Maryam Kamgarpour is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), School of Engineering. She previously held faculty positions at the University of British Columbia and ETH Zürich. Her work bridges stochastic control , multiagent learning , and game theory , focusing on safety-critical systems. Education: PhD in Engineering from UC Berkeley, BSc in Applied Science from University of Waterloo. Research Interests: Control under uncertainty, game theory, mechanism design, mixed-integer optimization, and applications to transportation, robotics, power grids, and healthcare. Her recent publications emphasize safe reinforcement learning , multirobot coordination , and stochastic trajectory planning , with applications to aircraft navigation and energy systems. She has received the European Union ERC Starting Grant, NASA High Potential Individual Award, and IEEE Transactions on Control of Network Systems Outstanding Paper Award. Scientific Awards: ERC Starting Grant (2016-2021) NASA High Potential Individual Award (2010) NASA Excellence in Publication Award IEEE Outstanding Paper Award (2022) PhD Students: Jordan Philip Christopher Maddux Anna Maria Ni Tingting Ren Kai Salizzoni Giulio Schlaginhaufen Andreas Vaishampayan Saurabh Dilip Vallat Gabriel Rémi Former EPFL student: Guo Baiwei
Christopher Pal is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. With a Ph.D. from the University of Waterloo, he has held academic positions at the University of Rochester and the University of Toronto, and industry roles at Interval Research and Microsoft Research's Interactive Visual Media Group. Fields of Expertise: Artificial Intelligence, Computer Vision, Pattern Recognition, Machine Learning, and Natural Language Processing Affiliations: CIFAR Chair in Artificial Intelligence, Institute for Data Valorization (IVADO) Member His research focuses on deep learning applications in visual question answering , medical image segmentation , and generative models . Recent work involves multimodal data analysis for climate modeling and vision-language systems for code generation. Key projects include CarbonSense for climate flux modeling and GeoCoder for geometry problem-solving AI. His 15 most recent publications (2023-2025) span topics from diffusion models to multi-agent systems , with emphasis on video generation , 3D animation , and environmental applications . Scientific recognition includes: CIFAR Chair in Artificial Intelligence IVADO Institute Membership Top-2% cited researcher (2021) He has supervised 22 Ph.D. and Master's students, with recent graduates working on generative AI , reinforcement learning , and medical imaging . Current research grants include MITACS-funded projects in software engineering agents and drone imagery analysis for tropical forest conservation.
Ioannis Lambadaris is a Full Professor and Chancellor’s Professor at Carleton University's Department of Systems and Computer Engineering, Faculty of Engineering and Design. Holding a Ph.D. from the University of Maryland, he has contributed extensively to network performance analysis over 25+ years. Specializes in stochastic processes, cloud computing, and wireless edge systems Led Ericsson 5G Chair initiatives Supervised over 70 graduate students His research spans QoS control , VNF placement optimization , and IoT indoor localization , with over 170 publications. Recent work focuses on reinforcement learning and deep learning in network resource allocation. Scientific Recognition: Chancellor’s Professor Ericsson 5G Chair Contact: ioannis@sce.carleton.ca | Office: Mackenzie 4448, Ottawa, ON
Nicholas Bambos is the R. Weiland Professor in the School of Engineering at Stanford University, holding a joint appointment in the Department of Electrical Engineering and the Department of Management Science & Engineering. He served as the Fortinet Founders Department Chair of the Management Science & Engineering Department from 2016 to 2020. His academic career spans over three decades, with previous positions as an assistant professor (1989-1995) and tenured associate professor (1995-1996) at UCLA before joining Stanford in 1996. Prof. Bambos's primary research interests focus on the architecture and high-performance engineering of computer systems and networks, along with data analytics emphasizing medical and health-care applications. His work spans multiple domains including networking and the Internet, cloud computing, multimedia streaming, computer security, and digital health. Methodologically, his contributions extend to network control, online task scheduling, routing and distributed processing, and machine learning and artificial intelligence. His research has resulted in over 300 peer-reviewed publications that demonstrate a strong interdisciplinary approach, bridging theoretical computer science with practical healthcare applications. The trajectory of Prof. Bambos's recent publications reveals a strategic expansion from traditional networking and systems research into healthcare analytics, particularly opioid use prediction and digital health monitoring. His work increasingly integrates machine learning techniques with domain-specific medical knowledge, showing a clear evolution toward solving complex societal challenges through technological innovation. Many publications demonstrate collaborative work across engineering, medical, and data science disciplines, reflecting the growing importance of interdisciplinary research in addressing modern healthcare challenges. His significant scientific achievements have been recognized through numerous prestigious awards: R. Weiland Professorship in Engineering (2016-present) Eugene L. Grant Teaching Award (2014) IBM Faculty Award (2002) Cisco Systems Faculty Scholar (1999-2003) National Young Investigator Award from NSF (1992-1997) Prof. Bambos has graduated over 40 doctoral students who have gone on to leadership positions in academia, Silicon Valley industries, technology startups, finance, and venture capital. His research has been supported by significant funding, including a $30 million Stanford Networking Research Center which he directed from 1999 to 2005. Beyond traditional academic roles, he has served on various editorial boards, scientific committees, and as a consultant and co-founder of technology startups, demonstrating his commitment to translating academic research into real-world impact. He leads the Computer Systems Performance Engineering Lab (Perf-Lab) at Stanford, which comprises doctoral students and industry visitors engaged in various research projects. His lab serves as an interdisciplinary hub connecting theoretical computer science with practical applications in healthcare, energy, and networking domains. The lab's collaborative environment fosters innovation across traditional academic boundaries, reflecting Prof. Bambos's broader research philosophy of addressing complex problems through integrated, multi-disciplinary approaches.
Professor Minyue Fu is an Honorary Professor in the School of Engineering at the University of Newcastle, Australia, specializing in Electrical and Computer Engineering. With over 30 years of research experience, he has established himself as a leading expert in control systems and signal processing, having published over 500 research papers with an H-index of 55. His academic journey began with a Bachelor's degree from the University of Science and Technology of China, followed by M.S. and Ph.D. degrees from the University of Wisconsin-Madison. Prof. Fu's research interests span a broad range of topics in control theory and signal processing. His work consistently focuses on fundamental theoretical problems with practical applications in diverse fields including power systems, sensor networks, multi-agent systems, and cyber-physical systems. He has made significant contributions to distributed control algorithms, stochastic systems, quantization effects in control, and networked systems. His recent publications (2021-2024) demonstrate continued productivity and relevance in the field, with research spanning decentralized optimal control, anomaly detection in cyber-physical systems, cart-pole control systems, and mean-field games. These works reflect his ability to bridge theoretical control concepts with practical engineering challenges, particularly in the context of modern networked and distributed systems. Fellow of IEEE (2004) Fellow of IFAC (2022) Fellow of Engineers Australia Fellow of Chinese Association of Automation (2018) Throughout his career, Prof. Fu has held significant editorial positions including Editor of IEEE Transactions on Signal Processing (2010-2014) and Associate Editor for several prestigious journals. His research has been supported by numerous grants, though specific details aren't provided in the current text. His laboratory work has focused on practical implementations of control algorithms in various systems, demonstrating the real-world applicability of his theoretical contributions. Prof. Fu has also supervised numerous students throughout his career, though specific names aren't listed in the available information.
Giuseppe FRANZE' is a Full Professor at the Department of Mechanical, Energy and Management Engineering (DIMEG) of the University of Calabria since 2022. He has over 200 publications in archival journals, book chapters, and conference proceedings, with a focus on constrained predictive control, networked control systems, and resilient control for cyber-physical systems. His research includes theoretical and applied projects funded by MIUR/MUR, European Union, and international institutions. IEEE Senior Member (2019) Associate Editor for IEEE/CAA Journal of Automatica Sinica Guest Editor for Special Issue on Resilient Control in Large-Scale Networked Cyber-Physical Systems Organized sessions at CoDIT, ETFA, CASE conferences Collaborations with Carnegie Mellon, Concordia University, Georgia Tech, and others His research spans constrained predictive control, fault-tolerant strategies, obstacle avoidance for autonomous vehicles, and machine learning integration in control systems. Recent articles emphasize resilient control under network attacks, encrypted MPC, and reinforcement learning for multi-agent systems. He received Best Paper and Best Reviewer awards at international conferences. Best Paper - CoDIT’19 Best Reviewer - IEEE ICAS 2021 FRANZE' has taught undergraduate and graduate courses in Automatic Control, Digital Control, and Robotics at the University of Calabria for over 25 years. He has chaired institutional committees, including the Degree Course Council for Automation Engineering and the Research Committee at DIMEG. His scientific partnerships include institutions like Concordia University, Northeastern University, and Université Libre de Bruxelles.
Mohammadhossein Malmir is a Researcher at the Chair of Robotics, Artificial Intelligence and Real-time Systems at Technische Universität München (TUM). He joined the chair in November 2019 and currently contributes to the A-IQ Ready project (successor of AI4DI ), focusing on learning algorithms for industrial manipulators and mobile robots in manufacturing and intralogistics tasks. Education B.Sc. in Electrical Engineering (Control Systems) from Amirkabir University of Technology (Tehran Polytechnic) (2014) M.Sc. in Automation and Control Engineering from Politecnico di Milano (2018) His research addresses Sim2Real Transfer , Reinforcement Learning , Robust Control , and Model Predictive Control for autonomous robotic systems. He has published extensively on sim2real policy transfer, domain randomization, and control algorithms for manipulation and navigation tasks. Recent publications highlight his work in dexterous grasping , continual domain adaptation , and deep reinforcement learning for trajectory optimization . His advising roles include supervising 13 master's students and interdisciplinary projects, with co-advisorship on topics like vision-based robotic grasping and socially compliant path planning . He teaches courses on Cognitive Systems and Cloud-Based/Simulation-Based Machine Learning in Robotics , emphasizing reinforcement learning and sim2real methodologies.
Georgios Vouros is a Professor at the Department of Digital Systems within the School of Information and Communication Technologies at the University of Piraeus. He previously served as a professor at the Department of Information and Communication Systems Engineering of the University of the Aegean (1998-2011), where he was department president for five years (2000-2005) and later dean of the School of Sciences (2006-2010). He currently directs the Artificial Intelligence Laboratory and the Inter-Institutional Master's Degree in Artificial Intelligence. Dr. Vouros holds a degree in Mathematics and a PhD in Artificial Intelligence from the National and Kapodistrian University of Athens. His research spans theoretical and applied artificial intelligence with emphasis on knowledge representation systems. His work integrates cognitive modeling with practical AI applications, particularly in multi-agent environments where reinforcement learning techniques are deployed for complex decision-making processes. He has pioneered approaches in conceptual knowledge representation that bridge symbolic AI with modern machine learning paradigms. Professor Vouros has served as president of the Hellenic Society for Artificial Intelligence for six years and has extensive experience in international research collaboration, having directed multiple EU-funded projects including datAcron and DART, with current focus on Reinforcement Machine Learning applications in Air Traffic Management. He has supervised 12 completed doctoral theses and currently guides 3 doctoral candidates and 3 post-doctoral fellows, in addition to numerous undergraduate and graduate students throughout his academic career. His research has been supported through significant grants including FP7/Grid4All and FP7/SEMAGROW projects. Professor Vouros directs the Artificial Intelligence Laboratory at the Department of Digital Systems and co-directs the Inter-Institutional Master's Degree "Artificial Intelligence" in collaboration with the Institute of Science & Technology of the NCSR "Demokritos".
Dr Vladimir Gusev is an interdisciplinary researcher at the University of Liverpool , currently serving as a Lecturer in Computer Science within the School of Electrical Engineering, Electronics and Computer Science. He previously led the Leverhulme Research Centre for Functional Materials Design for four years, integrating Chemistry and Computer Science to advance materials discovery. Research Focus: Optimization algorithms, machine learning, automated reasoning, and combinatorial problems in discrete structures. Collaborations: Active in the Materials Chemistry research group, working with teams across Mathematics, Computer Science, and Chemistry departments. His recent work involves multi-agent reinforcement learning for crystal structure optimization, data-driven property prediction in materials science, and automated reasoning techniques for exploring chemical space. Publications emphasize geometric optimization, materials informatics, and algorithmic complexity. Dr Gusev supervises graduate theses on topics like machine learning for compositional data , crystal structure optimization , and experimental design in materials discovery .
Joachim Baumeister is a Professor at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems within the Institute of Computer Science at the University of Würzburg's Faculty of Mathematics and Computer Science. While his primary employment since September 2010 has been at denkbares GmbH, a company specializing in knowledge-based systems, he continues to regularly give lectures at the university. His research focuses on Semantic Information Systems, Knowledge Graphs, Deep Learning applications, Natural Language Processing, and Knowledge-based Configuration for Industry 4.0. Professor Baumeister's work bridges theoretical AI research with practical industry applications, particularly in knowledge-based configuration systems and semantic technologies. His recent publications (2020-2024) reveal a strong emphasis on product configuration systems, semantic knowledge representation, regulatory document processing, and knowledge-based systems. His research has evolved from foundational work on semantic wikis and knowledge engineering to more recent applications involving deep learning and large language models, demonstrating adaptability to emerging technologies while maintaining focus on practical knowledge representation problems. Professor Baumeister's work demonstrates significant contributions to case-based reasoning, knowledge configuration, and semantic technologies, with applications spanning regulatory compliance, industrial configuration systems, and document processing. His current research areas include: Semantic Information Systems and Knowledge Graphs Deep Learning for Image Recognition and Language Understanding Knowledge-based Configuration for Industry 4.0 Natural Language Processing Intelligent Personal Assistants and Chat Bots Though specific students aren't listed in the provided information, Professor Baumeister actively invites students to contact him regarding projects, bachelor theses, and master theses in his areas of expertise. His work at denkbares GmbH focuses on the design, implementation, and evolution of knowledge-based systems and semantic information systems.
Kunal Garg is an Assistant Professor in the Mechanical and Aerospace Engineering program at Arizona State University's School of Engineering for Matter, Transport and Energy. He holds a PhD and Master of Engineering in Aerospace Engineering from the University of Michigan (2021, 2019). Prior to joining ASU, he was a Postdoctoral Associate at MIT's Laboratory for Information & Decision Systems (LIDS) and Department of Aeronautics and Astronautics. His research focuses on integrating control theory with machine learning for autonomous systems, with emphasis on: Robust control synthesis for multi-agent coordination Finite/fixed-time control under spatiotemporal constraints Deadlock resolution in robotics using foundation models (LLMs/VLMs) Safety-critical control via barrier functions Recent publications demonstrate strong focus on neural control barrier functions, deadlock resolution for multi-robot systems, and failure prediction methods. His work frequently appears in top robotics and control conferences including ICRA, CDC, and TRO. Awards & Honors: 2022 DAAD AInet Fellow (AI and Robotics domain) Pierre T. Kabamba Award for Excellence in Control Systems (2021) Richard and Eleanor Towner Prize for Distinguished Academic Achievement (2021) He advises MS students through programs like ASU's MORE fellowship and teaches MAE 494/598: Design and Analysis of Nonlinear Controls. His research group develops open-source tools like the NeuralFaultDetector framework for model-free fault detection.
Elizabeth M. Daly is a Research Scientist at IBM Research Laboratory, Dublin, and an Adjunct Assistant Professor at Trinity College Dublin's School of Computer Science and Statistics. Her work focuses on interactive AI , human-centered design , and fairness in algorithmic systems . She contributes to AI governance and LLM safeguarding through projects like AutoFair and Granite Guardian. Ph.D. in Computer Science (2007), Trinity College Dublin Thesis: Social Network Analysis for Routing in Disconnected Delay-Tolerant MANETs Her research interests span: Interactive AI : Facilitating AI-human negotiation for common objectives Trustworthy AI : Addressing fairness, accountability, and transparency in industrial applications Explainable AI : Developing tools like AIMEE for model exploration and editing She serves on the program committees of top conferences (RecSys, IUI, WWW, UMAP, ICWSM) and the Royal Irish Academy’s committee on Engineering and Computer Science. Notable scientific award: ACM Distinguished Member . Projects: AutoFair : Human-compatible automation of fairness in AI AIMEE : AI model explorer and editor tool Usage Governance Advisor : Translating AI intent into governance frameworks She leads the Interactive AI Group at IBM Research Europe - Ireland.
Arend Hintze is a Professor of Microdata Analysis at Dalarna University's Department of Information and Technology. His research bridges artificial intelligence, evolutionary biology, and computational psychiatry, focusing on neuroevolution, cellular automata, and digital health applications for bipolar disorder. Key research themes include: Evolutionary dynamics in computational models AI for mental health prediction Cellular automata and self-replication Large language model behavior analysis Recent publications demonstrate interdisciplinary work across computer science, genetics, and psychiatry, with emphasis on: Neuroevolution and information transfer Fitness landscape navigation Bipolar disorder early warning systems Evolutionary game theory applications His work frequently employs agent-based modeling, digital evolution, and deep learning techniques.
Roozbeh Mottaghi is a Senior AI Research Scientist Manager at Meta's Fundamental AI Research (FAIR) group and an Affiliate Associate Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington. His work bridges academic research and industrial AI development, focusing on embodied artificial intelligence, robotics, and computer vision. Dr. Mottaghi received his Ph.D. in Computer Science from UCLA under the supervision of Alan Yuille. He completed his Master's degrees at Simon Fraser University and Georgia Institute of Technology, and earned his Bachelor's degree from Sharif University of Technology. Prior to his current positions, he was a Postdoctoral Researcher at Stanford University and Research Manager of the PRIOR team at the Allen Institute for AI. Dr. Mottaghi's research focuses on embodied AI , where agents learn to interact with and understand their physical environments. His work spans robotics , computer vision , and human-robot interaction , with particular emphasis on 3D scene understanding, visual reasoning, and language-vision integration. His research addresses fundamental challenges in how AI systems can perceive, navigate, and manipulate the physical world through embodied experiences. His recent publications demonstrate a strong trend toward increasingly sophisticated embodied AI systems capable of complex multi-agent collaboration, open-vocabulary understanding, and human-like reasoning about physical environments. His work bridges simulation and real-world robotics, with significant contributions to benchmark creation and standardized evaluation frameworks for embodied AI. Dr. Mottaghi's work has been recognized with several prestigious honors including: Outstanding Paper Award at NeurIPS 2022 Multiple oral presentations at top-tier conferences (CVPR, ICCV) Spotlight presentations at major computer vision conferences Dr. Mottaghi has mentored numerous students and researchers, including PhD students and Pre-doctoral Young Investigators. His advising style emphasizes both theoretical rigor and practical implementation, preparing students for successful careers in both academia and industry. His work has been supported by significant research funding from Meta and previously from the Allen Institute for AI. Dr. Mottaghi has been instrumental in developing AI2-THOR, RoboTHOR, and Habitat simulation platforms, which have become standard tools in the embodied AI research community. His leadership in the PRIOR team at AI2 and currently at Meta's FAIR has driven significant advances in how AI systems understand and interact with the physical world.
Mengxiao Zhang is an Assistant Professor in the Department of Business Analytics at the University of Iowa's Tippie College of Business. Her research focuses on online learning, reinforcement learning, and game theory, with applications in fair multi-agent systems and contextual bandits. Her work spans algorithmic design for online convex optimization, fairness-aware social welfare optimization, and supply chain coordination under unknown demand distributions. Key contributions include advancements in contextual bandit algorithms with general value functions and feedback graphs. Recent publications highlight trends in no-regret learning, personalized reward optimization, and budget-constrained autobidding systems. She has presented at premier venues including NeurIPS and COLT, with invited talks at institutions like the University of Illinois Urbana-Champaign.
