Chris Atkeson is a Professor at the Robotics Institute of Carnegie Mellon University. His research focuses on achieving human-level competence in machines through humanoid robotics and human-aware environments. He explores machine learning techniques such as reinforcement learning, nonparametric methods, and memory-based learning to develop robots capable of complex tasks like manipulation, locomotion, and perception. His work emphasizes bridging the gap between simulation and real-world applications (sim2real transfer), with contributions to tactile sensing (e.g., FingerVision), dynamic walking control, and human-robot collaboration. Notable projects include participation in the DARPA Robotics Challenge with Team WPI-CMU, where his team developed reliable humanoid behavior for disaster response scenarios. Atkeson’s research spans robotics, computer vision, and control systems, with a focus on enabling robots to perceive, learn, and act in unstructured environments. His recent work includes advancements in 3D scene capture, soft robotics, and energy-based planning for compositional tasks.
Daniel W. Bliss is a Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University and Director of ASU's Center for Wireless Information Systems and Computational Architectures (WISCA). With over $50 million in research funding as principal investigator from organizations including DARPA, ONR, Google, and Airbus, his work bridges theoretical foundations with practical implementations across multiple domains of wireless systems. Dr. Bliss received his educational foundation with a B.S.E.E. from Arizona State University (1989), followed by M.S. and Ph.D. degrees in Physics from the University of California-San Diego (1995, 1997). His academic journey includes significant industry experience at General Dynamics (1989-1993) and MIT Lincoln Laboratory (1997-2012) before joining ASU. His research program focuses on advanced wireless systems spanning radar, communications, precision positioning, computational architectures, and medical monitoring applications. Bliss employs information theory, estimation theory, and signal processing to develop novel system concepts with disruptive capabilities. Current research emphasizes RF convergence, integrated sensing and communications, and anticipatory medical analytics using wireless technologies, with particular focus on extracting physiological data from radar signals. Analysis of recent publications reveals a strong trend toward integrated sensing and communications systems, particularly utilizing mmWave and radar technologies for medical monitoring applications. His work increasingly bridges traditional communications and radar domains while expanding into physiological monitoring, demonstrating a clear trajectory toward convergence of wireless technologies for healthcare applications and remote vital sign detection. Dr. Bliss has received significant recognition for his contributions: Fellow of the IEEE (2015) 2021 IEEE Warren D. White Award for Excellence in Radar Engineering 2016-2017 Top 5% Teaching Award at ASU 2017 ASU Fulton Engineering Exemplar Faculty As a dedicated mentor, Dr. Bliss has supervised numerous graduate students through successful dissertation and thesis defenses across both PhD and Master's programs. His research portfolio includes substantial funding from diverse sources with over $50 million secured as principal investigator. Current projects include the $17M DARPA DASH project focused on advanced software-reconfigurable heterogeneous SoCs for next-generation RF systems, and multiple initiatives in contactless vital sign monitoring using radar technologies. Dr. Bliss leads the BLISS Lab and serves as director of WISCA, fostering interdisciplinary research in wireless systems. His team includes researchers working on distributed coherent systems, MIMO radar, RF convergence, and medical monitoring applications, with recent successes including the Making Waves team that tied for first place in the Air Force Spark Tank challenge. He has founded two startup companies: DASH Tech Integrated Circuits Company and the Big Little Sensor Company, focusing on high-performance embedded processing and small-scale radar physiological monitoring, respectively.
Andrew Zisserman is a Royal Society Research Professor at the University of Oxford's Department of Engineering Science, affiliated with the Visual Geometry Group (VGG). His research focuses on computer vision, artificial intelligence, and neural networks, with significant contributions to multimodal learning, video understanding, and 3D scene analysis. He leads projects exploring visual-language models, audio-visual synchronization, and clinical imaging applications. Key research areas include: Video analysis and temporal modeling Multimodal systems for sign language translation and action recognition 3D shape estimation and physical property inference Foundation models and cross-modal retrieval Recent work highlights: Developed Flamingo and Tapir models for video-language tasks Advancements in spinal MRI analysis and clinical imaging Leadership in EGO4D and VoxCeleb challenges Honors include Fellowship of the Royal Society (FRS) and the ISSLS Prize in Clinical Science 2023 for spinal analysis innovations. His lab collaborates globally, emphasizing real-world applications in healthcare and autonomous systems.
Minseok Ryu is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Industrial & Operations Engineering from the University of Michigan (2020). Prior to ASU, he was a postdoctoral appointee at Argonne National Laboratory’s Mathematics and Computer Science Division. His research focuses on optimization methodologies for decentralized and stochastic decision-making distributed algorithms for machine learning and operations research applications in healthcare systems and energy grids Teaching responsibilities include courses on applied deterministic operations research (IEE 574), optimization (IEE 622), and research practicums (IEE 792). His work emphasizes computational challenges in decision-making under uncertainty, with recent projects addressing nurse staffing optimization, federated learning frameworks (e.g., APPFL/APPFLX), and resilient power grid systems. He has no recorded academic awards but actively contributes to open-source software and cross-disciplinary research collaborations. Research interests bridge theory and practice, targeting social goods through optimization techniques like distributionally robust optimization, federated learning, and heuristic algorithms for energy and healthcare systems.
Sarah Billington is the UPS Foundation Professor of Civil and Environmental Engineering at Stanford University and a Senior Fellow at the Woods Institute for the Environment. Her research program focuses on sustainable building design, human wellbeing, and ethical supply chain practices. Education: PhD, University of Texas at Austin, Structural Engineering (1997) MSE, University of Texas at Austin, Structural Engineering (1994) BSE, Princeton University, Civil Engineering & Operations Research (1990) Her lab employs interdisciplinary methods to study how built environments impact human physical, psychological, and social wellbeing. Current projects include developing tools to quantify nature exposure in buildings, exploring affordable housing's role in wellbeing, and using AI to assess forced labor risks in material supply chains. While no longer active in this area, her group previously pioneered research on sustainable construction materials like bio-based composites and ductile cement-based composites. Her recent publications address topics such as nature integration in architecture, workplace design optimization, and health impacts of building materials. The Billington Lab at Stanford fosters collaborative team science, welcoming partnerships to advance occupant-centric engineering solutions.
Osman Yağan is a Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with affiliate faculty status in the School of Computer Science. He is also a core member of CyLab Security and Privacy Institute. Prior to joining CMU in 2013, he was a Postdoctoral Research Fellow at CyLab. He holds a Ph.D. in Electrical and Computer Engineering from the University of Maryland (2011) and a B.S. from Middle East Technical University (2007). His research focuses on modeling, analysis, and optimization of computing systems, leveraging applied probability, network science, data science, and machine learning. Key areas include multi-armed bandits, resilient machine learning, contagion processes in networks, and cybersecurity. Research Interests include Machine Learning, Data Science, Network Science, Cybersecurity, and Robustness in Cyber-Physical Systems. He has advised numerous students, including current PhD candidates Yurun Tian, Orkun İrsoy, and Ishank Juneja, as well as notable alumni such as Mansi Sood (now at MIT) and Jun Zhao (Assistant Professor at Nanyang Technological University). Key Awards include the CIT Dean's Early Career Fellowship, IBM Academic Award, and Best Paper Awards at ICC 2021, IPSN 2022, and ASONAM 2023. His work spans theoretical contributions (e.g., contagion models in multi-layer networks) and applied research (e.g., mitigating cascading failures in power systems). He leads or co-leads grants from ONR, NSF, and ARO, focusing on resilient machine learning, network robustness, and pandemic modeling.
Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML
Kenneth P. Birman is the N. Rama Rao Professor of Computer Science at Cornell University, where he has had a long and impactful career in distributed systems, cloud computing, and AI/ML infrastructure. He is known for foundational contributions to reliable and scalable distributed systems, and for leading high-impact projects such as Cascade, Vortex, and Derecho. He is also the author of a widely used textbook on reliable distributed systems and has founded multiple companies based on his research. Education: Ph.D. in Computer Science, University of California, Berkeley M.S. in Computer Science, University of California, Berkeley B.A. in Computer Science, Columbia University Research Interests: Professor Birman's research focuses on building reliable, secure, and scalable distributed systems . His current emphasis is on AI and ML infrastructure , particularly in reducing latency and improving performance through hardware acceleration, RDMA-based communication, and edge computing. He explores how to eliminate data movement bottlenecks in AI pipelines and how to support real-time, mission-critical applications in domains like healthcare, smart grids, and industrial IoT. His work spans systems programming, cloud computing, fault tolerance, and formal verification . He has designed systems that have been deployed in high-stakes environments such as the New York Stock Exchange, the Swiss Exchange, and the French Air Traffic Control system. Scientific Awards: ACM Fellow (1999) IEEE Fellow (2014) IEEE Tsutomu Kanai Award for innovations in distributed computing Teaching and Mentorship: Professor Birman teaches two courses in the fall semester: CS4414: Systems Programming and CS5416: Cloud and ML Systems Programming . He has advised numerous Ph.D. and M.S. students, including Alicia Yang, Tiancheng Yuan, Yifan Wang, Weijia Song, Edward Tremel, Sagar Jha, Jonathan Behrens, and Mae Milano. He has announced that Fall 2025 will be his last semester teaching, and he is no longer recruiting new students, though he will continue supervising current ones. Labs and Projects: He leads the Derecho Project and the Cascade/Vortex Project , both focused on high-performance distributed systems. These projects are collaborative efforts with students and industry partners, and the software is released under open-source licenses. He also maintains strong ties with Cornell's systems group and collaborates with faculty across CS, ECE, IS, and the Cornell Tech NYC campus.
Ram Vasudevan is an Associate Professor and Associate Chair of Graduate Studies in the Department of Robotics at the University of Michigan. His research focuses on developing tools for safe and robust deployment of robotic systems, emphasizing optimization, nonlinear control, and real-world applications. Key areas include legged robot locomotion, shared control systems, and safety-critical autonomous systems. Research Interests: Optimization and control of nonlinear systems, locomotion of legged robots, shared control active safety systems, and automation of diagnostic/rehabilitative tasks. His ROAHM Lab prioritizes mathematical guarantees for robotic performance, with applications in medical robotics, autonomous vehicles, and soft robotics. Recent work emphasizes trajectory optimization, sensor fusion, and safety-aware control strategies. He has contributed to benchmarks for autonomous vehicle perception and novel methods in thermal image restoration using neural radiance fields. Awards: None explicitly listed in provided text. Labs/Teams: Directs the ROAHM Lab, collaborating on projects like robotic tail mechanics, real-time motion planning, and sensor data analysis. Active in academic conferences including RSS and ICRA.
Prof. Laura Leal-Taixé is a Professor at the Technical University of Munich (TUM) in the Department of Informatics, leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Assistant Professorship, promoted to W3 Associate Professorship in 2022. Her research focuses on computer vision and machine learning, particularly video analysis, multi-object tracking, and autonomous driving applications. Education: B.Sc./M.Sc. in Telecommunications Engineering (Technical University of Catalonia, UPC) Ph.D. in Information Processing (Leibniz University of Hannover, Germany) Postdoctoral Research at ETH Zürich (Switzerland) and TUM Research Interests: Laura’s work addresses challenges in video analysis, including motion analysis, semantic segmentation, and integrating social dynamics into urban traffic modeling. Her project socialMaps , funded by the Sofja Kovalevskaja Award, explores decoupling vehicle and pedestrian traffic using dynamic maps. Her research combines optimization techniques, deep learning, and sensor data for real-world applications like autonomous systems. Recent Trends: Her publications highlight advancements in multi-object tracking, 3D LiDAR segmentation, and trajectory forecasting, emphasizing neural networks and geometric approaches. Collaborations with industry and academia underscore her work’s practical impact. Awards: 2017 Sofja Kovalevskaja Award (€1.65M, Humboldt Foundation) 2017 DAAD Australia-German Joint Research Grant Travel grants from Women in CV, CVPR Doctoral Consortium, and Vodafone Foundation Labs & Projects: Leads the Dynamic Vision and Learning group at TUM, focusing on vision-based AI for autonomous systems and environmental monitoring. Active in developing datasets like DynamicEarthNet for semantic change analysis.
Moe Z. Win is the Robert R. Taylor Professor at the Massachusetts Institute of Technology (MIT), specializing in wireless communications, optical communications, and space communications systems. His research bridges theoretical and applied domains, including quantum sensing, network localization, and signal processing. B.S.E.E., Texas A&M (1987) M.S.E.E. & Ph.D., University of Southern California (1989, 1998) Recent work focuses on quantum-enhanced positioning, machine learning for localization, and next-generation (xG) non-terrestrial networks. He leads research at the Quantum neXus Laboratory (QX Lab), Wireless Information & Network Sciences Lab, and Laboratory for Information and Decision Systems. His career spans the Jet Propulsion Laboratory (1987-1995) and AT&T Research Laboratories (1998-2002). Key methodologies include soft information fusion, variational quantum sensing, and robust beam tracking for terahertz communications.
Prof. Dr. Thomas Brinkhoff is Chair of the Institute Board and Chair of Geoinformatics at Oldenburg University of Applied Sciences. He leads the Institute for Applied Photogrammetry and Geoinformatics (IAPG) and contributes to institutions like the Association for the Promotion of Geoinformatics in Northern Germany (GiN e.V.) and the Oldenburg Research and Development Institute for Computer Science (OFFIS e.V.). Education: Diploma in Computer Science (Informatik), Universität Bremen (1990) Doctorate in Computer Science (Dr. rer. nat.), Ludwig Maximilian University of Munich (1994) Brinkhoff's research spans geodatabase systems, spatiotemporal data processing, geosensor analytics, and location-based services. His work addresses Volunteered Geographic Information (VGI), web-based geospatial visualization, and mobile data integration, with applications in traffic management and forensic science. Recent projects include ProSaDi (Digital Provenance and Collection Research) and contributions to the Laboratory for optical 3D metrology . He has served on program committees for ACM SIGSPATIAL (2002-2019), AGILE conferences (2010-2025), and editorial boards of journals like GeoInformatica and TGIS. Notable Lectures: 2024: Forensic applications of tachograph data 2023: Geoinformatics in homicide investigations 2022: Spatiotemporal analysis for sustainability projects 2015: Open geodata standards at FOSSGIS 2014: Mobile sensor data processing
Dr. Jiamin Li is an Associate Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech), Shenzhen, China. She holds a Ph.D. and B.Eng. in Electrical and Computer Engineering from the National University of Singapore (NUS). Her research focuses on biomedical integrated circuits, energy harvesting, and wireless body-area networks. Education: Ph.D. and B.Eng. in Electrical and Computer Engineering from NUS Employment: Associate Professor (2024–Present), Assistant Professor (2022–2024) at SUSTech; Research Fellow at NUS Dr. Li's research addresses wireless body area powering , low-power biosignal interfaces , and intelligent biosignal processors . Her work leverages microsystem design for applications in biomedical engineering , particularly in energy-autonomous medical devices and human body-coupled communication . Her recent publications highlight advancements in concurrent power/data transmission , subharmonic pulse injection , and triboelectric energy harvesting for biomedical systems. Key venues include IEEE JSSC , Nature Electronics , and conferences like ISSCC and BioCAS. Awards: IEEE SSCS Predoctoral Achievement Award, ISSCC Best Demo Award, ASSCC SDC Best Design Award Committees: IEEE SSCS Women-in-Circuit (WiC) Committee, ISSCC Student Research Preview Committee Opportunities: Positions available for Ph.D., Master's, Postdoctoral Fellows, and Visiting Scholars
Jon Simon is the Joan Reinhart Professor and Professor of Applied Physics at Stanford University . He leads the Simon Lab , which explores the convergence of condensed matter physics , quantum optics , and quantum information science , focusing on creating synthetic materials from light and investigating topological and strongly correlated quantum systems. His research spans constructing photonic materials in quantum circuits, studying small quantum systems with strong correlations, and applying Hamiltonian engineering to realize exotic states of matter. The lab has achieved milestones like the first Mott insulator of photons and topologically insulating circuits . Collaborative projects with the Schuster Lab leverage superconducting quantum circuits for synthetic matter studies. Jon's students include Adam Shaw (PhD, now at Stony Brook) Lavanya Taneja (PhD, now at Atom Computing) Ruichao Ma (Postdoc, now faculty at Purdue) among others. The lab's recent publications focus on cavity arrays, hybrid quantum systems, and topological photonics. Research is supported by grants and affiliations with Stanford's Department of Applied Physics and interdisciplinary institutes.
Kaiyuan Yang is an Associate Professor in the Department of Electrical and Computer Engineering at Rice University, leading the Secure and Intelligent Micro-Systems (SIMS) Lab. His research focuses on low-power integrated circuits and bioelectronic implants for applications like the Internet of Everything and medical devices. He holds a B.S. from Tsinghua University (2012) and M.S./Ph.D. degrees from the University of Michigan (2017). Research Interests: Low-power digital/analog/mixed-signal systems Bioelectronics and implantable devices Hardware security and PUF design Mixed-signal computing and emerging materials Recent work emphasizes magnetoelectric-powered implants, secure backscatter communication, and in-memory computing architectures. His publications span top venues like IEEE ISSCC, IEDM, and ACM MobiCom. Awards: 2022 NSF CAREER Award 2022 IEEE Top Picks in Hardware Security 2016 IEEE SSCS Predoctoral Achievement Award Dr. Yang serves on editorial boards for IEEE TVLSI and program committees for ISSCC/CICC. His lab develops miniature, secure, and energy-efficient systems for healthcare and IoT applications.
