Find Academic Supervisors

Santiago SegarraView profile

Associate Professor

• Network Theory
• Machine Learning
• Graph Signal Processing
• +5 more

Santiago Segarra is the W. M. Rice Trustee Associate Professor in the Department of Electrical and Computer Engineering at Rice University, with courtesy appointments in Computer Science and Statistics. He joined Rice in 2018 and collaborates with Microsoft Research since 2022. His expertise spans network theory, machine learning, graph signal processing, and optimization. Segarra earned his B.Sc. in Industrial Engineering from ITBA (2011), and M.S. and Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2014-2016), followed by a postdoc at MIT (2016-2018). Research Focus: His work integrates algebraic topology, signal processing, and machine learning to analyze networked systems. Key areas include social/technological network clustering, graph-based data analysis, and applications in neuroscience and communication networks. Recent projects address fair graph learning, distributed GNN training, and network topology inference. Awards: Penn’s Wolf Award for Best Dissertation (2017), Argentine National Engineering Honors (2011), and ITBA’s Best Thesis Award (2011). Grants/Sponsors: Supported by NSF, ONR, and industry collaborations. Labs/Groups: Leads the Rice Wireless group and collaborates with Microsoft Research on applied network science. Advises students in interdisciplinary research combining theory and real-world applications.

Antonia Krefeld-SchwalbView profile

Assistant Professor

• Cognitive Science
• Consumer Behavior
• Sustainable Marketing
• +5 more

Antonia Krefeld-Schwalb is an Assistant Professor at the Department of Marketing Management, Rotterdam School of Management, Erasmus University. With a background in cognitive science and management, her research bridges computational modeling, eye-tracking, and consumer decision-making to address sustainability challenges. Current Affiliation: Assistant Professor, Rotterdam School of Management Research Focus: Sustainable consumer behavior, decision-making processes, and methodological improvements Key Collaborations: Columbia University, University of Geneva, Erasmus Sustainability Program Her cognitive science training informs methodological approaches like mouse/eye tracking and computational modeling applied to marketing problems. She investigates structural parameter interdependencies, external validity threats in surveys, and climate risk communication effectiveness. Recent research trends include climate adaptation strategies, sustainable behavior interventions, and meta-scientific analyses of statistical practices in consumer research. She advocates for heterogeneous population sampling and preregistration to enhance validity. Scientific Honors: Veni Grant (NWO) She develops targeted sustainability interventions through collaborations like the Erasmus Sustainability Program. Her work appears in journals such as PNAS, Journal of Marketing Research, and Psychological Review.

• Algorithms
• Computational Complexity
• Theoretical Computer Science

Yassine Ghannane is a Research Fellow at the Department of Computer Science , University of Copenhagen , specializing in Algorithms and Complexity . University: University of Copenhagen Department: Department of Computer Science Research Focus: Theoretical computer science, permutation-based evolutionary algorithms, computational complexity His recent work includes runtime analysis and theory development for permutation-based evolutionary algorithms, as well as module-based neural network mapping heuristics. Publications span 2022–2024 with interdisciplinary applications in machine learning and optimization. Contact: yagh@di.ku.dk | Office: Universitetsparken 1, 2100 København Ø, Denmark

• Combinatorial Optimization
• Scheduling Theory
• Operations Research
• +3 more

Professor Daniel Oron is affiliated with the University of Sydney, where he joined in 2004 after completing his PhD in Operations Research at the Hebrew University of Jerusalem. His research focuses on Combinatorial Optimization, particularly Scheduling Theory, addressing challenges like batch scheduling with setups, customer delivery models, and scheduling under deteriorating conditions. He teaches courses such as Quantitative Business Analysis, Management Science, and Business Analytics Honours. His editorial role includes serving on the board of the Journal of Industrial & Management Optimization . Recent research contributions span multi-agent scheduling, energy recharging in scheduling, and coupled task optimization. He advises two current PhD students: Johnson (Two-agent scheduling problems) and Renjie Yu (Multi-agent scheduling with parallel batching). Publications highlight advancements in scheduling algorithms, resource allocation, and optimization under constraints. Notable works include minimizing late jobs with step-learning models and analyzing parameterized complexity in single-machine scheduling.

• Malware Analysis
• Symbolic Execution
• Machine Learning
• +3 more

Charles-Henry Bertrand Van Ouytsel is a Research Assistant and Visiting Lecturer at Université catholique de Louvain , affiliated with the Louvain Polytechnic School (EPL) and the Computer Engineering Center (INGI) . His work focuses on malware analysis , symbolic execution , and machine learning for cybersecurity applications. Research Areas : Packing detection, intrusion detection systems, side-channel security, and adversarial machine learning. Teaching : Involved in courses like Secured systems engineering (LINFO2144) and Software engineering and programming systems seminar (LINFO2359) . His recent publications emphasize malware obfuscation techniques and security evaluation frameworks . Collaborations with Axel Legay and others highlight his contributions to tool development (e.g., Packing-Box , SEMA ). No scientific awards are explicitly mentioned.

Quan Quan TanView profile

Research Fellow

• Cryptography
• Symmetric-key Cryptanalysis
• Automated Cryptanalysis
• +2 more

Quan Quan Tan is a Research Fellow at Nanyang Technological University (NTU), Singapore, specializing in symmetric-key cryptanalysis and automation tools. He previously served as a Cybersecurity Engineer at CSIT, Singapore for nearly two years. His educational background includes: Ph.D. in Mathematical Sciences from NTU (2023) under Prof. Thomas Peyrin. Thesis: "Cryptanalysis of Lightweight Symmetric-Key Cryptographic Algorithms" M.Sc. in Mathematical Sciences from NTU, with research on optimization techniques for block cipher hardware implementations B.Sc. in Mathematical Sciences from NTU Dr. Tan's research focuses on automation in cryptographic design and analysis, emphasizing secure symmetric-key primitives and advanced cryptanalysis tools. His work bridges theoretical cryptography with practical security engineering through algorithm development and vulnerability assessment. Analysis of his 2020-2025 publications reveals dominant themes in symmetric-key cryptanalysis, including innovative meet-in-the-middle attacks, differential cryptanalysis frameworks, and automated verification systems. His contributions span attack methodologies (e.g., higher-order differential-linear techniques), tool development (Trail-Estimator), and novel cipher design (uKNIT-BC), demonstrating consistent advancement in lightweight and low-latency cryptographic systems.

Pierre FlenerView profile

Professor

• Artificial Intelligence
• Constraint Programming
• Discrete Optimization
• +5 more

Pierre Flener is a Professor at the Department of Information Technology, Division of Computing Science at Uppsala University. He leads the Optimisation Group and is a member of the Centre for Interdisciplinary Mathematics. His work focuses on constraint programming and discrete optimization, addressing complex scheduling, routing, and resource allocation challenges. Flener is an Officer of the Order of Merit of Luxembourg and co-founder of NordConsNet, the Nordic Network for Constraint Programming researchers. Research Interests: Flener’s research spans constraint programming, combinatorial optimization, and algorithm design. He develops models and tools for automated decision-making in domains like air traffic management, sensor networks, and industrial robotics. His work emphasizes practical applications, leveraging constraint satisfaction techniques to solve real-world puzzles such as vehicle routing and personnel allocation. Key Contributions: Flener has authored over 100 publications on constraint solving, symmetry breaking, and CP-based approaches to industrial problems. Notable projects include airspace sectorization optimization, energy-efficient sensor networks, and financial portfolio design. He has led initiatives like Auto-Tabling for MiniZinc and collaborated on CP applications in bioinformatics and image processing. Labs & Teams: He heads the Optimisation Group at Uppsala, fostering research in CP and its applications. NordConsNet, co-founded by Flener, connects Nordic researchers and practitioners in constraint technology.

David ChurchillView profile

Associate Professor

• Artificial Intelligence
• Real-Time Strategy Games
• Game Design
• +5 more

David Churchill is an Associate Professor in the Department of Computer Science at Memorial University of Newfoundland (MUN), specializing in Artificial Intelligence and Real-Time Strategy (RTS) Game AI. He holds a PhD from the University of Alberta and has been actively involved in AI research since 2009. His work focuses on AI for RTS games like StarCraft, emphasizing heuristic search, combat simulation, and build order optimization. He organizes the annual AIIDE StarCraft AI Competition and maintains open-source projects like UAlbertaBot and SparCraft. Education: BSc in Pure Mathematics and Computer Science (MUN) MSc in Computer Science (MUN, 2009) PhD in Computing Science (University of Alberta, 2016) Research Interests: AI in video games, heuristic search algorithms, RTS game strategies, multi-agent systems, and robotics. His work bridges theoretical AI with practical applications in competitive gaming and robotics. Publications & Awards: Notable contributions include Search Ordering for StarCraft Build Order Optimization (2024) and Hierarchical Portfolio Search in Prismata (2017, Best Student Paper Award). His research has advanced combat simulation (SparCraft) and build-order planning (BOSS) in RTS games. Awards: Best Student Paper Award (2017) Best Paper Award (2013, 2022) Advising & Labs: Supervised over 20 graduate and undergraduate theses at MUN. Leads the StarCraft AI Competition and contributes to open-source projects like UAlbertaBot and STARTcraft. Currently not accepting new graduate students due to funding constraints.

• Automated Planning
• Goal and Plan Recognition
• Multiagent Systems
• +4 more

Felipe Meneguzzi is a Professor of Computing Science at the University of Aberdeen, where he leads research in automated planning, goal and plan recognition, multiagent systems, BDI agents, and machine learning. He also holds a Bridges Professorship at the Pontifical Catholic University of Rio Grande do Sul (PUCRS) in Brazil and leads the Group on Artificial Intelligence at PUCRS. He is a Senior Member of the ACM and AAAI. PhD in Artificial Intelligence (2009) from King's College London Postdoctoral Fellowship at Carnegie Mellon University His research spans theoretical and applied artificial intelligence, with a focus on automated planning, decision-making in autonomous agents, and AI applications in neuroscience. He has contributed to landmark-based methods in plan recognition, generalized decision-making in BDI agents, and clinical AI for autism spectrum disorder detection. Key publications include: Landmark-based approaches for goal recognition as planning (IJCAI 2024) Empowering BDI Agents with Generalised Decision-Making (AAMAS 2024) Identification of autism spectrum disorder using deep learning (Neuroimage: Clinical, 2017) Visually-impaired accessibility application via CNNs (IJCNN 2017) Norm conflict identification with deep learning (AAMAS 2017 workshop) Scientific honors include: Best SPC member at AAMAS 2021 Blue-Sky Paper award at AAMAS 2024 Google Research Awards for Latin America (2016, 2019) Runner-up for Microsoft Research Faculty Fellowship (2013) CNPq Highly Productive Researcher Fellowship (Brazil) As an advisor, he supervised Ramon Pereira's MSc dissertation and PhD thesis, both recognized as top works in Brazilian AI. He actively mentors students in automated planning, machine learning, and multiagent systems through projects like the final year project repository and the graduate student repository .

Gustavo VulcanoView profile

Adjunct Professor

• Revenue Management
• Pricing Analytics
• Retail Operations
• +6 more

Gustavo Vulcano is an Adjunct Professor in the Department of Information, Operations and Management Sciences at the Leonard N. Stern School of Business, New York University, where he has been affiliated since 2002. He served as Assistant Professor (2002–2010), Associate Professor (2010–2017, tenured in 2012), and has held an adjunct role since 2017. His academic work bridges theoretical and applied operations management with strong industry engagement. Education: Ph.D. in Operations Management, Columbia University, 2003 M.Phil. in Operations Management, Columbia University, 2000 M.S. in Computer Science, University of Buenos Aires, 1997 B.S. in Computer Science, University of Buenos Aires, 1994 His research focuses on revenue and pricing analytics , retail operations , and supply chain management , particularly emphasizing customer choice modeling , data-driven optimization , and computational methods in network revenue management . He integrates stochastic modeling and behavioral insights to develop practical pricing and operational strategies. His work is deeply rooted in real-world applications across airlines, retail, and financial services. The analysis of his publications reveals a consistent trend in leveraging data-driven decision-making under uncertainty, with a focus on dynamic pricing, demand learning, and robust optimization. His articles span premier journals such as Operations Research and Management Science , reflecting a strong theoretical foundation combined with empirical and computational rigor. Key thematic areas include customer behavior modeling, network revenue management, and stochastic optimization for service industries. Scientific Awards and Leadership: Chair, INFORMS Revenue Management and Pricing Section (2016–2017) Associate Editor, Operations Research and Management Science Prof. Vulcano has advised numerous PhD and master’s students and has secured research grants through industry collaborations. His consulting projects with Delta Airlines, Sabre Holdings, Aerolíneas Argentinas, and ICBC demonstrate a strong commitment to translating academic research into practical solutions. He has taught core courses such as Operations Management , Pricing and Revenue Management , and Dynamic Programming across undergraduate, MBA, PhD, and MSBA programs, shaping future leaders in data-driven decision-making. He is actively involved in research labs and teams focused on operations analytics and pricing strategy , often collaborating with interdisciplinary groups at NYU Stern and industry partners. His ongoing editorial roles and consultancy reflect sustained engagement in advancing the field of revenue management and operations science.

• Operations Research
• Combinatorial Optimization
• Artificial Intelligence
• +3 more

Omar Rifki is an Associate Professor (Maître de Conférences) specializing in combinatorial optimization and artificial intelligence applications. His research bridges theoretical computer science with practical logistics challenges, focusing on routing problems, process mining, and machine learning integration for complex decision systems. His core research interests include phase transitions in NP-hard problems, vehicle routing optimization under time constraints, and healthcare process modeling. Rifki's work demonstrates a consistent pattern of integrating reinforcement learning with traditional optimization techniques to solve large-scale real-world problems in transportation and logistics, with particular emphasis on spatio-temporal data effects and collaborative systems. Analysis of his 15 publications (2019-2025) reveals three dominant research thrusts: (1) Fundamental studies of combinatorial problem hardness using phase transition frameworks, (2) Practical applications of deep reinforcement learning in vehicle routing and taxi assignment, and (3) Healthcare process optimization through advanced process mining techniques. His work consistently addresses scalability challenges in real-world implementations while maintaining theoretical rigor. No scientific awards were documented in the provided materials. His collaborative work with researchers like Christine Solnon and Thierry Garaix indicates active participation in European operations research communities, though specific grant details remain unreported. Rifki's research shows increasing integration of graph theory and machine learning in transportation applications, particularly evident in his Lyon City case studies on autonomous ride-sharing systems.

• Algorithms and Complexity
• Logic
• Database Theory
• +2 more

Martin Grohe is a Professor at the School of Logic and Theory of Discrete Systems , part of the Department of Computer Science at RWTH Aachen University . His research spans Algorithms and Complexity , Logic , Database Theory , Graph Theory , and Machine Learning , with a focus on integrating logical frameworks into computational models. His recent work explores graph neural networks , Weisfeiler-Leman algorithms , and parameterized complexity , as seen in publications on isomorphism testing , database repairing , and probabilistic query evaluation . While no specific scientific awards are mentioned, his contributions to graph theory and machine learning are widely recognized through numerous peer-reviewed publications.

Frank MeiselView profile

Professor

• Supply Chain Management
• Logistics
• Operations Research
• +4 more

Prof. Dr. Frank Meisel holds the Chair for Supply Chain Management at the Christian-Albrechts-University of Kiel, within the Faculty of Law, Economics and Business. His research focuses on mathematical modeling and quantitative solution methods for the design and operation of production, distribution, and service networks. 1998-2003: Studied Traffic Engineering at the Technical University of Dresden 2008: Received Dr. rer. pol. with thesis "Seaside Operations Planning in Container Terminals" 2014: Completed Habilitation in Business Economics with thesis "Papers on the Design and Operations of Production-, Distribution- and Service-Networks" Since 2013: Professor of Supply Chain Management at Kiel University Prof. Meisel's research spans multiple areas of logistics and operations research. His primary interests include maritime logistics, particularly container terminal operations; vehicle routing problems with synchronization requirements; and integrated planning of production-distribution networks. He develops mathematical models and optimization algorithms to address complex logistical challenges in various industry contexts, from seaport operations to healthcare logistics and urban waste management. His work bridges theoretical operations research with practical logistics applications across diverse sectors. His publication record demonstrates a progression from specialized maritime logistics problems to broader supply chain applications. Recent work shows increasing focus on sustainability considerations and the integration of multiple operational decisions across different parts of supply networks. His research has significant practical implications for container terminal operations, intermodal transportation, and service logistics. Prof. Meisel actively contributes to the academic community through conference presentations at major events including EURO, IFORS, and specialized workshops on maritime logistics. His work appears in top-tier journals such as Transportation Science, European Journal of Operational Research, and Computers & Operations Research. He teaches courses including Supply Chain Management, Production and Logistics, and Operations Management at both undergraduate and graduate levels. His educational contributions include developing curriculum for English-language business programs and supervising student research projects in logistics optimization.

• Distributed Algorithms
• Programmable Matter
• Bio-inspired Algorithms
• +5 more

Andrea W. Richa is a President's Professor at Arizona State University (ASU), holding positions in the School of Computing and Augmented Intelligence (SCAI), Barrett Honors College, and multiple research centers including the Biodesign Institute's Center for Biocomputing, Security, and Society. She specializes in distributed algorithms, programmable matter, and bio-inspired computing. Richa has led major research initiatives, including a DoD MURI award and an NSF CAREER Award, and has delivered keynote speeches at top conferences like DISC and LATIN. Her work focuses on self-organizing particle systems, wireless networks, and algorithmic foundations of active matter. Educations: PhD (Computer Science, Carnegie Mellon University, 1998), M.S. (Computer Science, Carnegie Mellon University, 1995), B.S. (Computer Science, Federal University of Rio de Janeiro, Brazil, 1989). Research Interests: Distributed algorithms, programmable matter, bio-inspired systems, wireless communication models, graph algorithms, combinatorial optimization, and resource allocation. She leads the Self-Organizing Particle Systems Lab and is part of SCAI's Theory and Algorithms group. Awards: 2024 ASU Mentorship Award, 2021 Mentor of the Year, 2017 SCAI Research Excellence Award, NSF CAREER Award (1999), and multiple grants including DoD MURI. Her research spans theoretical and applied domains, with over 150 publications in top venues. Grants: Current DoD MURI funding (2019-25), NSF awards on Markov chain algorithms and active matter (2021-25), and prior funding on programmable matter (2014-2017). Labs/Teams: SOPS Lab (sops.engineering.asu.edu), contributing to interdisciplinary research in algorithmic matter and bio-inspired systems.

Vassilios TzerposView profile

Associate Professor

• Audio Processing
• Machine Learning
• Digital Signal Processing
• +3 more

Vassilios Tzerpos is an Associate Professor at the Lassonde School of Engineering, York University, where he has been since 2001. He holds a Ph.D. in Computer Science from the University of Toronto (2001). His research focuses on audio processing for musical applications, deep learning, digital signal processing, machine listening, and software engineering education. He directs the APTLY lab exploring music-technology intersections and leads the LaSSoftE lab developing socially-oriented software solutions. Education: Ph.D. in Computer Science, University of Toronto, 2001 Research Highlights: Dr. Tzerpos' work spans music information retrieval (e.g., automatic music classification), synthetic speech detection using neural networks, and software engineering pedagogy. His recent projects include Music-STAR for audio re-instrumentation and OER-based learning path creation systems. He has pioneered methods in design pattern detection and software clustering evaluation. Grants & Labs: Leads two research groups: APTLY (music-tech) and LaSSoftE (social impact software). Active in developing adaptive cybersecurity solutions against DoS attacks and refining software architecture recovery techniques. Key Themes in Publications: Recent work emphasizes machine learning applications in music technology and cybersecurity, with foundational contributions to software clustering methodologies and design pattern detection algorithms. His work bridges theoretical computer science with practical applications in education and creative industries.