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Bob KirschView profile

Professor

• Brain-Computer Interfaces
• Functional Electrical Stimulation
• Neuroprosthetics
• +3 more

Bob Kirsch serves as Professor and Department Chair of Biomedical Engineering at Case Western Reserve University, where he also directs the Cleveland FES Center. His research focuses on restoring motor function for paralyzed individuals through neural engineering innovations in brain-computer interfaces and functional electrical stimulation systems. His educational background includes: Ph.D. in Biomedical Engineering from Northwestern University M.S. in Biomedical Engineering from Northwestern University (1986) B.S. in Electrical Engineering from the University of Cincinnati (1982) Dr. Kirsch's research spans intracortical brain-computer interfaces for movement restoration, neural decoding algorithms for grasp force and speech, and functional electrical stimulation integration with BCI systems. His work emphasizes closed-loop control architectures , artifact reduction in neural recordings , and reinforcement learning approaches for neuroprosthetic control, targeting clinical translation for tetraplegia rehabilitation. Analysis of his 2016-2020 publications reveals consistent focus on improving BCI calibration speed, enhancing movement decoding accuracy, and developing robust systems for real-world use. Key trends include leveraging human-generated rewards for controller training, addressing signal-independent noise limitations, and creating hybrid approaches combining cortical recordings with FES for functional movement restoration. His recognition includes: Faculty Distinguished Research Award from Case Western Reserve University (2023) As Department Chair and Cleveland FES Center Director, Dr. Kirsch leads multidisciplinary teams developing next-generation neuroprosthetics. His research program, supported by NIH and NSF grants, trains graduate students in neural engineering while advancing clinical applications through the BrainGate consortium collaboration. Current efforts focus on improving BCI communication rates and expanding movement restoration capabilities for paralyzed individuals. The Cleveland FES Center under his direction serves as a hub for neuroprosthetics innovation, integrating expertise in neural signal processing, biomechanics, and clinical rehabilitation to develop practical solutions for restoring lost motor function through implanted and non-invasive technologies.

Stefana ParaschoView profile

Assistant Professor

• Digital Fabrication
• Robotic Construction
• Sustainable Architecture
• +3 more

Stefana Parascho serves as Tenure Track Assistant Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL) within the School of Architecture, Civil and Environmental Engineering (ENAC), specifically in the Institute of Architecture (IA). She leads the Laboratory for Creative Computation (CRCL) and concurrently heads the Diversity Office at ENAC, demonstrating dual commitments to technological innovation and institutional equity. Her research pioneers intersections of digital fabrication, robotics, and sustainable architecture, with particular focus on transforming construction waste into valuable resources. Through computational design and robotic assembly, she develops novel methodologies for upcycling concrete rubble and timber into structurally sound building components, directly addressing circular economy challenges in construction. Her work bridges theoretical frameworks with physical prototyping, emphasizing human-robot collaboration and material reintegration. Analysis of her recent publications reveals a cohesive trajectory toward waste valorization through digital processes, with concrete rubble masonry and timber reuse forming the core technical pillars. These investigations consistently integrate robotic fabrication, structural validation, and circular design principles across 15+ publications since 2023, establishing new paradigms for resource-efficient construction. As an academic advisor, she supervises four PhD candidates (Grangeot Maxence, Skevaki Eleni Maria, Vallat Gabriel Rémi, Wang Jingwen) while contributing to architectural education through courses like "Digital design and making: A critical introduction" that merge technical skill development with theoretical critique. Her leadership extends to the ENAC Teaching Committee and institutional diversity initiatives, reflecting comprehensive engagement with academic community development. Her operational base at CRCL (https://crcl.epfl.ch) provides the technical infrastructure for robotic experimentation, while her Diversity Office role (https://www.epfl.ch/schools/enac/about/diversity-office/) demonstrates parallel commitment to inclusive academic environments. This dual focus positions her at the forefront of both technological innovation and cultural transformation within architectural academia.

• Data management and data science
• Artificial intelligence
• Distributed computing and systems software
• +6 more

Thomas C Henderson is a tenured Professor at the School of Computing within the College of Engineering at the University of Utah. His research focuses on autonomous systems , computational models , and intelligent machine systems , with specific interests in biosystem simulation, distributed systems, and adaptive algorithms. He has contributed to Bayesian sensor networks , UAS traffic management , and probabilistic logic frameworks for intelligent agents. Current academic appointments since 1989 Adjunct roles in Biomedical Engineering Active in IEEE committees (2024-2025) Research trends in recent publications emphasize autonomous aircraft coordination , probabilistic reasoning , and multi-sensor integration . Key subfields include lane-based airspace modeling, reinforcement learning for UAS, and pseudogradient navigation techniques. His work has received recognition through a Best Paper Award (IEEE 2008) and the US Air Force Summer Faculty Fellowship . Strategic deconfliction protocols Dynamic data-driven applications Structural health monitoring systems He actively mentors undergraduate research through courses like Deep Learning Capstone and Senior Capstone Design . Current grants include "Deep Learning in AI and Robotics" (2024-2029) and "Robust Reasoning using Geometric SAT/PSAT" (2022-2023).

Ioannis HavoutisView profile

Associate Professor

• Robotics
• Machine Learning
• Dynamic Motion Planning
• +5 more

Ioannis Havoutis is an Associate Professor in Engineering Science at the Oxford Robotics Institute , University of Oxford. His research integrates dynamic whole-body motion planning , control systems , and machine learning for robotics with arms and legs . He holds a PhD (2011) and MSc with distinction (2007) from the University of Edinburgh, focusing on machine learning for motion planning of articulated robots. University of Edinburgh: PhD in Robotics (2011), MSc in Machine Learning (2007) His work spans dynamic legged locomotion , learning from demonstration , and terrain-adaptive control , with applications in quadruped robots and teleoperation . Key contributions include symplectic meta-learning , skill switching criteria , and flow-based trajectory optimization . Recent publications focus on 2025 advancements in adaptive manipulation , generalist parkour policies , and diffusion models for locomotion. He has received awards such as the Frontiers in Robotics & AI Best Paper Award (2019) and CLAWAR 2015 Best Technical Paper Award . Teaching roles include B16 Operating Systems courses at Oxford and Advanced Robotics lectures at IIT. Contact: havoutis@robots.ox.ac.uk | ioannis@robots.ox.ac.uk | Address: Oxford Robotics Institute, 17 Parks Road, Oxford, OX1 3PJ, UK

• Reinforcement Learning
• Computational Decision-Making
• Machine Learning
• +3 more

Carlo D'Eramo is a Professor in Reinforcement Learning and Computational Decision-Making at the Center for Artificial Intelligence and Data Science (CAIDAS) of Julius-Maximilians-Universität Würzburg (JMU). He leads the LiteRL research group and the hessian.AI independent research group, focusing on lightweight methods for adaptive autonomous agents in complex real-world environments. B.Sc., M.Sc. in Computer Engineering - Politecnico di Milano (2011, 2015) Double M.Sc. in Computer Science - University of Illinois at Chicago (2015) Ph.D. in Information Technology - Politecnico di Milano (2019) His research spans multiple RL domains including multi-task learning , curriculum learning , adversarial RL , options learning , and multi-agent coordination . Recent work explores physics-informed ML, neural network distillation, and uncertainty-driven exploration. 2025 publications highlight advancements in: Bellman update optimization via adaptive distillation uncertainty propagation in tree search multi-agent policy gradients real-world educational applications The group's work shows increasing specialization in physics-integrated RL and efficient neural architectures. Spotlight Presentation - ICML (2025) Spotlight Presentation - ICLR (2024) Oral Presentation - NeurIPS (2020) As Senior Area Chair for RLC and Area Chair for major AI conferences (AAAI, NeurIPS, ICLR), he contributes to academic governance. His team includes researchers like Ahmed Hendawy, Théo Vincent, and Georgia Chalvatzaki, with collaborations spanning TU Darmstadt and hessian.AI.

Jun WuView profile

Researcher

• Vibration and Dynamics
• Medical Image Segmentation
• Surgical Artificial Intelligence
• +3 more

Dr. Jun Wu is a Postdoctoral Research Assistant at the Department of Biology, University of Oxford, where he conducts research in the Animal Vibration Lab. Previously, he served as a research associate in the Department of Aerospace Engineering at the University of Bristol. His academic credentials include: PhD in Sound and Vibration Research from the University of Southampton Dr. Wu's research bridges vibration dynamics and biomedical engineering, with current work developing engineering technologies inspired by biological dynamic systems. His publication record reveals a significant pivot toward medical artificial intelligence since 2023, with dominant contributions in surgical vision-language models and medical image segmentation. This dual expertise enables unique cross-pollination between biological vibration principles and clinical AI applications. Analysis of his 2023-2025 publications shows concentrated innovation in adapting foundation models like Segment Anything Model (SAM) for medical contexts. Key advances address uncertainty quantification in segmentation, human-in-the-loop refinement systems, multi-modal diagnostic reasoning, and real-time surgical video analysis. These works consistently target clinical deployment challenges including safety verification, computational efficiency, and human-AI collaboration. Professional recognition includes: Associate Fellow of the Higher Education Academy (AFHEA) Member of the Royal Aeronautical Society (MRAeS) As a core member of Oxford's Animal Vibration Lab, Dr. Wu contributes to research investigating biological dynamic systems for engineering innovation while leading medical AI initiatives that translate these principles into surgical and diagnostic technologies.

Antonio PietrabissaView profile

Associate Professor

• Networked Systems
• Time-delay Systems
• Non-cooperative Control
• +4 more

Antonio Pietrabissa is an Associate Professor at the Department of Computer, Control, and Management Engineering “Antonio Ruberti” (DIAG) of the University of Rome Sapienza, where he earned his degree in Electronics Engineering (2000) and PhD in Systems Engineering (2004). He has been teaching Automatic Control and Process Automation since 2010 and holds the National Scientific Qualification as Full Professor in Systems and Control Engineering (09/G1). Research interests include networked systems, robust control, Markov decision processes, and deep reinforcement learning. His work spans telecommunications, biomedical applications, and space systems, with a focus on federated learning and decentralized control. He has authored ~70 journal papers (Scopus h-index 22) and co-invented a patent for model predictive control in motor disability assistance. Awards include the 2021 Cybersecurity Award and ETRI Journal Best Paper. Current projects are NANCY (6G networks) and CADUCEO (AI-driven medical diagnostics). He is also CEO of Sapienza startup Automation Intelligence and Control (AICO), commercializing AI solutions for space, telecom, and biomedical sectors.

• Mobile Robotics
• Autonomous Robotics
• Computer Vision
• +4 more

António Fernando Macedo Ribeiro is an Associate Professor at the Department of Industrial Electronics within the School of Engineering at the University of Minho. He is also a Senior Researcher at the Algoritmi Research Center and a member of both the IE R&D Group and CAR R&D Lab. He graduated in Computer Science (1988), earned an MSc in Industrial Robotics (1992), and a PhD in Industrial Robotics and Advanced Manufacturing Technology (1995). His academic career includes roles as department director (2013-2016) and co-director of Algoritmi (2006-2010). He founded the Robotics Laboratory (LAR) at the University of Minho and the spin-off SAR (botnroll.com). His research focuses on mobile robotics , autonomous systems , computer vision , and industrial automation . He has led over 50 projects, including RoboParty (since 2007) and RoboCup participation (since 1999). His work spans robot localization , gesture recognition , and adaptive control . Recent publications highlight trends in autonomous navigation , deep learning for robotics, and human-robot interaction . Key subfields include hexapod locomotion, multi-agent systems, tactile sensing, and 3D recognition. He has organized over 30 events like RoboCup editions and RoboParty. He actively promotes robotics education for youth through TEDx talks, school visits, and robotics clubs.

• Artificial Intelligence
• Deep Learning
• Action Recognition in Videos
• +4 more

Catherine Achard is a full Professor at Sorbonne University, affiliated with the RPI-Bio team under Polytech Sorbonne. She has served as Deputy Director at Polytech Sorbonne since 2020 and has been actively involved in research and teaching in artificial intelligence and computer vision. Research Focus: Artificial Intelligence, Deep Learning, Action/Gesture Recognition, Multi-modal Interaction, Person Re-identification Teaching: AI courses at Sorbonne University (M1/M2) and Polytech Sorbonne, Image Processing for L3. Her recent publications (2023-2025) demonstrate expertise in 3D point cloud registration, adaptive virtual agents, and multimodal interaction analysis. Key trends include geometric constraint handling, deep reinforcement learning for action spotting, and biomedical applications of AI. Technical Contributions: Development of RoCNet++ for point cloud registration, ASAP framework for agent adaptation, and SALAD for action detection. Collaborations span robotics, medical imaging, and human-computer interaction projects across international conferences.

Laure SoulierView profile

Lecturer

• Design of language models for Information Retrieval (IR) and Natural Language Processing (NLP)
• Data-to-Text Generation
• Search-Oriented Conversational Systems
• +3 more

Laure Soulier is a HDR (Habilitation à Diriger des Recherches) Lecturer at Sorbonne University within the MLIA team at the ISIR (Institute for Intelligent Systems and Robotics) laboratory. Her research focuses on the design of language models for Information Retrieval (IR) and Natural Language Processing (NLP) applications, including data-to-text generation , search-oriented conversational systems , language models for robotics , and continual learning with domain adaptation . Recent publications highlight her work on cross-encoders, co-speech gesture generation, and latent space metrics. She supervises an ANR-funded postdoctoral researcher position (SCAI/BnF program) and collaborates on projects involving multimodal techniques, user interaction analysis, and document vectorization. Her scientific contributions include best paper awards at CORIA 2021, SCAI@EMNLP 2019, CORIA 2015, and AIRS 2013. Laure Soulier’s work spans collaborative information retrieval models, entity ranking in heterogeneous networks, and neural approaches for knowledge-based IR. She has contributed to evaluation frameworks for LLMs in IR and co-speech gesture generation, with applications in e-commerce search, medical information retrieval, and social media-based collaboration. Her research integrates user roles, document representations, and reinforcement learning techniques.

• Autonomous Vehicles
• Vehicle Control Systems
• Traffic Safety
• +5 more

Jonas Sjoberg is a Professor of Mechatronics at Chalmers University of Technology and leader of the Mechatronics research group. His work spans over 30 years with 143 publications and leadership in 29 research projects. His current research focuses on autonomous vehicle systems, vehicle dynamics, and traffic safety applications. Dr. Sjoberg's research interests center on mechatronic systems for transportation applications. His work bridges theoretical control systems with practical automotive implementations, particularly in autonomous vehicle technology. His research group investigates vehicle dynamics, path planning, intersection management, and safety systems, with growing emphasis on micromobility applications including autonomous bicycles and pedestrian interaction. Recent work demonstrates strong focus on real-world applications of control theory to improve vehicle safety and performance. His publication record shows consistent output with 15+ papers annually, demonstrating sustained research activity. The work spans fundamental control theory (system identification, nonlinear control) to applied transportation problems (intersection management, road surface estimation, autonomous docking). Recent publications show increasing focus on micromobility applications, particularly autonomous bicycles, and integration of machine learning techniques with traditional control approaches. Dr. Sjoberg leads multiple significant research projects including MicroSIM (2026-2027), Mintox (2025-2026), and MicroITS, with funding from VINNOVA, EU, and industry partners. His projects address critical challenges in autonomous vehicle safety, micromobility integration, and traffic management. As leader of the Mechatronics research group, Dr. Sjoberg oversees research on vehicle control systems, autonomous driving technologies, and safety applications. His laboratory work focuses on practical implementation of theoretical control concepts, with recent emphasis on bicycle dynamics and micromobility safety systems.

Emre UgurView profile

Associate Professor

• Robotics
• Robot Learning
• Cognitive Robotics
• +5 more

Emre Ugur is an Associate Professor in the Department of Computer Engineering at Bogazici University, where he serves as the head of the Cognition, Learning and Robotics (CoLoRs) laboratory. His research focuses on bridging the gap between continuous sensorimotor experiences and discrete symbolic representations in robotics. Funded by major international sources including the European Commission's Horizon 2020 program and TUBITAK, his work has significant implications for cognitive robotics and autonomous systems. Education: PhD in Computer Engineering from Middle East Technical University (METU, Turkey) Ugur's research interests center on cognitive and developmental approaches to robotics, with particular emphasis on neuro-symbolic integration, affordance learning, and symbol emergence. His work explores how robots can autonomously develop high-level cognitive capabilities through continuous interaction with their environment, similar to human cognitive development. His approach combines machine learning, cognitive science, and robotics to create systems that can learn, predict, and reason about their actions. His recent publications reveal a strong trajectory toward neuro-symbolic robotics, where he develops methods for extracting discrete symbolic representations from continuous sensorimotor experiences. This work enables robots to perform complex planning and reasoning tasks while maintaining connection to physical reality. There is also significant focus on social robotics, particularly in human-robot interaction, social navigation, and embodied cognition. Scientific Awards: The Young Scientist Award by the Science Academy (BAGEP) The Excellence in Teaching Award by the Faculty of Engineering (2023) As Principal Investigator of major projects including INVERSE (EU Horizon 2025), DEEPPLAN (TUBITAK), and previously DEEPSYM and IMAGINE, Ugur has established a robust research program that bridges theoretical advances with practical applications. He has supervised numerous PhD and Master's students who have made significant contributions to the field. His leadership extends to organizing major workshops at top robotics conferences including IROS, RSS, and ICRA. At the Cognition, Learning and Robotics (CoLoRs) lab, Ugur leads research on cognitive robotics, developmental robotics, and neuro-symbolic AI. The lab explores fundamental questions about how robots can develop understanding of their actions, learn from interaction, and form abstract representations necessary for high-level cognition. Current projects focus on symbolic reasoning, prediction, and planning in robotic systems.

• Design automation for intelligent cyber-physical systems
• Internet-of-Things applications
• Safe and robust machine learning for embodied AI
• +7 more

Professor Zhu Qi is a faculty member in the Department of Electrical and Computer Engineering at Northwestern University's McCormick School of Engineering, with courtesy appointment in Computer Science. He leads the IDEAS Lab (Design Automation of Intelligent Systems Lab) where his research focuses on design automation for intelligent cyber-physical systems and Internet-of-Things applications. His research interests include safe and robust machine learning for embodied AI systems, cyber-physical security, energy-efficient CPS, and system-on-chip design. Professor Zhu's work particularly addresses safety, robustness, security, adaptability, resiliency, and energy challenges in the design and operation of embodied AI systems. His applications span connected and autonomous vehicles, robotics, advanced manufacturing, wearable computing, smart buildings and infrastructures, and IoT. Professor Zhu's recent publications reveal a strong focus on safety verification of neural network controlled systems, robust reinforcement learning methods, and applications of large language models in autonomous systems. His work often combines formal verification techniques with machine learning approaches to provide safety guarantees for AI-enabled cyber-physical systems. DATE 2022 Best Paper Award AutoSec 2021 Best Short Paper Award ACM TODAES 2016 Best Paper Award IEEE TCCPS Early-Career Award (2017) Humboldt Research Fellowship for Experienced Researchers (2017) NAE US Frontiers of Engineering participant (2020) Professor Zhu has secured multiple research grants from NSF (including FM, DESC, and Fuse grants), DOE, ONR, and industry partners including GM and Toyota. His advising includes PhD students Shuyue Lan and Hengyi Liang, and postdoc Chao Huang who became a Lecturer at University of Liverpool. He leads the IDEAS Lab which focuses on cross-layer design, verification, and adaptation of learning-enabled cyber-physical systems.

• Mechanical Engineering
• Artificial Intelligence
• Tribology
• +3 more

Hassan Shirvani is a Professor of Engineering Design and Simulation at the School of Engineering and the Built Environment, Anglia Ruskin University. He serves as Director of the Engineering Analysis Simulation and Tribology (EAST) Research Group, focusing on industry collaborations to solve engineering challenges. PhD in Mechanical Engineering, University of Bath MSc in Mechanical Engineering, University of Birmingham Member, Institute of Mechanical Engineers (IMechE) His research spans mechanical engineering, artificial intelligence, and biomedical applications, including: Thermal system optimization Machine learning in clinical decision-making Composite metal foil manufacturing Virtual reality medical training systems Flow dynamics in heat exchangers and nozzles AI-assisted diagnostics Hybrid manufacturing processes Hassan's publications reflect expertise in computational modeling, multi-physics simulations, and industrial applications. Notable areas include deep learning for suicide prediction, thermodynamic analysis of sustainable energy systems, and tribology in mechanical components.

• Robust Machine Learning
• Trustworthy AI
• Bayesian Uncertainty Propagation
• +5 more

Dimah Dera is an Endowed Assistant Professor at the Chester F. Carlson Center for Imaging Science, College of Science, Rochester Institute of Technology (RIT). She holds a Ph.D. and M.S. in Electrical and Computer Engineering, and an M.A. in Mathematics from Rowan University. Her research focuses on robust and trustworthy machine learning, integrating Bayesian theory and statistical signal processing into modern ML frameworks for healthcare, remote sensing, and surveillance systems. She is an NVIDIA Deep Learning Institute University Ambassador and active in IEEE Signal Processing and ACM SIGHPC. Dr. Dera has received prestigious awards including the NSF CRII Award (2023), NSF REU Supplement (2024), and IEEE Benjamin Franklin Key Award (2021). Her work emphasizes Bayesian uncertainty propagation for robust AI systems, with applications in sequential time-series analysis and medical imaging. Her scholarly contributions span robust image classification, uncertainty-aware neural networks, and Bayesian vision transformers. Teaching includes courses like Mathematical Methods for Imaging and Image Processing & Computer Vision II . She actively mentors students and leads research initiatives funded by NSF and industry collaborations.