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Ye (Sarah) SunView profile

Associate Professor

• Wearable sensors
• Wearable robotics
• Smart and connected health
• +3 more

Ye (Sarah) Sun is an Associate Professor in the Department of Mechanical Engineering at the University of Virginia (UVA), part of the School of Engineering and Applied Science. She joined UVA in 2021 after serving as an Associate Professor at Michigan Technological University. Her work focuses on wearable sensors, robotics, smart health systems, and cyber-physical systems. She leads the WEARLab research group. Education: Ph.D. in Electrical Engineering from Case Western Reserve University (2021), B.S. in Instrumentation Engineering from Tianjin University (not specified). Research interests include wearable electronics, health monitoring, and human-technology interaction. Her interdisciplinary approach integrates engineering innovations with healthcare applications. Notable projects involve self-powered triboelectric sensors and optical fiber-based health monitoring systems. Recent publications highlight advancements in photodiode technologies for high-frequency applications, including millimeter-wave generation and photonic integrated circuits. Awards include the NSF CAREER Award (2018) and NSF BRITE Award (2022). She has organized major conferences and holds editorial roles in health technology journals. Grants include NSF funding for cyber-physical systems and smart health initiatives. Her lab collaborates on projects involving wearable robotics and connected health solutions, with a focus on real-world applications in healthcare and IoT.

Panayiotis KoliosView profile

Assistant Professor

• Autonomous Systems
• Intelligent Transportation Systems
• Cyber-Physical Systems
• +7 more

Panayiotis Kolios is an Assistant Professor at the Department of Computer Science, University of Cyprus (UCY). Previously, he served as a Research Assistant Professor at the KIOS Research and Innovation Centre of Excellence (2013–2024) and a Visiting Lecturer at UCY. He holds a BEng and PhD in Telecommunications Engineering from King’s College London (2008 and 2012, respectively). His research focuses on networked intelligent systems, emergency management using AI and UAV technologies, and cyber-physical systems. Education: BEng in Telecommunications Engineering, King’s College London, 2008 PhD in Telecommunications Engineering, King’s College London, 2012 Research Interests: His work centers on autonomous systems, intelligent transportation, and emergency management. Key areas include AI-driven disaster response, UAV-based surveillance, and algorithmic optimization for critical infrastructure. He develops solutions for real-time situational awareness and decision-support in emergencies. Recent work trends show a focus on multi-UAV coordination, disaster management platforms (like AIDERS), and AI applications in emergency response. His team’s 2023 win in the Cooperative Aerial Robots Inspection Challenge highlights advancements in UAV inspection algorithms. Scientific Awards: First Prize in Cooperative Aerial Robots Inspection Challenge (CDC 2023) Grants and Advising: He has secured over €40 million in EU and industrial grants, leading projects like PREDICATE, SWIFTERS, and AIDERS. His team advises on emergency response strategies and has trained first responders through EU-funded programs such as the Exchange of Experts training. Labs and Teams: He leads the Security and Emergency Response Group at KIOS CoE and established the Cyprus Civil Defence Aerial Observation Unit. His team collaborates with institutions like the Cyprus Police and Fire Service to operationalize UAV technologies in disaster scenarios.

Aniket BeraView profile

Associate Professor

• Affective Computing
• Computer Graphics
• Augmented Reality/VR
• +8 more

Dr. Aniket Bera is an Associate Professor in Computer Science at Purdue University and holds an Adjunct Associate Professor role at the University of Maryland at College Park (UMIACS). He directs the IDEAS Lab at Purdue and previously served as a Research Assistant Professor at UNC Chapel Hill. His research focuses on Affective Computing, Computer Graphics (AR/VR), AI & Robotics, Social Robotics, and medical AI applications for mental health diagnostics. Affiliations: Purdue University (Primary), University of Maryland (Adjunct), UMIACS Career: Joined Purdue in 2017, extensive industry collaborations with Disney Research, Intel, and C-DAC Research Interests: Affective Computing: Emotion perception via gait analysis, speech, and facial/body expressions AR/VR: Redirected walking, virtual environments, and human motion modeling Medical AI: AI-driven mental health detection systems (e.g., VidSole dataset) in collaboration with medical schools Key Contributions: Developed Project Dost (mental health initiative) Received 2020 Brain & Behavior Seed Grant ($X) for emotion-gait research Authored 65+ papers (1,800+ citations) with awards at IEEE VR 2021 Funding & Leadership: Serves as Senior Editor for IEEE RA-L (Planning/Simulation) Conference Chair for ACM SIGGRAPH MIG 2022 Labs/Teams: IDEAS Lab (Purdue), UMD GAMMA Group

Carlo D'EramoView profile

Professor

• Reinforcement Learning
• Decision-Making
• Multi-task Reinforcement Learning
• +3 more

Carlo D'Eramo is a Professor of Reinforcement Learning and Computational Decision-Making at the University of Würzburg. He leads the LiteRL group at hessian.AI until 2025 and is affiliated with the Intelligent Autonomous Systems group at TU Darmstadt's Computer Science Department, as well as the Hessian Centre for Artificial Intelligence. Ph.D. : Information Technology, Politecnico di Milano (2019) Double MSc : Computer Engineering, Politecnico di Milano (2015) and University of Illinois at Chicago (2015) BSc : Computer Engineering, Politecnico di Milano (2011) His research focuses on lightweight reinforcement learning methods for adaptive autonomous agents, spanning multi-task/curriculum RL, multi-agent RL, deep RL, uncertainty quantification, residual learning, and planning. He developed MushroomRL, a widely adopted RL library, and investigates how agents can acquire real-world expert skills efficiently. The 15 most recent publications highlight trends in deep reinforcement learning architectures, adversarial and multi-agent systems, domain randomization, and curriculum design. Key subfields include optimal transport applications, entropy maximization, neural network distillation, and bounded rationality frameworks for robust learning. He has contributed to top venues like ICML, NeurIPS, AAAI, ICLR, JMLR, and IEEE Transactions on Pattern Analysis and Machine Intelligence, with a focus on advancing scalable and adaptive RL methodologies.

• Biomedical Signal Processing
• Respiratory Monitoring
• Sleep Disorders
• +4 more

Prof. Raimon Jané Campos is a leading figure in biomedical signal processing at the Universitat Politècnica de Catalunya (UPC) and Universitat de Barcelona (UB). As co-director of UPC's Biomedical Signal and System Group (CREB) and coordinator of the Biomedical Engineering PhD Programme, he bridges engineering and clinical applications. His work focuses on respiratory and sleep disorder diagnostics, with significant contributions to COPD and sleep apnea monitoring through wearable devices and machine learning. PhD in Biomedical Engineering (UPC, 1989) Visiting researcher at Université de Nice-Sophia Antipolis Vice-president of Spanish Society of Biomedical Engineering Research spans respiratory mechanics , sleep-disordered breathing , acoustic biomarkers , bioimpedance , and machine learning in biomedical contexts . His 2025 work on microcalorimetric pathogen classification and 2024 spiking neural networks for apnea detection demonstrate cutting-edge integration of computational methods with physiological monitoring. Articles from 2017-2024 reveal consistent focus on non-invasive diagnostics , cardiorespiratory synchronization , and smartphone-based health solutions . Awarded the Barcelona City Technology Research Award (2005) and serving on the International Advisory Board for Physiological Measurement since 2010, his career combines academic leadership with real-world clinical translation through IBEC's technology transfer initiatives.

Abdon Pena-FranceschView profile

Assistant Professor

• Biomaterials science
• Polymer chemistry
• Soft matter physics
• +5 more

Abdon Pena-Francesch is an Assistant Professor in the Department of Materials Science and Engineering at the University of Michigan. He is also affiliated with the Macromolecular Science and Engineering Program, Chemical Engineering, and the Michigan Robotics Institute. His interdisciplinary research integrates biomaterials science, polymer chemistry, soft matter physics, and nanotechnology to develop programmable soft materials for applications in healthcare, robotics, and environmental science. Education: Ph.D. in Engineering Science and Mechanics, The Pennsylvania State University, 2017 M.Sc. in Chemical Engineering, Institut Químic de Sarrià (Barcelona, Spain), 2013 B.Sc. in Mechanical Engineering, Institut Químic de Sarrià (Barcelona, Spain), 2011 Research Interests: His work focuses on bioinspired materials , soft robotics , self-healing polymers , and biodegradable microrobots . By engineering molecular and nanoscale structures, his lab designs materials with programmable properties for soft robotic systems and biomedical devices. The group emphasizes both fundamental science and translational applications, including tissue repair, actuation, and environmental sensing. Awards and Honors: Humboldt Research Fellowship for Postdoctoral Researchers (2018–2020) Alumni Association Dissertation Award, Penn State University (2017) Rustum and Della Roy Innovation in Materials Research Award (2016) Materials Research Society Graduate Student Award (2016) First Prize, Penn State ESM Graduate Research Symposium (2015) AGAUR MOBINT Fellowship, Government of Catalunya (2012) Labs and Affiliations: He leads the Bioinspired Materials Lab , an interdisciplinary group within the University of Michigan’s Materials Science & Engineering Department. The lab collaborates with the Macromolecular Science & Engineering Program, Chemical Engineering, and the Michigan Robotics Institute.

James A. SethianView profile

Professor

• Applied Mathematics
• Computational Physics
• Partial Differential Equations
• +6 more

James A. Sethian is a Professor in the Department of Mathematics at the University of California, Berkeley , with additional affiliation at Lawrence Berkeley National Laboratory . His work focuses on developing and applying Level Set Methods and Fast Marching Methods to track evolving interfaces across diverse scientific domains. Education: Ph.D. in Applied Mathematics , University of California, Berkeley (1982) B.A. in Mathematics, Princeton University (1976) Research spans Applied Mathematics , Computational Physics , and Numerical Analysis , with applications in Semiconductor Manufacturing , Fluid Dynamics , Medical Imaging , Image Processing , Seismic Analysis , and Optimal Control . His publications demonstrate expertise in modeling interfaces that develop sharp corners, break apart, and merge, particularly through PDE-based numerical techniques. Key contributions include algorithms for noise removal , minimal surface computation , and multi-layer coating flows . As a mentor, he has advised numerous PhD students in computational methods and applied mathematics, including Robert I. Saye , Jon Arthur Wilkening , and David Layne Chopp . Projects under his leadership integrate ViscoElastic Flow , Tumor Modeling , and Robotics via curvature-driven evolution and interface tracking.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +9 more

Jim Torresen is a Professor at the Norwegian University of Science and Technology (NTNU), specializing in Computer Science, Artificial Intelligence, and Robotics. He earned his M.Sc. and Dr.ing. (Ph.D.) in computer architecture and design from NTNU in 1991 and 1996 respectively, followed by industry experience in hardware design before transitioning to academia in 1999. Research Interests: His work spans Machine Learning, Evolvable Hardware, and Ethical AI, with notable contributions to music technology, facial expression recognition, and healthcare monitoring systems. He actively explores interdisciplinary applications of AI in creative domains and clinical environments. Publications & Editorial Roles: Torresen has published extensively in journals like Frontiers in Artificial Intelligence and Genetic Programming and Evolvable Machines . He serves as a Topic Editor for Frontiers in Explainable AI and has editorial roles in robotics and biomedical AI domains.

Aafke FraaijeView profile

Research Fellow

• Ethics of Technology
• Responsible Innovation
• Citizen Engagement
• +3 more

Aafke Fraaije is a postdoctoral researcher at the Section Ethics & Philosophy of Technology , Faculty of Technology, Policy and Management , Delft University of Technology. Her work bridges art, ethics, and technology through interdisciplinary approaches to citizen engagement and responsible innovation. Institution: Delft University of Technology School: Faculty of Technology, Policy and Management Department: Section Ethics & Philosophy of Technology Academic Rank: Research Fellow Research Interests : Ethics of human-nature-technology relationships Art-based methods for public engagement with technology Participatory futuring in smart city development Inclusive approaches to technology governance Communication frameworks in clinical and engineering contexts Critical analysis of responsible research and innovation (RRI) Recent Research Trends : Her publications focus on using artistic practices (dance, theatre) to explore ethical implications of smart city technologies, developing frameworks for responsible innovation, and analyzing communication dynamics in high-stakes environments like surgery and robotics. Key themes include citizen participation in technological development, interdisciplinary methodologies, and ethical accountability across sectors.

Leila NotashView profile

Professor

• Robotics
• Mechatronics
• Kinematics
• +3 more

Dr. Leila Notash is a Professor in the Department of Mechanical and Materials Engineering at Queen's University, where she has been a faculty member since 1997. She is a Fellow of Engineers Canada (FEC) and a licensed Professional Engineer with Professional Engineers Ontario (PEO), with significant contributions to engineering education and professional service. Her educational background includes: Bachelor of Science in Mechanical Engineering, Middle East Technical University (Ankara, Turkey) - High Honor Student (2nd out of 166) Master of Applied Science in Mechanical Engineering, University of Toronto PhD in Mechanical Engineering, University of Victoria Dr. Notash's research centers on robotics and mechatronics, with specialized expertise in cable-driven parallel manipulators. Her work integrates kinematics, fault-tolerant design, and neural network applications to address challenges in robot calibration, workspace analysis, and motion control under real-world constraints like cable mass and elasticity. She investigates both theoretical frameworks and practical implementations for industrial and specialized robotic systems. Analysis of her recent publications (2020-2024) reveals a clear trajectory toward intelligent control systems, where machine learning techniques—particularly neural networks and reinforcement learning—are increasingly applied to solve complex problems in cable-driven robotics. This includes motion control optimization, path generation, and kineto-static analysis while accounting for physical limitations such as cable elasticity and mass effects, demonstrating a shift from traditional mechanical analysis to data-driven adaptive control methodologies. Her scientific recognition includes: Fellow of Engineers Canada (FEC) University of Toronto Open Fellowship University of Toronto International Differential Fee Waiver Charles S. Humphrey Graduate Student Award NSERC Doctoral Prize Nominee (1996) Dr. Notash has mentored 161 undergraduate students as Faculty Advisor for the Mechanical '06 cohort and pioneered international educational initiatives like the International Undergraduate Student Design project (IVDS), connecting Queen's University with Middle East Technical University and Union College. Her service extends to editorial leadership for Mechanism and Machine Theory and ASME journals, and governance roles including Faculty Senator at Queen's University (2009-2025) and PEO Council Councillor-at-Large (2019-2025). She has established collaborative research networks through initiatives like the Reading Week shop course 'Design Basics 1.0' and sustained leadership in the Canadian Committee for the Promotion of Mechanism and Machine Science (CCToMM) and the International Federation for the Promotion of Mechanism and Machine Science (IFToMM), where she chaired the Permanent Commission on Communications (2006-2011).

Marilena VendittelliView profile

Professor

• Robotics
• Control Systems
• Biomedical Engineering
• +7 more

Marilena Vendittelli is a Professor at Sapienza University of Rome, affiliated with the Robotics group. Her research spans control systems, biomedical robotics, motion planning, and autonomous systems. Research Areas: Control systems for robotics and nonholonomic systems Biomedical applications (hyperthermia therapy, needle insertion) UAV navigation and obstacle avoidance Haptics and human-robot interaction Adaptive estimation algorithms Recent Publications focus on adaptive control of bio-heat equations, safe UAV motion planning, soft robotics actuation, and haptic feedback in medical procedures.

• Machine Learning
• Electrical Engineering
• Computer Science
• +3 more

Luís Miguel Mendonça Rato is an Associate Professor at the Universidade de Évora and a Senior Researcher with a PhD at Centro ALGORITMI. He is affiliated with the CST R&D Group and VISTA Lab R&D Lab, focusing on interdisciplinary research at the intersection of Electrical Engineering, Computer Science, and Agricultural/Biomedical applications. Academic Degree: PhD Current Position: Associate Professor Labs: VISTA Lab Researcher IDs: ORCID 0000-0003-4492-7548, ResearcherID A-9152-2013, CiênciaID A914-6344-CD2D His research spans machine learning applications in Agricultural Engineering (Sentinel-2 satellite data for nutrient analysis), Biomedical Imaging (MRI-ADC texture analysis for tumor classification), and Control Systems (predictive control algorithms for water delivery canals and solar fields). With an h-index of 11 and 51 publications, his work emphasizes hybrid systems combining traditional engineering with computational innovation. Recent publications highlight trends in SLAM efficiency (2024), cloud service optimization (2022), and deep learning for medical imaging (2022-2023). He has contributed to Smart Cities initiatives through projects like M-Traffic (2006) and NanoSen-AQM (2020). As a senior researcher, he leads projects in the CST R&D Group and VISTA Lab , with notable work in the Universidade de Évora ecosystem.

Taylor WareView profile

Associate Professor

• Biomaterials
• Micro/Nanotechnology
• Medical Devices
• +7 more

Taylor Ware is an Associate Professor in Biomedical Engineering and Materials Science & Engineering at Texas A&M University's College of Engineering, holding the Cain Faculty Fellowship. Her research focuses on designing structured biomaterials and medical devices using stimuli-responsive polymers for clinical applications. Education: Ph.D. in Materials Science and Engineering, The University of Texas at Dallas, 2013 Research Interests: Dr. Ware pioneers the development of liquid crystal elastomers as artificial muscles and implantable electronics substrates, engineered living materials for infection treatment, and directed self-assembly of hydrogels. Her lab specializes in polymer formulation, thermomechanical testing, and microfabrication. Key research thrusts include: Smart elastomers, hydrogels, and composites for dynamic medical devices Programming liquid crystalline polymers for shape-morphing applications Engineered living materials that respond to biomolecular cues in urinary tract environments Publication Trends: Recent work (2023-2025) demonstrates convergence of materials science, microbiology, and medical device engineering. Her group advances liquid crystal elastomers for soft robotics and implantable electronics, develops engineered living materials for UTI treatment using microbial competition, and creates novel hemostats and urethral support devices. Publications emphasize translational applications in urology, wound healing, and neural interfaces. Scientific Awards: Invited Participant, NAE Japan-USA Frontiers of Engineering Bilateral (2023) Senior Member, National Academy of Inventors (2022) NSF CAREER Award (2018) Air Force Young Investigator Award (2017) NSF Graduate Research Fellowship (2011) Fellow of AIMBE (American Institute for Medical and Biological Engineering) Advising and Grants: Dr. Ware leads the Ware Lab with significant funding including an NIH R01 grant (with UT Dallas and Case Western collaborators) and the NSF CAREER award. Her lab mentors postdoctoral fellows like Mustafa (winner of a prestigious postdoctoral fellowship) and graduate students. Current projects are supported by the NSF Engineering Research Center HAND, focusing on advanced materials for healthcare applications. Laboratory and Teams: The Ware Lab collaborates globally and is featured in Texas Monthly, Houston Chronicle, and National Geographic for breakthroughs in engineered living materials. As part of the NSF HAND ERC, the lab develops dynamic materials for stress urinary incontinence treatment and collaborates with medical institutions on UTI therapies using engineered E. coli strains.

Lifeng ZhouView profile

Assistant Professor

• Robotics
• Algorithms
• Combinatorial Optimization
• +4 more

Lifeng Zhou is an Assistant Professor in the Department of Electrical and Computer Engineering at Drexel University, where he leads the Zhou Lab focused on advancing robustness and reliability in multi-robot systems through integration of foundation models. His research addresses real-world challenges in environmental monitoring, disaster response, and urban mobility. Education PhD, Electrical and Computer Engineering, Virginia Tech, 2020 MS, Control Science and Engineering, Shanghai Jiao Tong University, 2016 BS, Automation, Huazhong University of Science and Technology, 2013 Research Focus Dr. Zhou's work integrates robotics, algorithms, game theory and machine learning to develop secure and scalable autonomous systems. Primary research thrusts include: Resilient multi-robot coordination in adversarial environments Large language model integration for robotic decision-making Game-theoretic resource allocation strategies Risk-aware planning for autonomous vehicles Publication Trends Recent work (2024-2025) demonstrates strong focus on large language model applications in multi-robot systems, with 12/15 articles exploring LLM integration for flocking, scene segmentation, and decision-making. Additional emphasis includes adversarial robustness in target tracking (5 articles) and autonomous driving applications (4 articles). Awards and Recognition Best Paper Award, WACV 2025 LLVM-AD Workshop Professional Service Associate Editor, ICRA Conference Editorial Board Laboratory Focus The Zhou Lab develops foundational algorithms for secure and scalable multi-robot systems, with current projects spanning environmental monitoring drones, disaster response coordination, and autonomous vehicle perception systems.

Nazanin TajikView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Infrastructure Resilience
• +4 more

Nazanin Tajik is an Assistant Professor in the Department of Industrial and Systems Engineering at Mississippi State University (MSU). She holds a Ph.D. from the University of Oklahoma, an M.S. from the University of Tehran, and a B.S. from Sharif University of Technology. Her research focuses on integrating artificial intelligence, machine learning, and social science concepts to develop cross-disciplinary frameworks for infrastructure resilience, smart transportation systems, and disaster management. Her academic journey includes a Ph.D. at the University of Oklahoma where she contributed to the Risk-Based Systems Analytics Laboratory. At MSU, she established a research center bridging AI/ML tools with socio-technical systems. Key research domains include cyber-physical-social infrastructure resilience, search-and-rescue planning, and game-theoretic robotic designs. Tajik's work emphasizes optimization algorithms for network vulnerability assessment, resource allocation in disaster scenarios, and adaptive recovery strategies. She is actively involved with professional organizations such as INFORMS, ISE, and POMS, reflecting her commitment to advancing operations research and systems engineering methodologies.