Find Academic Supervisors

Lorraine AyadView profile

Lecturer

• String Algorithms
• Computational Biology
• Data Structures
• +5 more

Lorraine Ayad is a Lecturer in Computer Science at Brunel University of London, affiliated with the College of Engineering, Design and Physical Sciences. She completed her PhD at King’s College London in 2019 with a thesis on efficient sequence comparison techniques. Research Interests: Algorithm Design for Computational Biology String Indexing and Pattern Matching Alignment-Free Sequence Analysis Data Structures for Long Pattern Matching Genomic Data Processing Resilient Pattern Mining Recent Research Trends: Her work focuses on creating universally applicable string algorithms for biological sequence analysis, particularly through hybrid alignment/alignment-free methods and locally consistent anchor-based indexing. This includes optimizing minimizers, constructing antidictionaries, and developing sparse suffix arrays for efficient genomic pattern matching. Scientific Awards: Brunel University Mid and Early Career Academic Research Support Scheme (2024) London Mathematical Society Conference Grants (2023) Fellow of the Higher Education Academy (Distinction) (2022) Brunel University Research Seminar Series Awards (2021-2022) London Mathematical Society Computer Science Small Grants (2019) Associate Fellow of the Higher Education Academy (2017) Outstanding Teaching Assistant Award at King’s College London (2016-2017) EPSRC Doctoral Training Account studentship (2015-2019) Academic Activities: Active in conference chairing (SPIRE 2020), program committees (WABI 2024, SPIRE 2021), and organizing committees (BBW 2023, MatBio 2018). Regular paper reviewer for journals and conferences including the Journal of Supercomputing and ECCB.

Kerttu Pollari-MalmiView profile

Senior Lecturer

• Programming Education
• Computer Science Education
• Educational Technology
• +4 more

Kerttu Pollari-Malmi is a Senior Lecturer at the Department of Computer Science, Aalto University, Finland. Her work focuses on programming education, educational technology, and interactive learning systems, with a particular emphasis on Python and SQL pedagogy. Research Interests Programming Education Educational Technology Interactive Learning Systems Information Retrieval Curriculum Development Recent publications include studies on clustering student feedback data, personalized remedial recommendations, and the design of integrated practice systems for programming. Her work spans algorithm optimization and pedagogical innovations in higher education. Notable collaborations include Peter Brusilovsky, Lauri Malmi, and Teemu Sirkiä, with publications in ACM conferences and journals such as International Journal of Artificial Intelligence in Education.

Cristina SirangeloView profile

Professor

• Database Theory
• Logic
• Automata Theory

Cristina Sirangelo is a Professor of Computer Science at Université Paris Cité, affiliated with the Institut de Recherche en Informatique Fondamentale (IRIF) and on INRIA delegation at ENS Paris within the VALDA team. Her work bridges database theory, logic, and automata theory, focusing on query answering in incomplete data environments and XML processing. Ph.D., University of Calabria (2005) Habilitation à diriger des recherches, ENS de Cachan (2014) Her research explores computational complexity in consistent query answering, Datalog and first-order logic for XML querying, and automata-based validation. Recent projects emphasize dichotomy theorems for query complexity and algorithmic solutions for database constraints under primary keys. She has been recognized with the ICDT 2013 Test of Time Award for foundational work on XML with incomplete information. Her publications span top-tier venues like PODS, ICDT, JACM, and LMCS, with a focus on logical frameworks and efficient data processing. ICDT 2013 Test of Time Award : For pioneering work on XML with incomplete data. As a teaching leader, she co-organizes the master DATA program and the MIDS double master in data science. Her collaborative efforts include co-authoring key papers on XML reasoning, data exchange, and histogram-based data summarization.

Giorgio Stefano GneccoView profile

Professor

• Optimization
• Machine Learning
• Game Theory
• +6 more

Giorgio Stefano Gnecco is a Full Professor in Mathematical Methods of Economics and of Actuarial and Financial Sciences at IMT School for Advanced Studies Lucca, where he works within the Analysis of compleX Economic Systems (AXES) research unit. His academic career spans multiple disciplines including optimization, machine learning, game theory, and their applications in economics, finance, and engineering. His research interests focus on optimization applied to actuarial sciences, economics, finance, and engineering; game theory; statistics; machine learning theory and applications; causal inference for economic policy evaluation; and environmental economics. His work demonstrates interdisciplinary connections between mathematical theory and practical applications across diverse fields, with particular emphasis on developing computational methods for complex economic systems. His publication record shows consistent output across multiple domains, with recent work spanning machine learning algorithms, image processing techniques, economic modeling, and applications in music performance analysis. The breadth of his research indicates strong methodological foundations in mathematical optimization and statistical learning, applied to problems ranging from flood hazard assessment to Parkinson's disease classification. Among his notable achievements are five Italian National Scientific Qualifications for professorial positions in various fields, demonstrating his recognized expertise across multiple academic disciplines. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, Action Editor for Neural Networks, and Associate Editor for Neurocomputing. Professor Gnecco leads numerous research projects including the "PRIN PNRR 2022" project "MOTUS - Automated Analysis and Prediction of Human Movement Qualities," the "INdAM-GNAMPA 2023" project on machine learning methods for Shapley Value estimation, and the "ROBOFARM" project on edge computing for precision agriculture. He has coordinated multiple international research collaborations between Italy and France through the Galileo program. His research unit (AXES) focuses on complex economic systems analysis, with applications spanning environmental economics, financial systems, and human movement analysis. The collaborative nature of his work is evident through his extensive network of domestic and international collaborators across multiple universities and research institutions.

Chris WatkinsView profile

Professor

• Reinforcement Learning
• Evolutionary Algorithms
• Theoretical Biology
• +7 more

Chris Watkins is Professor of Machine Learning at the Department of Computer Science at Royal Holloway, University of London . His research spans reinforcement learning , evolutionary algorithms , kernel methods in machine learning , epidemiological modeling , and financial mathematics . Key research contributions include: Invention of Q-learning in the 1980s Formal equivalence between evolutionary processes and Bayesian inference Pioneering work on string kernels for non-vectorial data His recent publications focus on evolutionary models satisfying detailed balance (2023), metastability in genetic systems (2022), and error-correcting codes in evolutionary contexts . He has also contributed to understanding fitness fluctuations and genetic architecture . Scientific recognition includes the ECML Innovative Contribution Award (2006) for work on string kernels and grammatical inference. His 1996-1999 research on portfolio optimization anticipated critical issues in financial risk estimation that resurfaced during the 2008 crisis.

Jean-Marc LasgouttesView profile

Researcher

• Probabilistic Modeling
• Statistical Physics
• Intelligent Transportation Systems
• +5 more

Jean-Marc Lasgouttes is a Researcher at Inria Paris working within the Astra project team, a joint research initiative between Inria Paris and Valeo. He also holds a teaching position at INSA Rouen Normandie's Department of Mathematical Engineering, where he has instructed courses including Boosting methods (until 2024), Functional Data Analysis (until 2018), and general Data Analysis (until 2024) for both the Mathematical Engineering department and the Specialized Master's program in Data Science. Dr. Lasgouttes' primary research focuses on probabilistic modeling of large systems using statistical physics tools, with particular emphasis on Intelligent Transportation Systems. His work spans traffic flow modeling, vehicle platooning, urban traffic prediction, and geopositioning systems. He frequently employs Markov Random Fields, statistical physics approaches, and game theory to address complex transportation challenges, bridging theoretical statistical methods with practical applications. His research methodology often involves developing novel algorithms like the ★-IPS family for incremental GMRF estimation. Analysis of his recent publications reveals a consistent trajectory of applying advanced probabilistic models to increasingly sophisticated transportation scenarios. His work demonstrates strong expertise in spatio-temporal modeling, with publications covering car-following dynamics, landmark-based positioning, cooperative ITS, and autonomous vehicle systems. The interdisciplinary nature of his research connects statistical physics, machine learning, and transportation engineering to solve real-world mobility challenges. At INSA Rouen Normandie, Dr. Lasgouttes has developed comprehensive teaching materials for data analysis courses, including practical applications of principal component analysis and correspondence analysis using real-world datasets such as European protein consumption patterns and Titanic passenger data. His educational approach emphasizes hands-on implementation with R programming, reflecting his commitment to practical statistical applications. The Astra project team serves as Dr. Lasgouttes' primary research environment, facilitating collaboration between academic researchers and industry partners to address contemporary transportation challenges. His work has contributed to significant research events including the 2018 workshop on Large Random Networks and Constrained Walks honoring Guy Fayolle's 75th birthday, and the 2012 interdisciplinary workshop on inference associated with the Travesti ANR grant.

Wen-Chen HuView profile

Associate Professor

• Handheld/Mobile/Smartphone/Spatial Computing
• Location-Based Services
• Web-Enabled Information Systems
• +6 more

Wen-Chen Hu is an Associate Professor in the Department of Computer Science at the University of North Dakota's School of Electrical Engineering and Computer Science. He has held academic positions at Auburn University (Assistant Professor, 1998–2002) and the University of North Dakota (Assistant/Associate Professor since 2002). His teaching spans courses like .NET/Web Programming, Computer Architecture, Electronic Commerce Systems, and Advanced Database Systems, with over 100 graduate students advised. Education PhD in Computer and Information Science and Engineering, University of Florida (1998) MS in Computer Science, University of Iowa (1993) ME in Information Science and Electronic Engineering, National Central University, Taiwan (1986) BE in Computer Science, Tamkang University, Taiwan (1984) Research Interests : Dr. Hu's work focuses on handheld/mobile/smartphone/spatial computing, location-based services (LBS), web-enabled information systems (e.g., search engines, web mining), database-driven systems (electronic/mobile commerce), and web technologies. His publications emphasize privacy-preserving LBS, spatial trajectory prediction, mobile security, and big data applications. Publication Trends : His recent work (2020–2023) explores pandemic response via mobile analytics, privacy in LBS, and smartphone computing. Earlier (2014–2017), he addressed geometric LBS algorithms, cognitive radio networks, and super-resolution image processing. Foundational work (1998–2005) includes XML search engines, handheld programming languages, and mobile commerce security. Scientific Awards : Best Paper Award, MICS 2015 Best Paper Award, ACM Southeast Conference 2003 Best Reviewer Awards Community Service Awards Advising & Academic Leadership : Dr. Hu has advised over 100 graduate students and served as General Chair for ICETA 2018 and Associate Editor for the Journal of Information Technology Research . He has organized editorial/review roles for over 100 journals and conferences.

Gen-Huey ChenView profile

Professor

• Graph Theory
• Combinatorial Optimization
• Algorithm Analysis and Design
• +1 more

Gen-Huey Chen is a Distinguished Professor in the Department of Computer Science and Information Engineering at National Taiwan University, where he has served since 1987 (Associate Professor 1987-1992, Professor since 1992). He previously held leadership roles as Dean of the College of Science and Technology (2001-2005) and Department Chair (2001-2002) at National Chi Nan University. Education Ph.D. in Computer Management Decision, National Tsing Hua University, 1987 B.S. in Computer Science and Information Engineering, National Taiwan University, 1981 Research Interests Professor Chen's work centers on Graph Theory , Combinatorial Optimization , and Algorithm Analysis and Design , with significant contributions to discrete mathematics and network theory. His research bridges theoretical foundations and practical applications, particularly in structural graph problems and wireless network protocols, emphasizing efficient algorithmic solutions for complex computational challenges. Publications Trends His 2007-2009 publications reveal a cohesive research trajectory focused on graph-theoretic applications in networking. Key patterns include structural analysis of specialized graphs (chordal/circular-arc), fault tolerance in multiprocessor topologies (grids/tori), and bandwidth-optimized routing in mobile ad-hoc networks. These works consistently apply combinatorial optimization to solve real-world networking constraints while advancing theoretical graph algorithms. Advising and Grants No specific information regarding student advising or research grant funding is provided in the source material. Laboratory He directs the Discrete Algorithm Lab at National Taiwan University, which specializes in discrete algorithm development and wireless network protocol research.

Leen StougieView profile

Professor

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

Leen Stougie is a part-time Full Professor of Operations Research at Vrije Universiteit Amsterdam and holds positions as Senior Researcher and Head of the Life Sciences and Health group at CWI (Centrum Wiskunde & Informatica). He has been affiliated with TU Eindhoven and Tinbergen Institute. His research focuses on Operations Research, Combinatorial Optimization, Scheduling, Approximation Algorithms, and Computational Biology. Education: PhD in Operations Research from Erasmus Universiteit Rotterdam (1985). Teaching includes the Advanced Linear Programming course via LNMB/Mastermath/DIAMANT. His work contributes to UN Sustainable Development Goals through algorithmic solutions in biology and optimization. Research highlights include phylogenetic network construction, string matching algorithms, and scenario-based scheduling. He has secured grants like the Marie Skłodowska-Curie Actions (2020) and NWO-GROOT (2001). Advised six PhD theses but no student names listed. Active in organizing conferences and reviewing activities.

• Applied Cryptography
• Theoretical Cryptography
• Computer Security

David Wu serves as a Visiting Professor in the Department of Computer Science at the University of Virginia, having joined the institution in January 2019 as an Assistant Professor after completing his PhD at Stanford University. His academic career focuses on advancing cryptographic research and its practical applications in computer security systems. His educational background includes: PhD in Computer Science, Stanford University (2018) Dr. Wu's research spans Applied and Theoretical Cryptography with particular expertise in attribute-based encryption (ABE), broadcast encryption, and zero-knowledge proof systems. His work addresses foundational challenges in cryptographic protocols while developing practical solutions for data privacy, secure access control, and efficient secure computation. Recent investigations explore lattice-based constructions and privacy-enhancing technologies for real-world deployment. Analysis of his 2023-2025 publications reveals a concentrated focus on registered ABE systems, adaptively-secure broadcast encryption, and succinct non-interactive arguments (SNARGs). A significant portion leverages learning with errors (LWE) assumptions to achieve security and efficiency, with notable contributions to private information retrieval (PIR) systems and witness encryption. His research consistently bridges theoretical cryptography with practical security implementations.

• Gravitational Wave Astronomy
• Astrophysics
• Quantum Engineering
• +3 more

Dr. Alexander Wanner holds management roles at several institutions within Leibniz University Hannover, including the QUEST-Leibniz Research School, Quest Centre for Quantum Engineering and Space-Time Research, QuantumFrontiers, and the HITec Institute of Technology. His email is alexander.wanner@quest.uni-hannover.de . His research focuses on gravitational wave detection, quantum engineering, and detector technology. He contributes to projects like the Einstein-Elevator facility and the AEI 10m prototype interferometer, emphasizing precision measurement and noise reduction in gravitational wave observatories. Wanner's work bridges astrophysics and experimental physics, addressing topics such as binary coalescence modeling, cosmic string constraints, and multi-messenger observations. He collaborates with LIGO-Virgo and ANTARES networks, advancing understanding of gravitational wave sources and their astrophysical implications. His role in managing interdisciplinary research initiatives highlights expertise in coordinating large-scale projects and integrating cutting-edge technologies for fundamental physics research.

Alessio IovineView profile

Researcher

• Control Systems
• Power Systems
• Cyber-Physical Systems
• +4 more

Alessio Iovine is a CNRS researcher at the Laboratory of Signals and Systems (L2S) affiliated with CentraleSupélec (Paris-Saclay University). His research focuses on control systems for cyber-physical applications, including energy systems (e.g., microgrids, renewable integration) and transportation (e.g., autonomous vehicles, traffic control). He holds a European Doctorate in Information Science and Engineering from the University of L’Aquila and CentraleSupélec, with postdoctoral experience at institutions like UC Berkeley and Efficacity. Research Areas: Cyber-Physical Systems DC/AC Microgrid Control Autonomous Vehicles & Adaptive Cruise Control Predictive Grid Management Multi-Agent Systems Publications Trends: Recent works emphasize mesoscopic control approaches for vehicular systems, distributed control for multi-agent networks, and predictive algorithms for power grid congestion management. Over 20 peer-reviewed articles since 2017 reflect his expertise in nonlinear control, hybrid systems, and renewable integration. Opportunities: Actively recruiting PhD/Master students for projects in cyber-physical power systems, machine learning for grid identification, and autonomous traffic control. Hosts seminars on energy systems and signal processing at Paris-Saclay. Labs/Teams: Member of L2S’s Modelling and Estimation team, collaborating with groups like ILOCOS (Information, Learning, Optimization & Communication Sciences).

Dong DengView profile

Associate Professor

• Data Science
• Data Management
• Data Curation
• +7 more

Dong Deng is an Associate Professor in the Department of Computer Science at Rutgers University, School of Arts and Sciences. He joined Rutgers University in 2019 as an Assistant Professor and has since been promoted to Associate Professor. His research is conducted through the Data Curation Lab within the Database Group. Dong Deng received his PhD from Tsinghua University and completed postdoctoral training at MIT CSAIL. His academic journey has positioned him as a leading researcher in database systems and data management. Dong Deng's research focuses on data management, data science, and database systems, with an emphasis on developing novel algorithms and building practical systems to address data problems. His primary research areas include scalable data curation (covering textual, structured, and feature data curation), data manipulation and wrangling at scale, data integration, data cleaning, data discovery, and scientific dataset management. His work bridges theoretical foundations with practical implementations, particularly in the areas of similarity search, approximate nearest neighbor algorithms, and data integration techniques. His research has significant applications in big data processing, entity resolution, and data lake management. Dong Deng has published extensively in top venues including SIGMOD, PVLDB, and ICDE. His recent publications demonstrate a strong focus on near-duplicate detection, efficient algorithms for similarity search, and data curation techniques. His work shows a consistent trajectory toward more complex and scalable solutions for data management challenges, with increasing emphasis on high-dimensional data and large language model applications. NSF III: Small: Large-Scale High Dimensional Dense Vector Management (2022) NSF CDSE: Computation-Informed Learning of Melt Pool Dynamics for Real-Time Prognosis (2022) SIGMOD Student Programming Contest 2022 Second Place Dong Deng has secured significant research funding and actively mentors students. He has served in various leadership roles within the academic community including Digital Platform Chair for VLDB 2023 and Student Mentorship co-Chair for SIGMOD 2022 and 2021. He teaches advanced courses in database systems and data management at Rutgers University. Dong Deng leads the Data Curation Lab, which focuses on developing innovative solutions for data management challenges. The lab has produced influential research with practical applications across multiple domains requiring sophisticated data processing capabilities.

Alpay DORUKView profile

Lecturer

• Information Security
• Cryptology
• Embedded Systems
• +5 more

Alpay DORUK is a Lecturer and the Head of the Cyber Security Department at the Faculty of Engineering and Natural Sciences, Bandirma Onyedi Eylul University. Previously, he served as a Lecturer at Tekirdag Namik Kemal University (2011–2020) and a Research Assistant at Izmir Institute of High Technology (1999–2011). His expertise spans Information Security, IoT, and Embedded Systems, with a focus on ontology engineering, machine learning applications, and sensor networks. Research highlights include developing ontology-based frameworks for IoT sensors and web services, designing wheelchair control systems using image processing, and analyzing superconductor properties via SEM image statistics. He has supervised over a dozen academic projects and one Master’s thesis on facial movement tracking using image processing. Active in academic administration, he coordinated Erasmus and Farabi programs and led departmental initiatives as Department Head. His refereeing roles include peer review for journals like Turkish Journal of Electrical Engineering and conferences such as ICECENG. He has authored/co-authored two books on cybersecurity and computer engineering, and contributed to international projects like the Sayısal Kontrol Sistemleri software development (2006–2010). Recent work emphasizes IoT device monitoring, blockchain-inspired data structures, and machine learning for automotive efficiency analysis. His teaching spans operating systems, software engineering, and agile development, reflecting his blend of theoretical and applied expertise.

Zvi GalilView profile

Professor

• Algorithms
• Complexity Theory
• Cryptography
• +3 more

Zvi Galil is a distinguished academic and former Dean of Computing at Georgia Institute of Technology (2010-2019). He holds the title of Storey Chair and serves as Executive Advisor for Online Programs. His academic journey includes leadership roles at Columbia University (Fu Foundation School of Engineering Dean, 1995-2007) and Tel Aviv University (President, 2007-2009). He earned degrees in Applied Mathematics from Tel Aviv University and a PhD in Computer Science from Cornell University. Galil’s research focuses on algorithms, complexity theory, cryptography, and stringology. He has authored over 200 papers and edited 5 books, with contributions to graph algorithms, parallel computing, and data structures. He is a Fellow of the ACM and American Academy of Arts and Sciences, and a member of the National Academy of Engineering. His work has influenced fields like online education through initiatives like OMSCS (Georgia Tech’s Online Master of Science in Computer Science). Educations: BS and MS (summa cum laude), Applied Mathematics, Tel Aviv University PhD, Computer Science, Cornell University His research trends span dynamic graph algorithms, real-time string processing, and scalable graph isomorphism techniques. He has also contributed to foundational areas like suffix trees and text indexing. His awards include the Columbia Great Teacher Award (2009) for pedagogical excellence. Awards: ACM Fellow Member, American Academy of Arts and Sciences Member, National Academy of Engineering Columbia Society of Graduates Great Teacher Award Galil advises on online education programs and collaborates with the Algorithms and Randomness Center (ARC) at Georgia Tech. He has held editorial roles at major journals and advised Oxford University Press on computer science publications.