Find Academic Supervisors

Edwin van der HeideView profile

Lecturer

• Sound Art
• Interactive Installations
• Spatial Composition
• +3 more

Edwin van der Heide is a part-time Lecturer and researcher at Leiden University, affiliated with the Leiden Institute of Advanced Computer Science (LIACS) and the Academy for Creative and Performing Arts (ACPA). His work bridges sound, space, and interaction through installations, performances, and environments. Key roles include: Co-head of ArtScience Interfaculty (Royal Conservatoire & Royal Academy of Art, The Hague) until 2016 Edgard Varèse Guest Professor at TU Berlin (2009) Invited artist at Le Fresnoy (France) and HKB Bern University of the Arts (2019) Research Interests: Spatial sound composition, audience interaction, interdisciplinary art, and the intersection of technology with auditory perception. Notable projects include Whispering Wind (permanent installation at Leiden University) and Spiral of Time (MACBA, Barcelona). Recent Articles & Exhibitions (2023–2024): Focus on large-scale installations like Pneumatic Sound Field and Spiral of Time , emphasizing public engagement and spatial acoustics. Awards: Witteveen+Bos Art+Technology Award (2009), Best Paper Award for BAI (摆) (2018). Grants & Labs: Collaborations with institutions like NCCA (Russia), MAXXI (Italy), and involvement in projects like Evolving Spark Network (global sound art initiatives).

Wai Pang NgView profile

Professor

• Radio-over-Fiber
• Optical Communications
• Fiber Optic Sensors
• +3 more

Wai Pang Ng is a Professor and Head of the Department of Mathematics, Physics and Electrical Engineering at Northumbria University. He holds a BEng (Hons) in Communications and Electronic Engineering from the University of Northumbria and a PhD in Electronic Engineering from the University of Wales, Swansea. His research focuses on radio-over-fiber systems, distributed fiber sensing, high-speed optical communications, and adaptive signal processing. Ng has held leadership roles in IEEE chapters and conferences, including chairing the IEEE UK&RI Communications Chapter (2011–2015) and serving as publicity chair for IEEE ICC 2015 and 2016. His research interests include innovative fiber optic sensor designs, acoustic wave devices for biomedical applications, and hybrid communication systems combining radio-over-fiber and free-space optics. Recent work emphasizes ultra-sensitive pressure/temperature sensors using microstructured fibers and acoustofluidic platforms for lab-on-a-chip applications. Ng has supervised seven PhD/MSc projects and actively contributes to standards development in optical communication systems. Ng’s publications span advanced sensor technologies, nonlinear compensation in optical systems, and turbulence-resistant free-space optical links. His work bridges academic research with practical applications in telecommunications, environmental monitoring, and healthcare diagnostics. Professional affiliations include IEEE technical committees (SPCE, TCGCC, ONTC) and guest editorships for IET Communications.

Torsten Wolfgang KuhlenView profile

Professor

• Virtual Reality
• Immersive Visualization
• Scientific Visualization
• +5 more

Prof. Torsten Wolfgang Kuhlen serves as a Universitätsprofessor at RWTH Aachen University, leading the Teaching and Research Area for Virtual Reality and Immersive Visualization within the Department of Computer Science. He is affiliated with Chair of Computer Science 12 (High Performance Computing), the Visual Computing Institute, and remains an integral part of the RWTH IT Center where his research group operates one of the world's largest Virtual Reality laboratories including the 30 sqm aixCAVE visualization chamber. The group maintains strong connections with Computational Science & Engineering Division, National High Performance Computing Center for Computational Engineering Science (NHR4CES), and VR in Science and Industry Network NRW e.V. Prof. Kuhlen's research spans virtual reality, immersive visualization, and multimodal 3D user interfaces with applications across simulation science, production technology, neuroscience, and medicine. His work combines basic research on advanced methods and algorithms with interdisciplinary collaborations involving RWTH Aachen institutes, Forschungszentrum Jülich, and industry partners. Recent publications demonstrate strong focus on audiovisual perception, immersive analytics, collaborative virtual environments, and practical VR applications in education and manufacturing. His research group has produced significant work on listening effort in virtual environments, immersive authoring techniques, and VR applications for scientific visualization. Notable projects include VRScenarioBuilder for automated vehicle testing and applications in monitoring additive manufacturing processes. The group actively participates in major conferences including IEEE VIS and EuroVis, with several award-winning contributions. Prof. Kuhlen has advised PhD students including Martin Bellgardt who recently completed his doctoral degree on "Increasing Immersion in Machine Learning Pipelines for Mechanical Engineering". The research group maintains state-of-the-art VR infrastructure including the aixCAVE facility which is open to all RWTH research groups.

Henry F. (Hank) KorthView profile

Professor

• Database Systems
• Blockchain Systems
• Distributed Systems
• +7 more

Henry F. (Hank) Korth is a Professor of Computer Science and Engineering at Lehigh University, with a courtesy appointment in the Department of Decision and Technology Analytics in the College of Business. He serves as Director of the Blockchain Lab in the Center for Financial Services and Co-Director of the Computer Science and Business Program. Korth is a Fellow of the ACM and IEEE, and a recipient of the VLDB 10-Year Award and Bell Labs President's Silver Award for contributions to database technologies. PhD in Computer Science from Princeton University MA, MSE in Computer Science from Princeton University BA in Mathematics from Williams College Korth's research spans database systems, blockchain systems, distributed systems, and real-time systems. He has pioneered transaction management in parallel and distributed systems, query processing, and the impact of modern computing architectures on database performance. His recent work focuses on blockchain applications in enterprise databases, including acceleration of zero-knowledge proofs, benchmarking frameworks, central-bank digital currencies, and private-yet-provable accounting systems. His contributions are rooted in both theoretical advancements and practical implementations, such as the QTM™ aggregation engine and the DataBlitz™ main-memory storage manager. Scientific awards include: ACM Fellow IEEE Fellow 10-Year Award at the VLDB Conference Bell Labs President's Silver Award Korth actively supervises research within the Blockchain Lab and is affiliated with the Scalable Software Systems Research Group at Lehigh. His scholarly output reflects a deep engagement with blockchain benchmarking, concurrency control, verifiable databases, and the evolution of database systems in response to technological shifts.

• Decision Support Systems
• Energy Management
• Environmental Analysis
• +6 more

John Psarras is a Professor at the National Technical University of Athens (NTUA) in the School of Electrical and Computer Engineering, specifically within the Division of Industrial Electric Devices and Decision Systems. He serves as the Director of the Decision Support Systems Laboratory (DSSlab) and the University Research Institute of Communication and Computer Systems. He holds a Diploma in Mechanical Engineering (1982) and a Ph.D. in Electrical and Computer Engineering (1989), both from NTUA. His research specializes in decision support systems with applications in energy management, environmental analysis, and information systems. Key areas include: Multi-criteria analysis for energy policy and renewable integration AI-driven optimization of smart grids and building efficiency Sustainable finance mechanisms for green projects Blockchain applications in education and data security His recent publications (2023–2025) demonstrate a strong focus on AI-enhanced decision tools for energy transitions, smart infrastructure, healthcare diagnostics, and cross-border renewable cooperation, reflecting interdisciplinary innovation. He has supervised 22 PhD theses and coordinates EU-funded projects in energy policy, clean technology, and capacity building. No scientific awards are listed in available sources. He leads the Decision Support Systems Laboratory (DSSlab), advancing research in energy analytics, and directs the University Research Institute of Communication and Computer Systems, facilitating large-scale interdisciplinary collaborations.

Mohsen LesaniView profile

Associate Professor

• Reliability of software systems
• Security of software systems
• Concurrent systems
• +12 more

Mohsen Lesani is an Associate Professor in the Computer Science and Engineering Department at the University of California, Santa Cruz's Baskin School of Engineering. His research focuses on reliability and security of software systems, particularly concurrent and distributed systems, with recent emphasis on secure replicated systems and distributed machine learning. Dr. Lesani received his PhD from UCLA, MS in artificial intelligence from Sharif University of Technology, and BS in software engineering from University of Tehran. He was previously a postdoc at MIT. His educational background provides a strong foundation for his interdisciplinary research spanning programming languages, distributed systems, and security. His research interests center on creating reliable and secure distributed systems. Current projects include resilient and secure distributed systems, heterogeneous and reconfigurable secure distributed systems, automatic analysis and synthesis of replicated objects, verification of distributed systems, data analytics, secure exchange across blockchains, machine learning for performance models, domain-specific languages and type systems, and automatic fence insertion for concurrent systems. His work bridges theoretical foundations with practical implementations to address real-world challenges in distributed computing. Lesani's research has been recognized with several prestigious awards including the NSF CAREER award in 2020 and DARPA YFA award in 2022. His work has also received the SIGPLAN Research Highlight in 2019, a distinguished paper award at OOPSLA 2018, and a best paper award at ISSRE 2015. These accolades reflect the impact and quality of his contributions to the field. He actively mentors PhD students in the Safe and Secure Software (S3) lab, including Xiao Li, Eric Chan, Javad Saber-Latibari, and Tejas Mane. His research has been supported by multiple NSF grants, demonstrating sustained funding for his innovative work. Lesani serves on program committees for major conferences including POPL, PLDI, OOPSLA, and DISC, contributing to the academic community. Lesani leads the Safe and Secure Software (S3) lab at UC Santa Cruz, where his team works on cutting-edge research in distributed systems, programming languages, and security. The lab fosters a collaborative environment where theoretical insights are translated into practical systems that address real-world challenges in reliability and security of distributed applications.

Marios PolycarpouView profile

Professor

• Intelligent Systems
• Adaptive Control Systems
• Fault Diagnosis
• +3 more

Marios Polycarpou is a Professor of Electrical and Computer Engineering and Director of the KIOS Research and Innovation Center of Excellence at the University of Cyprus. He holds honorary positions at Imperial College London and is a member of Academia Europaea. His expertise spans intelligent systems, adaptive control, machine learning, and critical infrastructure. Education: B.A. Computer Science (Rice University, 1987) B.Sc. Electrical Engineering (Rice University, 1987) M.S. Electrical Engineering (University of Southern California, 1989) Ph.D. Electrical Engineering (University of Southern California, 1992) Research Focus: Polycarpou’s work emphasizes fault diagnosis in cyber-physical systems, water distribution networks, and adaptive control. He pioneers digital twin technologies for infrastructure resilience and develops algorithms for real-time anomaly detection and system optimization. Article Trends: His recent publications address adaptive control strategies, cybersecurity in networked systems, and AI-driven solutions for water management. Key themes include distributed control, event-triggered mechanisms, and transformer-based anomaly localization. Awards: 2023 IEEE Frank Rosenblatt Technical Field Award 2016 IEEE Neural Networks Pioneer Award Fellow of IEEE and IFAC Grants & Leadership: He secured prestigious grants including ERC Advanced and Synergy Grants. He led KIOS CoE’s Horizon 2020 projects and served as IEEE Computational Intelligence Society President (2012–2013). Labs & Teams: Directs the KIOS CoE, a hub for AI in critical infrastructure. Collaborates on projects like ERC Water-Futures, focusing on long-term water system transitions and contamination mitigation.

Aaron RothView profile

Professor

• Algorithms
• Machine Learning
• Differential Privacy
• +10 more

Aaron Roth is the Henry Salvatori Professor of Computer and Cognitive Science at the University of Pennsylvania, affiliated with the Department of Computer and Information Science in the School of Engineering and Applied Science. He holds a secondary appointment in the Department of Statistics and Data Science at the Wharton School and is associated with several research centers including PRiML, the Warren Center for Network and Data Sciences, and the AMCS program. He received his PhD from Carnegie Mellon University under Avrim Blum and was a postdoc at Microsoft Research New England. His research focuses on algorithms and machine learning, particularly in private data analysis, fairness in machine learning, game theory, mechanism design, and learning theory. His work bridges theoretical computer science with societal concerns, advocating for ethically aware algorithm design. He co-authored the book The Ethical Algorithm with Michael Kearns, which explores how to embed social values like privacy and fairness into algorithmic systems. His recent publications show a strong trend toward uncertainty quantification, multicalibration, conformal prediction, and fairness in reinforcement learning and high-dimensional settings. He frequently publishes in top-tier venues such as STOC, FOCS, ICML, NeurIPS, and COLT, often with a focus on rigorous theoretical foundations with practical implications. Hans Sigrist Prize Presidential Early Career Award for Scientists and Engineers (PECASE) Alfred P. Sloan Research Fellowship NSF CAREER award Google Faculty Research Award Amazon Research Award Yahoo Academic Career Enhancement award Roth has advised numerous PhD students and postdocs, many of whom now hold academic or industry research positions. He is also an Amazon Scholar at AWS and has served in advisory roles for companies like Apple, Facebook, Leapyear, and Spectrum Labs. He has been active in organizing workshops and tutorials on differential privacy, fairness, and adaptive data analysis, and has given keynotes at major conferences and institutions worldwide. He leads research groups and collaborates widely across Penn, focusing on responsible AI, privacy, and algorithmic fairness. His lab produces foundational work on calibration, unlearning, privacy-preserving learning, and equitable decision-making systems.

Laura ToniView profile

Associate Professor

• Large-scale signal processing for machine learning
• Graph signal processing
• Reinforcement learning
• +9 more

Laura Toni is an Associate Professor in the Department of Electronic and Electrical Engineering at University College London's Faculty of Engineering Sciences. She serves as the leader of a research team focused on advanced signal processing and machine learning applications, documented at https://lasp-ucl.github.io . Additionally, she holds prestigious affiliations as an ELLIS (European Laboratory for Learning and Intelligent Systems) Member and Turing Fellow Alumni. PhD in Electrical Engineering, University of Bologna (2009) MS in Electrical Engineering, University of Bologna (2005) Professor Toni's research spans theoretical and applied aspects of machine learning with particular emphasis on graph-based approaches. Her work integrates signal processing techniques with modern AI methodologies to address complex problems in communication systems, multimedia processing, and scientific discovery. She has made significant contributions to reinforcement learning theory, graph signal processing, and their applications across diverse domains including drug discovery and immersive technologies. Analysis of her recent publications reveals a strong focus on graph-based machine learning approaches, with increasing emphasis on reinforcement learning applications. Her work demonstrates a progression from theoretical foundations to practical implementations, particularly in multimedia processing, network science, and drug discovery applications. Many of her recent papers combine graph neural networks with diffusion models and reinforcement learning for complex prediction and generation tasks. Professor Toni has received notable recognition through her ELLIS membership and Turing Fellow Alumni status, which represent significant achievements in the European AI research community. ELLIS (European Laboratory for Learning and Intelligent Systems) Member Turing Fellow Alumni As an academic leader, Professor Toni supervises postgraduate students and leads a research team at UCL, focusing on cutting-edge projects at the intersection of signal processing and machine learning. Her team has secured research funding through various channels including European initiatives and industry partnerships, enabling them to pursue ambitious projects in graph learning, reinforcement learning, and multimedia processing. The team actively collaborates with institutions worldwide, including previous connections with UCSD and EPFL. Professor Toni leads the LASP research group at UCL (https://lasp-ucl.github.io), which focuses on Large-scale Adaptive Signal Processing for intelligent systems. The team comprises researchers working on graph signal processing, reinforcement learning, and multimedia applications, with strong connections to both theoretical foundations and practical implementations across various domains including healthcare, communications, and immersive technologies.

Sri KollaView profile

Professor

• Electrical Engineering
• Renewable Energy and Energy Sustainability
• Control Systems
• +5 more

Sri Kolla, Ph.D. is a tenured Professor in the Department of Electronics and Computer Engineering Technology at Bowling Green State University (BGSU) , where he has served since August 2002. He also served as a Visiting Professor at the Indian Institute of Science (2017) and as a Fulbright Research Scholar (2008-2009). His academic career spans faculty roles at Penn State University, University of Toledo, and consortium graduate faculty at Indiana State University. Education: Ph.D. in Electrical Engineering and Computer Science (University of Toledo, 1989) M.S. in Electrical and Computer Engineering (University of Saskatchewan, 1986) M.E. in Electrical Engineering (Indian Institute of Science, 1983) B.E. in Electrical Engineering (Andhra University, 1981) Research Interests: Dr. Kolla specializes in Electrical Power and Energy Systems with Smart Grid applications, Control Systems for networked environments, and Machine Learning techniques for power system diagnostics. His work focuses on fault detection in microgrids using LSTM networks, stability robustness of discrete-time systems, and multi-agent protection schemes for power infrastructure. Scientific Contributions: Developed robust control frameworks for microgrid systems under parameter variations (2023-2025) Pioneered AI-based fault identification in induction motors and transformers (1995-2000) Advanced networked control system designs addressing time delays (2002-2012) Published 82+ technical articles in IEEE, ISA Transactions, and conference proceedings Honors and Recognition: Recipient of the Fulbright-Nehru Academic and Professional Excellence Award and Whiteford Scholarship . Senior member of IEEE and ISA , with listings in Marquis Who’s Who and fellowships in The Institute of Engineers (India) .

• Decision and Control
• Game Theory
• Multi-Agent Systems
• +7 more

Jeff Shamma is the Department Head and Professor of Industrial and Enterprise Systems Engineering (ISE) at the University of Illinois at Urbana-Champaign, holding the Jerry S. Dobrovolny Chair. He is also courtesy Professor in Aerospace Engineering and Mechanical Science and Engineering. Formerly, he held the Julian T. Hightower Chair at Georgia Institute of Technology and faculty positions at KAUST. Dr. Shamma earned his PhD in Systems Science and Engineering from MIT (1988) and a BS in Mechanical Engineering from Georgia Tech (1983). He is a Fellow of IEEE and IFAC, recipient of the IFAC High Impact Paper Award, AACC Donald P. Eckman Award, and NSF Young Investigator Award. His research spans Decision and Control , Game Theory , and Multi-Agent Systems , focusing on human-machine networks, distributed autonomy, and adaptive robotic systems. Recent work examines crowd dynamics, risk-sensitive control, and feedback linearization for constrained optimization. Jeff has served as Editor-in-Chief of IEEE Transactions on Control of Network Systems (2020–2024) and held editorial roles in journals like Annual Reviews in Control and IEEE Transactions on Robotics . His 15 most recent publications (2024–2025) analyze learning dynamics, multi-agent optimization, and UAV-crawler systems, reflecting trends in autonomous systems, game-theoretic modeling, and industrial inspection technologies. Scientific distinctions include: Fellow of IEEE and IFAC IFAC High Impact Paper Award (2020) AACC Donald P. Eckman Award (1996) NSF Young Investigator Award (1992) Mohammed Dahleh Distinguished Lecture Award (2013) Dr. Shamma advises current PhD students Hassan Abdelraouf, Aya Hamed, and Nawaf Otaibi, with former advisees including Sarah Toonsi (2025) and Fat-hy Rajab (2025). His lab integrates theoretical research with applied projects like FalconScan, a UAV-crawler system for industrial inspection, and develops magnetic legs for curved surface UAV landing.

Themistoklis SapsisView profile

Professor

• Probabilistic Prediction
• Nonlinear Systems Analysis
• Machine Learning in Fluid Dynamics
• +3 more

Themistoklis Sapsis is a Professor in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he also holds an affiliation with the MIT Institute for Data, Systems, and Society. He earned his Ph.D. in Mechanical Engineering from MIT in 2011 and previously served as an Assistant Research Scientist at NYU’s Courant Institute of Mathematical Sciences. His research focuses on developing analytical, computational, and data-driven methods to predict and quantify extreme events in high-dimensional nonlinear systems, such as turbulent fluid flows and mechanical systems. Key areas include probabilistic modeling of climate extremes, machine learning for climate simulation corrections, and uncertainty quantification in complex dynamical systems. Recent work emphasizes applications in ocean engineering (e.g., vortex-induced vibrations, wave energy systems) and environmental science (e.g., spatially resolved climate extremes, bias correction in Earth system models). His methodologies combine stochastic emulators, Bayesian experimental design, and neural networks to address challenges in data sparsity and model fidelity. Notable contributions include frameworks for correcting coarse-scale climate simulations using machine learning, real-time ocean temperature reconstruction from satellite data, and data-driven modeling of hydrodynamic interactions in marine risers. His research bridges theoretical developments with practical applications in energy systems, structural monitoring, and autonomous systems. Prof. Sapsis collaborates with interdisciplinary teams and has contributed to initiatives such as FIRSTLING-DIGIMAR (a marine riser digital twin) and multi-fidelity frameworks for autonomous seakeeping. His work is supported by grants focused on advancing machine learning in scientific modeling and extreme event prediction.

Sajid AlaviView profile

Professor

• Food Engineering
• Extrusion Processing
• Pet Food and Feed Applications
• +4 more

Dr. Sajid Alavi is a Professor in the Department of Grain Science and Industry at Kansas State University. He joined the faculty in 2002 after earning his Ph.D. in Food Science/Food Engineering from Cornell University (2002), M.S. in Agricultural and Biological Engineering from Penn State (1997), and B.S. in Agricultural Engineering from IIT (1995). His research focuses on extrusion processing in food, pet food, and feed applications, with expertise in rheology, food microstructure imaging, and process sustainability. He leads global projects in Africa, Brazil, India, and beyond, emphasizing sustainable food technologies and AI-driven processing innovations. Dr. Alavi is a recipient of the 2010 Young Research Scientist Award from the Cereals & Grains Association. He teaches GRSC 620 (Intro to Extrusion Processing) and GRSC 820 (Advanced Extrusion Processing), and has trained over 1,000 industry leaders through his renowned 'Extrusion Processing: Technology and Commercialization' short course. His work bridges food science and engineering, addressing challenges in plant-based meat analogs, nutrient bioavailability, and food aid product development. Key facilities associated with his work include the BIVAP Feed Quality Assurance Lab and Hal Ross Flour Mill. His research spans sensory analysis of meat alternatives, fiber utilization in pet food, and sustainability assessments of novel crops like intermediate wheatgrass. Recent studies explore insect protein in pet food, AI-driven extrusion optimization, and iron bioavailability in fortified foods. Dr. Alavi’s contributions span academic, industrial, and global food security domains, reflecting a commitment to innovative, scalable food solutions.

David LillisView profile

Associate Professor

• Natural Language Processing
• Artificial Intelligence
• Digital Forensics
• +4 more

David Lillis is an Associate Professor in the School of Computer Science at University College Dublin (UCD). His research focuses on Natural Language Processing (NLP), Artificial Intelligence (AI), and their applications in legal and forensic contexts. He leads projects like CeADAR (Ireland’s Applied AI Center) and the Transpire project, collaborating with organizations such as Corlytics and the Department of Enterprise, Trade and Employment. He holds adjunct roles as a Guest Professor at Beijing University of Technology’s Data Mining and Security Lab and has been a Fulbright Scholar at the University of New Haven’s Cyber Forensics Research and Education Group. Education: B.A. (Hons) in Law and Accounting, University of Limerick Higher Diploma in Computer Science, UCD M.Sc., Ph.D. in Computer Science, UCD Professional Certificate in University Teaching & Learning, UCD Research Interests: Legal AI, digital forensics, machine learning, multi-agent systems, and information retrieval. Recent work includes NLP for regulatory analysis, crop yield prediction via neural networks, and AR-driven decision support systems. Grants & Projects: Principal Investigator: Transpire (AI Platform for Regulation) SFI Funded Investigator: CONSUS (Crop Optimization) PI: CeADAR Technology Centre Teaching roles include Deputy Programme Director for Software Engineering at Beijing-Dublin International College (BDIC) since 2014. Labs & Groups: UCD Forensics and Security Research Group, ML-Labs (SFI Centre for ML Training), and the Data Mining and Security Lab (BJUT).

• Artificial Intelligence
• Optimization Algorithms
• Machine Learning
• +5 more

Grégoire DANOY is a Researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability, and Trust (SnT) and Head of the Parallel Computing and Optimization Group (PCOG). He specializes in artificial intelligence, with a focus on optimization algorithms, machine learning, and swarm intelligence. His work addresses challenges in cloud computing, high-performance computing, smart mobility, and unmanned autonomous systems like drone swarms. He has authored over 150 publications, including articles in IEEE Transactions and conferences like NeurIPS and GECCO. He currently leads major projects such as UltraBO (€1.019M), ADHOC (€1.291M), and SERENITY (€1.228M), collaborating with institutions in France and Poland. Education: PhD in Computer Science (2008) from École Nationale Supérieure des Mines de Saint-Étienne, Master’s in Computer Science (2004), and Industrial Engineering Degree (2003) from Luxembourg University of Applied Sciences. Research Interests: Developing novel AI techniques for solving large-scale optimization problems, with applications in distributed systems, autonomous robotics, and federated learning. He emphasizes scalable solutions for combinatorial challenges using parallel computing and swarm intelligence. Grants & Projects: Principal Investigator for EU-funded initiatives like ADARS (2021–2024) and FNR PoC/SIMMS (2019–2021). His work bridges academia and industry, with technology transfer projects in autonomous robot swarms. Awards: Recognitions include the Best Student Paper Nomination (2022), IEEE CybConf Best Paper Award (2017), and ACM GECCO nominations (2016, 2009). He serves on the editorial board of Engineering Applications of Artificial Intelligence (EAAI). Labs & Teams: Leads the Parallel Computing and Optimization Group (PCOG), focusing on interdisciplinary research in AI and distributed systems. He also contributes to outreach programs like FNR's Researchers at School.