Dr. phil. Stefan Ladwig is a researcher at the Institute for Automotive Engineering (Institut für Kraftfahrzeuge) at RWTH Aachen University, specializing in traffic psychology, user acceptance, and human-machine interaction for automated driving systems. His work addresses psychological and ergonomic aspects of emerging mobility technologies, including seating ergonomics, trust-building mechanisms, and communication strategies for autonomous delivery robots. Research Focus: Automated driving, interior design psychology, driver/passenger behavior, and safety requirements. Projects: Lead roles in UrbANT (autonomous delivery robot) and SteeringBow (dynamic driving experience). Collaborations: Affiliated with Aldenhoven Testing Center, Future Mobility Partnership e.V., and innocam.NRW. Ladwig’s recent publications examine rotated seating positions, visual dominance in speed adaptation, and trust-relevant driving scenarios, reflecting his interdisciplinary approach to optimizing human-vehicle interactions. His work spans ergonomics, energy management, and behavioral interventions to enhance safety and usability in next-generation mobility solutions.
David Sivak is a Professor and Canada Research Chair Tier 2 in the Department of Physics at Simon Fraser University (SFU). His research focuses on theoretical and computational biophysics, exploring the physical limits of energy and information transduction in molecular machines. He investigates how biological systems operate efficiently under fluctuating conditions, with emphasis on molecular motors, free-energy transduction, and nonequilibrium thermodynamics. Education: Ph.D. in Biophysics, University of California, Berkeley B.A. in Philosophy, Politics & Economics, University of Oxford A.B. with Honors in Applied Mathematics, Harvard University Research Interests: Sivak's work integrates statistical mechanics, information theory, and molecular biophysics to understand how microscopic systems achieve functional efficiency. Key areas include: Optimal control strategies for nonequilibrium systems Energy-information trade-offs in molecular machines Stochastic thermodynamics of biochemical processes Design principles of ATP synthase and similar rotary motors Publications & Awards: Sivak has authored over 60 peer-reviewed articles, including foundational work on nonequilibrium fluctuation theorems and optimal control in stochastic systems. His awards include the Canada Research Chair and the Young Investigator Award from the Biophysical Society of Canada. Grants & Collaborations: He leads projects funded by the Simons Foundation and collaborates with experimental groups globally. His lab actively mentors graduate students and postdocs, emphasizing interdisciplinary approaches to biophysical problems. Labs & Teams: The Sivak Group at SFU develops theoretical models and numerical simulations to analyze molecular-scale processes. Current projects explore information engines, microbiome dynamics, and the thermodynamics of cellular processes.
Henry Hoffmann is a Professor and Liew Family Chair in the Department of Computer Science at the University of Chicago. His research focuses on self-aware computing systems that adapt to meet goals like power efficiency, performance, and security. He leads the SEEC project and has contributed to advancements in computer architecture, embedded systems, and quantum computing. Hoffmann received the PECASE (2019), DOE Early Career Award (2015), and was inducted into the Samsung Hall of Fame for discovering vulnerabilities in SmartTVs. He holds a PhD from MIT (2013) and has co-founded Config Dynamics (2019). His work bridges control theory, machine learning, and traditional computer systems to create adaptive solutions for modern computing challenges. Education: PhD in Electrical Engineering and Computer Science from MIT (2013), SM (2003), and B.S. (1999) with highest honors from UNC Chapel Hill. Professional experience includes roles at Tilera Corporation and MIT Lincoln Laboratory. Research Interests: Self-aware systems, adaptive resource management, quantum computing optimization, and cybersecurity. His SEEC framework enables systems to autonomously adapt to constraints like energy and performance. Recent work explores applying adaptive techniques to AI/ML models for energy-efficient inference and security. Awards: Over $19M in research funding, 100+ publications, and leadership roles in NSF Expedition EPiQC (quantum computing). Named Chair of UChicago CS Department (2023-2024). Labs/Teams: Systems Group, EPiQC (quantum computing), and CERES (unstoppable computing systems). Current students include Jerry Ding and Ryien Hosseini. Notable alumni include Yi Ding (now faculty at Purdue) and Nikita Mishra.
Dr. Srinivas Peeta is the Frederick R. Dickerson Chair and Professor in Transportation Systems Engineering at the Georgia Institute of Technology’s School of Civil and Environmental Engineering. He previously held the Jack and Kay Hockema Professorship at Purdue University, where he served for 24 years. He is also the Associate Director of the USDOT Center for Connected and Automated Transportation. Education: B. Tech. from IIT Madras, M.S. from Caltech, and Ph.D. from UT Austin, all in Civil Engineering. His research focuses on large-scale transportation systems, infrastructure interdependencies, and connected/automated vehicles. He has authored over 345 publications and secured over $48M in research funding. Research Interests: Dynamic traffic networks and driver behavior modeling Information-based navigation in vehicular systems Systems perspectives for complex adaptive infrastructure Autonomous vehicle integration and human-vehicle interactions Key Achievements: Developed DYNASMART software for traffic operations Recipient of NSF CAREER Award (1997) and ASCE Walter Huber Prize (2009) Directed NEXTRANS UTC and pioneered USDOT’s real-time route guidance systems Grants & Outreach: Secured funding from USDOT, NSF, FHWA, and international agencies Initiated NEXTRANS internship programs and K-12 outreach Labs/Teams: Active in Georgia Tech’s ACT Lab, focusing on autonomous transportation systems and human-vehicle-environment interactions.
Brian D. Taylor is a Professor of Urban Planning and Public Policy at UCLA’s Luskin School of Public Affairs, and a Research Fellow at the Institute of Transportation Studies. His academic roles include Director of the UCLA Institute of Transportation Studies and former leadership of the Lewis Center for Regional Policy Studies. Education: PhD in Urban Planning (UCLA), MCP in City and Regional Planning (UC Berkeley), MS in Civil Engineering (UC Berkeley), and BA in Geography (UCLA). Research focuses on transportation equity, finance, policy, and behavior. Key areas include post-pandemic travel trends, transit ridership decline, transportation pricing, and the socio-political dimensions of urban mobility. Recent work explores falling public transit use, pandemic-induced activity changes, and equity impacts of new mobility systems. Publications highlight studies such as The Drive for Dollars (Oxford Press, 2023), analyzing freeway financing and urban transformation, and influential papers on congestion economics and transit fiscal challenges. Over 50 students advised, many now leading academics and practitioners. Awards include the YPT Excellence in Innovation Award (2021) and recognition as a Top 10 Transportation Thought Leader (2016). Teaching includes courses on transportation economics, land-use integration, and shared mobility policy. Administrative leadership roles span institutes and transportation centers across California.
Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.
Cathy Wu is an Associate Professor at MIT, with affiliations in the Laboratory for Information and Decision Systems (LIDS), Department of Civil and Environmental Engineering (CEE), and Institute for Data, Systems, and Society (IDSS). Her research group focuses on integrating machine learning with model-based optimization to solve complex problems in transportation systems and cyber-physical systems. Academic Leadership: Class of 1954 Career Development Associate Professor (MIT) Research Grants: NSF CAREER Award, Amazon Robotics, Mathworks, MIT Mobility Initiative, US DOT, Microsoft Research, Cintra, Symbotic Research Interests : Wu's work bridges AI and engineering challenges in transportation. Key areas include: Hybrid ML/Model-based Optimization (large neighborhood search, branch-and-cut) Sustainable Mobility (Project Greenwave, eco-driving) Multi-Agent Coordination (warehouse automation, cooperative driving) Cyber-Professional Systems (generalization in RL, transfer learning) Recent work demonstrates significant advances in eco-driving (11-22% emissions reduction), large-scale multi-agent path finding (1000+ agents), and foundational RL methods for traffic control. Her group has produced 15+ major publications since 2015, with notable media coverage in Science, Wired, and NewScientist. Selected Scientific Awards NSF CAREER Award (2023) Ole Madsen Mentoring Award (2025) IEEE ITSS WiE/YP Fellowship (2024) Harold L. Hazen Teaching Award (2022) Her lab has advised 12+ graduate students and postdocs, including: Vindula Jayawardana (PhD '24, now at Anthropic) Sirui Li (PhD '25, now at Microsoft Research) Yining Ma (Postdoc, active researcher) Zhongxia Yan (PhD '24, now at Anthropic)
Susan Helper is the Frank Tracy Carlton Professor of Economics at the Weatherhead School of Management , Case Western Reserve University . She also holds the title of Professor in the Department of Economics at the same institution. Beyond academia, she served as Chief Economist of the U.S. Department of Commerce (2013–2015) and as Senior Economist on the White House Council of Economic Advisers (2012–2013). She is a Research Associate at the National Bureau of Economic Research, Non-resident Senior Fellow at the Brookings Institution, and Co-Chair of the National Academy of Sciences. Education: PhD in Economics, Harvard University (1987) BA in Economics, Government, and Spanish, Oberlin College (1979) Research Focus: Susan Helper's research centers on the globalization of supply chains , manufacturing revitalization , and the interplay between technology, work systems, and policy . She explores how organizational structures, relational contracts, and digital transformation influence innovation, productivity, and job quality. Her work spans both theoretical and empirical studies, with a strong emphasis on policy implications for U.S. manufacturing competitiveness. Scientific Awards & Honors: Becker Friedman Fellow , University of Chicago (2018) Women of Achievement Award , Flora Stone Mather Center for Women, CWRU (2009) Professional Service & Impact: Helper has held influential roles such as Program Organizer for the Canadian Institute for Advanced Research (2019–2024), Editorial Review Board Member for the Strategic Management Journal (2008–present), and Member of the Bureau of Labor Statistics Technical Advisory Committee. Her advisory work bridges academic research and federal policy, shaping strategies for industrial innovation and workforce development.
Alyssa Pierson is an Assistant Professor in the Department of Mechanical Engineering at Boston University, with affiliations in Robotics & Autonomous Systems and Systems Engineering. She leads the Collaborative Autonomy Group and serves as Chief Scientist at Ava Robotics. Her research focuses on trust, cooperation, and distributed control in multi-agent systems, emphasizing socially-compliant and autonomous robotic systems interacting in dynamic environments. Education: PhD in Mechanical Engineering, Boston University (2017) BS in Engineering, Harvey Mudd College Research Interests: Multi-agent systems, distributed robotics control, socially-compliant autonomous systems, and human-robot interaction. Her work addresses challenges in cooperative teaming, trust modeling, and safe navigation in complex environments. Key Awards: NSF Career Award (2023) MassRobotics Rising Star in Robotics Medal (2023) Clare Booth Luce Fellowship Grants & Projects: Recipient of NSF CAREER Award for 'Decentralized and Online Planning for Emergent Cooperation in Multi-Robot Teams'. Active in projects like heterogeneous teaming and privacy-aware trajectory planning. Labs & Affiliations: Principal Investigator of the Collaborative Autonomy Group. Affiliated with BU's Intelligent, Autonomous & Secure Systems division and the Hariri Institute for Computing.
Jaime S. Cardoso is an Associate Professor with Habilitation at the Faculty of Engineering of the University of Porto (FEUP) and a Senior Researcher in the 'Information Processing and Pattern Recognition' Area at INESC TEC's Telecommunications and Multimedia Unit. He has been serving as Research Coordinator since September 15, 1998, and is a Senior Member of IEEE as well as co-founder of ClusterMedia Labs. His educational background includes a Licenciatura in Electrical and Computer Engineering (1999), an MSc in Mathematical Engineering (2005), and a Ph.D. in Computer Vision (2006), all from the University of Porto. Cardoso's research focuses on three major areas: computer vision, machine learning, and decision support systems. His work spans medical image analysis, explainable AI, semantic audio-visual analysis, and pattern recognition. He has co-authored over 150 papers, with more than 50 published in international journals, and has accumulated over 6,500 citations. His recent publications demonstrate expertise in cell nuclei segmentation, ordinal regression for CNNs, semantic segmentation with ordinal relationships, face recognition using synthetic data, and explainable vision language models for medical applications. Honorable Mention in the Exame Informática Award 2011 for 'Semantic PACS' First Place in the ICDAR 2013 Music Scores Competition Cardoso has supervised numerous graduate students at UP-FEUP, with recent theses focusing on multimodal explanations, autonomous driving, medical diagnosis, and explainable AI. His research group works at the intersection of computer vision, machine learning, and practical applications in healthcare and autonomous systems.
Jens Behley is a Lecturer (Privatdozent) and postdoctoral researcher at the Department of Photogrammetry, University of Bonn. He completed his habilitation in 2023 with a thesis on LiDAR-based spatio-temporal scene understanding for autonomous vehicles and earned his PhD in 2014 under Prof. Armin Cremers. His research focuses on LiDAR perception, agricultural robotics, and 3D scene understanding. Behley is an Associate Editor at IEEE Robotics and Automation Letters (RA-L) and has authored influential datasets like SemanticKITTI and BonnBeetClouds3D. Education: PhD in Computer Science, University of Bonn, 2014 Habilitation in Photogrammetry, University of Bonn, 2023 Research Interests: LiDAR-based perception in urban and agricultural environments, machine learning for robotics, semantic mapping, SLAM algorithms, and 3D reconstruction. His work bridges computer vision and robotics, with applications in autonomous vehicles and precision agriculture. Awards: Best Agri-Robotics Paper Award (IROS 2024) Outstanding Reviewer Awards (ECCV, CVPR, ICRA) Faculty Award for Geodesy (2021) Advisees & Grants: Behley collaborates extensively with the PRBonn lab and researchers like Cyrill Stachniss, focusing on projects funded by EU Horizon and industry partners. His team develops open-source tools for LiDAR processing (e.g., KISS-ICP, VDBFusion). Labs/Teams: Part of the Photogrammetry and Robotics Institute (IGG) at the University of Bonn, contributing to the PRBonn research group.
Calin Belta is the Brendan Iribe Endowed Professor of Electrical and Computer Engineering and Computer Science at the University of Maryland, College Park. He is affiliated with the Institute of Systems Research (ISR) and the Maryland Robotics Center (MRC), and holds a Research Professor position at Boston University's College of Engineering. His work bridges control theory, formal methods, and machine learning to ensure safety in cyber-physical and data-driven systems, with applications in robotics, autonomous driving, and systems biology. Research Interests: Focus on dynamics and control theory, formal methods for verification and control synthesis, robotics, autonomous systems, and synthetic biology. Recent projects include PROGENIC (collaborating with MIT, UChicago, and UDelaware) and safety-critical control for heterogeneous robotic teams. Key Achievements: General Chair of the 2025 MRC Symposium, recipient of AFOSR Young Investigator Award (2008), NSF CAREER Award (2005), and IEEE Fellow. His work on formal methods for autonomous systems has led to impactful tools for safety assurance in robotics and AI. Grants: NSF EFRI PROGENIC grant (2024), multiple industry partnerships. Advising: Mentored students like Wenliang Liu (PhD 2024, now at Amazon), and collaborator Marius Kloetzer (shared HSCC Test of Time Award 2025). Labs/Teams: Maryland Robotics Center, Institute for Systems Research, and Boston University collaborations.
Gianni Franchi is an Assistant Professor at ENSTA Paris, part of Institut Polytechnique de Paris. His research focuses on robust computer vision, uncertainty quantification, and explainable AI (XAI). He has been teaching Deep Learning, Computer Vision, and Machine Learning courses since 2020 at ENSTA Paris and Télécom Paris. PhD in Fusion of Information, Machine Learning, and Image Processing (2016) from Mines de Paris Postdoctoral experience at Paris Saclay University (2018-2020) and Seigen University (2016-2018) Current PhD students: Rémi Kazmierczak, Olivier Laurent, Adrien Lafage, Mouïn Ben Ammar Alumni: Xuanlong Yu (2020-2023) Research interests include robust computer vision, anomaly detection, uncertainty quantification, out-of-distribution detection, certifiable AI, and explainable AI. He leads the development of the PyTorch library Torch Uncertainty for uncertainty quantification in deep learning. Recent publications span uncertainty quantification in foundation models, trajectory forecasting, vision-language adaptation, and explainability benchmarks. Gianni actively collaborates on multimodal autonomous driving datasets and uncertainty-aware systems for human-agent interaction.
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.
Jiaxin Lin is an Assistant Professor in the Department of Electrical and Computer Engineering at Cornell University , affiliated with the Computer Systems Laboratory . She earned her Ph.D. in Computer Science from UT Austin (2025) , preceded by an M.S. from University of Wisconsin-Madison and a B.S. from ShenYuan Honors College at Beihang University. Her research focuses on co-designing software and hardware systems to enable high-performance data center communication, particularly through: Programmable network interface controllers (SmartNICs) Terabit network system stacks Cache/memory interconnects Compilers for in-network computing Chip-to-chip interconnects Her work addresses challenges in portability across heterogeneous SmartNICs, demonstrated through the development of the Alkali compiler framework (NSDI '25). Key themes include hardware abstraction, data center scalability, and network-compute co-design. Scientific Awards: Google Junior Faculty Award (2025) MIT EECS Rising Star (2024) Google Ph.D. Fellowship (2021) Meta Ph.D. Fellowship (2021)
