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Alexander ArtikisView profile

Associate Professor

• Artificial Intelligence
• Distributed Systems
• Complex Event Recognition
• +4 more

Dr. Alexander Artikis is an Associate Professor of Artificial Intelligence at the University of Piraeus and a Research Associate at the National Centre for Scientific Research (NCSR) "Demokritos". He leads the Complex Event Recognition (CER) group , focusing on symbolic and probabilistic approaches to event recognition and forecasting. University of Piraeus (2025–present) NCSR Demokritos (2017–present) Complex Event Recognition Group (2017–present) His research spans Artificial Intelligence and Distributed Systems , with a focus on: Complex Event Recognition (CER) : Developing logic-based systems for detecting events in real-time data streams Event Calculus : Creating probabilistic and incremental versions for runtime reasoning Multi-Agent Systems : Modeling norm-governed interactions Maritime Informatics : Applying CER to vessel trajectory analysis and fleet management Key publications reveal trends in: Neuro-symbolic forecasting models combining deep learning and logic-based reasoning Symbolic automata with memory for pattern detection Online learning techniques for dynamic event rule generation Tensor-based formalizations for efficient temporal reasoning Handling uncertainty in real-time maritime data streams Optimizing memory usage for scalable stream processing He contributes to open-source tools like RTEC (Run-Time Event Calculus) and holds a European patent on complex event forecasting. His work addresses challenges in: Proactive decision-making systems Knowledge Graph consistency Hybrid human-machine discovery of movement patterns Big Data analytics for time-critical applications

• Control Theory
• Distributed Parameter Systems
• Model Predictive Control
• +3 more

Lorenz Dörschel is an Adjunct Professor (Lehrbeauftragter) at the Institute of Automatic Control at RWTH Aachen University. He holds the academic title PD Dr.-Ing. habil, signifying post-doctoral research qualifications. His position is part-time, focusing on advanced control theory and applications. His primary research interests include: Control of distributed parameter systems (e.g., fluid dynamics, thermal processes) Model predictive control for industrial and automotive systems Parameter space methods for robust controller design Model reduction techniques for complex nonlinear systems Dörschel's recent publications (2018-2024) demonstrate broad applications across biomedical engineering, renewable energy, automotive systems, and industrial automation. His work consistently integrates mathematical rigor with practical implementations, emphasizing advanced control methodologies like nonlinear MPC, Lyapunov-based design, and Bayesian optimization. A recurring theme is the development of computationally efficient control strategies for distributed parameter systems. No scientific awards, student advising relationships, or research grants are documented in the available information.

• Machine Learning
• Optimization
• Federated Learning
• +8 more

Peter Richtarik is a Professor at the King Abdullah University of Science and Technology (KAUST), specializing in Machine Learning, Optimization, and Federated Learning. He actively teaches courses such as Stochastic Gradient Descent Methods and mentors PhD and MS students like Konstantin Burlachenko, Kai Yi, and Lukang Sun. His research spans distributed optimization, parameter-efficient fine-tuning, and theoretical frameworks for non-convex and non-smooth problems. Recent work includes 2025 contributions to Bernoulli-LoRA (theoretical PEFT) and Gluon (LMO-based optimizers). He co-developed Thanos (block-wise pruning) and BurTorch (CPU-optimized training framework). His publications focus on communication efficiency ( ATA , LoCoDL ), differential privacy ( DP-RBCD ), and stochastic proximal methods. Richtarik received the Charles Broyden Prize for his work on quasi-Newton methods. He frequently presents at workshops like MLSS in Senegal and FLOW seminars.

Franziska KlüglView profile

Professor

• Multi-Agent Systems
• Agent-Based Simulation
• Artificial Intelligence
• +4 more

Franziska Klügl is a Professor in Computer Science at Örebro University's Faculty of Business, Science and Engineering, affiliated with the Center for Applied Autonomous Sensor Systems (AASS). She currently leads the KKS-funded TeamRob project on Human-Robot Teamwork and serves as Deputy Dean of the faculty since January 2023, chairing the academic appointment committee. Previously, she headed the Computer Science department (2020-2022) and served on the faculty board (2019-2022). Her research focuses on: Multi-agent systems : Development of languages, processes, and tools for agent-based simulation Interdisciplinary applications : Transportation, economics, epidemics, production, and mining simulations Simulation engineering : Integrating AI, machine learning, and formal methods to create accessible modeling tools for domain experts She created SeSAm , a visual programming tool for agent-based simulation that enables rapid prototyping of complex models. Analysis of her recent publications reveals three dominant themes: Human-robot collaboration frameworks and intention recognition systems Economic impacts of automation on labor markets and engineering services Advanced simulation methodologies using affordance theory and reinforcement learning She teaches software engineering, multi-agent systems, and agent-based modeling across multiple programs, including the WASP AI&ML PhD course. She leads research groups at the Machine Perception and Interaction Lab and oversees the TeamRob human-robot teamwork project.

Narges MahyarView profile

Associate Professor

• Human-Computer Interaction (HCI)
• Information Visualization
• Digital Civics
• +4 more

Narges Mahyar is an Associate Professor at the University of Massachusetts Amherst in the Manning College of Information and Computer Sciences (CICS) . She is currently on sabbatical with the Aviz team at the Inria Center, University of Paris-Saclay . Her research focuses on Human-Computer Interaction (HCI) , Information Visualization , and Digital Civics , aiming to empower communities through technology. Education: She holds a PhD in Computer Science from the University of Victoria, an MS in Information Technology from the University of Malaya, and a BS in Electrical Engineering from Tehran Azad University. She completed postdoctoral fellowships at the University of British Columbia (2014–2016) and the University of California San Diego (2016–2018). Research Interests: Her work addresses complex societal challenges like climate change, urban planning, and healthcare by designing inclusive technologies. Key areas include civic engagement, data visualization for equity, and integrating AR/VR for public participation. Notable projects include CommunityClick and RisingEMOTIONS , which enhance public input in decision-making. Publications & Awards: With over 50 publications, her work has received prestigious awards including Best Paper Awards at CHI 2023 , Eurovis 2022 , and CSCW 2020 . Her research emphasizes ethical design and inclusivity, particularly involving marginalized communities. Grants & Advising: She has secured grants totaling over $1.4 million, including NSF funding for projects like "Mapping Instability" . Advises PhD student Mahmood Jasim and collaborates with Ali Sarvghad and Pari Riahi on interdisciplinary research. Labs & Teams: Leads the HCI-VIS Lab at UMass, focusing on social computing and visualization. Collaborates internationally with teams like Aviz and Inria on civic tech initiatives.

Alexander RodríguezView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Time Series Analysis
• +7 more

Alexander Rodríguez is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on advancing AI methods for modeling complex spatiotemporal dynamics, particularly in applications related to population health and community resilience. He specializes in machine learning, time series analysis, uncertainty quantification, and multi-agent systems, with an emphasis on scientific modeling and data-driven decision-making. Recent contributions include keynote talks at AAMAS 2025 (Autonomous Agents for Social Good workshop), presentations at the US National Academies Symposium, and invited talks at AAAI 2025 on topics like knowledge-guided machine learning and public health prediction. He co-organizes AAMAS 2025 as sponsorship co-chair and leads initiatives in AI for science and epidemic forecasting. His publications emphasize neural networks for time series forecasting, biomedical foundation models, and epidemic surveillance systems. Notable work includes 'Neural Conformal Control for Time Series Forecasting' (AAAI 2025) and 'Deepcovid: An operational deep learning-driven framework for explainable real-time forecasting' (2021). No scientific awards explicitly listed in available texts. His research group actively collaborates on grants related to AI applications in public health and infrastructure resilience, with a focus on data-centric methodologies and multi-agent systems.

Shufang ZhuView profile

Lecturer

• Artificial Intelligence
• Formal Methods
• Automated Reasoning
• +3 more

Dr. Shufang Zhu is a Lecturer (Assistant Professor equivalent) at the Department of Computer Science, University of Liverpool , and an Associate Member at the University of Oxford’s Department of Computer Science . Previously, she held roles including Senior Research Associate at Oxford (2023–2024) and Postdoctoral Researcher at Sapienza Università di Roma (2020–2022). She earned her Ph.D. in Software Engineering from East China Normal University (ECNU, 2020) under Prof. Geguang Pu, with a visiting Ph.D. at Rice University (2016–2018) under Prof. Moshe Y. Vardi. Education: B.Sc./Ph.D. in Software Engineering from ECNU (2010–2020). Scholarships include the Chinese Scholarship Council (CSC) and UT Austin EECS Rising Star (2022). Her research focuses on interdisciplinary areas of Formal Methods and Artificial Intelligence , emphasizing automated reasoning, planning, and synthesis. Key topics include temporal logics (LTL/LTLf), symbolic synthesis frameworks, and applications in reactive systems. Notable work addresses finite-trace specifications, best-effort strategies, and coordination in multi-agent systems. Teaching: Lecturer for Game-Theoretic Approach to Planning and Synthesis (European Summer School) and Foundations of Self-Programming Agents (Oxford). She also supervises funded Ph.D. positions, including a 2025 deadline for CSC-Liverpool scholarships. Awards: Future Digileader (Digital Futures, 2023), UT Austin EECS Rising Star (2022). Erdős number ≤3 via Moshe Y. Vardi. Collaborations: Co-chair of AAAI 2023 symposium on temporal logics in AI. Active in open-source tools like LydiaSyft for LTLf synthesis. Engages with academic networks through Google Scholar, DBLP, and GitHub.

Penny KyburzView profile

Professor

• Video Games
• Game Design
• AI
• +4 more

Prof. Penny Kyburz is a Professor and Associate Director (Engagement & Impact) at the School of Computing, Australian National University. She leads the GameFlow Lab and teaches Game Development, focusing on AI, human-AI interaction, and game design. Her work bridges academia and industry, with 20+ award-winning games including four BAFTA nominations. She holds a PhD, B.InfoTech(Hons), G.C.Ed.(Higher Ed.), and G.C.Acad.Prac. Education: Ph.D. B.InfoTech(Hons) Graduate Certificate in Education (Higher Education) Graduate Certificate in Academic Practice Research Interests: Video games, game design, and player experience AI and human-AI collaboration Virtual Reality and Mixed Reality applications Technology policy and digital rights Educational games and accessibility Awards: 4 BAFTA nominations for game contributions High critic scores for usability-tested AAA games Grants/Projects: IDEATE (2024–2028) MEC23: Advanced Biometrics (2023) C2 Sociotechnical Collaboration (2022–2024) Role-Based Deception in Games (2020–2021) Her GameFlow model and book on emergence in games are foundational in player experience research. She advocates for diversity in tech and has advised on digital rights in the Senate.

• Transportation Engineering
• Urban Planning
• Sustainable Mobility
• +5 more

Ravi Seshadri is an Associate Professor in the Transport Division at the Department of Technology, Management and Economics, Technical University of Denmark (DTU). His research focuses on designing equitable, efficient, and sustainable mobility solutions with a focus on fiscal instruments like congestion pricing and tradable permits, as well as emerging mobility modes such as shared and demand-responsive transit. He employs methods from transportation network equilibria, dynamic traffic assignment, and agent-based simulation. His research interests span transportation economics, urban planning, and intelligent transportation systems. Key areas include evaluating the impacts of automated mobility-on-demand systems, optimizing tolling strategies using predictive control and reinforcement learning, and integrating multi-modal transportation networks through game-theoretical frameworks. His work emphasizes real-world applications in urban freight systems, e-commerce logistics, and sustainable urban mobility policies. Recent projects include studying congestion pricing schemes via agent-based microsimulation, analyzing behavioral responses to decarbonization policies, and developing frameworks for tradable credit systems with peer-to-peer trading. He has contributed to both theoretical advancements (e.g., robust traffic assignment models) and applied tools like the SimMobility simulation platform. Ravi's research demonstrates a strong focus on bridging transportation engineering with policy analysis, using cutting-edge computational methods to address complex urban mobility challenges. His work spans academic publications, industry collaborations, and policy consultations to advance sustainable transportation systems.

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +7 more

Vahid Behzadan is an Assistant Professor in Data Science and Computer Science at the University of New Haven's Tagliatela College of Engineering. He leads the SAIL Lab, focusing on AI safety and security, particularly in autonomous systems like driverless cars and smart cities. His work addresses adversarial attacks on machine learning and reinforcement learning, with applications in cybersecurity and healthcare. Behzadan has held prior positions at Kansas State University, University of Nevada Reno, and University of Birmingham, UK. He holds a Ph.D. in Computer Science and an M.S. from the University of Nevada Reno, and a B.Eng. from the University of Birmingham. His research spans AI ethics, cybersecurity, and complex systems. Behzadan advises the UNH hacking team and actively participates in policy initiatives, including Connecticut's AI Working Group and the Connecticut AI Alliance. He has contributed to over 30 peer-reviewed articles and frequently engages in media discussions on topics like AI safety, facial recognition, and cybersecurity threats. Key research areas include adversarial machine learning, AI forensics, and ethical AI design. His work bridges theoretical advancements with real-world applications in transportation, healthcare, and national security. Behzadan collaborates with organizations such as the Transportation Research Laboratory (TRL) and Open Web Application Security Project (OWASP).

Alexandra KirschView profile

Assistant Professor

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +3 more

Alexandra Kirsch is an Assistant Professor in the Media Informatics Department at the University of Tübingen's Faculty of Informatics. She held the Carl von Linde Junior Fellowship at the Technical University of Munich (TUM) Institute for Advanced Study (TUM-IAS) from 2010. Previously, she was a senior research scientist at TUM's Intelligent Autonomous Systems Group and led the independent Junior Research Group “Planning for Adaptive Robot Assistance” within the Excellence Cluster CoTeSys (Cognition for Technical Systems). Education: Diploma in Computer Science from TUM, 2003 Doctoral degree from TUM, completed between 2003-2007 Research Interests: Kirsch focuses on developing control mechanisms for autonomous robots using artificial intelligence, aiming to create systems that collaborate closely and transparently with humans. Her work emphasizes models of world dynamics, robot action effects, and human behavior. She created the Robot Learning Language (RoLL) to automate model acquisition and update processes during robot operations. Collaborations with psychologists and neuroscientists explore joint human-robot planning tasks and model development for seamless interaction. Scientific Awards: Member of the Bayerische Akademie der Wissenschaften Förderkolleg (2012) Award by Comet Computer GmbH for excellent graduation results (2003) Advising & Grants: Managed interdisciplinary research projects during her junior fellowship at TUM. Previously worked as a management consultant at Booz & Co., 2007-2008. Her grants include the Carl von Linde Fellowship and support for the Junior Research Group. Labs/Teams: Active in the Planning for Adaptive Robot Assistance group (CoTeSys) and collaborates with the Cognitive Technology focus group at TUM-IAS. Engages in cross-disciplinary teams involving neuroscience and psychology for human-robot interaction studies.

Dimitri BertsekasView profile

Professor

• Optimization
• Control Systems
• Reinforcement Learning
• +3 more

Dimitri Bertsekas is the Fulton Professor of Computational Decision Making at the School of Computing and Augmented Intelligence , Arizona State University (ASU), where he has been a full-time faculty member since 2019. Previously, he held the position of McAfee Professor of Engineering at MIT (1979–2019). He is renowned for his contributions to optimization, control theory, and artificial intelligence. Education: Bachelor's degree in Engineering from the National Technical University of Athens (Greece). MS in Electrical Engineering from George Washington University (1969). PhD in System Science from MIT (1971). Research Interests: Bertsekas' work bridges theory and application in optimization, control systems, and reinforcement learning. His research emphasizes large-scale computational methods, dynamic programming, and stochastic systems, with significant impacts on machine learning and AI. Publications & Awards: He has authored over 20 books and numerous papers, including seminal works like Neuro-Dynamic Programming and Dynamic Programming and Optimal Control . His accolades include the INFORMS John von Neumann Theory Prize (2018), the IEEE Control Systems Award (2022), and membership in the U.S. National Academy of Engineering. Affiliations: Bertsekas founded Athena Scientific (1995), a publisher of technical books, and serves as Chief Scientific Advisor at Bayforest Technologies (London). He has advised numerous students and held editorial roles for scientific journals.

Lillian RatliffView profile

Associate Professor

• Game Theory
• Optimization
• Machine Learning
• +3 more

Lillian Ratliff is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Washington, holding additional Adjunct Associate Professor positions in the Allen School of Computer Science and Engineering and the Department of Aeronautics and Astronautics. She specializes in research at the intersection of game theory, optimization, machine learning, and control theory, with a focus on decision-making in intelligent systems with learning-enabled components and strategic agents. Her research interests span game theory and economics, optimization, machine learning, and control theory. She develops theoretical frameworks for understanding how intelligent systems make decisions when interacting with strategic agents. Her work bridges theoretical foundations with practical applications in multi-agent learning systems, where she examines how learning algorithms converge to equilibria in strategic environments. She has made significant contributions to understanding the dynamics of gradient-based learning in games, Stackelberg games, and decision-dependent distributions. Her recent publications demonstrate a strong focus on theoretical foundations of game-theoretic learning, with particular attention to convergence properties, equilibrium analysis, and strategic behavior in multi-agent systems. Her work spans zero-sum games, matrix games, Stackelberg games, and decision-dependent learning scenarios, with applications across multiple domains including human-machine interaction and networked systems. NSF Graduate Research Fellowship (2009) NSF CISE Research Initiation Initiative award (2017) NSF CAREER award (2019) ONR Young Investigator award (2020) UW CoE Junior Faculty Award (2021) Invited speaker at NAE China-America Frontiers of Engineering Symposium (2019) Dhanani Endowed Faculty Fellowship (2020) Professor Ratliff's research is supported by multiple NSF grants (current: CNS-1736582, CNS-1836819, CNS-1931718, CNS-1907907, CNS-1844729, CNS-1952011; previous: CNS-1634136, CNS-1646912, CNS-1656873) and an Office of Naval Research Young Investigator award. She has mentored numerous students and collaborators across her extensive publication record, with work appearing in top venues including NeurIPS, ICML, AISTATS, and IEEE conferences.

Abbas EdalatView profile

Professor

• Computer Science
• Mathematics
• Exact Computation
• +7 more

Abbas Edalat is a Professor of Computer Science and Mathematics at Imperial College London, and an Adjunct Professor at the Institute for Research in Fundamental Sciences, Tehran. He leads two research groups: Algorithmic Human Development and Continuous Data-Types and Exact Computation. His work spans computational mathematics, psychotherapy models, and exact real-number computation. Notably, he received the LICS 2017 Test-of-Time Award for foundational contributions to logic in computer science. Research interests include self-attachment psychotherapy, computational differential calculus, topology, and bisimulation in probabilistic systems. He has pioneered exact computation frameworks for real numbers, geometry, and dynamical systems, with applications in neuroscience and artificial intelligence. Professional activities include keynote talks at conferences like IJCNN and workshops on psychotherapy in Iran and the UK. Teaching includes advanced courses on dynamical systems, quantum computing, and computational techniques. He advises PhD students globally and chairs initiatives like the Science and Arts Foundation to expand educational access in developing nations. His interdisciplinary work bridges mathematics, computer science, and clinical psychology.

Joe GeunesView profile

Professor

• Production Planning
• Supply Chain Management
• Logistics
• +3 more

Joe Geunes is a Professor and Associate Department Head for Graduate Affairs in the Department of Industrial & Systems Engineering at Texas A&M University, holding the Mike and Sugar Barnes Professorship. His research focuses on production planning, supply chain management, logistics, and operations optimization. He earned his Ph.D. in Business Administration (Management Science & Operations Research) and M.B.A. from The Pennsylvania State University in 1999 and 1993, respectively. Dr. Geunes has received notable accolades including Fellow of the Institute of Industrial Engineers (2015), Marilyn and L. David Black Faculty Fellow (2022), and Best Reviewer Award from Omega (2022). His work spans infrastructure network restoration, supply chain resilience, and optimization algorithms for logistics systems. Recent projects address railcar operations, distribution network fortification, and disaster response strategies. Education: Ph.D., Business Administration (Management Science & Operations Research), The Pennsylvania State University – 1999 M.B.A., The Pennsylvania State University – 1993 Awards: Fellow, Institute of Industrial Engineers – 2015 Marilyn and L. David Black Faculty Fellow – 2022 Best Reviewer Award, Omega – 2022 Best Application Paper, IISE – 2018 His research integrates mathematical modeling and computational methods to address real-world challenges in supply chain design, inventory management, and infrastructure resilience. Recent publications emphasize multi-modal logistics, robust optimization under uncertainty, and post-disaster network recovery strategies.