Petar Kormushev is a Senior Lecturer (Associate Professor) in Robotics at the Dyson School of Design Engineering, Imperial College London, and the founder/director of the Robot Intelligence Lab. He holds a PhD in Computational Intelligence from Tokyo Institute of Technology. His research focuses on robotics and machine learning, particularly reinforcement learning for autonomous robots. Key projects include the WALK-MAN humanoid robot for disaster response and contributions to the PANDORA and STIFF-FLOP EU projects. He has received the 2013 John Atanasoff Award for scientific excellence in ICT. His lab develops machine learning algorithms applied to humanoid robots like COMAN and iCub, with interests in robot learning, compliant control, and autonomous systems. He has supervised numerous PhD students and led research teams at IIT and Imperial College.
Stéphane Doncieux is a University Professor in Computer Science at Sorbonne University, where he is affiliated with the Institute of Intelligent Systems and Robotics (ISIR), a joint research laboratory with CNRS. Since January 2024, he has served as Director of ISIR, following a term as Deputy Director from 2019 to 2023. He leads the ASIMOV research team and is based at the Pierre and Marie Curie Campus in Paris. His primary research interests lie in cognitive and developmental robotics, with a strong focus on open-ended learning, evolutionary algorithms, and adaptive systems. He investigates how robots can autonomously learn diverse skills through mechanisms such as novelty search, quality-diversity optimization, and intrinsic motivation. His work bridges theoretical foundations in artificial life and practical applications in robotic manipulation, perception, and control. The recent publications highlight a consistent trend in advancing robotic learning under sparse rewards and in open-ended environments. Key themes include quality-diversity optimization for grasping, state representation learning, sim-to-real transfer, and the development of behavioral repertoires. These works are published in high-impact journals such as IEEE Transactions on Robotics, Evolutionary Computation, and Frontiers in Robotics and AI. Coordinator, DREAM FET H2020 project (2015–2018) Principal Investigator, ANR projects on Creative Adaptation by Evolution, Learning Movement Skills, and Grasping with Multimodal Feedback Involved in European initiatives including VeriDREAM and HumanE-AI-Net He has supervised numerous PhD and Master’s students, including Leni Le Goff, Giuseppe Paolo, Alban Laflaquière, and Achkan Salehi, often in collaboration with leading researchers like Olivier Sigaud and Jean-Baptiste Mouret. He teaches computer science and robotics at both undergraduate and graduate levels at Sorbonne University. Doncieux has been instrumental in shaping research directions in evolutionary and developmental robotics, notably through his leadership in the IEEE Task Force on Evo-Devo-Robotics and his editorial contributions. His lab, ASIMOV, fosters interdisciplinary research integrating computer science, neuroscience, and engineering to create more autonomous and intelligent robotic systems.
Sriram Subramanian is an Assistant Professor at the School of Computer Science in Carleton University since July 2025. He holds affiliations with the Vector Institute for Artificial Intelligence and the Schwartz Reisman Institute for Technology and Society in Toronto, and serves as a mentor in the Indigenous Black Engineering and Technology (IBET) PhD Project . Ph.D. in Electrical and Computer Engineering, University of Waterloo (2022) MASc in Electrical and Computer Engineering, University of Waterloo (2018) BE in Geomatics Engineering, Anna University (2016) His research focuses on advancing Multi-agent Systems and Reinforcement Learning through intersections with Game Theory , with applications in generative AI , robotics, finance, and autonomous driving. Recent work emphasizes cooperation mechanisms, constraint learning, and theoretical robustness in large-scale environments. Articles demonstrate cross-disciplinary impacts in chemistry (ChemGymRL) and societal systems. Notable awards include the MITACS Globalink Research Award , Pasupalak Fellowship in AI , and the CAIAC Best Doctoral Dissertation Award (2023) . Publications span top venues like AISTATS, ICML, AAAI, IJCAI, JAIR , and TMLR . He has collaborated with Microsoft, Royal Bank of Canada, Denso, ESRI, and Borealis AI. As a Distinguished Postdoctoral Fellow at the Vector Institute (2022-2025), he advanced algorithmic frameworks while maintaining active roles in conference reviewing and committee work. His advocacy for equity and diversity drives mentorship initiatives in Canadian institutions.
Dr. Sam Ferguson is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), with a multidisciplinary background in music performance, cognitive science, and psycho-acoustics. His research explores the intersection of sound, music, and human experience through creative coding, machine learning, and interactive systems. Key Research Areas: Sound and Music Computing, Human-Computer Interaction, Creative Coding, Cognitive Science, Installation Art, and Acoustics. Current Projects: ARC Linkage project on creative coding and multiplicitous media; industry collaborations on IoT-based audiovisual systems. Recent Publications: Focus on spatial audio complexity, gestural interaction with networked sound, music emotion recognition frameworks, and robotic performance through genre-based cultural platforms. Leadership Roles: Director of Teaching & Learning Engagement; former Deputy Head of School (Teaching and Learning); active in ACM Creativity and Cognition Steering Committee. Teaching: Courses like Digital Media Studio , Prototyping Physical Interaction , and Data Processing using R within UTS's interdisciplinary Software Development Studio.
Dr Fabio Pierazzi is an Associate Professor in Information Security at the Department of Computer Science, University College London. His research focuses on enhancing systems security through AI, particularly in environments where attackers rapidly adapt to defenses. He investigates adversarial attacks, concept drift mitigation, and explainability of ML-based security systems. Research emphasizes adversarial machine learning in security contexts Works on practical applications in malware analysis and network intrusion detection Explores concept drift robustness and problem-space constraints Collaborates with industry to improve real-world security solutions His publications span top-tier venues like IEEE Security & Privacy, ACM CCS, and USENIX Security. Key themes include adversarial robustness, security evaluation methodologies, and AI's limitations in practice. He supervises research degrees and provides consultancy for security projects.
Professor Robert Eason is a leading academic at the University of Southampton, specializing in photonics and laser technology. His research spans interdisciplinary areas combining Machine Learning , Medical Diagnostics , and Microfluidics . Research Interests : Eason focuses on AI-driven laser applications, including deep learning for phototherapy , autonomous laser machining , and low-cost paper-based diagnostic devices . His work bridges photonics with biomedicine and advanced manufacturing. Recent Publications : His 2025 article in Scientific Reports explores AI simulations for psoriasis treatment, while 2024-2022 works address laser-controlled microfluidics, deep learning in microscopy, and reinforcement learning for laser machining. Supervision : He supervises PhD student Georgia Mourkioti in laser-based research projects. External Roles : Eason has served as a speaker at international conferences including the International Symposium on Laser Precision Microfabrication (2018), LAISER (2019), and Deep Learning for Control of Light-Matter Interactions (2022).
Dr. Gary Glover is a Professor of Radiology (Radiological Sciences Lab) at Stanford University , with courtesy appointments in Psychology and Electrical Engineering. His work focuses on the physics and mathematics of MRI, particularly rapid scanning methods using spiral k-space trajectories for functional brain imaging and multimodal neuroimaging (fMRI/EEG/fPET/fNIRS) combined with neuromodulation techniques like TMS and transcranial ultrasound. Academic Appointments: Radiology, Psychology, Electrical Engineering Professional Affiliations: Bio-X, Stanford Cancer Institute, Wu Tsai Neurosciences Institute Research Interests include: Development of blood oxygen level-dependent (BOLD) and viscoelastic contrast in MRI Functional MR Elastography for brain activation mapping Optimization of MR-ARFI for transcranial ultrasound guidance Automated spinal cord segmentation (EPISeg) using machine learning Scientific Awards : National Academy of Engineering (2013) Gold Medal, ISMRM (2000) Steinmetz Award, General Electric (1985) Lauterbur Lecture, ISMRM (2018) Recent Publications analyze: Fast fMRI sampling and spurious signal correction Dissociated patterns in default mode network anti-correlations Neural correlates of collaborative behavior in triadic fMRI Salience network contributions to depression pathophysiology
Massimo Canale is a Tenured Associate Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino , and a member of the CARS@PoliTO Interdepartmental Center for Automotive Research and Sustainable Mobility. His academic career spans over two decades, focusing on control systems engineering with applications in automotive technology. Scientific Branch: Systems and Control Engineering (IINF-04/A) ERC Sectors: Automotive Engineering, Control Engineering, Control Theory Dr. Canale's research bridges theoretical advancements in Model Predictive Control (MPC) with practical applications in autonomous vehicles , hybrid/electric propulsion , and active suspension systems . His work integrates reinforcement learning and dynamic programming for optimizing vehicle performance and energy efficiency. Recent publications demonstrate trends in autonomous driving architectures (2024), sliding mode control for highway scenarios (2024), and energy management for sustainable mobility (2023-2024). He has developed patented solutions for semi-active suspension control and autonomous vehicle guidance. Award: IEEE Transactions on Control Systems Technology Outstanding Paper Award (2011) Editorial Roles: Associate Editor, IEEE Open Journal of Control Systems (2022–present) Dr. Canale supervises PhD students like Francesco Cerrito and teaches courses on digital control technologies , automatic control , and reinforcement learning at Politecnico di Torino. His research is funded through competitive grants (e.g., MPC4AVP 2021-2022) and commercial contracts (AD Shuttle 2024).
Professor Ioannis Katakis is a Faculty Member at the University of Nicosia, where he is affiliated with the School of Sciences and Engineering and the Department of Computer Science. He has held various academic positions across multiple institutions including Aristotle University of Thessaloniki, University of Cyprus, Cyprus University of Technology, Open University of Cyprus, Hellenic Open University, Athens University of Economics and Business, and National and Kapodistrian University of Athens. His educational background includes a PhD in Machine Learning for Automated Text Classification (2005-2009), a Master's in Information Systems (2005-2007), and a Bachelor's in Computer Science (2000-2004), all from Aristotle University of Thessaloniki. Professor Katakis specializes in several cutting-edge areas of computer science and data analysis. His primary research interests include Mining Social, Web and Urban Data , Sentiment Analysis and Opinion Mining , Data Streams , and Multi-label Learning . His work bridges theoretical machine learning approaches with practical applications in social media analysis, healthcare informatics, privacy protection, and smart city technologies. He has published extensively in top venues including CIKM, ECML/PKDD, IEEE TKDE, and ECAI. His recent publications demonstrate a clear trend toward applying machine learning techniques to real-world problems with societal impact. He has focused on areas such as GDPR compliance in smart devices, sentiment analysis in crowd-sourced content, healthcare applications including drug reaction classification and brain disease monitoring, and privacy protection in wearable technologies. His work often involves multi-modal data analysis and addresses challenges in data streams and multi-label classification. Professor Katakis has made significant contributions to his field, with his research cited over 4,200 times. He serves as an Editor for the journal Information Systems and has edited four special issues in journals such as DAMI and InfSys. He regularly contributes to the academic community by serving on program committees for major conferences including ECML/PKDD, WSDM, DEBS, and IJCAI, and by reviewing for prestigious journals like TPAMI, DMKD, TKDE, TKDD, JMLR, TWEB, and ML. He has been actively involved in European research projects, notably serving as Quality Assurance Coordinator and Senior Researcher for projects such as VAVEL (www.vavel-project.eu) and INSIGHT (www.insight-ict.eu). His grant activities demonstrate a strong focus on collaborative, interdisciplinary research with practical applications in urban data management, social media analysis, and healthcare informatics. He has organized three workshops at major conferences (ICML, ECML/PKDD, EDBT/ICDT) and has extensive experience translating research into practical applications through his involvement in European projects.
Dr. Christian Jaeger is a Researcher at the Zurich University of Applied Sciences (ZHAW) School of Engineering, focusing on Machine Learning in Optimal Control for Industry. His work bridges engineering and computer science with applications in industrial automation and building systems. His research interests span Machine Learning , Optimal Control , Reinforcement Learning , Energy Management Systems , and Industrial Automation . Jaeger has led multiple research projects including a preliminary study on automated IBN heat pumps and a feasibility study on Reinforcement Learning Control for heating systems. His work demonstrates a clear trajectory from traditional manufacturing technology toward contemporary AI-driven control systems. Jaeger's publication record shows consistent output from 2005 to 2024, with recent focus on energy optimization in building control using reinforcement learning, 3D printing techniques, and model predictive control. His research demonstrates strong interdisciplinary connections between computer science, engineering, and practical industrial applications. His scientific contributions include publications in journals such as Applied Sciences and the Journal of the British Interplanetary Society, along with numerous conference proceedings from international events including EuroSun and the International Symposium on Nonlinear Theory and its Applications. At ZHAW, Jaeger has served as project leader for multiple completed research initiatives including adaptive energy management systems for buildings and automated heat pump systems. His work demonstrates strong industry connections with applications in building automation and industrial manufacturing processes.
Professor David Abbink is a Full Professor of Haptic Human-Robot Interaction at Delft University of Technology, holding a joint appointment between the Department of Cognitive Robotics in the Faculty of Mechanical Engineering and Industrial Design Engineering since November 2023. He founded the Delft Haptics Lab and co-founded the Cognitive Robotics Department in 2017. Abbink leads the transdisciplinary research and innovation centre FRAIM, which was awarded the prestigious NWO Stevin Premie (Dutch Nobel Prize equivalent) in June 2024. Trained as a mechanical engineer specializing in biomechanics, Abbink's research focuses on human behavior adaptations when interacting with autonomous systems. He has published over a hundred scientific articles on human-robot interaction, haptics, shared control, tele-operation, driver assistance systems, and sensorimotor control. His research has been funded by industry partners (Nissan, Boeing, Renault), RVO (Brightsky project 2022-2026), and the Dutch Science Foundation NWO through personal grants (VENI 2010-2014, VIDI 2015-2019). Abbink's recent work centers on worker-robot relations as an academic focus, collaborating with organizations like Erasmus Medical Centre for nursing work, Schiphol and KLM for baggage handling, and KLM Engine Repair Services for maintenance work. He also serves as scientific director for the Centre for Meaningful Human Control, launched in October 2024. His work bridges engineering, social sciences, and practical applications to responsibly shape the future of work with emerging robotic capabilities. NWO Stevin Premie (2024) Best IEEE SMC journal paper on Cybernetics (2019) Top 25 scientific talents according to New Scientist (2015) Best teacher of Faculty 3mE (2013, 2014) Best teacher of Department of BioMechanical Engineering (seven consecutive years) Abbink has supervised over 110 MSc students and 11 PhD students. His educational contributions include developing the Master Programme in Robotics at TU Delft and receiving international recognition for his course 'The Human Controller.' He is also a prominent science communicator, featured on national television, radio, and major Dutch newspapers, and has delivered lectures at venues like The Royal Institution and Lowlands Festival. Despite his academic commitments, Abbink maintains a drummer persona, having recorded four albums and performed over 400 shows across three continents between 1999-2014.
Xiaofeng Liu is an Assistant Professor at Yale University School of Medicine in the Departments of Radiology & Biomedical Imaging and Biomedical Informatics & Data Science. He is also an Associate Member at the Broad Institute of MIT and Harvard. Previously, he held faculty positions at Harvard Medical School and research roles at Massachusetts General Hospital and Beth Israel Deaconess Medical Center. PhD in Mechatronics from University of Chinese Academy of Sciences Dual Bachelor's degrees in Automation (Wang-Daheng Elite Class) and Communication from University of Science and Technology of China His research integrates trustworthy AI, medical imaging, and data science to improve diagnosis, prognosis, and treatment monitoring for neurological disorders, cancer, and cardiovascular diseases. Key focus areas include domain adaptation techniques, diffusion models, and interpretable AI systems. Led special issues in IEEE Transactions on Pattern Analysis and Medical Image Analysis Developed novel frameworks like Ordinal UDA and Memory-Consistent Adaptation Scientific accolades include the Trailblazer R21 Award (NIBIB), OpenAI Research Award, and National Artificial Intelligence Research Resource Pilot Award. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and actively contributes to MICCAI and NIH review panels. His lab at Yale (XLiu Lab) investigates neural basis of intelligence to inspire AI development, with applications in brain tumor segmentation, cardiac imaging, and cross-modal medical diagnostics.
Cheng Zhang is an Associate Professor (with Tenure) in Information Science and a Field Member in Computer Science at Cornell University. He directs the Smart Computer Interfaces for Future Interaction (SciFi) Lab , focusing on integrating human-centered AI with advanced sensing technologies to empower everyday wearables. Ph.D. in Computer Science, Georgia Institute of Technology (2020) M.S. in Software Engineering, Chinese Academy of Sciences B.S. in Software Engineering, Nankai University His research examines how to solicit information on and around the human body to address real-world challenges in interaction, health sensing, and activity recognition. He builds novel sensing systems spanning hardware prototypes, algorithm design (machine learning and physics-based modeling), and high-impact applications in accessibility and health. Article Trends : His recent work includes low-power, minimally intrusive wearables (e.g., EchoForce for muscle activity tracking, Ring-a-Pose for hand poses, SeamFit for smart clothing) using acoustic sensing and machine learning. The 15 most recent articles span 2025–2023, with applications in silent speech, authentication, and pose estimation. Scientific Awards : NSF CAREER Award Ubicomp 10-Year Impact Award Best Paper Honorable Mentions at ISWC’24 and ISWC’23 Advising : Mentored Ph.D. students like Ruidong Zhang (Qualcomm Fellowship recipient) and Ke Li, with research featured in Cornell Chronicle and IEEE Spectrum .
Chris Fuller, Ph.D., is the Samuel Langley Distinguished Professor of Engineering at the College of Engineering , Virginia Tech. He leads the Vibrations and Acoustics Laboratory (VAL) , focusing on active/passive noise control systems, metamaterials, and their application to aerospace, medical devices, and industrial machinery. Education: Ph.D. (1979) and B.E. (1974) from the University of Adelaide, Australia. Research Interests: Structural acoustics, adaptive materials, machine learning in noise prediction, and biomedical acoustics (e.g., neonatal incubators). Awards: ASME Rayleigh Award (2017), NASA Team Achievement Award (1996), and Fellow of the Acoustical Society of America. Recent Publications: Highlight advancements in drone noise reduction using neural networks, metamaterials for HVAC systems, and poro-elastic materials for low-frequency noise control.
Guo Ping is an Associate Professor of Mechanical Engineering at Northwestern University, leading the Advanced Intelligent Manufacturing Laboratory (AIM). His research focuses on precision manufacturing, intelligent metrology via deep learning, and advanced manufacturing applications. He holds a Ph.D. from Northwestern University and a B.S. in Automotive Engineering from Tsinghua University. Education: Ph.D. in Mechanical Engineering, Northwestern University, Evanston, IL B.S. in Automotive Engineering, Tsinghua University, Beijing, China Research Interests: Dr. Guo’s work emphasizes innovations in precision engineering, including ductile-regime machining, smart metrology systems, and robotics-driven manufacturing. Key areas include structural coloration, additive manufacturing, and human-robot collaboration in industrial settings. His lab explores cutting-edge techniques like ultrasonic vibration machining and machine learning for defect detection and process optimization. Publications Trends: Recent work spans AI-driven quality control (e.g., photometric stereo networks), robotic swarm patterning, and wearable fatigue monitoring systems. His research bridges machine learning, robotics, and traditional manufacturing to address scalability and precision challenges. Awards: F.W. Taylor Medal (CIRP, 2023) ASME Kornel F. Ehman Manufacturing Medal (2021) SME Outstanding Young Manufacturing Engineer Award (2020) Professional Service: Associate Editor of the Journal of Manufacturing Processes (2017–present). Active in organizing conferences and reviewing for top journals. Labs & Teams: Directs the AIM Lab, which integrates robotics, AI, and advanced materials to solve problems in precision fabrication and smart manufacturing. Current projects include structural coloration for anti-counterfeiting and fatigue prediction in industrial workers.
/prod01/channel_3/media/migration/research-groups/Petar-Kormushev--tojpeg_1463870974521_x4.jpg)
