Swiss Federal Institute of Technology in LausanneSwitzerland
Amirreza Razmjoo Fard is a PhD student and Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering (STI) and the Robot Learning and Interaction (RLI) Group at the Idiap Research Institute. Supervised by Dr. Sylvain Calinon, he focuses on developing adaptive, efficient, and intelligent robotic control methods for contact-rich environments and constrained scenarios. Research Areas: Generative AI (diffusion models, flow matching), Model composition (product of experts), System dynamics, Control theory, Physics-based simulation (Isaac Sim) Key Goals: Bridging theory and real-world applications, enhancing robot autonomy, interaction, and physical intelligence His work spans publications at top robotics conferences like IROS, CoRL, RSS, and ICRA, with a Best Paper Finalist recognition at RSS 2024. Notable methods include CCDP for diffusion policy composition, CDF for differentiable robot geometry, and D-LGP for hybrid planning. He has also collaborated with Honda Research Institute Europe during a six-month internship.
Dr. Tim Welschehold is a Junior Research Group Leader and former Substitute Professor at the Department of Computer Science, University of Freiburg, Germany. He is affiliated with the Faculty of Engineering and conducts research in the Robot Learning Lab and Autonomous Intelligent Systems group. He completed his PhD in Computer Science under Prof. Wolfram Burgard and has held senior research and leadership roles since 2020. Research Interests: His work centers on reinforcement learning, imitation learning, mobile manipulation, and dynamical systems. He explores how robots can learn complex manipulation tasks from human demonstrations and improve autonomy through deep learning and adaptive policies. His research integrates perception, reasoning, and action for real-world robotic applications. The recent publications highlight a strong trend in mobile manipulation, with a focus on learning from demonstrations, uncertainty-aware perception, and task-driven co-design of robotic systems. His work frequently appears in top-tier robotics conferences such as ICRA, IROS, CoRL, and RA-L, often in collaboration with Prof. Abhinav Valada and other members of the Freiburg robotics community. Scientific Awards: Best Paper Award, IROS 2022 Workshop on Mobile Manipulation and Embodied Intelligence Advising and Grants: Dr. Welschehold has co-supervised several Master’s students, including Abdelrahman Younes, Erick Rosete-Beas, and Iman Nematollahi. His research has been supported by major funding bodies such as the German Research Foundation (DFG), NVIDIA, Toyota Motor Europe, and the Carl Zeiss Foundation through projects like ReScale and BrainLinks-BrainTools. Labs and Teams: He is a key member of the Robot Learning Lab and the Autonomous Intelligent Systems group at the University of Freiburg, contributing to projects such as OpenDR, OML, and ReScale, which aim to advance scalable and responsible learning for assistive robotics.
Sumeet S. Aphale is a Professor and Academic Line Manager at the School of Engineering, University of Aberdeen, where he also leads the Electrical and Electronic Systems research group. He holds a Personal Chair and is actively involved in research, teaching, and academic leadership. He is the Programme Leader for the MSc in Robotics & Artificial Intelligence and MSc in Industrial Robotics. Education: BEng Electrical Engineering, University of Pune, India (1999), First Class with Distinction MS Electrical Engineering (Robotics and Control), University of Wyoming, USA (2003) PhD Electrical Engineering (Robotics and Control), University of Wyoming, USA (2005) His research focuses on control systems, mechatronics, robotics, nanopositioning, soft robotics, and vibration control. He leads the Artificial Intelligence, Robotics and Mechatronic Systems (ARMS) group, emphasizing practical, implementable solutions for complex engineering problems. His work spans applications in MEMS, drill-strings, biomedical systems, and renewable energy. The 15 most recent publications reflect a strong trend in advanced control strategies, including fractional-order, sliding-mode, and AI-based control, applied to nanopositioning, soft actuators, and industrial systems. Key themes include precision enhancement, dynamic modeling, and real-time performance optimization. Scientific Awards: Principal's Excellence Award for Outstanding Postgraduate Research Supervisor (2023) Best Paper Award, AsiaSim (2017) Best Paper Award Finalist, Advanced Intelligent Mechatronics (2008) Tau Beta Pi - Engineering Honors Society (2003) Top 5 UG GPA (1999) Dr. Aphale has supervised numerous PhD students to completion and currently mentors several doctoral candidates in areas such as soft robotics, motor optimization, and unconventional control. He has secured over £2.7M in research funding from EPSRC, SFC, Innovate UK, and international sources, and has led industrial consultancies with BP, Aramco, Aker, and LeapAI. He is actively involved in knowledge exchange through KTP projects and industrial collaborations. He teaches courses in signals, systems, and advanced control engineering and serves as a personal tutor and examination officer. He leads the ARMS research group and collaborates with institutions in Spain, China, India, France, Australia, and the USA. His editorial roles include Associate Editor for IEEE Control Systems Letters and Frontiers in Mechanical Engineering.
Thomas Henderson is a Professor of Computer Science at the University of Utah, specializing in robotics, artificial intelligence, and computer vision. His work emphasizes multisensor data fusion, autonomous navigation, and industrial automation. He has contributed to foundational algorithms in constraint satisfaction and robotic manipulation. Research interests include 3D object representation, sensor integration, and system architecture design for industrial applications. Notable contributions span wireless sensor networks, neural controller evolution for mobile robots, and safety-critical simulations of fire/explosion scenarios. Collaborations include projects with Nanyang Technological University and industry partners in manufacturing automation. His publications (over 200 cited works) demonstrate interdisciplinary impact across robotics, computer vision, and AI. Current focus areas include strategic conflict management for unmanned aircraft systems (UAS) and resilient networked robotics solutions.
Sylvaine Tuncer is a Lecturer in Work, Interaction & Technology at King’s Business School, King’s College London. She specializes in qualitative sociological research focusing on work practices, collaboration with technologies, and video-based ethnographic methods. Her work spans human-robot interaction, healthcare technology, auction sales dynamics, and urban mobility studies. She actively contributes to interdisciplinary research and innovation in data collection methodologies. Dr. Tuncer teaches at both undergraduate and graduate levels, leading modules such as Communication in Organisations and Work Practice & Technology. She currently serves as Deputy Programme Director for the Business Management BSc. Her research group, Work, Interaction & Technology (WIT), explores video-based studies of social interaction in collaborative settings. Notable projects include studies on hybrid auction sales adaptation and the role of frontline workers in digital strategy. Her recent publications emphasize human-robot interaction design, trust dynamics in hybrid environments, and the interplay between technology and social practices. She is involved in organizing international workshops on healthcare and technology, reflecting her commitment to fostering academic exchange and applied research.
João Carvalho is a Postdoctoral Researcher at the Intelligent Autonomous Systems (IAS) group within the Technische Universität Darmstadt . He obtained his PhD in Computer Science from TU Darmstadt in 2025, advised by Jan Peters, following a MSc in Computer Science from Albert-Ludwigs-Universität Freiburg and a Master's in Electrical and Computer Engineering from Instituto Superior Técnico (University of Lisbon). His work focuses on developing machine learning and reinforcement learning algorithms for robot manipulation, particularly in motion planning, grasping, and contact-rich tasks like insertions. Education: PhD in Computer Science, TU Darmstadt (2025) MSc in Computer Science, Albert-Ludwigs-Universität Freiburg Master's in Electrical and Computer Engineering, Instituto Superior Técnico (University of Lisbon) His research integrates generative models for motion planning and grasping, reinforcement learning for contact-rich tasks, and policy gradient methods with variance reduction. Recent publications highlight applications of diffusion models and tensor planning in robotics, with a focus on spatial symmetry and efficient policy generation. Key contributions include work on Motion Planning Diffusion , Grasp Diffusion Networks , and Model Tensor Planning . He actively supervises thesis students in areas related to robot learning , generative models , and residual reinforcement learning .
Álvaro Jesús López López is a researcher at the Instituto de Investigación Tecnológica (IIT) and a professor at the Higher Technical School of Engineering (ICAI), both part of Comillas Pontifical University. He holds the position of Associate Research Fellow and serves as a principal investigator at the Chair of Smart Industry, where he leads applied research on digital technologies such as generative AI, computer vision, and synthetic data generation. He also directs executive programs in generative artificial intelligence for professionals at Comillas Onexed. His research interests include Artificial Intelligence, Reinforcement Learning, Generative AI, Smart Industry, and Railway Systems . His work focuses on applying advanced AI techniques to industrial and transportation challenges, particularly in energy efficiency and automation. He has published extensively in high-impact journals such as Engineering Applications of Artificial Intelligence , Applied Intelligence , and Transportation Research Part C . The recent articles highlight a strong trend toward applied AI in industrial and robotic systems , with a focus on sim-to-real transfer, keyphrase extraction, and reinforcement learning enhanced with semantic knowledge. His work bridges the gap between theoretical AI and real-world industrial applications. Scientific Awards: Honorable Mention from the Industry 4.0 Observatory (2021) Honorable Mention at the 6th Connected Industry Promotion Award Erasmus Teaching/Research Staff Training Grant (2014) He has advised multiple PhD students and leads several industry-funded research projects with organizations such as Ferrovial, Endesa, Metro de Madrid, and the European Commission. He has also organized scientific events and delivered numerous invited talks on AI and digitalization. His research has led to practical applications, including a patent on utilizing regenerative braking energy in railways. He completed a research stay at the Royal Institute of Technology (KTH) in Stockholm and has contributed to books and policy reports on digitalization in industry.
Stanimir Yordanov Yordanov is an Associate Professor at the Department of Automation, Information and Control Technology within the Faculty of Electrical Engineering and Electronics at Technical University - Gabrovo. With a Doctorate in Technical Sciences and over 30 years of professional experience since 1993, he has established himself as a leading researcher and educator in control systems and automation. His educational background includes a Master of Engineering from VMEI - Gabrovo (1992) with specializations in Computer Engineering, Management Technologies, and Pedagogy, followed by a Doctorate (2006) and Associate Professor qualification (2010) in specialized technical fields. His teaching portfolio encompasses System Programming, Operating Systems, Digital Control Systems, and Industrial Robotics, among others. Professor Yordanov's research focuses on automated control systems, intelligent management of technological processes, industrial system monitoring, and object/system modeling. His work demonstrates a consistent trajectory toward increasingly sophisticated control algorithms and applications across diverse domains from electrohydraulic systems to environmental monitoring. The recent publications reveal a strong emphasis on advanced control techniques including neuro-PID regulators, model predictive control, and applications of artificial intelligence in industrial contexts. His extensive project portfolio includes 22 significant research initiatives, ranging from national projects like the 'Competence Center for Intelligent Mechatronic Systems' to international collaborations such as the 'MechMate' project focused on European SME growth. These projects demonstrate his ability to secure funding and lead research teams across various technical domains. Professor Yordanov has mentored six PhD students to completion, with several successfully defending dissertations on topics including intelligent energy systems, embedded real-time operating systems, and robotic systems. His academic leadership extends to serving as an academic mentor for over 600 student internships. His laboratory work centers on electrohydraulic control systems, robotic manipulation, and intelligent monitoring applications, with recent projects developing smart dispensers, beehive monitoring systems, and low-cost health monitoring devices for pregnant women, demonstrating practical applications of his theoretical research.
Philipp Cimiano is a Professor at Bielefeld University, Germany , with a prolific research record in Artificial Intelligence, Semantic Web, Natural Language Processing, Knowledge Graphs, Explainable AI, Clinical Decision Support Systems, Ontology Engineering, and Federated Learning . His work bridges theoretical AI concepts with practical applications in healthcare and robotics. Key research themes include dialogue-based XAI for user understanding, federated learning for healthcare data privacy, and LLM-driven robotics for embodied commonsense reasoning. Recent publications analyze dynamic explanatory interactions , perspectivized argumentation frameworks , and benchmarks for robot manipulation using large language models. His collaborations span institutions in Germany and Europe, with frequent co-authorship on topics like counterfactual generation , stakeholder group analysis , and lexicalization in QALD systems .
Edward S. Boyden is the Y. Eva Tan Professor in Neurotechnology at MIT, where he holds appointments in the Department of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering. He is a full member of the McGovern Institute for Brain Research, co-director of the Center for Neurobiological Engineering and the K. Lisa Yang Center for Bionics, and an investigator at the Howard Hughes Medical Institute. Boyden joined the MIT faculty in 2007 and was awarded tenure as a full professor seven years later. Boyden's research spans multiple areas of neurotechnology, with groundbreaking contributions in optogenetics, expansion microscopy, deep brain stimulation, and multiplexed imaging. His work has transformed neuroscience by providing researchers with powerful tools to observe and manipulate brain activity at unprecedented resolution. Boyden's research integrates principles from physics, engineering, chemistry, and biology to develop novel approaches for understanding and treating brain disorders. His publications reveal a consistent focus on developing innovative technologies that push the boundaries of what's possible in neuroscience. The trend shows increasing clinical translation of his basic science discoveries, with recent work moving from fundamental tool development toward therapeutic applications, particularly in vision restoration through optogenetics and non-invasive brain stimulation for memory enhancement. Breakthrough Prize in Life Sciences (2016) The Brain Prize (2013) Rumford Prize (2019) National Academy of Sciences (2019) Gairdner Foundation International Award (2018) Warren Alpert Foundation Prize (2019) Wilhelm Exner Medal (2020) Boyden has mentored numerous students who have gone on to establish their own research programs, including Deblina Sarkar, Christian Wentz, and Kate Adamala. His research has been supported by significant grants from the NIH, NSF, and private foundations. Through his leadership of the Synthetic Neurobiology Group, Boyden has fostered interdisciplinary collaborations across multiple institutions and fields. Boyden leads the Synthetic Neurobiology Group at MIT, which brings together researchers from diverse backgrounds including neuroscience, engineering, physics, and computer science. The group operates state-of-the-art facilities for developing and testing new neurotechnologies, with close connections to clinical researchers for translational work.
Southern University of Science and Technology (SUSTech)China
Hu Chengzhi is a tenured Associate Professor at the Department of Mechanical and Energy Engineering , Southern University of Science and Technology (SUSTech), where he has worked since 2018. He leads the SUSTech-Zifu Medical Joint Laboratory (5 million RMB funding) and serves as Deputy Director of the Guangdong Provincial Key Laboratory of Human Augmentation and Rehabilitation Robots . A National Young Talent Program recipient and Shenzhen Peacock Plan B-category Talent , he earned his PhD in Micro-Nano Systems Engineering from Nagoya University (2014) under Professor Toshio Fukuda (Chinese Academy of Sciences foreign member) and conducted postdoctoral work at ETH Zurich with Professor Bradley Nelson. Education B.S. & M.S. in Mechanical Engineering, Huazhong University of Science and Technology (2008, 2010) Ph.D. in Micro-Nano Systems Engineering, Nagoya University (2014) Research Interests focus on micro-nano robotic technologies for single-cell analysis and tumor-targeted therapy , including magnetic manipulation, microfluidic chips, and swimming microrobots. His work also extends to medical microrobotics for diagnosing and treating gastrointestinal diseases. Over the past five years, he has led 14 competitive projects, including the National Key R&D Program and National Natural Science Foundation of China grants. Scientific Awards include the 2024 MINE Young Scientist Award , Best Paper Awards at IEEE MHS 2012, IEEE ICRA 2015, IEEE CBS 2023, and IEEE ICMA 2024. He has published 65 papers in top journals like Nature Communications , Advanced Science , and ACS Nano , and holds over 30 patents. Leadership and Service : Served as General Chair for IEEE ICMA 2025 (500+ participants), Director of SUSTech-Zifu Medical Joint Laboratory , and editorial board member of Cyborg and Bionic Systems . He mentors PhD and Master's students and actively recruits researchers for his lab.
Dong Yang is a Researcher and PhD candidate at the Chair of Media Technology, Technical University of Munich (TUM) , since October 2021. His work bridges Computer Vision, Machine Learning, and Robotics to advance haptic teleoperation systems. B.Eng in Electrical Engineering and Automation (Fuzhou University, China) B.Sc in Electrical Engineering and Information (Technical University of Kaiserslautern, Germany) M.Sc in Electrical Engineering and Information (TUM, Germany) Research focuses on haptic communication , teleoperation , and scene understanding for robots. Key contributions include: Developing SRI-Graph for scene-robot interaction Creating ISSC for semantic shared control Advancing depth estimation in adverse weather Designing HPF-SLAM for visual navigation His publications span top conferences like ICRA , IROS , and RO-MAN , addressing topics in haptic teleoperation , sensor fusion , and collaborative robotics . Supervises thesis projects including Diffusion Model-based Imitation Learning and Scene Graph-based Real-time Scene Understanding Collaborates on projects like Centre for Tactile Internet with Human-in-the-Loop (CeTI) and Teleoperation over 5G
Rachel Holladay is an incoming Assistant Professor in the Mechanical Engineering and Applied Mechanics (MEAM) Department at the University of Pennsylvania's School of Engineering and Applied Science, with an anticipated start date of Fall 2025. She will hold the Asness Family Foundation Assistant Professor of Mechanical Engineering title and join the prestigious GRASP Laboratory as a key faculty member. Education: B.S. in Computer Science and Robotics, Carnegie Mellon University (2017) Ph.D. in Electrical Engineering and Computer Science, MIT (2024) Her research focuses on enabling robots to execute complex, contact-rich manipulation tasks requiring long-horizon planning and robustness against partial or uncertain information. Key areas include: Robotics Mechanical Engineering Control Systems Artificial Intelligence Computer Science Her publication record demonstrates expertise in: Mechanics-based manipulation planning Soft robotics and compliant grasping Task and motion planning integration Force-constrained tool use Motion cone analysis Fréchet error minimization Rachel is actively recruiting graduate students for Fall 2025 and works with the GRASP Lab's interdisciplinary environment that combines computer vision, machine learning, and mechanical design. She maintains collaborative ties with experts like Cynthia Sung and Daniel Koditschek at UPenn, and previously worked with Tomás Lozano-Pérez and Alberto Rodriguez at MIT's LIS Group and MCube Lab.
Dr. Gangbing Song is a Moores Professor in the Department of Mechanical and Aerospace Engineering at the University of Houston , where he has been a tenured Full Professor since 2008. He previously served as an Assistant Professor (1998–2002) and Associate Professor with Tenure (2002–2008) at the University of Houston. Ph.D. , Mechanical Engineering, Columbia University, 1995 M.S. , Mechanical Engineering, Columbia University, 1991 B.S. , Energy Engineering, Zhejiang University, 1989 His research spans structural health monitoring , machine learning , and smart materials , focusing on non-destructive testing , wind energy , and structural vibration control . Recent work includes entropy-based fault diagnosis , underwater bolt monitoring , and upcycling of wind turbine blades in a circular economy framework. Dr. Song has secured over $3.2 million in external funding (including 13 NSF awards) and established the Smart Materials and Structures Laboratory . He has published 96 peer-reviewed journal papers , 191 conference papers , and holds 3 U.S. patents . Outstanding Technical Contribution Award (ASCE, 2008) NSF CAREER Award (2001) Best Paper Award (Earth and Space’08) General Chair, ASCE Earth and Space Conference (2010) He has advised 13 Ph.D. and 31 M.S. students , delivered 10 keynote speeches , and developed educational programs including a remote laboratory and REU initiative . His work integrates robotics , smart sensors , and data-driven algorithms for structural integrity and sustainability applications.
Raffaella Carloni is a Professor at the Faculty of Medical Sciences and Faculty of Science and Engineering of the University of Groningen, holding the chair in Artificial Intelligence and leading the Robotics and image-guided minimally-invasive surgery group. Her work bridges robotics , biomedical engineering , and artificial intelligence , focusing on prosthetics , soft actuators , and human-robot interaction . Research Interests : Robotics, Soft Actuators, Prosthetic Design, Biomechanics, Artificial Intelligence, Mechatronics Her recent research explores variable stiffness actuators for prosthetic limbs, piezoelectric nanofibers in self-sensing materials, and deep reinforcement learning for musculoskeletal simulations. Publications emphasize energy-efficient designs , rehabilitation technologies , and adaptive control systems for amputees and robotic platforms.
