Michael Lepech is a Professor of Civil and Environmental Engineering and Senior Fellow at the Woods Institute for the Environment at Stanford University. His research focuses on integrating sustainability into civil engineering through quantitative assessment and multi-scale modeling, particularly via the Sustainable Integrated Materials, Structures, Systems (SIMSS) framework. He also leads the Stanford Center at the Incheon Global Campus (SCIGC) in South Korea, exploring smart city technologies for urban sustainability. Education : PhD in Civil and Environmental Engineering (2006), MBA in Finance and Strategy (2008) from the University of Michigan. Research Areas : Sustainable infrastructure design, biopolymer composites, life cycle assessment, digital twinning, smart city technologies, and multi-physics deterioration modeling. Leadership : Director of SCIGC, advancing research on smart and sustainable urban environments in Songdo, South Korea. His recent publications focus on biopolymer-bound composites, traffic signal optimization, and life cycle sustainability analysis. He has received recognition as a Senior Fellow at Stanford’s Woods Institute for environmental research.
Hamsa Bastani is an Associate Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania, with a secondary appointment in Statistics and Data Science. She co-directs the Wharton Healthcare Analytics Lab and serves as an Associate Editor for Operations Research, M&SOM and OR Letters. Her academic journey began with summa cum laude graduation from Harvard in 2012 with an A.M. in physics and A.B. in physics and mathematics. She completed her PhD in Stanford's Electrical Engineering department under Mohsen Bayati, followed by a Herman Goldstine postdoctoral fellowship at IBM Research. Professor Bastani's research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications spanning healthcare operations, social good, and revenue management. Her work demonstrates particular expertise in sequential decision-making (bandits, reinforcement learning), learning from auxiliary data sources (transfer learning, meta-learning), and designing effective human-AI interfaces (interpretability, fairness). She has made significant contributions to understanding how AI systems affect and augment human behavior, with the goal of designing AI tools that help humans thrive. Her publications reveal a strong trend toward high-impact applications of machine learning in critical societal domains. A significant portion of her recent work focuses on healthcare applications, including optimizing health supply chains in low- and middle-income countries, designing clinical trial protocols, and creating targeted public health interventions. Another major theme examines the complex relationship between humans and AI systems, particularly how AI affects learning outcomes and decision-making processes. Her work frequently bridges theoretical advances with practical implementation, as evidenced by country-scale deployments in Greece and Sierra Leone. Wagner Prize for Excellence in Operations Research Practice (2021) Pierskalla Award for Best Paper in Healthcare (2021, 2019, 2016) Behavioral OM Best Paper Award (2021) Public Sector in OR Best Paper Award (2024) INFORMS Data Mining Best Paper Award (2022) Wharton Teaching Excellence Award (2019, 2020, 2021) Professor Bastani has advised numerous PhD students who have gone on to prominent positions, including Pia Ramchandani (Director of Responsible AI at PwC), Arielle Anderer (Assistant Professor at Cornell Johnson), and Kan Xu (Assistant Professor at ASU Carey). Her research has been supported by collaborations with national governments, including the Greek government where she co-designed Eva, the national-scale reinforcement learning system for targeted COVID-19 testing, and the Government of Sierra Leone where she improved patient access to essential medicines by nearly 20% via decision-aware learning. She has also conducted the first large field study deploying generative AI tutors in high school math classes. She leads the Wharton Healthcare Analytics Lab and serves on the Steering Committee for the Penn Center for Health Incentives and Behavioral Economics and on the statistics advisory committee for the AHA Food is Medicine Initiative. Outside academia, she serves on the Workday AI Advisory Board, demonstrating her commitment to translating academic research into practical applications.
Magnus Liebherr is a Professor at the University of Duisburg-Essen , focusing on Technology Acceptance , Artificial Intelligence , and Sustainable Transportation . His work bridges Business Administration with Human-Computer Interaction , exploring how users adapt to emerging technologies like autonomous vehicles and AI systems. Key research areas include: Acceptance of AI applications in mobility Business model development for climate-neutral transportation Cognitive and psychological factors in technology interaction Digital media effects on adolescent well-being Trust calibration in automated systems His recent publications examine Large Language Model dependency , gamified language learning , and mental workload metrics for autonomous vehicles. While no formal awards are listed, his interdisciplinary work spans psychology , engineering , and business strategy .
Prof. Dr. Rudi Zagst is a Professor of Mathematical Finance at the Technical University of Munich (TUM), where he serves as Head of the Department of Mathematical Finance within the TUM School of Computation, Information and Technology. He has held this position since 2001 and is actively involved in teaching, research, and academic leadership. In 2003, he was appointed as a second member of the Faculty of Economics, and since 2004, he has served as Deputy Chairman of the joint elite degree program 'Finance & Information Management' of the University of Augsburg and TUM. Prof. Zagst earned his doctorate in business mathematics from the University of Ulm, where he later completed his habilitation in 2000. His academic journey began with a professional career at HypoVereinsbank AG, where he served as Head of Product Development in Institutional Investment Management before becoming Managing Director of RiskLab GmbH in 1997. His research focuses primarily on financial engineering, risk management, and asset management, with particular emphasis on portfolio optimization, mathematical finance, and quantitative risk management. His work bridges theoretical finance with practical applications, often incorporating advanced mathematical techniques to solve complex financial problems. Recent publications demonstrate his continued interest in GARCH models, portfolio optimization under various constraints, and the application of machine learning techniques to financial problems. Analysis of his recent publications (2024-2025) reveals a strong focus on portfolio optimization under complex market conditions, particularly using GARCH models to capture volatility dynamics. His work increasingly incorporates machine learning techniques (as seen in the credit spread analysis paper) while maintaining rigorous mathematical foundations. Many papers explore the intersection of theoretical finance with practical investment strategies, reflecting his commitment to bridging academic research with real-world financial applications. Professor of the Year 2007 (awarded by Unicum Profession magazine) Prof. Zagst has supervised numerous bachelor's, master's, and doctoral theses through TUM's Finance and Actuarial Science research group. His collaborative work with industry partners through the TUM CAIR Labs and RiskFactory demonstrates strong connections between academic research and practical financial applications. He has received research funding through various industry partnerships with major financial institutions including Allianz, Munich Re, and ERGO Group AG. Prof. Zagst leads the Research Group Finance and Actuarial Science at TUM, which includes Professors Matthias Scherer, Aleksey Min, and Christoph Knochenhauer. The group maintains strong industry connections through the TUM CAIR Labs initiative, collaborating with over 25 financial institutions including Allianz, Munich Re, Deloitte, PwC, and KPMG. Their RiskFactory laboratory serves as a bridge between academic research and practical financial risk management applications in the industry.
Minsu Kim is a CIFAR AI Safety Post-doc Fellow at KAIST and Mila, collaborating with Prof. Yoshua Bengio, Prof. Sungjin Ahn, and Prof. Sungsoo Ahn. His work bridges System 2 Deep Learning, Bayesian posterior inference, and combinatorial optimization. Ph.D., Industrial Engineering, KAIST (2025) M.S., Electrical Engineering, KAIST (2022) B.S., Mathematics and Computer Science (Dual Degree), KAIST (2020) Kim's research focuses on enabling AI systems to measure uncertainty, represent causality, and perform sequential reasoning for safety-guaranteed planning. His methodology integrates GFlowNets and diffusion models with off-policy amortized inference, targeting applications in scientific discovery , hardware design optimization , and large language model alignment . Recent work explores Bayesian posterior inference through GFlowNets, aiming to unify deep learning with probabilistic reasoning. His 15 most recent publications (2025–2024) reveal a trend of combining combinatorial optimization with generative models for tasks like molecular graph discovery, vehicle routing, and neural architecture search. He also investigates diffusion samplers for Bayesian inverse problems and symmetry-based neural methods (Sym-NCO) to enhance sample efficiency. Jang Yeong Sil Fellowship (2025) KAIST Presidential Best Ph.D. Thesis Award (2025) Qualcomm Innovation Fellowship (2023) DesignCon Best Paper Awards (2021–2022) Kim's collaborations span KAIST's Industrial Engineering department and Mila's AI research groups. He actively contributes to academic peer review for top conferences (NeurIPS, ICML, ICLR) and journals (IEEE TNNLS, TPAMI), emphasizing the intersection of AI safety , uncertainty quantification , and systemic reasoning .
Prof. Dr. Raphael Sznitman serves as Director of the ARTORG Center for Biomedical Engineering Research and Head of the Artificial Intelligence in Medical Imaging group at the University of Bern, Switzerland, holding a Full Professor position in AI for Medical Imaging since 2015. Education: PhD in Computer Science, Johns Hopkins University (2011) MSc in Computer Science, Johns Hopkins University (2009) BSc in Cognitive Systems, University of British Columbia (2007) Research Interests: Sznitman's work centers on computational vision , probabilistic methods , and statistical learning applied to medical imaging challenges. His group develops AI algorithms for ophthalmic diagnostics, surgical robotics, and medical image analysis, with emphasis on OCT, surgical phase recognition, and domain adaptation techniques. Key application areas include retinal disease detection and cataract surgery automation. Publication Trends: His 2021-2025 publications reveal concentrated efforts in deep learning for medical imaging , particularly in ophthalmology (OCT analysis) and surgical video understanding. Emerging themes include LLM applications for clinical monitoring, unsupervised out-of-distribution detection for surgical safety, and physics-informed AI for multimodal medical data fusion. Research Leadership: As ARTORG Center Director, Sznitman oversees interdisciplinary research bridging computer science and clinical medicine. His group collaborates extensively with Bern University Hospital clinicians on translational projects, securing funding for AI-driven diagnostic tools and surgical assistance systems. Current initiatives focus on real-time intraoperative guidance and spaceflight ophthalmology applications. Laboratory: The Artificial Intelligence in Medical Imaging group operates within ARTORG's dedicated facilities, maintaining partnerships with surgical robotics labs and ophthalmology departments for clinical validation of AI systems. Their work integrates multimodal data streams including OCT, VR perimetry, and surgical video feeds.
Miroslav Pajic serves as a Professor in the Department of Electrical and Computer Engineering at Duke University's Pratt School of Engineering. He also holds joint appointments as Associate Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science and Associate Professor of Computer Science. As Director of Master's Studies, he oversees the graduate program in Electrical and Computer Engineering and teaches numerous courses spanning embedded systems, cyber-physical systems design, and robotics. Education: Ph.D. in Electrical and Computer Engineering from University of Pennsylvania (2012) Miroslav Pajic's research focuses on the design and analysis of cyber-physical systems (CPS) with varying levels of autonomy and human interaction. His work spans the intersection of embedded systems, artificial intelligence, machine learning, control theory, formal methods, and robotics. He specializes in developing high-assurance autonomous systems with applications in robotics, automotive systems, and medical devices, with particular emphasis on CPS security and resilient autonomy. His research addresses fundamental challenges in creating systems that can operate reliably in uncertain environments while maintaining security against potential cyber attacks. Analysis of Pajic's recent publications reveals a strong interdisciplinary research program bridging theoretical foundations with practical applications. His work spans secure sensor fusion for distributed autonomy, medical applications of CPS (particularly deep brain stimulation for neurological disorders), and innovative sensing technologies for autonomous vehicles. A significant portion of his research addresses security challenges in cyber-physical systems, including stealthy GPS attacks on UAVs and methods for attack-resilient state estimation. His publications increasingly integrate machine learning techniques with traditional control theory to create more adaptive and robust autonomous systems. Pajic actively mentors graduate students and leads research groups focused on cyber-physical systems security and high-assurance autonomy. His research is supported by multiple grants, including the NSF AI Institute for Edge Computing (Athena), which he co-leads. He has received funding from various sources to support his work on secure and resilient cyber-physical systems, medical device security, and autonomous vehicle technologies. Pajic collaborates extensively with medical researchers on applications of cyber-physical systems in healthcare, particularly in deep brain stimulation for neurological disorders. His work bridges the gap between theoretical control systems and practical implementations in safety-critical domains, with a growing emphasis on translating research into real-world applications that improve system security and reliability.
Petri Mähönen is a Full Professor at the Department of Information and Communications Engineering , Aalto University. His research focuses on networked systems, machine learning applications in telecommunications, and smart grid technologies. University: Aalto University Department: Information and Communications Engineering Research Interests span networked systems, IoT security, UAV communication, and AI-driven network optimization. His work addresses predictive QoS in cellular-connected drones and generative adversarial networks for cybersecurity. Recent Publications include studies on GAN-based traffic augmentation, anomaly detection in mobile networks, and regulatory frameworks for data platforms. His articles reflect expertise in both theoretical and applied network science.
Prof. Dr.-Ing. Ahmad Osman is a Professor at the Saarland University of Applied Sciences (htw saar), specializing in Test Technologies and Test Methods within the Faculty of Engineering. He also holds an Adjunct Professor position at Laval University in Quebec, Canada, in the Department of Electrical Engineering and Computer Science. His research focuses on Artificial Intelligence applications in Signal and Image Processing for Non-destructive Testing (NDT) , with extensive work on Deep Learning , 3D Ultrasound Tomography , and Sensor Data Fusion in industrial contexts. Engineering Artificial Intelligence Signal Processing Image Processing Non-destructive Testing Quality Control Augmented Reality Osman leads the AutomaTiQ research group and serves as Head of the Algorithms/Signal and Data Processing Department at Fraunhofer IZFP . His recent publications (2017–2022) emphasize Deep Learning for defect detection in CFRP , Terahertz Imaging for artwork diagnostics, and Acoustic Sensors for agricultural quality control. He has organized international conferences on Structural Health Monitoring and contributed to Springer books on NDT technologies. His projects include ComforTex-AI (2024) and development of 3D positioners for ultrasound measurements. Collaborations span institutions in Germany, Canada, Italy, and Brazil, with advisory roles in the German Society for NDT and technical committees for conferences in Montreal and Egypt.
Hedvig Kjellström is a Professor at the Division of Robotics, Perception and Learning within the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology. She holds significant affiliations with the Swedish e-Science Research Centre and the Max Planck Institute for Intelligent Systems in Germany. Her work spans multiple interdisciplinary domains and she serves as Editor-in-Chief for CVIU and was Program Chair for CVPR 2025. Her research centers on Computer Vision as a sub-field of AI, with three interconnected themes: Computational Aesthetics (exploring aesthetic aspects of human communicative behavior), Communicative Behavior (developing models of how humans and animals perceive and produce non-verbal communication), and Embodied Artificial Intelligence (creating methodologies for robots and autonomous agents to perceive the world through sensors, primarily vision). Her work has significant applications in medical diagnostics, animal welfare, human-robot interaction, and creative arts. Analysis of her recent publications reveals a strong trend toward multimodal AI systems that integrate vision, language, and action understanding. Her research increasingly focuses on animal-centered applications, particularly equine pain detection and behavior analysis, while maintaining strong foundations in human communication modeling, gesture recognition, and 3D reconstruction techniques. The interdisciplinary nature of her work bridges computer science with veterinary medicine, neuroscience, and performing arts. Hedvig Kjellström actively supervises numerous PhD and Master's students across various projects and maintains extensive collaborations with institutions including Karolinska Institutet, Swedish University of Agricultural Sciences, and international partners. Her research is supported by major funding bodies including WASP, VR, and SeRC. She leads or participates in several notable projects including OrchestrAI (communication between conductor and orchestra), ANITA (Animal Translator), MARTHA (3D horse motion analysis), and STING (synthesis and analysis with transducers and invertible neural generators). Her work with ACAI (Animal Centered Artificial Intelligence), which she co-founded and directs, demonstrates her commitment to applying AI for animal welfare.
Deji Akinwande is a Professor and holds the Cockrell Family Regents Chair in Engineering #8 at The University of Texas at Austin's Chandra Family Department of Electrical and Computer Engineering. He earned his PhD in Electrical Engineering from Stanford University (2009) and an MS in Applied Physics from Case Western Reserve University. His research focuses on 2D materials, nanotechnology, and flexible electronics, with breakthroughs in atomristors, graphene-based biosensors, and wearable electronic tattoos. Key achievements include pioneering work on silicene, being elevated to IEEE Fellow (2021), and receiving the PECASE Award (Obama administration). His lab, the Akinwande Nano Research Group, explores nanoelectronics, bioelectronics, and RF systems for societal applications like health monitoring and 6G communications. Education: PhD, Electrical Engineering, Stanford University, 2009 MS, Applied Physics, Case Western Reserve University Awards: 2021 IEEE Fellow APS Fellow (2017) PECASE Award Moore Inventor Fellowship His research spans neuromorphic computing, flexible sensors, and energy-efficient memory devices. Over 100+ publications highlight his work on graphene, MXenes, and 2D material applications. He co-authored a textbook on carbon nanotubes and graphene (Cambridge University Press, 2011) and serves as an IEEE Distinguished Lecturer and editor for Nature NPJ 2D Materials . Lab and Collaborations: The Akinwande Nano Lab develops scalable 2D electronics, wearable health monitors, and next-gen RF components. Recent grants include NSF CHIPS Act funding and DoD support for 6G switches and neuromorphic hardware.
Timothy Bretl is a Professor of Aerospace Engineering at the University of Illinois at Urbana-Champaign, holding the Severns Faculty Scholar position since 2021. He also serves as Associate Head of the Aerospace Engineering department. His research focuses on robotics, control systems, rehabilitation robotics, and engineering education. Bretl earned his Ph.D. from Stanford University (2005), with prior degrees from Swarthmore College. He holds affiliate roles across multiple departments, including Neuroscience, Coordinated Science Laboratory, and Computer Science. Education: Ph.D. in Aeronautics and Astronautics, Stanford University (2005) B.A. in Mathematics and B.S. in Engineering, Swarthmore College (1999) His research spans engineering education innovations, robotic manipulation, and brain-machine interfaces. Notable awards include the NSF CAREER Award (2010), Best Manipulation Paper (2012), and multiple teaching honors like the Rose Award for Teaching Excellence (2016). Bretl’s work integrates theoretical foundations with practical applications in prosthetics, autonomous systems, and educational technology. He has advised numerous projects on robotics, control systems, and human-robot interaction. His lab explores advanced topics like elastic rod manipulation, magnetic positioning, and curriculum reform in STEM education. Collaborative projects include partnerships with industry and interdisciplinary teams at the Beckman Institute.
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.
Dr. Zheng Yuan is an Associate Professor (Senior Lecturer) in the School of Computer Science at the University of Sheffield. Previously, they held roles as an Assistant Professor at King's College London and a Research Associate at the University of Cambridge's Department of Computer Science and Technology. Their primary research focuses on machine learning and deep learning applications in natural language processing (NLP), particularly in educational technology, healthcare, creativity, and multilingual contexts. Key projects include computer-assisted language learning (CALL), human-centered NLP in education, computational code-switching, and creative AI. Education includes a PhD and MPhil in Natural Language Processing from the University of Cambridge, and a BSc(Eng) from Queen Mary University of London. They hold affiliated positions at the University of Cambridge, King's College London, and are a Fellow of Trinity College, Cambridge. They contribute to The Alan Turing Institute's Data-Centric Engineering Programme and hold FHEA status (2024-). Research interests span educational NLP, multilingual systems, transfer learning, and explainable AI. They actively organize workshops and serve on editorial boards (e.g., PeerJ Computer Science) and conference committees (ACL/EMNLP). Recent activities include co-organizing NLP workshops at ACL 2025 and NAACL 2024, alongside roles in professional societies like the ACL Professional Conduct Committee. Awards include Fellowship of the Higher Education Academy (2024-) and ASEFClassNet18 Faculty Collaboration (2025-). They welcome PhD applications in NLP and machine learning, emphasizing interdisciplinary applications.
Endadul Hoque is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Syracuse University (SU), leading the SYNE Lab. Previously, he held a similar position at Florida International University (FIU). He earned his Ph.D. in Computer Science from Purdue University in 2015, with postdoctoral research at Northeastern University. His research focuses on cybersecurity, particularly in automated vulnerability detection, network/system security, and IoT defense mechanisms. He draws on program analysis, formal verification, and fuzz testing. Key honors include the NSF CAREER Award (2024) and a Google Research Scholar Award (2022). His work has been published in top venues like ACM CCS, NDSS, and IEEE S&P. He actively seeks students for PhD/MSc positions in security, IoT, and program analysis. His grants include NSF funding for IoT policy enforcement and context-sensitive fuzzing. Education Ph.D., Computer Science, Purdue University, 2015 M.S., Computer Science, Marquette University, 2010 B.S., Computer Science and Engineering, Bangladesh University of Engineering and Technology, 2008 Research Interests Dr. Hoque’s work spans secure network systems, IoT security, vulnerability detection, and automated reasoning methods like SMT solving and symbolic execution. Recent innovations include VetIoT (runtime IoT defense validation) and LLM-driven security tools like iConPAL. He emphasizes practical defenses and user-centric security solutions, such as the SeQR enterprise Wi-Fi configurator. Awards NSF CAREER Award (2024) Google Research Scholar Award (2022) NDSS Distinguished Paper (2018) Grants & Service He leads NSF-funded projects on IoT policy enforcement and context-sensitive fuzzing. Served on program committees for S&P, SecDev, and DSN. Teaches courses on systems security and operating systems at SU and FIU. Lab/Team SYNE Lab at Syracuse University focuses on securing networked systems through automated analysis, runtime verification, and resilient protocols.
