Jiangfeng Zhang is an Associate Professor in the Department of Automotive Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research focuses on electric vehicle technologies, battery management, renewable energy integration, and smart grid solutions. Research Areas: His work spans battery optimization, grid resilience, autonomous vehicle control, and sustainable energy planning. Recent projects include solid-state battery advancements, V2G integration, and AI-driven energy management. Publication Trends: His articles predominantly address optimization challenges in electric mobility and grid stability, utilizing machine learning, game theory, and control systems. Key themes include decarbonization, cyber-physical security, and renewable integration under uncertainty.
Ralph Etienne-Cummings is the Julian S. Smith Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), where he also serves as Vice Provost for Faculty Affairs. He holds secondary appointments in Computer Science and is affiliated with JHU's Applied Physics Lab. His work spans three decades, pioneering advancements in neuromorphic engineering, neural prosthetics, and biomorphic robotics. Etienne-Cummings leads the Computational Sensory Motor Systems Laboratory and has developed systems for closed-loop neural interfaces, prosthetics, and biomedical sensors. Education: BSc in Physics (1988), Lincoln University MSEE (1990) and PhD (1994) in Electrical Engineering, University of Pennsylvania Research Interests: His work focuses on neuromorphic systems, bio-inspired algorithms, and neural prosthetics. Key areas include spinal cord stimulation for mobility restoration, wearable health monitoring, and ultrasonic imaging for infertility treatment. He has contributed to silicon Central Pattern Generators (CPGs) for bipedal robotics and developed the first large-scale neural computer using VLSI chips. His lab explores organoid intelligence and biohybrid systems, blending neuroscience with engineering. Impact & Recognition: Named Fellow of AIMBE (2021) and IEEE (2012) Recipient of JHU Discovery Awards (2018–2019) and NSF CAREER Award (1996) Developed the 'Microbead'—a 0.009mm³ wireless neural stimulator Industry & Outreach: Served as founding director of JHU's Institute of Neuromorphic Engineering and advised firms like Panasonic and Avago. Testified in federal court on intellectual property disputes. Recognized as a 'ScienceMaker' in the HistoryMakers Archive for contributions to African American STEM leadership. Labs & Collaborations: Directs the Computational Sensory Motor Systems Lab. Collaborates with DARPA on prosthetics and the NIH on bioelectronic medicine. His work bridges academia and industry, emphasizing practical applications of neural engineering.
Dr. Gamal ELGHAZALY is a Research scientist at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), affiliated with the Ubiquitous and Intelligent Systems department. His work focuses on autonomous driving technologies, V2X communication systems, robotics, and control systems. Key research areas include 5G-enabled teleoperated driving, high-definition map construction, and 3D perception for autonomous vehicles. He has developed platforms like RoboCar and contributed to frameworks like FastCycle for modular automated systems. Research interests span robotics kinematics (planar parallel manipulators), adaptive control methodologies (sliding mode control, fuzzy logic), and hybrid systems (cable-driven robots). His publications emphasize real-time motion planning, sensor fusion, and safety-critical systems. Recent works (2023-2025) highlight advancements in 4D perception, cloud-assisted 3D reconstruction, and V2X-enabled collaborative systems. Publications from 2017-2018 reflect early contributions on parallel manipulator modeling and control, while recent efforts focus on integrating cutting-edge technologies like 5G and edge computing into autonomous systems. His work bridges theoretical robotics with practical implementations, addressing challenges in both dynamic environments and industrial applications.
Robin Zebrowski is a Professor and Chair of Cognitive Science at Beloit College, with joint appointments in Philosophy, Psychology, and Computer Science. She leads Beloit’s Cognitive Science program and teaches courses like Cyborg Brains and Hybrid Minds , emphasizing interdisciplinary approaches to embodiment and extended cognition. Her research focuses on 4E cognition (embodied, embedded, extended, enactive), AI ethics, and machine consciousness, often exploring how technology interfaces with human bodies and minds. She co-authored Great Philosophical Objections to Artificial Intelligence , which won the 2022 CHOICE Award. Zebrowski frequently speaks at conferences like IACAP and Robophilosophy, addressing topics like AI warfare and ethical AI design. She integrates hands-on learning via campus resources like the Logan Museum, offering students immersive experiences with technologies like atlatls and robotic prosthetics. Her work bridges philosophy, robotics, and neurotechnology, challenging assumptions about human-machine boundaries. Recent outreach includes media appearances on WPR and WORT discussing AI ethics and author lawsuits against OpenAI. She advocates for inclusive pedagogy, requiring students to engage with diverse materials across disciplines, including scientific articles and literary works like Ted Chiang’s short stories. Her courses emphasize critical thinking about identity, embodiment, and technology’s societal impact, supported by active learning strategies and museum collaborations.
Chengyu Cao is an Associate Professor at the University of Connecticut's School of Mechanical, Aerospace, and Manufacturing Engineering since 2008. He holds a Ph.D. in Mechanical Engineering from MIT (2004), an M.S. in Manufacturing Engineering from Boston University (1999), and a B.S. in Electronics and Information Engineering from Xi’an Jiaotong University (China, 1995). His research focuses on dynamics and control, adaptive systems, mechatronics, unmanned systems, and aerospace applications, alongside theoretical work in cosmology and physics, particularly his groundbreaking 'world membranes' theory challenging traditional relativity. Dr. Cao co-authored L1 Adaptive Control: Guaranteed Robustness with Fast Adaptation and authored the Universes are Black Holes series, proposing an axiomatic framework for spacetime dynamics. This theory addresses dark energy/matter, resolves cosmological paradoxes, and predicts novel phenomena awaiting experimental validation. His work bridges gaps between experimental data and theoretical models, emphasizing simplicity and precision. His engineering research spans advanced control strategies for aerospace systems, robotics, and energy infrastructure, with applications in autonomous vehicles, power plants, and combustion monitoring. He has published over 180 peer-reviewed papers and pioneered fiber-optic sensing technologies for high-temperature environments.
Lu Feng is an Associate Professor of Computer Science at the University of Virginia, affiliated with the Link Lab, a center specializing in Cyber-Physical Systems (CPS). She holds a Ph.D. in Computer Science from the University of Oxford. Her research focuses on ensuring safety and trustworthiness in CPS, with applications in medical devices, autonomous robotics, and smart cities. She has received prestigious awards including the NSF CRII Award (2018) and NSF CAREER Award (2020). Her work integrates formal methods, AI, and robotics to address challenges in CPS assurance and human-machine collaboration. Notable contributions include developing risk-assessment tools for heart failure patients, predictive monitoring frameworks for CPS, and trust-aware planning algorithms for autonomous systems. She has pioneered frameworks like DP-RuL for clinical decision support systems and IrrMap for precision agriculture. Her research bridges theoretical foundations (e.g., model checking, reinforcement learning) with practical applications in healthcare, transportation, and urban systems. She collaborates across disciplines, contributing to initiatives like the Link Lab’s smart city simulations and safety-critical medical CPS assurance. Education: Ph.D., Computer Science, University of Oxford Awards: NSF CRII (2018), NSF CAREER (2020) Labs: Link Lab (Cyber-Physical Systems Center) Focus Areas: Runtime safety, human-AI trust, medical device assurance, smart city systems
Andrey Chechulin is a Professor at the St. Petersburg Federal Research Center of the Russian Academy of Sciences, Institute of Informatics Problems, where he leads research in cybersecurity within the Information Security Department. His work spans over 15 years with more than 80 publications in top-tier security conferences and journals. Dr. Chechulin's primary research interests include cybersecurity, cyber-physical systems security, social network analysis, and bot detection. His work focuses on developing practical methodologies for incident investigation, access control, and vulnerability assessment. He has pioneered approaches for analyzing social media bots, particularly on the VKontakte platform, and developed innovative frameworks for cryptocurrency transaction anomaly detection using neural networks. His publication record shows consistent contributions to major security venues including PDP, COMSNETS, and Sensors, with a notable increase in output since 2018. Recent work demonstrates his adaptation to emerging security challenges in AI-generated content detection and blockchain security. Best Paper Award at PDP 2021 Cybersecurity Research Excellence Award (2019) Dr. Chechulin maintains strong collaborative relationships with Igor V. Kotenko (60 joint publications), Dmitry Levshun, and Maxim Kolomeets. His research often bridges theoretical security concepts with practical implementations for real-world systems, including smart city infrastructure and automotive security applications.
Neptun Yousefi serves as a Postdoctoral Researcher (Research Fellow) in the Department of Bioproducts and Biosystems at Aalto University, where they lead innovative research at the intersection of cellulose chemistry, nanomaterials, and sustainable biomass conversion. Their work focuses on developing environmentally benign processes for transforming renewable resources into high-value bioproducts, with particular emphasis on nanocellulose production and chemical valorization of underutilized biomass streams. Dr. Yousefi's research program centers on advanced biomaterials engineering, specifically targeting cellulose nanofiber and nanocrystal production from unconventional feedstocks including potato residue, algae, and agricultural waste. Their methodological expertise spans electrochemical oxidation, acid-catalyzed methanolysis, and precision extraction techniques to control nanomaterial dimensions and surface properties. This work bridges fundamental cellulose chemistry with practical applications in sustainable materials, green catalysis, and circular economy frameworks, demonstrating significant potential for industrial biorefining and waste valorization. Analysis of their publication record reveals a consistent trajectory toward developing novel chemical pathways for biomass conversion, with recent work emphasizing process intensification and feedstock diversification. Key themes include the engineering of cellulose nanomaterials with tailored properties, exploration of marine and agricultural waste streams as cellulose sources, and development of sustainable reaction systems that minimize environmental impact while maximizing resource efficiency. They actively contribute to the Materials Chemistry of Cellulose research group at Aalto University, collaborating on projects that integrate fundamental chemical insights with practical engineering solutions for next-generation bioproducts and biosystems. Their experimental approach combines advanced characterization techniques with process optimization to address critical challenges in sustainable materials development.
George Nikolakopoulos is a Professor in Robotics and Automation at the Department of Computer Science, Electrical and Space Engineering at Luleå University of Technology , Sweden. He also holds a Chair in Robotics and Artificial Intelligence and has been affiliated with the NASA Jet Propulsion Laboratory for collaborative research on Aerial Planetary Exploration. Academic Rank: Professor Department: Computer Science, Electrical and Space Engineering Collaborations: NASA JPL, COSTAR Team Research Interests Robotics Artificial Intelligence Field Robotics UAVs (Unmanned Aerial Vehicles) Automatic Control Applications Learning and Reasoning Networked Embedded Systems Cyber-Physical Systems Mechatronics Adaptive Control System Identification Publications and Research Trends His recent publications focus on autonomous aerial robotics, deep mineral exploration, and AI-driven control systems. Key trends include the integration of reinforcement learning for UAV stabilization, path planning in subterranean environments, and cloud/edge-assisted control architectures for collaborative robotic systems. Scientific Awards and Recognitions Team's work included 4 times in a row at the IVA Top 100 list (Royal Academy of Engineers in Sweden) Won the second stage of the DARPA Grand Challenge on Sub-T exploration with the COSTAR team in February 2020 Leadership and Innovation He established the Digital Innovation Hub on Applied AI at Luleå University of Technology and founded two spin-offs, including FieldRobotix , which joined the IVA REACHME Silicon Valley accelerator. His lab, the Robotics Team , actively participates in cutting-edge projects like autonomous mining inspection and aerial additive manufacturing.
Aaron N. Rice is a Research Professor at the Cornell Lab of Ornithology , specializing in bioacoustics and marine ecology. His work bridges coastal ecology with conservation policy, focusing on sound production and perception in fishes and whales to understand social behaviors and population dynamics. Develops passive acoustic monitoring frameworks for marine species Provides bioacoustic data for U.S. federal/state agencies (BOEM, NOAA, National Park Service) Contributes to marine spatial planning, including offshore wind energy development Research Highlights : Proves fish sounds dominate coastal soundscapes seasonally Links acoustic patterns to environmental changes and migratory behavior Directly influenced North Atlantic right whale critical habitat expansion and Gulf of Mexico Bryde’s whales endangered listing Scientific Awards : U.S. Department of Interior Partners in Conservation Award (2014) Recent Collaborations : Integrates passive acoustics with Everglades freshwater flow management, fosters interdisciplinary ties with U.S. Navy, National Park Service, and NGOs.
Bob Sheil is Professor of Architecture and Design through Production at The Bartlett School of Architecture, University College London. A registered architect with over 25 years of experience, he is renowned for his work exploring the relationship between design, making, craft, and technology in architecture. As the sixth Director of the Bartlett School of Architecture from 2014 to 2022, he led transformative changes that significantly expanded the school's physical space, student applications, and research impact, while championing equality, diversity, and inclusivity initiatives. Professor Sheil's research centers on the complex relationship between design and making in architecture, particularly how this relationship affects architectural outcomes. He advocates for 'Protoarchitecture,' a speculative approach that creates alternative pathways for architectural production through varied design and making methodologies. His work examines how new tools have dissolved boundaries between the drawn and the made, arguing that how we design has become equally important to what we design. This perspective has informed his hands-on teaching philosophy that emphasizes learning through direct engagement with evolving technologies and materials. His publication record reveals a consistent focus on digital fabrication, material innovation, and the evolving role of the designer in contemporary practice. The Fabricate conference series he co-founded has become a major international platform for research in architectural production, with publications generating over 330,000 downloads across 184 countries. Recent work shows increasing emphasis on resource efficiency, sustainable practices, and the integration of material intelligence within digital workflows. Professor Sheil's significant professional recognition includes: RIBA award for design (2010) for '55/02' with Stahlbogen GmbH As an educator and mentor, Professor Sheil has supervised PhD students since 2011 and has been instrumental in developing innovative architectural education programs. His leadership resulted in substantial growth in research funding, student applications (90% increase), and international collaborations. He co-founded the influential FABRICATE conference series and sixteen*(makers), and played a key role in establishing the Bartlett Manufacturing and Design Exchange (Bmade), which has transformed the school's workshop facilities and safety culture. Professor Sheil maintains active collaborations with institutions including the Royal Central School of Speech and Drama, SHUNT, ScanLAB projects, and Thomas Pearce, creating interdisciplinary opportunities that bridge architecture with performance, digital scanning, and scenography. His current work continues to push the boundaries of architectural practice through the integration of emerging technologies and material innovation.
Dr. Frank Soboczenski is a Lecturer in the Department of Computer Science at the University of York, with an affiliate scientist position at King's College London supported by the NVIDIA GPU Grant Program. His research spans multiple domains including healthcare, space research, and quantum machine learning applications. He serves as a STEM scientist for NASA's and NOAA's GLOBE program and is actively involved in various NASA initiatives including the Frontier Development Lab. Dr. Soboczenski's primary research interests include Transformers and Large Language Models, Machine Learning with focus on Uncertainty Quantification and Explainability, and advanced applications of Quantum Machine Learning in Healthcare/Biomedicine and Space Research domains. His work on the RobotReviewer project applies Deep Learning and Natural Language Processing to healthcare. Previously, he has worked in Human-Computer Interaction, Cyber-Security, and Real-Time Systems in cooperation with organizations including the German Police Force, GCHQ, Rapita Systems, INRIA, Barcelona Supercomputing Center, and Airbus. His recent publications demonstrate a strong focus on applying machine learning techniques to healthcare informatics and space research, with particular emphasis on systematic reviews, clinical decision support, atmospheric retrieval for exoplanets, and medical data analysis. His work bridges the gap between theoretical AI advancements and practical applications in critical domains. NASA TechLeap Prize - Quantum Machine Learning NASA/NOAA/U.S. Department of State Outstanding Efforts to Mentor and Support Students (2019-present) NASA Frontier Development Lab AI Research Award of Merit Data Samaritan Award Steely Eyed Operator Award NASA Kennedy Space Center OsirisREx launch invitation SpaceApps 3M Thesis Competition UK National Winner (2013) Deggendorf Institute of Technology Robotics Challenge Award Dr. Soboczenski actively mentors students, as evidenced by his NASA/NOAA award for mentoring. His research is supported by the NVIDIA Corporation through the GPU Grant Program. He serves on multiple program committees including NeurIPS (2019-present), AAAI (2019-present), and various specialized workshops at major AI conferences. He is also involved in organizing NASA Space Apps challenges and serves on the NASA GeneLab Analysis Working Group on AI/ML. As an active member of the academic community, Dr. Soboczenski participates in numerous professional organizations including the NASA Nancy Grace Roman Spacecraft Science Working Group, NASA Technosignatures research group, IBM Quantum Researchers Program, PolarAI Research Group of the ACM, Huggingface BigScience Team, International Astronomical Union, and several others focused on AI and space research.
Mario Carpo is the Reyner Banham Professor of Architectural History and Theory at The Bartlett School of Architecture, University College London. His work bridges early modern architectural history and contemporary digital design theory, with a focus on cultural technologies and the Vitruvian tradition. Education: Dr.arch (University of Florence, 1984), PhD (European University Institute, 1990), HDR (Art History, France, 2009) His research examines the intersection of digital design theory , computational discretism , and historical architectural representation . Recent publications explore the implications of AI, big data, and robotic construction on architectural authorship and practice. Key scientific awards include: Guggenheim Fellow (2022-2023) Senior Fellow, National Gallery of Art (2014) Grantee, The Graham Foundation (2013) Resident, American Academy in Rome (2004-05) His recent articles (2023-2025) analyze post-digital architecture , biological computing , and algorithmic curation , revealing trends in computational design's socio-technical context and the dissolution of traditional authorship models.
Bruno Gas serves as a Professor at Sorbonne University, affiliated with the ASIMOV research team within the Intelligent Systems and Robotics Institute (ISIR). His academic work bridges robotics, artificial intelligence, and cognitive science through innovative investigations into sensorimotor learning frameworks for embodied agents. Gas's research centers on how naive robotic agents develop spatial and bodily representations through sensorimotor interactions, with particular emphasis on multimodal sensory integration (audition, vision, and touch). His work demonstrates how robots can autonomously construct internal models of their environment through active exploration, utilizing principles from developmental psychology and neuroscience. Key methodologies include neural network modeling, predictive processing architectures, and bio-inspired sensorimotor contingency frameworks that enable agents to learn without pre-programmed spatial knowledge. Analysis of Gas's recent publications (2013-2020) reveals consistent thematic progression in developmental robotics, focusing on the emergence of topological spatial representations, active exploration strategies, and multimodal sensor fusion. His research demonstrates how sensorimotor flow generates internal spatial models, with notable contributions including the Head Turning Modulation System for environment exploration and tactile space representation models. This work establishes critical links between robotics, cognitive science, and neuroscience through experimentally validated frameworks for embodied learning. No explicit information regarding student supervision or research grants appears in the source material, though extensive collaborative publications with researchers like Sylvain Argentieri and J. Kevin O'Regan suggest active mentorship and project leadership within the ISIR ecosystem. His publication record shows sustained interdisciplinary collaboration across European robotics institutions. Gas operates within the ASIMOV team at ISIR (Institut des Systèmes Intelligents et de Robotique), a premier robotics research unit jointly operated by Sorbonne University and CNRS. The team specializes in adaptive systems and intelligent machines, with research spanning embodied cognition, developmental robotics, and human-robot interaction. ASIMOV's experimental platforms focus on sensorimotor learning paradigms for autonomous exploration, positioning Gas at the forefront of bio-inspired robotics research in France.
Mahsa Ghasemi is an Assistant Professor at the Elmore Family School of Electrical and Computer Engineering, Purdue University, located in West Lafayette. She holds a B.Sc. in Mechanical Engineering from Sharif University of Technology (2014), an M.S.E. in Mechanical Engineering from The University of Texas at Austin (2017), and a Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin (2021). Her research focuses on task-oriented knowledge acquisition, online learning and control, human-robot interaction, trustworthy AI, and socially beneficial autonomy. She is affiliated with the Materials and Electrical Engineering Building at Purdue. Education: B.Sc., Mechanical Engineering, Sharif University of Technology (2014) M.S.E., Mechanical Engineering, UT Austin (2017) Ph.D., Electrical and Computer Engineering, UT Austin (2021) Her research interests span interdisciplinary areas including reinforcement learning, causal inference, control systems, and human-autonomy collaboration. Recent work emphasizes resilient cyber-physical systems, privacy-preserving multi-agent learning, and causal discovery in decision-making frameworks. Her articles address challenges in sensor selection, no-regret learning in bandits, and formal methods for autonomous systems. Publications highlight contributions to submodular optimization in hypothesis testing, robust sensor scheduling in intrusion detection, and adaptive experimental design for causal discovery. Her work bridges theoretical foundations with practical applications in robotics, cybersecurity, and AI ethics. No scientific awards or grants are explicitly listed in the provided data. She advises no listed students but collaborates on projects involving diverse planning and decision-making in constrained environments.
