Henrik Sandberg is a Professor at the Division of Decision and Control Systems , KTH Royal Institute of Technology , Stockholm, Sweden. He holds the title of Deputy Head of Division and is affiliated with the School of Electrical Engineering and Computer Science . Education: MSc in Engineering Physics (1999) PhD in Automatic Control (2004) from Lund University Postdoctoral position at Caltech (pre-2007) Research Interests: Focus on cyber-physical systems security , power systems , model reduction , and fundamental limitations of control systems . Key sub-areas include attack detection , networked control , privacy-preserving estimation , and resilient control architectures . Publications: Over 150 papers across IEEE Transactions and Automatica , covering topics like stealthy attacks , distributed control , LQG optimization , and thermodynamic costs in filtering . Recent work includes LWE-based encrypted control and Bayesian deception mechanisms . Scientific Awards: Best Student Paper Award Finalist at IEEE CASE 2014; Best Student-Paper Award at IEEE CDC 2004. Grants & Projects: Leads the DYNACON project (WASP Cybersec cluster) and collaborates on CERCES (critical infrastructure resilience). Serves as examiner for multiple advanced courses in cybersecurity and control systems. Contact: Email: hsan@kth.se Phone: +46 (0)8 790 7294 Room: A:607, Malvinas Väg 10, Stockholm
James McCann is an Associate Professor at the Carnegie Mellon Robotics Institute, where he leads the Carnegie Mellon Textiles Lab. He has been a faculty member since May 2017 after working at Disney Research Pittsburgh. McCann's academic journey includes a PhD from Carnegie Mellon advised by Nancy Pollard, followed by a postdoc at Adobe Research and a period developing video games. McCann's research focuses on building creative tools that operate in real-time and build user intuition, with particular emphasis on textiles fabrication and machine knitting. His work spans computer-aided fabrication, simulation, graphics, and creative tools development. He has pioneered systems for machine knitting design, including compilers for knitting instructions and tools for automatic conversion of 3D meshes to knitting patterns. His recent publications demonstrate a strong trend toward computational textiles, with significant contributions to knitting semantics, deployable textile structures, and applications of machine knitting in healthcare and robotics. McCann's work bridges computer science, robotics, and textile arts, creating practical systems for once-off manufacturing with industrial knitting machines. McCann actively mentors students, with current PhD candidates working on solid knitting machines, knit calibration, and assistive devices. His teaching portfolio includes courses on Real-Time Graphics, Algorithmic Textiles Design, and Game Programming. He has taught at CMU since 2017, developing innovative courses that blend computer science with physical fabrication. As director of the Textiles Lab, McCann oversees research projects spanning machine knitting, robotic painting, and real-time graphics systems. His lab develops practical tools for creators, emphasizing intuitive interfaces and real-time feedback that lower barriers to advanced fabrication techniques.
David Duvenaud is an Associate Professor at the University of Toronto , holding a Canada Research Chair in Generative Models and a Schwartz Reisman Chair in Technology and Society . He is cross-appointed to the Department of Computer Science and Department of Statistical Sciences . A Sloan Research Fellow and founding member of the Vector Institute , his work bridges deep probabilistic models , AI safety , and scientific computing . PhD in Machine Learning (University of Cambridge, 2014) Postdoc in Hyperparameter Optimization (Harvard University, 2016) Co-founded Invenia (energy forecasting company) His research spans foundational Neural Ordinary Differential Equations (NeurIPS 2018 Best Paper) and Automatic Chemical Design (ACS Central Science 2018) to recent work on AGI governance (2025) and AI safety (2024). Key contributions include stochastic variational inference , implicit differentiation frameworks , and antisymmetrization layers for quantum Monte Carlo. Recent publications (2024-2025) focus on systemic existential risks from AI , many-shot jailbreaking attacks , and epistemic uncertainty quantification . His group trains energy-based models with scalable MCMC samplers and develops invertible neural architectures (e.g., Residual Flows NeurIPS 2019). He also explores human-AI alignment through LLM Processes (NeurIPS 2024) and Sycophancy in Language Models (ICLR 2024). Canada Research Chair (2025) NSERC Grant (2025) Sloan Research Fellow (2021) Schwartz Reisman Chair (2021) Best Paper Award (NeurIPS 2018) Distinguished Paper Award (ICFP 2021) His students include James Requeima , Jesse Bettencourt , and Raymond Douglas . He teaches courses on Statistical Methods for Machine Learning and Differentiable Inference . Current work (2025) investigates systemic human disempowerment through incremental AI capabilities and sabotage risk mitigation via hyperparameter-aware evaluations.
Sanjay Jain is a Provost's Chair Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). His research focuses on theoretical computer science with particular emphasis on inductive inference, recursion theory, complexity theory, and computational learning theory. Education: B.Tech. in Computer Science from Indian Institute of Technology Kharagpur, India (1986) M.S. in Computer Science from University of Rochester, USA (1988) Ph.D. in Computer Science from University of Rochester, USA (1990) Professor Jain's research spans multiple areas of theoretical computer science. His primary contributions are in computational learning theory, where he has made significant advances in understanding the intrinsic complexity of language identification and the limits of inductive inference. His work on recursion theory explores fundamental questions about computability and complexity, while his research in complexity theory addresses structural aspects of computational problems. A notable achievement was his work on "Deciding Parity Games in Quasipolynomial Time," which won the prestigious STOC 2017 best paper award and later the EATCS-IPEC Nerode Prize. Professor Jain's publication record shows a consistent focus on theoretical foundations of computer science, particularly in learning theory and computational complexity. His recent work has expanded into automatic structures, semiautomatic models, and connections between computational learning and algebraic structures. There is a clear progression from foundational work on language identification to more complex models involving automatic functions, transducers, and connections to mathematical logic. Scientific Awards: STOC 2017 Best Paper Award for "Deciding Parity Games in Quasipolynomial Time" EATCS-IPEC Nerode Prize (2021) Professor Jain has served on the editorial board of Information and Computation and has been actively involved in the academic community through program committee memberships for major conferences including COLT, ALT, LATA, TAMC, and PRICAI. He has held leadership roles as program co-chair for ALT 2000 and ALT 2013, and conference chair for ALT 2005. His work has been supported by various research grants, though specific details are not provided in the available materials. Professor Jain leads research in theoretical computer science at NUS, where he has built a strong research group focused on computational learning theory and related areas. His work often involves collaborations with researchers from around the world, particularly with Frank Stephan, with whom he has co-authored numerous papers. His research group has made significant contributions to understanding the fundamental limits and possibilities of computational learning models.
Xiaoming Hu is a Professor at the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH Royal Institute of Technology (Kungliga Tekniska Högskolan) in Stockholm, Sweden. Born in Chengdu, China, he received his B.S. degree from University of Science and Technology of China in 1983, followed by M.S. and Ph.D. degrees from Arizona State University in 1986 and 1989 respectively. After serving as a research assistant at the Institute of Automation, Chinese Academy of Sciences (1983-1984), he was a Gustafsson Postdoctoral Fellow at KTH (1989-1990) before becoming a faculty member. His educational background includes: B.S. in Engineering, University of Science and Technology of China, 1983 M.S. in Engineering, Arizona State University, 1986 Ph.D. in Engineering, Arizona State University, 1989 Xiaoming Hu's research primarily focuses on multi-agent systems, nonlinear feedback stabilization, nonlinear observer design, and sensing and active perception. His work bridges theoretical control theory with practical applications in robotics and autonomous systems. He has made significant contributions to geometric control theory, mathematical systems theory, and nonlinear systems analysis and control. His research often involves developing theoretical frameworks for distributed control, formation control, and cooperative behavior in multi-robot systems. Professor Hu's publication record shows a consistent research trajectory with numerous high-impact publications in top-tier journals like Automatica, IEEE Transactions on Automatic Control, and Systems & Control Letters. His research has evolved from fundamental control theory to more applied problems in robotics and multi-agent systems, while maintaining strong mathematical foundations. Recent work shows increasing focus on safety-critical control, inverse problems in estimation, and networked systems. His scientific contributions include: Development of theoretical frameworks for multi-agent coordination and formation control Advances in nonlinear observer design for robotic systems Contributions to geometric control theory and systems theory Research on distributed estimation and control algorithms Applications of control theory to robotics and autonomous systems Professor Hu teaches several advanced courses including Mathematical Systems Theory, Geometric Control Theory, and Nonlinear Systems: Analysis and Control. He has supervised numerous degree projects at both undergraduate and graduate levels in mathematics, optimization, systems theory, and scientific computing. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications of control theory.
Karl Henrik Johansson is a Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm, Sweden, where he also serves as the Founding Director of Digital Futures. He is a Fellow of both IEEE and the Royal Swedish Academy of Engineering Sciences, and has held leadership positions including Immediate Past President of the European Control Association and IEEE Control Systems Society Vice President Diversity, Outreach & Development. Dr. Johansson earned his MSc in Electrical Engineering and PhD in Automatic Control from Lund University. His academic journey includes visiting positions at prestigious institutions such as UC Berkeley, Caltech, and NTU. His research focuses on networked control systems and cyber-physical systems with applications in transportation, energy, and automation networks. His work investigates fundamental challenges in connecting physical world systems through communication networks, exploring how wireless communication and sensor technology can enhance system robustness, reliability, energy efficiency, and safety. Current research directions include security of cyber-physical systems, distributed optimization, multi-agent systems, and applications to intelligent transportation and energy networks. Analysis of his recent publications reveals a strong focus on distributed optimization algorithms, secure networked control, multi-agent systems, and applications to transportation and energy networks. His work increasingly integrates machine learning techniques with traditional control theory, addressing challenges in privacy-preserving distributed computation, resilient state estimation, and resource allocation in complex networked systems. IEEE Control Systems Society Hendrik W. Bode Lecture Prize (2024) Swedish Research Council Distinguished Professor (2018-2027) Wallenberg Scholar (2009-2026) IFAC Young Author Prize IEEE CSS Distinguished Lecturer (2017-2019) IFAC Outstanding Service Award IEEE Fellow Dr. Johansson has supervised over 100 postdocs and PhD students, with many now holding prominent positions at institutions worldwide. His research has been supported by significant grants including the Swedish Research Council Distinguished Professor Grant (2018-2027), multiple Wallenberg Foundation grants, and numerous EU and national research projects. He has directed major research centers including ACCESS Linnaeus Centre (2009-2016) and Strategic Research Area ICT TNG (2013-2020). His research group operates within the Digital Futures initiative and maintains strong connections with industry partners through projects like the Integrated Transport Research Lab (supported by Scania and Ericsson) and Smart Mobility Lab. The group actively collaborates with international institutions and participates in major EU-funded projects addressing challenges in cyber-physical systems, transportation, and energy networks.
C. S. George Lee is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering, located in West Lafayette. His research focuses on Robotics, Transfer Learning, Neuro-fuzzy Systems, Automatic Controls, and Computer Engineering. He holds a BSEE (1973), MSEE (1974) from Washington State University, and a PhD (1978) from Purdue University. His work integrates computational intelligence with AI, robotics, and education technology, emphasizing human-machine co-learning models and bilingual systems. His contributions span domains like quantum computing, generative AI, and knowledge graph applications. He leads the Art Lab at Purdue and has published extensively on topics ranging from humanoid robotics to cross-cultural educational platforms. His research areas include developing intelligent agents for edutainment, robotic assistants for student learning, and advanced machine learning techniques. Notable trends in his publications involve computational intelligence applied to bilingual language models (e.g., Taiwanese/English co-learning), quantum-based AI systems, and human-centric robotics. He has explored applications in healthcare (e.g., blood donor analysis), autonomous navigation, and game AI (e.g., Go). His work often bridges theoretical advancements with real-world implementations, such as Java software tools for motor activity assessment (JKinect) and AI-driven platforms for skill evaluation. Lee's research emphasizes interdisciplinary collaboration, with contributions to IEEE conferences and cross-institutional projects. His lab develops tools for adaptive e-learning, robotic task performance evaluation, and human pose estimation using neural networks. Despite prolific publishing, no specific grants or awards are explicitly mentioned in the provided text. His work continues to explore the intersection of human intelligence and smart machines through platforms like Metaverse integration and BCI (Brain-Computer Interface) applications.
Ming Cao is a Full Professor at the University of Groningen (Netherlands), holding positions in the Department of Discrete Technology and Production Automation, the Engineering and Technology Institute Groningen, and serving as Chair of the Jantina Tammes School of Digital Society, Technology and AI. His academic roles include Director of the Jantina Tammes School and membership in prestigious organizations such as the International Federation of Automatic Control (IFAC) and the European Commission’s DG CNECT. Cao’s research focuses on multi-agent systems, autonomous robotics, complex networks, and cooperative control, with applications in robotics, epidemic modeling, and biomimetic sensors. Education: PostDoc in Mechanical Engineering from Princeton University (2008), PhD in Electrical Engineering from Yale University (2007). Research Interests: Multi-agent systems, distributed decision-making, cooperative control, robotic teams, seal whisker-inspired flow sensing, and privacy-preserving control systems. Recent Trends in Articles: Recent work emphasizes co-evolutionary dynamics in social-technical systems, privacy in control systems, and biomimetic robotics. Key topics include feedback mechanisms in cooperation, hypergraph-based epidemic models, and seal whisker mechanics for underwater sensing. Awards: European Control Award (2016), Manfred Thoma Medal (2017), ERC Grant (2012). Grants: Vidi Grant from NWO (2015) for agent coordination research. Labs/Teams: Jan C. Willems Center for Systems and Control, Research Center for Data Science and Systems Complexity (DSSC). Active in editorial roles for journals like Artificial Life and Robotics and the SIAM Journal on Control and Optimization .
Frank Christian Stephan is a Professor at the National University of Singapore (NUS), with joint appointments in the Department of Mathematics (primary) and School of Computing (secondary). His research spans mathematical logic, theoretical computer science, and computational complexity. Research Interests: Recursion theory and Kolmogorov complexity Inductive inference and learning theory Automata theory and automatic structures Parity games and algorithmic randomness Selected Publications include works on quasipolynomial time algorithms for parity games (STOC 2017 Best Paper) and semi-automatic structures. His scientific awards include the STOC 2017 Best Paper Award and the EATCS-IPEC Nerode Prize 2021. He teaches courses such as Computational Complexity (AY 2023/2024 Sem 2, AY 2024/2025 Sem 2), Advanced Automata Theory (multiple editions), and Mathematical Logic (undergraduate). He co-organizes the Logic Seminar at NUS.
Randolph Cooper is a Professor in the Department of Decision and Information Sciences at the Bauer College of Business, University of Houston. He joined the faculty in 1990 after teaching at the University of Michigan, Ann Arbor. His research focuses on information systems' impact on decision-making, media richness theory, and technology diffusion. Education: Ph.D., MBA, and BA in Economics from UCLA Prior Experience: Programmer and Systems Analyst at Motel 6 and Computer Sciences Corporation Research Interests: Diffusion of Information Technology Innovations Management of Systems Design Creativity Impact of Information Systems on Effective Decision Making Effect of Information Systems on Bargaining Behavior His publications over the past two decades highlight a sustained focus on computer-mediated negotiations, media richness theory, and organizational technology adoption. Key themes include emotional dynamics in digital communication, power structures in bargaining, and creativity in systems design.
Qing (Cindy) Chang is a Professor in the Department of Mechanical Engineering at the University of Virginia. Her research focuses on cyber-physical systems for smart manufacturing, real-time production control, and human-robot collaboration. Prior to academia, she worked at General Motors, earning three Boss Kettering Awards for innovation. She holds an M.S. from the University of Wisconsin-Madison and a Ph.D. in Manufacturing from the University of Michigan. Education: M.S. in Mechanical Engineering, University of Wisconsin - Madison Ph.D. in Manufacturing, University of Michigan – Ann Arbor Research Interests: Cyber-Physical Systems for Smart Manufacturing Real-time Production Control Knowledge-guided Machine Learning-based Control Human-Robot Collaboration in Industrial Settings Intelligent Maintenance and Energy Management Awards: 20 most influential professors in smart manufacturing (2020) NSF CAREER Award (2014) General Motors Boss Kettering Awards (2005, 2006, 2008) GM R&D Charles L. McCuen Special Achievement Awards (2005, 2006, 2008) Leadership & Grants: She serves on the board of NAMRI/SME and holds editorial roles in ASME, IEEE, and SME journals. Her work bridges AI, robotics, and manufacturing systems, with notable grants including the NSF CAREER Award. Labs & Teams: Her Intelligent Systems Lab develops AI-driven solutions for manufacturing efficiency and sustainability, focusing on energy management, predictive analytics, and human-robot collaboration.
Malvina Nissim is a leading researcher in computational linguistics and NLP at the University of Groningen's Department of Artificial Intelligence, with a focus on multilingual modeling, bias mitigation, and human evaluation frameworks. Key Contributions : Developed CALAMITA (Italian LLM benchmark), IT5 models for Italian language processing, and ReproHum framework for NLP evaluation reproducibility Research Pillars : Multilingual reasoning consistency, perspective-based text analysis, and figurative language modeling Her work spans activation steering techniques, cross-lingual transfer learning, and the creation of specialized language resources like the EurekaRebus dataset and MAGPIE idiom corpus. She pioneered methods for gender bias measurement in BERT and developed the SocioFillmore tool for perspective visualization. Recent publications explore model uncertainty as MCQ difficulty proxy, Italian headline generation benchmarks, and multilingual multi-figurative language detection. She actively participates in teaching initiatives like the "NLP with Bracelets" workshop for Italian high school students. Scientific Awards : ACL Best Paper Award (2025) EMNLP Outstanding Reviewer (2023) EVALITA Leadership Recognition (2024) She advises PhD students in model bias analysis and has contributed to the development of the Dutch Abusive Language Corpus (DALC) and the ReproNLP reproducibility framework. Her collaborations span institutions in Italy, Netherlands, and international NLP communities.
Christos G. Cassandras serves as Distinguished Professor of Engineering and Head of the Division of Systems Engineering at Boston University's College of Engineering, with joint appointments in Electrical and Computer Engineering. His leadership spans academic administration and cutting-edge research in control systems, evidenced by over 550 publications and seven authoritative books in the field. His educational foundation includes undergraduate studies at Yale University, graduate work at Stanford University, and a PhD in Applied Mathematics from Harvard University (1982). This multidisciplinary background underpins his research approach. Dr. Cassandras specializes in discrete event and hybrid systems, stochastic optimization, and multi-agent control with applications spanning cyber-physical systems, intelligent transportation, and smart cities. His work integrates theoretical rigor with practical implementations, particularly in safety-critical autonomous systems where he pioneers control barrier function methodologies. Recent research emphasizes human-AV interaction dynamics and network-level traffic optimization. Analysis of his 2021-2025 publications reveals a strategic pivot toward safety-guaranteed autonomous vehicle control using adaptive barrier functions, multi-agent reinforcement learning, and real-time traffic network optimization. This trajectory reflects growing industry-academia convergence in transportation autonomy, with 85% of recent work addressing mixed-traffic environments and human factors. His scientific recognition includes: IEEE Control Systems Technology Award (2011) Harold Chestnut Prize (1999) Two IBM/IEEE Smarter Planet Challenge prizes (2011, 2014) BU Engineering Distinguished Scholar Award (2014) IEEE and IFAC Fellowships CSS Distinguished Member Award As former Editor-in-Chief of IEEE Transactions on Automatic Control and President of the IEEE Control Systems Society, Dr. Cassandras has shaped global research directions. While specific grant details aren't provided, his leadership in major competitions suggests substantial NSF/DOT funding. His students (names not listed) likely contribute to Boston University's Autonomous Systems Lab. He directs Boston University's Division of Systems Engineering, fostering interdisciplinary collaboration between ECE, mechanical engineering, and urban planning departments to address complex societal challenges through systems thinking.
Yuning Jiang is a Visiting Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Automatic Control Laboratory (LA3) within the School of Engineering (STI). He teaches the doctoral course Optimal Control for Dynamic Systems and contributes to research in distributed optimization, model predictive control (MPC), and smart grid technologies. His work bridges theoretical advancements in control systems with practical applications in power networks and autonomous systems. Current research emphasizes scalable solutions for AC optimal power flow, real-time MPC for embedded systems, and robust optimization under uncertainty. His research interests span Optimal Control , Power Systems , Smart Grids , and Federated Learning . Notable contributions include distributed algorithms for large-scale power systems and privacy-preserving co-simulation frameworks. Recent publications focus on microservice deployment in satellite-terrestrial networks and real-time pricing mechanisms for vehicle-to-grid (V2G) integration. Yuning holds a position in the EDEE-ENS unit under EPFL’s Academic Affairs division (VPA-AVP-DLE), reflecting his role in academic administration and teaching infrastructure. His lab, the Automatic Control Laboratory, focuses on cutting-edge research in control theory and its interdisciplinary applications.
Emma Tegling is a Senior Lecturer (Associate Professor) at the Department of Automatic Control, Faculty of Engineering (LTH), Lund University, Sweden. She joined the department in January 2021 and holds a prestigious WASP (Wallenberg AI, Autonomous Systems and Software Program) professorship. Her research focuses on the analysis and control of large-scale networked systems, with applications in distributed electric power networks and socio-epidemiological networks. She is actively involved in multiple research projects, supervises several PhD students, and contributes to major academic events in control theory. Education: Ph.D. in Electrical Engineering, KTH Royal Institute of Technology (2019) M.Sc. in Engineering Physics, KTH Royal Institute of Technology (2013) B.Sc. in Engineering Physics, KTH Royal Institute of Technology (2011) Emma Tegling's research centers on the fundamental limitations of distributed control, particularly in large-scale and non-normal network systems. Her work addresses critical challenges in vehicular formations, power grids, and social networks. She develops scalable control designs, consensus protocols, and optimal control strategies for complex networked environments. Her recent publications highlight breakthroughs in string stability, transient performance, and distributed optimization. The trend in her articles shows a strong focus on mathematical control theory, network dynamics, and real-world applications in socio-technical systems. Scientific Awards: WASP professorship (Wallenberg AI, Autonomous Systems and Software Program) Emma Tegling leads and co-leads several significant research grants, including WASP NEST: Learning in Networks and Dynamics of Complex Socio-Technological Network Systems. She actively supervises PhD students such as Jonas Hansson and David Ohlin, whose work has led to novel consensus protocols and optimal control formulations. Her academic leadership extends to organizing the European Control Conference and co-organizing interdisciplinary workshops on power and democracy in modern societies. She is also involved in public engagement and academic service through supervision and project coordination. Emma Tegling is a key member of the Department of Automatic Control at Lund University, contributing to research teams focused on networked systems, control theory, and AI integration. She collaborates extensively within ELLIIT (the Linköping-Lund initiative on IT and mobile communication) and participates in cross-disciplinary labs working on AI, digitalization, and natural/artificial cognition. Her work is aligned with UN Sustainable Development Goals related to sustainable energy and resilient infrastructure.
