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Kyongbum LeeView profile

Professor

• Metabolic Engineering
• Tissue Engineering
• Systems Biology
• +3 more

Kyongbum Lee is the Karol Family Professor and Dean of Engineering at Tufts University School of Engineering, where he also serves as Professor in the Department of Chemical and Biological Engineering. His research integrates metabolic engineering, tissue engineering, and systems biology to study cellular metabolism and develop technologies for biomedical applications. Dr. Lee directs a laboratory focused on host-microbe interactions, cell-based bioprocesses, and metabolic regulation in diseases like obesity. Education Ph.D., Massachusetts Institute of Technology, 2002 B.S., Stanford University, 1995 Research Focus Dr. Lee's group employs systems approaches combining experimental and computational methods to investigate how metabolites regulate cellular communication. Current projects examine gut microbiome interactions, metabolic flux in engineered tissues, and development of therapeutic strategies targeting metabolic disorders. The lab specializes in bioreactor systems, metabolomic profiling, and mechanistic studies of metabolite signaling pathways. Publication Trends Recent publications emphasize gut microbiota metabolism, microbial metabolite therapeutics, and engineered tissue models. Work consistently demonstrates translational focus—from computational prediction of metabolic pathways (e.g., flavonoid biotransformation algorithms) to preclinical validation of microbial metabolites in disease models (e.g., steatosis reduction via indole derivatives). Recurring themes include nuclear receptor modulation, HDAC/IFN-γ pathway regulation, and high-throughput screening platforms. Awards Karol Family Professorship (endowed position recognizing scholarly excellence) Research Operations The laboratory actively recruits postdoctoral researchers and graduate students through Tufts' Chemical and Biological Engineering program. Current projects are supported by specialized infrastructure for metabolomics, bioreactor systems, and 3D tissue culture. The team emphasizes interdisciplinary collaboration, particularly at the interface of engineering, microbiology, and translational medicine.

Qi ZhangView profile

Assistant Professor

• Artificial Intelligence
• Reinforcement Learning
• Trustworthy AI Systems
• +4 more

Qi Zhang is an Assistant Professor in the Department of Computer Science and Engineering, AI Institute at the Molinaroli College of Engineering and Computing, University of South Carolina. His research focuses on developing safe, reliable, and trustworthy AI systems through advancements in reinforcement learning and decision-making algorithms for uncertain environments. He holds a Ph.D. from the University of Michigan (2020) and a B.E. from Shanghai Jiao Tong University (2015). Research interests include artificial intelligence, reinforcement learning, multi-agent systems, and decision-making under uncertainty. Key themes involve leveraging domain knowledge for robust AI solutions and ensuring ethical, transparent, and risk-aware system designs. His work spans applications in autonomous systems, healthcare, robotics, and materials science. Current projects emphasize improving algorithmic trustworthiness, safety, and adaptability across diverse contexts. He is affiliated with the AI Institute at USC and maintains an active research lab focused on these areas.

Alan TsangView profile

Assistant Professor

• Artificial Intelligence
• Multi-agent Systems
• Game Theory
• +7 more

Alan Tsang is an Assistant Professor at Carleton University's School of Computer Science. He specializes in Multi-agent Systems and Computational Social Choice, focusing on strategic interactions in social networks. Previously, he was a Post-Doctoral Researcher at the National University of Singapore and earned his PhD from the Cheriton School of Computer Science at the University of Waterloo under Dr. Kate Larson. His research integrates game theory and agent-based simulations to address fairness, voting mechanisms, and social network dynamics. He is affiliated with Carleton's Institute for Data Science and the Department of Human-Computer Interaction (HCI), and serves as Information Officer of ACM SIGAI. His educational background includes a Master of Mathematics in Graph Theory and a Bachelor of Mathematics in Bioinformatics, both from the University of Waterloo. His work explores interdisciplinary applications, such as modeling pandemic impacts and ethical computing challenges. He teaches courses like Multiagent Systems and Computing, Society, and Ethics, and actively mentors students in graduate and undergraduate research programs. Key research areas include algorithmic fairness, social choice mechanisms, and agent-based modeling of societal systems. He leads the Games, Agents, and Incentives Workshop and collaborates on projects analyzing voting behavior in homophilic networks and fair allocation strategies.

Kevin TomsovicView profile

Professor

• Power Systems Engineering
• Renewable Energy Integration
• Smart Grid Technologies
• +6 more

Kevin Tomsovic is the Chancellor's Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, and Director of the CURENT Engineering Research Center. His expertise spans smart grids, power systems optimization, and cyber-physical resilience. He holds a PhD from the University of Washington (1987) and prior roles at Washington State University. Research focuses on renewable integration, microgrid scheduling, and grid security, with notable contributions to IEEE standards. Awards include IEEE Fellow status. Education: PhD/M.S. Electrical Engineering, University of Washington (1987/1984); B.S. Electrical Engineering, Michigan Tech (1982). Research interests include: - Intelligent systems for power distribution design - Electricity market analysis and stability control - Cybersecurity for grid infrastructure - Resilience quantification in energy systems Publications highlight advancements in grid emulation platforms, adversarial ML defenses, and microgrid optimization. His work is supported by NSF/DOE grants (e.g., NSF EEC-1041877). CURENT testbed facilities enable real-time grid control validation. Awards: IEEE Fellow (20XX). Grants & Collaborations: Lead CURENT NSF ERC; industry partnerships include power utilities and tech firms. Active in developing standards for grid resilience metrics. Labs/Teams: CURENT Engineering Research Center, UT Power Systems Lab.

• Computational Neuroscience
• Neural Networks
• Machine Learning

Christodoulou Chris is a Professor at the Department of Computer Science, University of Cyprus. He joined in 2005 and holds a Visiting Research Fellowship at Birkbeck College, University of London. His educational background includes a BEng in Electronic Engineering from Queen Mary and Westfield College (1991), a PhD in Neural Networks from King's College London (1997), and a BA in German from Birkbeck College (2008). His research focuses on Computational Neuroscience, Neural Networks, and Machine Learning, with specific interests in neural coding, self-control modeling, computational neuronal modeling, multi-agent reinforcement learning, and practical machine learning applications. Recent publications (2013-2025) demonstrate interdisciplinary approaches combining neuroscience, computer science, and optimization techniques, with emerging emphasis on protein structure prediction and biomedical applications. Scientific awards include: Best Paper Award, ICSR 2015 Best Paper Award, MODELS 2008 Doctoral Symposium He leads the Computational Intelligence and Neuroscience (CIN) research group and has secured funding from European projects (SocioCoast, CYberSafety, TAMIT). He mentors students through Google Summer of Code and collaborates internationally.

Ralph DetersView profile

Professor

• Multi-Agent Systems
• Scalability & Dependability of Distributed Systems
• Mobile & Ubiquitous Computing
• +4 more

Ralph Deters is a Professor in the Department of Computer Science at the University of Saskatchewan , where he co-leads the MADMUC (Multi-Agent Distributed Mobile and Ubiquitous Computing) Lab . He has taught courses including CMPT 298, CMPT 350, CMPT 436, CMPT 842, and CMPT 898, and served as co-coordinator for CMPT 400/CMPT 405. Education Ph.D. in Computer Science, Federal Armed Forces University Munich (1998) MS (Diploma) in Computer Science, Technical University Berlin (1992) BS in Computer Science, Technical University Berlin (1988) Research Interests focus on multi-agent systems , scalable distributed systems , mobile/ubiquitous computing , and privacy-preserving technologies . Current lab projects include: Middleware infrastructures for decentralized systems (cloud/fog/mobile) Social software for sustainable user collaboration Decentralized trust-based data storage Persuasive technologies for health, physical activity, and learning Ethical AI and dark patterns in UI design Blockchain applications in IoT and access control Publications highlight trends in digital twins , blockchain integration , synthetic data for privacy , and machine learning for social impact , with recent work spanning health analytics, agricultural AI, and smart city applications. Lab Impact : The MADMUC Lab, established in 2000 with a $895,687 CFI grant, has graduated 120 researchers (17 PhD, 103 MSc), including two Vanier Scholars. The lab emphasizes awards, scholarships, and interdisciplinary collaboration with 152 total alumni.

Walter LuciaView profile

Associate Professor

• Secure and Resilient Control of Cyber-Physical Systems
• Model Predictive Control for Autonomous Vehicles
• Cybersecurity in Control Systems
• +3 more

Dr. Walter Lucia is an Associate Professor at the Concordia Institute for Information Systems Engineering, Concordia University. His research focuses on secure and resilient control of cyber-physical systems and model predictive control strategies for autonomous vehicles. He supervises MASc and PhD students in programs such as Information Systems Security, Electrical and Computer Engineering, and Information and Systems Engineering. His research interests encompass cybersecurity in control systems, including strategies against false data injection and setpoint attacks. He develops resilient control architectures and applies model predictive control to autonomous systems like self-driving cars and mobile robots. Recent work emphasizes data-driven safety mechanisms, encrypted control systems, and collision-free platooning of mobile robots. No scientific awards or grants are explicitly mentioned in the provided text. Dr. Lucia's advising roles include overseeing multiple graduate programs, though specific student names are not listed. His publications reflect a strong focus on control theory applications, cyber-physical system security, and optimization techniques. Labs or collaborative teams are not explicitly detailed in the text, but his research themes suggest involvement in interdisciplinary projects at the intersection of engineering and cybersecurity.

Aakriti UpadhyayView profile

Research Fellow

• Robotics
• Motion Planning
• Topological Data Analysis
• +3 more

Aakriti Upadhyay is a Research Fellow at the Colorado School of Mines (CSM), working in the Dynamic Automata Lab (DyALab) under Dr. Neil T. Dantam. She holds a Ph.D. in Computer Science from the University at Albany, SUNY, where she was part of the Robotics Algorithms and Computable Systems (RACS) lab under Dr. Chinwe P. Ekenna. Her research integrates Computational Geometry, Applied Mathematics, Machine Learning, and Topological Data Analysis (TDA) to advance motion planning for robotics and computational biology applications. Her work focuses on optimizing robot motion paths using topological methods, with extensions to identifying protein interaction sites. Key areas include robust path planning, fault tolerance in robotic systems, and topological modeling of biomolecular surfaces. Her research trends emphasize interdisciplinary applications of TDA, bridging robotics and computational biology. Collaborations include developing tools for Intrinsically Disordered Proteins (IDPs) analysis and enhancing path planning algorithms through geometric and topological insights. Aakriti has contributed to academic initiatives like the Student Activities Committee and advocated for women in engineering during the pandemic. Her work reflects a commitment to advancing both technical and community-oriented academic goals.

Pavan PoudelView profile

Assistant Professor

• Parallel and Distributing Computing Algorithms
• Robotics
• Internet of Things
• +3 more

Dr. Pavan Poudel is an Assistant Professor of Computer Science at the University of Houston-Clear Lake (UHCL), part of the College of Science and Engineering. He joined UHCL in Fall 2024 after working as a Research Engineer in the software industry and completing a postdoctoral research position at Augusta University's Department of Computer and Cyber Sciences. He holds a Ph.D. in Computer Science from Kent State University (2021) and a Bachelor's in Computer Engineering from Tribhuvan University, Nepal. His research focuses on Parallel and Distributed Computing, Robotics, and Fault-Tolerance, with expertise in optimizing scheduling algorithms and designing scalable systems for distributed environments. Dr. Poudel's research includes transactional memory systems, distributed robotics algorithms (e.g., gathering, scattering, and coverage), and fault-tolerant solutions for real-time applications like medical and disaster response. His work bridges theoretical computer science with practical applications, emphasizing efficiency and scalability. He has published extensively in venues like Theoretical Computer Science (TCS) and Information, addressing topics such as time-optimal robot navigation and adaptive versioning in transactional memory. His academic awards include the Best Presentation Award at Kent State’s Graduate Research Symposium (2021) and scholarships like the John Sechrist and Hine Scholarships (2019–2020). He teaches courses such as Computer Organization and Assembly Language and Computer Game Programming. His publications demonstrate a strong focus on distributed systems, robotics algorithms, and concurrency control, reflecting his commitment to advancing scalable and reliable computing solutions.

Anne TempletonView profile

Senior Lecturer

• Crowd Psychology
• Social Identity Theory
• Emergency Response
• +5 more

Anne Templeton serves as a Senior Lecturer in Social Psychology at the University of Edinburgh's School of Philosophy, Psychology and Language Sciences. She holds a UKRI Future Leaders Fellowship and leads the research group Identities and Collective Behaviour . Her academic journey includes a PhD from Sussex, MSc from Surrey, and MA (hons) from St Andrews. Her research centers on applying social identity theory to crowd psychology, particularly examining how group dynamics influence safety during emergencies and mass events. Key focus areas include: psychological crowd modeling, first responder communication strategies, and incorporating social identities into evacuation simulations. She develops behavioral interventions to improve public adherence during crises through computational modeling and virtual reality experiments. Her 15 most recent publications reveal strong thematic consistency across emergency management, crowd dynamics, and social identity applications. Recent work emphasizes high-rise evacuation protocols, tunnel fire egress, COP26 protest support mechanisms, and bystander CPR interventions. The research demonstrates interdisciplinary integration of psychology, computational modeling, and public safety practice. Scientific Recognition: UKRI Future Leaders Fellowship (2022) Research Leadership: Principal Investigator for ESRC, DCMS, and Chief Nursing Office grants totaling over £1.8 million, including the £1.377m UKRI project on first responder communication strategies. Current supervision of four PhD students (Waleed Alhajri, Sam Vo, Sayaka Hinata, Kayleigh Smith). Her knowledge exchange extends through government advisory roles including the Scottish Government COVID-19 Behavioural Insights team (2021-2022), UK Department of Digital, Culture, Media and Sport Science Board (2021), and Sports Grounds Safety Authority consultation (2020). Media engagements feature BBC, Reuters, and The Conversation coverage of Hajj crowd safety and stadium atmosphere research.

Jonathan SprinkleView profile

Professor

• Cyber-Physical Systems
• Autonomous Systems
• Traffic Control
• +3 more

Jonathan Sprinkle is Chair and Professor of Computer Science at Vanderbilt University's School of Engineering, with additional roles as Professor in Civil and Environmental Engineering and Electrical and Computer Engineering. Previously, he was Litton Industries John M. Leonis Distinguished Associate Professor at the University of Arizona and Interim Director of the Transportation Research Institute. His research focuses on model-based cyber-physical systems, autonomous systems, and traffic control, emphasizing industry impact. He holds a Ph.D., M.S., and B.S. in Electrical Engineering from Vanderbilt University and Tennessee Technological University. Key awards include the NSF CAREER Award (2013), UA Distinguished Scholar (2020), and NSF I-Corps Best Team (2012). His work spans traffic wave dissipation, autonomous vehicle safety, and large-scale field experiments like the CIRCLES consortium's 100-AV test. He leads initiatives in data-driven control systems and smart infrastructure through collaborations with NSF and industry. Research highlights include developing control barrier functions for automated vehicles, integrating LLMs for traffic analysis, and deploying ROS-based frameworks for vehicle communication. His contributions aim to bridge theoretical models with real-world applications, addressing challenges in energy efficiency, safety, and scalability.

Vytautas ValaitisView profile

Senior Lecturer

• Macroeconomics
• Labor Markets
• Housing Economics
• +7 more

Vytautas Valaitis is a Senior Lecturer at the University of Surrey , affiliated with the Department of Economics under the Faculty of Arts, Business and Social Sciences. His research focuses on macroeconomics, labor markets, housing economics, and fiscal policy, with a strong emphasis on heterogeneous-agent models and macro-finance intersections. Max Weber Fellow, European University Institute (previous role) PhD recipient from Duke University (2021) His recent work integrates machine learning into macroeconomic modeling to address multicollinearity in state variables, explores wealth distribution's impact on labor supply, and investigates optimal government debt strategies under geopolitical risk. Publications highlight innovative frameworks for defense financing, inflation control through real/nominal bonds, and housing market effects on asset pricing. Scientific awards include the prestigious Max Weber Fellowship . His research underscores the critical role of structural economic modeling in policy design and market stabilization.

Luca SchenatoView profile

Professor

• Control Systems
• Distributed Optimization
• Federated Learning
• +5 more

Luca Schenato is a Full Professor in the Department of Information Engineering at the University of Padova. His research focuses on distributed control systems, federated learning, multi-agent optimization, and wireless communication protocols. He has extensive experience in developing algorithms for cyber-physical systems, with applications in robotics, smart grids, and sensor networks. Education and Appointments section lists his academic journey but lacks explicit details. He has held positions related to control systems and information engineering throughout his career. Research interests include: Design of resilient wireless control systems Federated learning architectures for edge computing Distributed optimization under communication constraints Robotics and multi-agent coordination Smart energy management systems His recent publications (2021–2025) demonstrate a strong focus on: Over-the-air federated learning innovations High-speed wireless control systems (e.g., 1 kHz Wi-Fi control) Resilient distributed optimization algorithms Human-centric building automation He has contributed to numerous projects related to networked control systems and has organized conferences like ECC13. His work emphasizes bridging theoretical control principles with practical industrial applications.

Javad FattahiView profile

Assistant Professor

• Energy Equity and Inclusion
• Distributed Energy Resources (DER)
• Software-Defined Nanogrids
• +6 more

Dr. Javad Fattahi is an Assistant Professor at the School of Electrical Engineering and Computer Science, University of Ottawa. He holds roles as an IEEE Senior Member, Professional Engineer (P.Eng.), and member of the Standards Council of Canada. He leads the IEEE 2030.5 smart-grid interoperability verification initiative as its VP and chief editor. Dr. Fattahi earned his BSc in Electrical Engineering from Iran University of Science and Technology (IUT), followed by graduate studies at Sharif University of Technology (SUT). He completed his PhD and postdoctoral research at the University of Ottawa, specializing in energy systems and optimization. His research focuses on energy equity, distributed energy resources (DERs), software-defined nanogrids, game theory, smart mobility, and nonlinear systems. His recent work emphasizes data-driven digital twins for grid stability, V2G integration in residential complexes, and federated Byzantine agreement models for EV infrastructure. Key contributions include transactive energy systems under IEEE standards and energy disaggregation in institutional buildings. Dr. Fattahi has received accolades including IEEE Senior Membership and P.Eng. certification. His lab develops solutions for grid-edge technologies, smart mobility, and resilient energy systems. Graduate opportunities are available via his UniWeb page.

Elena TroubitsynaView profile

Professor

• Autonomous Systems
• Software Engineering
• Formal Verification
• +3 more

Elena Troubitsyna is a Professor of Computer Science with specialization in Software Engineering at KTH Royal Institute of Technology. Her research focuses on developing dependable, autonomous systems that ensure safety and reliability, particularly in complex environments like self-driving cars and drones. She employs rigorous mathematical modeling and verification techniques to address system complexity and real-time adaptability challenges. Her work emphasizes the co-engineering of safety and security in cyber-physical systems, integrating formal methods such as Event-B modeling with AI-driven solutions. Key research areas include cybersecurity for embedded systems, formal analysis of safety-security interactions, and resilient multi-agent systems. Elena has contributed to advancing methods for autonomous system navigation, fault tolerance, and privacy-preserving microservices architectures. Elena has organized international workshops like SENSEI (Safety-Security Interaction) and published extensively on topics such as model-driven engineering, formal verification of critical systems, and optimizing scheduling for distributed computing. Her research bridges theoretical foundations with practical applications, aiming to enhance societal trust in autonomous technologies.