neural Graphs) for efficient data representation. Scientific Collaborations: Collaborated with leaders in AI, including Prof. Leszek Rutkowski and Prof. Ryszard Tadeusiewicz Contributed to international competitions and publications, including monographs like Artificial Associative Systems and Associative Artificial Intelligence (EXIT Publishing, 2013)

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.

• Autonomous Mobile Robots
• Computer Vision
• Reinforcement Learning
• +5 more

Dr. Jaime Boal Martín-Larrauri is an Associate Professor at the School of Engineering (ICAI) of Comillas Pontifical University, affiliated with the Electronics, Control and Communications Department. He holds a PhD in Engineering Systems Modeling (2014) and coordinates the M.Eng. in Intelligent Industry since 2019. His research focuses on robotics, computer vision, reinforcement learning, IoT, energy efficiency, and Industry 4.0 applications. He co-founded Stemy Energy (2018–2024) as CTO, developing energy efficiency solutions. Education: PhD in Research in Engineering Systems Modelling, Comillas Pontifical University (2014) M.Sc. in Research in Engineering Systems Modelling, Comillas Pontifical University (2012) Industrial Engineer (Electronics), Comillas Pontifical University (2010) Research Interests: His work integrates hardware-software systems, emphasizing robotic control, energy-efficient IoT platforms, and AI-driven solutions for industrial automation. Key projects include drone detection systems, smart energy grids, and reinforcement learning for industrial robots. Publications & Trends: Recent articles emphasize robotic learning, energy efficiency, and AI interpretability. Notable contributions include reinforcement learning robustness improvements and IoT energy platform architectures. Awards: Multiple Chair for Smart Industry Awards (2021–2024) for outstanding student projects under his supervision Extraordinary End-of-Degree Award (2011) Grants & Labs: Lead projects funded by ENDESA, Ferrovial, MIT, and the Madrid Regional Government. Active in the Institute for Research in Technology (IIT), collaborating on robotics and energy systems. Labs/Teams: Contributes to the IIT’s robotics and IoT research groups, focusing on autonomous systems and smart industry applications.

• Semantic Web
• Linked Data
• RDF/S Provenance
• +4 more

Irini Fundulaki is a prominent researcher in the Semantic Web and Linked Data domains. Her work focuses on data provenance, access control, and benchmarking for RDF systems. She has collaborated extensively with institutions across Europe and contributed to annual Ontology Alignment Evaluation Initiatives. Research Interests: Dr. Fundulaki's research spans Provenance management in RDF/S datasets Usage control policies via reification Ontology alignment and semantic interoperability Benchmarking tools for linked data systems XML access control frameworks Publication Trends show sustained contributions to semantic data management, with recent works on SPARQL provenance (2024), Geo-Spatial link discovery (2023), and precision medicine ecosystems (2020).

Paul S. LaFollette, Jr.View profile

Associate Professor

• Algorithms
• Combinatorial Generation
• Program Visualization
• +4 more

Paul S. LaFollette, Jr. MD serves as Associate Professor in the Department of Computer and Information Sciences within Temple University's College of Science & Technology. With a unique dual background in medicine and computer science, he has maintained continuous faculty appointment since 1982 (promoted to Associate Professor in 1987) and served as Department Chair from 2002-2005. His current teaching responsibilities include CIS 2168 (Data Structures) as evidenced by active course materials. Education: B.S. in Mathematics, Duke University (1969) M.D., Temple University School of Medicine (1974) LaFollette's research traverses two distinct domains: early biomedical engineering work in ultrasound diagnostics (1970s-1980s) and sustained contributions to theoretical computer science (1990s-2000s). His primary expertise lies in combinatorial algorithms, particularly loopless generation techniques for trees, permutations, and combinatorial structures. Significant secondary interests include program visualization systems for education and medical imaging physics. This dual trajectory reflects his transition from medical practice to computer science academia. His 15 most recent publications reveal a clear evolution from biomedical engineering (1980s-early 1990s) to pure computer science (mid-1990s onward), with nearly all post-1998 work focusing on combinatorial generation algorithms. The overwhelming majority appear in top theoretical computer science venues, demonstrating sustained contributions to discrete mathematics and algorithm design. Medical imaging research continues to inform his approach to visual representation problems. Scientific Recognition: NIH Post-Doctoral Fellowship (1975-1977) LaFollette secured multiple competitive grants including two Ben Franklin Partnership awards ($40,000 each as PI for Advanced Visual Automata Systems I & II) and NIH funding ($39,800 as Senior Investigator for ultrasound artifact research). While no formal advising records appear in source materials, his program visualization research (1998-2000) directly supported classroom instruction through tools like the Visual Interface for C/C++ animation. His departmental leadership as Chair (2002-2005) further demonstrates institutional commitment. Though no dedicated lab is mentioned, his research manifests through collaborative projects with J. Korsh (algorithms) and R. Sangwan (visualization), suggesting a small research group focused on combinatorial generation and educational tools. Current course development indicates ongoing engagement with computer science pedagogy despite his 1947 birth year.

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.

Peter AdrianView profile

Associate Professor

• Machine Learning
• Signal Processing
• Computer Vision
• +3 more

Peter Adrian is an Associate Professor in the Department of Mathematics and Systems Engineering at Florida Institute of Technology's College of Engineering and Science. He also serves as Affiliate Faculty in the Department of Electrical Engineering and Computer Science. His research focuses on machine learning applications in signal processing, computer vision, and statistical analysis. He leads projects in environmental sound classification, seismic event discrimination, and structural health monitoring, often leveraging edge computing and deep learning frameworks. Adrian's work bridges theoretical advancements with practical systems engineering, such as developing acoustic situational awareness tools (e.g., ACE plugin) and rail infrastructure monitoring systems. Key contributions include physically-augmented deep learning models for seismic analysis and multimodal datasets for firearm acoustics research. His publications span over 5 decades, addressing challenges in sensor networks, geospatial modeling, and shape analysis using information geometry principles. While no formal awards are listed, his prolific output demonstrates sustained academic engagement. Adrian’s research website provides further details on ongoing projects, though currently inaccessible. His interdisciplinary approach integrates mathematics, computer science, and engineering to solve real-world problems in transportation safety, environmental monitoring, and astrophysics.

• Machine Learning
• Computer Vision
• Safety-Critical Systems
• +7 more

Ran Wei is a researcher at the University of Science and Technology of China, Department of Chemistry, with extensive contributions across interdisciplinary domains. His work bridges Computer Science , Biomedical Imaging , and Transportation Systems , focusing on active inference models, digital twins, and multimodal learning. Academic Affiliation : University of Science and Technology of China, Department of Chemistry Research Themes : Ran Wei's research explores active inference for adaptive systems, multimodal fusion in computer vision, and domain-specific knowledge graphs for engineering applications. He also investigates federated learning on heterogeneous networks and automated safety analysis for cyber-physical systems. Recent Article Trends : His publications from 2025–2021 span AI-driven engineering (e.g., construction project management), biomedical imaging (lesion detection, radiomics), and transportation systems (driver behavior modeling). Methodologically, he integrates transformers , Bayesian inference , and active learning frameworks. Scientific Collaborations : Ran Wei collaborates with experts in autonomous vehicles (Alfredo García, Anthony D. McDonald), biomedical engineering (Qiaolin Ye, Yifan Cai), and safety-critical systems (Tim Kelly, Simon Foster).

Vassilios PeristerasView profile

Associate Professor

• Business Information Systems
• eGovernment
• Digital Transformation
• +7 more

Vassilios Peristeras is an Associate Professor at the International Hellenic University (School for Science and Technology) and Associate Research Fellow at KU Leuven (Public Governance Institute). His work bridges business, technology, and public administration through research in Digital Transformation , eGovernment , and Semantic Web Technologies . Focus areas: Public Sector Innovation, Interoperability, Open Data Key projects: JustReDi, GR digiGOV-innoHUB Recent publications: Data-centric governance, AI ethics, and talent matching His research explores knowledge graphs , government big data ecosystems , and responsible AI adoption. Articles emphasize semantic technologies for policy documentation, cybersecurity cooperation , and digital transformation barriers in education and public sectors. Contact: dorg@ihu.edu.gr

Fabrizio GrandoniView profile

Professor

• Algorithms Design and Analysis
• Approximation Algorithms
• Parameterized Algorithms
• +3 more

Fabrizio Grandoni is a Professor in approximation algorithms and Area Responsible at the Department of Innovative Technologies (DTI) of SUPSI. Previously, he held positions at institutions including the University of Rome Tor Vergata (2007–2011), Max Planck Institut für Informatik (Germany), and École Polytechnique Fédérale de Lausanne. He earned a Master’s (cum laude) and PhD in Computer Science from the University of Rome Tor Vergata, with a Marie-Curie Fellowship at the Max Planck Institute during his PhD. His research focuses on algorithms design and analysis, particularly approximation algorithms, parameterized algorithms, distributed systems, dynamic algorithms, and fault-tolerant data structures. He has authored over 100 papers, co-authored with 80+ researchers, achieving 3,000+ citations and an h-index of 31. Notable awards include the ERC Starting Grant (2011), Best Paper Award at STOC (2010), and the EATCS-IPEC Nerode Prize (2017). He leads projects on network design, clustering algorithms, and optimization, and is affiliated with the IDSIA (Dalle Molle Institute for Artificial Intelligence).

• Financial Mathematics
• Stochastic Analysis
• Systemic Risk
• +3 more

Prof. Thilo Meyer-Brandis is a Professor of Mathematics at the University of Munich, affiliated with the Department of Mathematics and the Workgroup Financial and Insurance Mathematics. His research focuses on stochastic analysis, financial mathematics, systemic risk modeling, and quantitative finance. He teaches advanced courses such as Finanzmathematik III , Stochastic Calculus and Arbitrage Theory , and Advanced Topics in Mathematical Finance . His research interests include stochastic differential equations (SDEs), systemic risk in financial networks, asset pricing dynamics, and applications of machine learning in finance. Notable contributions involve modeling asset price bubbles using deep learning, analyzing McKean-Vlasov SDEs, and studying fire sales contagion in financial systems. Recent work explores liquidity-based market models, optimal risk transfer equilibria, and network-based contagion effects. His publications span journals like Mathematical Finance , SIAM Journal on Financial Mathematics , and Finance and Stochastics . Teaching responsibilities include courses on fixed income markets, credit derivatives, and mathematical finance seminars. He actively contributes to the quantLab initiative, fostering quantitative research and education in finance and insurance.

• Reinforcement Learning
• Machine Learning
• Artificial Intelligence
• +3 more

Marco Bagatella is a doctoral researcher at the Institute of Machine Learning (ETH Zürich), focusing on advanced machine learning and artificial intelligence research. His work spans reinforcement learning, behavioral cloning, and causal inference, often addressing challenges in generalization, exploration, and policy adaptation. Contact: marco.bagatella@inf.ethz.ch Specializes in Reinforcement Learning (including offline and multi-task settings) Expertise in causal modeling and counterfactual data augmentation Investigates graph neural networks for biological systems Active contributor to AI/ML publications (15+ recent works) His research explores problem space transformations, optimal transport for zero-shot imitation learning, and temporal logic-based exploration. Current projects focus on improving policy robustness and adaptability across diverse domains. Scientific awards: No formal honors listed yet. Collaborates with ETH Zurich's machine learning teams on cutting-edge algorithm development and theoretical analysis.