Emilio Frazzoli is a Full Professor at ETH Zurich’s Department of Mechanical and Process Engineering. He leads the Institute for Dynamic Systems and Control and the Center for Sustainable Future Mobility, focusing on autonomous systems, robotics, and socio-technical control frameworks. Current affiliations: ETH Zurich (Dynamic Systems and Control, Sustainable Future Mobility) Research: Autonomous mobility-on-demand, game theory for resource allocation, and safety verification in multi-agent systems His work bridges robotics, control theory, and economics, with projects like the open-source AMoDeus simulation framework for autonomous taxis and karma-based resource allocation systems. Recent publications emphasize trustworthy AI, reproducibility in autonomous vehicle control, and human-robot interaction challenges. Notable projects include nuReality (VR-based pedestrian interaction studies) and CARSI II (context-driven vehicle interfaces).
Karl Michael Göschka is an Associate Professor at Vienna University of Technology (TU Wien) in the Department of Distributed Systems (E194-02) within the Faculty of Computer Science. His academic title of Privatdozent indicates he has completed habilitation, the highest academic qualification in the German/Austrian system. He maintains an active research and teaching profile with courses scheduled through 2026S, including Bachelor Thesis supervision, Computer Science Projects, and PhD Seminars. His research spans over two decades with consistent publications from 2004 to 2022. Göschka's primary research interests focus on Dependable Distributed Systems, Web Engineering, and the convergence of voice and data technologies. His work demonstrates particular expertise in middleware, service-oriented architecture, wireless sensor networks, and fault tolerance mechanisms. He has supervised numerous master's and diploma theses since 2006, with his most recent doctoral supervision completed in 2022. Göschka's publication record shows a clear evolution from foundational work in distributed systems and replication (2004-2013) to more recent contributions in wireless sensor networks and dependable computing (2020-2022). His research consistently addresses the challenge of balancing dependability, performance, and security in distributed environments, with notable contributions in temporal decoupling techniques and adaptive systems. GIT-Preis des Österreichischen Vereins für Elektrotechnik (1999) Würdigungspreis des Ministeriums für Wissenschaft und Forschung (1998) GIT-Förderpreis des Österreichischen Vereins für Elektrotechnik (1994) Göschka has led and participated in multiple significant research projects including TRADE (2008-2011) on adaptive performance optimization, COMPASS (2005-2007) for automotive software systems, S-Cube (2008-2012), ALL-TIMES (2007-2010), and DeDiSys (2004-2007) on dependable distributed systems. His research has strong practical applications in automotive systems, online auctions, and critical infrastructure monitoring.
Dr. Gavin Mount is a researcher at UNSW Canberra , specializing in non-traditional security studies , ethnic conflict , and the sociopolitical implications of emerging technologies . His work bridges theoretical analysis with practical applications in conflict transformation and defense studies. Research Focus: Global politics of ethnic conflict, hybrid peace/war frameworks, and socio-political impacts of drone technology. Teaching: Courses like Ethnic Conflict in World Politics and Law, Force and Legitimacy , with a focus on military education and strategic studies. Publications: Recent work includes contributions to Thinking Swarms (2025) and Hybridity on the Ground in Peacebuilding and Development (2018). Grants: Leads the Curricula Integration of Student Wellbeing Resources project under HERDSA. Key Themes across his research include the intersection of technology and conflict, evolving defense paradigms, and pedagogical innovations in security studies.
Ambuj Varshney is an Assistant Professor at the National University of Singapore (NUS) School of Computing , leading the WEISER research group . His work bridges electronics, wireless communication, computer science, and AI with a focus on creating ultra-low-power embedded systems for sustainable IoT deployments. University of California, Berkeley: Postdoctoral Scholar (2020-2022) Uppsala University: PhD in Sustainable Networked Systems NXP Semiconductors: Software Engineer (prior to PhD) Bachelors in Information & Communication Technology Research interests center on overcoming wireless systems' energy asymmetry through tunnel diode oscillators , LiFi-RF hybrid networks , and battery-free communication architectures . His group develops STICORS —sticker-like computers for industrial and medical monitoring. Recent publications demonstrate AudioCast 's FM-band utilization for 130m transmission, TunnelSense 's vital monitoring, and PixelGen 's diffusion model cameras. These works combine IoT sustainability, spectrum efficiency, and hardware innovation . 2024: Google Research Scholar Award 2023: MobiSys Best Demonstration 2021: Berkeley FORM+FUND Fellowship 2019: ABB's $300K Research Award As an educator, he teaches CS4222 Wireless Networking and CS5272 Embedded Software Design . Past students include Wenqing Yan (NUS/UCB PhD), Qiao Yukai , and Kunjun Li . His team collaborates with Prabal Dutta (UCB), Christian Rohner (Uppsala), and Prateek Saxena (NUS).
Dr. Manish Kumar is a Professor in the Department of Mechanical Engineering at the University of Cincinnati, leading the Cooperative Distributed Systems (CDS) Laboratory and the UAV MASTER Lab. He specializes in robotics, unmanned aerial systems (UAVs), and swarm systems, with a focus on decision-making, control in complex systems, and multi-sensor data fusion. His research has been supported by grants from the National Science Foundation, Department of Defense, and industry partners. Education: Ph.D. in Mechanical Engineering, Duke University (2004) M.S. in Mechanical Engineering, Duke University (2002) Research Interests: Dr. Kumar's work addresses challenges in autonomous systems, including UAV coordination, swarm robotics, and emergency response technologies. He integrates machine learning, control theory, and optimization to develop robust systems for applications such as wildfire management, telehealth, and industrial automation. Grants & Labs: Directed projects funded by federal agencies (NSF, DoD) and industry, totaling over $5M. Co-directs the Collaboratory for Medical Innovation and Implementation and the UAV MASTER Lab. Key Contributions: Pioneered algorithms for UAV path planning and swarm coordination. Developed PDE-based models for epidemic spread prediction and control.
Stelios Timotheou is an Associate Professor at the Department of Electrical and Computer Engineering and faculty member at the KIOS Research and Innovation Center of Excellence, University of Cyprus. He holds a Dipl-Ing from National Technical University of Athens, and MSc/PhD from Imperial College London. His research focuses on developing real-time distributed methodologies using mathematical optimization, machine learning, and computational intelligence to enhance efficiency in critical infrastructure systems. Research interests center on data-driven decision making for urban mobility management, traffic control systems, and energy optimization. Key areas include: Intelligent UAV-based sensing for traffic monitoring Cooperative control algorithms for multi-agent systems Optimization of renewable energy integration Robust infrastructure protection strategies Recent publications demonstrate strong focus on traffic state estimation (76%), energy system optimization (16%), and UAV applications (8%). Awarded the Cyprus Research Award (2017) and ERC Consolidator Grant for URANUS project. Secured multiple research grants focusing on real-time control systems.
Mehrdad Ehsani is a Robert M. Kennedy Endowed Professor of Electrical Engineering at Texas A&M University, leading the Power Electronics and Motor Drives Laboratory. He holds a Ph.D. from the University of Wisconsin-Madison and has over four decades of expertise in power electronics, electric/hybrid vehicles, and energy systems. His research focuses on sustainable energy, advanced power conversion, and vehicle electrification. Educational Background: Ph.D., Electrical Engineering, University of Wisconsin-Madison (1981) M.S., Electrical Engineering, University of Texas at Austin (1974) B.S., Electrical Engineering, University of Texas at Austin (1973) Research Interests: Sustainable power systems, electric/hybrid vehicles, energy storage, power electronics, and aerospace power systems. His work emphasizes practical applications, such as transmotor technology for energy efficiency and grid-interactive buildings. Awards & Recognition: Life Fellow of IEEE SAE Fellow (2005) IEEE Vehicular Technology Society Avant Garde Award (2001) Recipient of multiple Prize Paper Awards (IEEE-IAS) Advising & Grants: Director of Advanced Vehicle Systems Research Program. His lab collaborates with industry on patents, including over 30 granted/pending patents, and advises on sustainable transportation technologies. He has consulted for over 60 companies and government agencies. Labs & Teams: Founder and director of the Power Electronics & Motor Drives Lab, focusing on electric vehicle propulsion, renewable energy integration, and advanced control systems.
Dr. Beibei Ren is an Assistant Professor in the Department of Mechanical Engineering at Texas Tech University. She earned her Ph.D. in Electrical and Computer Engineering from the National University of Singapore (NUS) in 2010, followed by postdoctoral work at UCSD and a research fellowship at NUS. Education: Ph.D. in Electrical and Computer Engineering (NUS, 2010) Previous Positions: Postdoctoral Scholar (UCSD, 2010-2013), Research Fellow (NUS, 2009-2010) Her research focuses on dynamic systems and control with applications in renewable energy integration, microgrids, UAVs, MEMS, marine systems, and manufacturing. At Texas Tech, she directs the Dynamic Intelligent Systems, Control and Optimization (DISCO) Group , emphasizing robust control strategies for uncertain systems. The 15 most recent publications highlight her expertise in uncertainty and disturbance estimator (UDE)-based control , with applications in smart grid technologies, wind and solar energy systems, quadrotor robotics, and power electronics. Her work bridges theoretical control theory with practical implementations in renewable energy and autonomous systems. STEM Outreach: Actively promotes diversity in engineering through Texas Tech's STEM CORE programs.
Jonathan P. Wong is a Senior Policy Researcher at the RAND Corporation and Professor of Policy Analysis at the RAND School of Public Policy. His work bridges academic research and defense policy, focusing on military strategy, acquisition, force planning, and the integration of emerging technologies into military operations. Education: Ph.D. and M.Phil. in Policy Analysis (Pardee RAND Graduate School), M.A. in Security Studies (Georgetown University), B.A. in Political Science (UC San Diego) Professional Experience: U.S. Marine Corps infantry officer (2001–2011), Consultant at Boston Consulting Group, current RAND researcher and professor Wong’s research interests center on defense innovation, military logistics, artificial intelligence in warfare, space capabilities, and the human dimension of military operations. He has led studies on human-machine teaming, non-lethal weapons, defense acquisition reform, and U.S. military posture in the Indo-Pacific. His work emphasizes practical, policy-relevant insights grounded in operational experience. The trends in his recent publications reveal a deep engagement with modern defense challenges: integrating commercial technologies into military systems, improving acquisition agility, enhancing battlefield awareness, and ensuring that defense innovation serves warfighter needs. His research spans strategic, operational, and tactical levels, often focusing on the U.S. Army and Marine Corps. Wong has not received public recognition in the form of listed awards in the provided texts, but his leadership in congressionally mandated studies and high-impact policy research suggests significant influence in defense circles. Advising & Grants: Co-led major projects such as the Long Range Precision Fires study and research on integrating women into Marine Corps infantry. His work is frequently funded by the Department of Defense and other federal entities, though specific grant details are not listed. Wong is affiliated with RAND’s defense and national security research teams, contributing to strategic analysis and policy development. He is not associated with a formal lab but works within RAND’s collaborative research environment, often partnering with other veterans and defense experts to produce actionable insights for military leaders and policymakers.
Brandon Lucia is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. He holds the Kavčić-Moura Professorship and leads the Abstract research group. As CEO and co-founder of Efficient Computer Corp., he bridges academic research with commercial applications in energy-efficient computing. Dr. Lucia received his Ph.D. in Computer Science and Engineering from the University of Washington in 2013, following an MS from the same institution in 2010 and a BS in Computer Science from Tufts University in 2007. His research focuses on the intersection of computer architecture, computer systems, and programming languages, particularly in energy-constrained environments. His primary research interests include intermittent computing, energy harvesting computers, orbital edge computing, and parallel computing systems. Lucia's work addresses fundamental challenges in creating programmable, reliable computing devices that operate without batteries by harvesting energy from their environments, with applications in sensing, medical implants, and space systems. He also investigates software systems and architectures for making parallel computing correct, reliable, and efficient in the post-Moore's Law era. Lucia's publication record shows a clear trajectory toward orbital edge computing and nanosatellite systems, with recent work focusing on computational constellations, visual navigation for satellites, and energy-efficient processing in space. His research spans both theoretical foundations of intermittent computing and practical implementations in hardware and software. 2021 Sloan Research Fellowship 2018 NSF CAREER Award 2018 ASPLOS Best Paper Award IEEE MICRO Top Picks in Computer Architecture (2009, 2010, 2016) 2015 OOPSLA Best Paper Award 2019 IEEE TCCA Young Computer Architect Award 2022 Engineering Faculty Award As an advisor, Lucia has mentored numerous graduate students including Brad Denby, Zhuo Cheng, and Kyle McCleary, many of whom have become co-authors on his significant publications. His lab developed the world's first batteryless PocketQube nanosatellite (Tartan-Artibeus-1), which was deployed to low-Earth orbit aboard the SpaceX Transporter-3 Rocket. Lucia's research has received funding from sources including NSF, DARPA, Google, and VMware, supporting both fundamental research and practical implementations of energy-harvesting computing systems.
Kwang Moo Yi is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), where he conducts research in computer vision and machine learning. He is affiliated with the Computer Vision Lab, CAIDA (Centre for Artificial Intelligence Decision-making and Action), and ICICS (Institute for Computing, Information and Cognitive Systems) at UBC. Education: B.Sc. from Seoul National University Ph.D. from Seoul National University under Prof. Jin Young Choi Post-doctoral researcher at École Polytechnique Fédérale de Lausanne (EPFL) with Prof. Pascal Fua and Prof. Vincent Lepetit Dr. Yi's research focuses on Visual Geometry with the goal of understanding local environments, adapting to them, and acting within them. His work spans applications in autonomous vehicles, drones, robots, and Augmented/Mixed Reality systems. He employs machine learning, particularly deep learning, as the primary tool for advancing computer vision capabilities. His recent publications demonstrate a strong focus on neural rendering techniques, especially 3D Gaussian Splatting and Neural Radiance Fields (NeRF). The research trends show increasing sophistication in handling occlusions, improving rendering quality, and developing more efficient training methods for neural fields. There's also significant work connecting computer vision with practical applications in industrial settings and energy systems. Dr. Yi serves as an area chair for top computer vision and machine learning conferences including CVPR, ICCV, ECCV, NeurIPS, ICML, and AAAI. He was part of the organizing committee for CVPR 2023. He supervises graduate students including Eric (who recently completed his PhD), Gopal (now at Samsung Research), and Jeong-Gi (joining as a postdoctoral fellow). His teaching includes CPSC 425: Computer Vision and CPSC 533Y: 3D Computer Vision with Deep Learning. Dr. Yi is actively involved with the Computer Vision Lab at UBC, collaborating with researchers across CAIDA and ICICS. His work bridges theoretical computer vision with practical applications in various domains including astronomy, industrial automation, and energy systems.
Tobi Delbruck is a titular professor of physics and electrical engineering at ETH Zurich, where he leads the Sensors Group at the Institute for Neuroinformatics (INI) in Zurich, Switzerland. He collaborates closely with Shih-Chii Liu and Giacomo Indiveri as part of the 'hardware groups' at INI. Delbruck has also served as visiting faculty at Caltech and is a Fellow of the IEEE. His work focuses on bio-inspired and neuromorphic event-based sensory processing systems. Professor Delbruck's research spans multiple areas of neuromorphic engineering, with particular emphasis on event-based vision systems and low-power analog VLSI circuits. His work has significantly advanced the field of Dynamic Vision Sensors (DVS), which mimic the human retina's response to changes in brightness rather than capturing full frames. This approach enables extremely low-latency vision processing with minimal power consumption, making it ideal for high-speed applications and robotics. His research has applications in robotics, autonomous systems, and low-power embedded vision. Delbruck is an active contributor to the neuromorphic engineering community, co-organizing the annual Telluride Workshop on Neuromorphic Engineering and serving in leadership roles with IEEE. He has authored numerous influential publications and co-authored books including "Event-Based Neuromorphic Systems" and "Analog VLSI: Circuits and Principles." His jAER (Java Address-Event Representation) project provides open-source tools for real-time event-based sensory processing. Analysis of his recent publications shows a clear trend toward integrating event-based vision with deep learning techniques and applying these systems to practical robotics problems. His scientific achievements have been recognized with multiple awards including: IEEE Fellow Winner of Best Live Demonstration award at ISCAS 2012 Honorable Mention Award from Sensory Systems Technical Committee at ISCAS 2012 Overall Best Student Paper Award and Best Paper Award from Sensory Systems Technical Committee at ISCAS 2010 Winner of the 2006 ISSCC Jan Van Vessem Outstanding European Paper Award Professor Delbruck actively mentors students and has supervised numerous PhD and Master's theses in the areas of neuromorphic engineering and event-based vision systems. His group has secured significant research funding from various sources to support their innovative work in bio-inspired sensory processing. He teaches courses on "Electronics for Physicists II (Digital)" and "Neuromorphic Engineering," helping to train the next generation of researchers in this field. The Sensors Group at INI, which Delbruck leads, operates state-of-the-art facilities for designing and testing neuromorphic vision systems. The group maintains close collaborations with researchers worldwide and has developed several important open-source resources including the jAER project and bias generator design kits. Their work continues to push the boundaries of what's possible with event-based sensory processing, with applications ranging from high-speed robotics to low-power embedded vision systems.
Ronald G. Dreslinski is the Morris Wellman Faculty Development Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, within the College of Engineering. He leads the Computer Engineering Lab (CEL) and directs multiple government-funded research initiatives. His research focuses on Computer Architecture , Hardware Design Automation , and Electronic Design Automation (EDA) , specializing in autonomous system-on-chip synthesis and reconfigurable computing. His work aims to democratize hardware design by eliminating barriers of cost, expertise, and uncertainty through tools enabling no-human-in-the-loop implementation. Dr. Dreslinski manages significant funding from defense and science agencies. Current projects include: DARPA DSSoC (DASH) DARPA Space-BACN (Cochon) DARPA Prowess (Dr. Dash) IARPA Agile (PANDO) NSF Fuse (Regaining the Edge) Past projects encompass: DARPA CRAFT (CERTUS) DARPA PERFECT DARPA SDH (Transmuter) DARPA IDEA (FASoC, OpenROAD) NASA/AFRL (HPSC) He drives the OpenROAD project toward 24-hour autonomous chip design, with industrial adoption by Intel and IHP for shuttle services. His lab develops open-source tools like FASoC for synthesizable analog-digital SoC composition, advancing portability and correctness-by-construction methodologies.
Robert Dick is a Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, part of the College of Engineering. He previously held roles as Associate Professor at Northwestern University and Visiting Professor at Tsinghua University. He earned his Ph.D. from Princeton University and a Bachelor's degree from Clarkson University. His research focuses on Embedded Systems, Learning Dynamics, Efficient Machine Learning, Privacy, and Censorship Resistance. Key themes include defining problems with correct costs/constraints, broadening access to information technology, mitigating negative tech impacts, and solving inference problems with limited resources. He leads the Embedded Systems Graduate Program and is a member of the Michigan Integrated Circuits Laboratory (MICL). His work spans thermal management, energy-efficient computing, and low-power wireless networks. Notable contributions include innovations in embedded system design, machine vision frameworks, and sensor networks. Courses taught include EECS 507 (Embedded Systems Research), EECS 373 (Embedded System Design), and ENGR 100 (Autonomous Systems). His recent publications emphasize AI model analysis, energy-efficient networks, and environmental sensing. Projects include MemX (attention-aware wearable tech) and LoRa-based LPWAN protocols. Dick co-founded Stryd, a company commercializing embedded systems innovations.
Professor Marta Zofia Kwiatkowska is Professor of Computing Systems and Fellow of Trinity College at the University of Oxford, where she has held a faculty position since 2007. She previously served as Professor of Computer Science at the University of Birmingham (2001–2007), Reader and Lecturer at the University of Birmingham (1994–2001), and Lecturer at the University of Leicester (1986–1994). Her academic career began as Assistant Professor at the Jagiellonian University in Kraków, Poland (1980–1988). Education: BSc/MSc in Computer Science, Jagiellonian University, Kraków MA, University of Oxford PhD, University of Leicester Research Interests: Professor Kwiatkowska spearheaded the development of probabilistic and quantitative verification methods on the international stage. Her work bridges theory and practice through the PRISM model checker—the leading software tool in probabilistic model checking—used worldwide for research and teaching. Application domains include communication and security protocols , nanotechnology designs , power management , ubiquitous computing and systems biology . She investigates automated verification , temporal logics , semantic models for concurrency , real-time systems , and biological process modelling . Current grant funding exceeds £3.7 million from EPSRC, EU and ERC, including the prestigious ERC Advanced Grant VERIWARE. Scientific Awards & Honours: Fellow of the Royal Society Fellow of the ACM Fellow of the European Association for Theoretical Computer Science (EATCS) Fellow of the British Computer Society (BCS) Fellow of the Polish Society of Arts & Sciences Abroad ERC Advanced Grant VERIWARE (€2.046 M, 2010–2015) Top Cited Article Award, Theoretical Computer Science (2005–2010) Best Paper Award, QEST 2006 Doctoral Supervision & Grants: Professor Kwiatkowska actively supervises doctoral students (D.Phil. at Oxford) and post-doctoral researchers. She welcomes applications in areas aligned with her research interests, detailed here . Current students include Charlie Griffin, Daqian Shao, Matthew Yuan and Minghao Liu; past students and researchers number over twenty, many now in faculty or industry leadership roles. Laboratory & Teams: She leads the Oxford Quantitative Verification group within the Department of Computer Science. Ongoing projects include FUN2MODEL, ELSA, FAIR and the flagship PRISM probabilistic model checker. The group maintains strong collaborations with biological, robotics and engineering teams worldwide.
