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Nuno FachadaView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Modeling and Simulation
• +4 more

Nuno Fachada is an Assistant Professor at Lusófona University's School of Communication, Arts and Information (ECATI) and a researcher at COPELABS (Cognitive and People-centric Computing). He teaches Programming and AI in the Videogames Bachelor's program and Research Software in the Informatics PhD program, with research spanning Artificial Intelligence, Machine Learning, Modeling and Simulation, High Performance Computing, and Computer Science Education. His work integrates computational methods with applications in game development, wildfire management, and networking. Education: Bachelor's degree in Electrical and Computer Engineering from IST (2005) Master's degree in Electrical and Computer Engineering from IST (2008), focusing on immune system simulation PhD in Electrical and Computer Engineering from IST (2016) with thesis 'Agent-Based Modeling on High Performance Computing Architectures', awarded 'Pass with Distinction and Honour' Research interests emphasize Agent-Based Modeling for complex systems like wildfires and immune responses, Particle Swarm Optimization algorithms, and OpenCL for parallel computing. He develops educational tools for game development curricula and applies AI to environmental monitoring and wireless networks, with strong output in simulation frameworks and human-centric computing. Recent publications (2024-2025) reveal three dominant trends: wildfire modeling using satellite data and agent-based approaches, large language models for engineering code generation (e.g., LoRaWAN), and game AI for procedural content and rehabilitation. His work bridges theoretical computer science with practical applications in environmental science and healthcare, while maintaining focus on education through tools like TextCL and cf4ocl. Scientific Awards: No scientific awards, fellowships, or medals were mentioned in the provided text Advising and Grants: Fachada serves as a researcher in ILIND-funded projects, notably the 'Cybersecurity Awareness Training Simulator' (2024-2025) with six collaborators. He supervises PhD students in Informatics but specific advisees aren't listed. His grant activity primarily involves institutional projects through Lusófona University's research center, with emphasis on simulation-based tools for real-world applications. Labs and Teams: He is a core researcher at COPELABS, focusing on cognitive and people-centric computing projects including wildfire simulation and cybersecurity training. Prior to Lusófona, he conducted postdoctoral work at LaSEEB/ISR (Institute for Systems and Robotics), maintaining connections to IST. His team collaborations span international researchers in environmental modeling and AI, with recent projects involving Portuguese and European institutions.

• Computational Robotics
• Robot Kinematics
• Robotic Manipulation
• +6 more

Alba Perez Gracia is an Associate Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Institute for Robotics and Intelligent Systems (IRI), a joint center with CSIC. She works in the Department of Robotics within the School of Engineering, focusing on computational robotics, kinematics, and mechanism design. Research Interests: Her research lies at the intersection of robotics, geometry, and mechanical design. She specializes in computational kinematics , geometric algebra (particularly Clifford algebras), robotic manipulation , and rehabilitation robotics . Her work often involves mathematical modeling of spatial mechanisms, grasping strategies, and the design of robotic hands and exoskeletons. Publication Trends: Over the period 2010–2019, her publications show a consistent focus on geometric methods in robotics, with increasing emphasis on grasping in unstructured environments and the use of algebraic tools for rotation and motion analysis. Key themes include screw theory, Cayley factorizations, superquadric modeling, and the synthesis of closed-loop linkages. Scientific Contributions: Developed geometric methods for orthonormalization of rotation matrices using Cayley factorization. Advanced grasping strategies for cluttered scenes using superquadric object representations. Contributed to the kinematic synthesis of multi-fingered and non-anthropomorphic robotic hands. Designed low-DOF mechanisms for finger rehabilitation exoskeletons. Advising and Grants: While specific advisees and funding sources are not mentioned in the text, her collaborative work with researchers such as F. Thomas, E. Simo-Serra, and D. Wolbrecht suggests active participation in research teams and potential supervision of students. Her publications in rehabilitation robotics suggest interdisciplinary collaboration with biomedical engineers. Labs and Teams: She is affiliated with the Institute for Robotics and Intelligent Systems (IRI), UPC-CSIC, a leading robotics research center in Spain, where she contributes to research in computational robotics and mechanism design.

Dip GoswamiView profile

Associate Professor

• Embedded Control Systems
• Cyber-Physical Systems
• Multi-core Platforms
• +3 more

Dip Goswami is an Associate Professor in the Electronic Systems group at Eindhoven University of Technology (TU/e), focusing on embedded control systems for automotive and robotics applications. His work spans resource-aware design, multi-core predictability, and cyber-physical systems (CPS) optimization. He has co-authored over 80 conference papers, 20 journal articles, and received three best paper awards (ASP-DAC 2011, EUC 2010, ECYPS 2019). His research integrates predictable multi-core architectures distributed automotive control image-based control systems real-time optimization . Recent publications highlight advancements in nonlinear MPC for water networks DNN-driven motion control multi-rate sensor fusion parallel GPU solvers resource-constrained CPS . Scientific Awards: Best Paper Award at ECYPS 2019 Best Paper Award in ASP-DAC 2011 Best Paper Award in EUC 2010 He actively contributes to journal editorial boards (e.g., Microprocessors and Microsystems ) and serves on program committees for DAC, DATE, RTSS, and EMSOFT. Goswami's work aligns with UN SDG 4 (Quality Education) and SDG 9 (Industry Innovation), bridging control theory and embedded systems implementation.

• Embedded Systems
• FPGA
• High Performance Computing
• +5 more

Alberto Dassatti is a Full Professor at HES-SO University of Applied Sciences Western Switzerland and Director of the Reconfigurable & Embedded Digital Systems (REDS) Institute. He is affiliated with the School of Engineering and Management of the Canton of Vaud. His work focuses on embedded systems, FPGA applications, high-performance computing, and topological data analysis. He has led and collaborated on numerous projects funded by institutions like Innosuisse and HES-SO Rectorat, totaling over CHF 1.2 million in grants. His research spans computational storage systems, medical sensor technologies, and signal processing frameworks for wireless communication. Key projects include developing the CarbonViz energy visualization tool, advancing topological methods for AI reliability, and creating the BB-Sensors system for neonatal vital monitoring. He has contributed to open-source frameworks like giotto-tda and gr-dnn, emphasizing interdisciplinary collaboration between hardware and software domains. His publications span topics such as FPGA-GPU communication, real-time embedded systems optimization, and heterogeneous computing architectures. He actively participates in conferences like NeurIPS and IEEE SOC, showcasing innovations in reconfigurable computing and energy-efficient systems.

Jocelyne Ventre-DomineyView profile

Researcher

• Neuroscience
• Cognitive Science
• Neurolinguistics
• +3 more

Jocelyne Ventre-Dominey is a Senior Researcher at INSERM, affiliated with the University of Burgundy's Faculty of Sports Sciences (UFR Staps). Her primary research focuses on embodied cognition, neurocognitive mechanisms of language and perception, and motor control systems. She is part of the INSERM U1093 Laboratory, collaborating extensively with Prof. Philippe F. Dominey and other international researchers. Her work bridges robotics and neuroscience, investigating how visuo-tactile feedback impacts telepresence embodiment, and explores neural systems underlying sentence-picture comprehension across languages. She has contributed to understanding the role of dopamine vs. STN stimulation in Parkinson's disease and the structural-functional connectivity of semantic systems. Recent studies also address human-robot interaction acceptability and parallel neural processes in motor sequence execution. Publications span journals like Scientific Reports, NeuroImage, and Frontiers in Human Neuroscience, reflecting her multidisciplinary approach. Her research integrates EEG/ERP, neuroimaging, and behavioral experiments to dissect cognitive and neural processes in embodied systems and language. Based in Dijon, France, she maintains ties with the University of Burgundy's sports sciences faculty while engaging in international collaborations. No specific grants or awards are listed, though her active publication record indicates sustained research engagement.

Ash LentonView profile

Associate Professor

• Colonial Encounters
• Social History
• Industrial Archaeology
• +9 more

Ash Lenton is an Associate Professor of Applied Cybernetics in the School of Computing at the Australian National University (ANU), within the ANU College of Systems & Society. He holds a PhD in Archaeology from the University of Reading (2013), specializing in Later Medieval Córdoba, Spain. His interdisciplinary career spans academic research, construction project management, and archaeological fieldwork, with expertise in colonial encounters, social history, and technological processes. Education: PhD Archaeology, University of Reading (2013) Research Interests: Colonial Encounters Medieval Architecture Ethnoarchaeology Technology & Identity Decolonisation Studies Public Archaeology His work emphasizes collaborative projects, such as the UNESCO Tentative World Heritage Listing for the Australian Cornish Mining Site and the Vice-Chancellor’s Award-winning ANU Archaeology education program. Awards: Vice-Chancellor’s Award for Excellence in Education (ANU Archaeology Program Leadership) Advising & Grants: Lead researcher for Australian Cornish Mining UNESCO project Project Convenor for Master’s Degree in Applied Cybernetics Collaborations include work with indigenous communities, museums, and the media. His podcast Foreign Countries: Conversations in Archaeology explores global archaeological narratives. Labs/Teams: ANU Archaeology Research Group Cybernetic Systems Research Team (2023 project on pre-historic robotics/AI parallels)

• Optical Physics
• Electrical and Electronic Engineering
• Medicinal and Biomolecular Chemistry
• +3 more

Sunil Kumar is a Research Fellow at the Department of Physics, Imperial College London, affiliated with the Photonics Group and Physics of Life research groups. His academic role within the Faculty of Natural Sciences focuses on interdisciplinary research spanning optical physics, biomedical engineering, and robotics. His work integrates mechanical engineering principles with control systems and rehabilitation technologies. Research interests include parallel manipulator dynamics, GUI development for medical devices, combustion science, and environmental sustainability assessments. Notable contributions involve designing lower limb rehabilitation systems using 3-PRRR parallel manipulators and analyzing combustion phenomena through computational models. Publications highlight a focus on robotics applications in healthcare, control system optimization for rehabilitation devices, and environmental impact studies of industrial processes. No scientific awards are explicitly mentioned in the provided text. No advisory roles or grant details are listed, but his affiliations suggest active participation in collaborative research teams. His lab and team involvement includes the Physics of Life group, focusing on biophysics and medical technology intersections.

Lorenzo RossiView profile

Assistant Professor

• Process Mining
• Robotics
• Business Process Management (BPM)
• +5 more

Lorenzo Rossi is an Assistant Professor specializing in the intersection of process mining, robotics, and business process management (BPM). His research focuses on advancing methodologies for analyzing robotic systems, IoT environments, and collaborative processes through formal models like BPMN. Key areas include process discovery, digital twins, and system resilience in smart environments. Expertise: Process Mining, Multi-Robot Systems, BPMN Semantics, Cyber-Physical Systems Key Tools: BEAR (BPMN animator), MIDA (multi-instance animator), UBBA (Unity-based BPMN animator) His work emphasizes reproducibility and practical frameworks for rapid prototyping. Recent trends in his articles highlight the integration of process mining with robotics for data-driven decision-making, as well as formal verification of BPMN collaborations to ensure system correctness. Awards: None explicitly mentioned in the provided texts. Advising & Grants: No student/advisor relationships or grant details documented here. Labs/Teams: Collaboration with Unity-based simulation tools (UBBA, MIDA) and serious game platforms (PlayWithUnicam).

• Artificial Intelligence
• Robotics
• Autonomous Systems
• +6 more

Sebastian B Thrun is an Adjunct Professor in the Department of Computer Science at Stanford University. His research focuses on artificial intelligence, robotics, autonomous systems, and medical imaging. He holds positions at Stanford and has affiliations with Udacity, Inc., contributing to innovations in autonomous driving systems, drone technology, and AI-driven healthcare solutions. His work often bridges theoretical advancements with practical applications in transportation and medicine. **Education**: Details not explicitly provided in the text. Research Interests include developing algorithms for autonomous vehicles, deep learning applications in healthcare (e.g., skin cancer detection), and improving medical imaging techniques. He explores Bayesian methods, decision trees, and clustering algorithms to enhance AI systems' efficiency and adaptability. His articles reflect a focus on autonomous systems (e.g., multicopter design, vehicle navigation), healthcare AI (skin cancer classification), and algorithm optimization. Notable trends include leveraging AI for real-world challenges in transportation and medical diagnostics. Scientific Awards**: None explicitly mentioned in the provided text. Advising & Grants**: No student advisees or grant details listed here. His contributions extend to industry partnerships, such as those in autonomous vehicle development and online education platforms like Udacity. Labs/Teams**: Likely associated with Stanford’s AI Lab and Udacity initiatives, though specific lab affiliations are not detailed in the text.

• Deep Learning
• Reinforcement Learning
• Robotics
• +7 more

Glen Berseth is an Assistant Professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute. He holds the prestigious Canada CIFAR AI Chair position and co-directs the Robotics and Embodied AI Lab (REAL). Previously, he was a postdoctoral researcher at Berkeley Artificial Intelligence Research (BAIR) working with Sergey Levine. Dr. Berseth's research focuses on solving sequential decision-making problems for real-world autonomous learning systems, with particular emphasis on human-robot collaboration, reinforcement learning, and various advanced learning paradigms including continual, meta, multi-agent, and hierarchical learning. His work bridges theoretical machine learning with practical robotics applications, aiming to create more capable and adaptable robotic systems. He has published extensively in top venues for robotics, machine learning, and computer animation. His recent publications demonstrate a strong focus on exploration strategies, representation learning, and efficient reinforcement learning algorithms. These works address critical challenges in scaling RL to real-world applications, improving generalization across different robot morphologies, and enabling autonomous learning without extensive human supervision. His research has practical implications for robotics, power grid control, and other complex systems requiring adaptive decision-making. Canada CIFAR AI Chair Dr. Berseth mentors a large group of graduate students across multiple institutions, supervising PhD and Master's candidates working on diverse aspects of robot learning and reinforcement learning. His lab receives significant research funding through his CIFAR AI Chair position and likely other grants supporting his robotics and AI research. The Robotics and Embodied AI Lab (REAL) he co-directs serves as a hub for interdisciplinary research at the intersection of machine learning and physical systems, fostering collaboration between computer scientists, roboticists, and domain experts.

Giovanni BeltrameView profile

Professor

• Intelligent Systems
• Embedded Systems
• Aerospace
• +1 more

Giovanni Beltrame is a Professor at Polytechnique Montréal's Department of Computer and Software Engineering, where he directs the MIST Laboratory. His work bridges academic research and aerospace engineering applications. Education: M.Sc. in Electrical Engineering and Computer Science, University of Illinois, Chicago (2001) Laurea in Computer Engineering, Politecnico di Milano (2002) MS in Information Technology, CEFRIEL, Milan (2002) Ph.D. in Computer Engineering, Politecnico di Milano (2006) Research Focus: His work spans four interconnected domains: Intelligent Systems (swarm robotics, distributed planning, SLAM), Embedded Systems (hardware/software modeling and verification), Aerospace (avionics, radiation-hardened systems), and Optimization (multi-objective algorithms, parallel programming). These areas converge in space-system applications and fault-tolerant computing. Leadership: As Principal Investigator, he leads multiple government and industry-funded projects. He actively recruits researchers through the MIST Laboratory's structured onboarding process, requiring candidates to demonstrate prior engagement with lab materials. Teaching: He instructs specialized courses including Swarm Intelligence (INF6593ME) and Space Systems Computing (AER8300), covering topics from microprocessor architecture to spacecraft data processing design. Lab Operations: The MIST Laboratory serves as the hub for his research activities, with documented procedures for student recruitment and project collaboration in swarm intelligence and embedded aerospace systems.

Massimo BertozziView profile

Associate Professor

• Computer Vision
• Real-Time Image Processing
• Autonomous Driving
• +12 more

Massimo Bertozzi is an Associate Professor in the Department of Engineering and Architecture at the University of Parma, Italy. He has held a permanent academic position since 1997 and is a leading researcher in computer vision and intelligent vehicles. His work spans real-time image processing, autonomous driving, pedestrian detection, and embedded systems. He earned his Dr.Eng. in Electronic Engineering from the University of Parma in 1994 and completed his PhD in Information Technologies in 1998. His academic journey progressed from PhD student to assistant researcher and eventually to Associate Professor. His research focuses on vision-based systems for autonomous vehicles, including stereo vision, CNNs, SWIR imaging, and sensor fusion. He has been instrumental in pioneering projects such as ARGO, VIAC, TerraMax, and V-Charge. His current work includes collaboration with the IMP Lab on deep learning for computer vision. His recent publications (2022–2025) demonstrate a strong trend in AI-driven image synthesis, disentangled representations, 3D reconstruction, and real-time autonomous driving algorithms. Key themes include diffusion models, GANs, transformers, and efficient neural architectures applied to vision tasks. Scientific Awards: Best Paper Award, CONTROLO 2022 Best Paper Award, VISAPP 2022 He has advised numerous researchers and co-authored with PhD students and collaborators such as Tomaso Fontanini, Leonardo Rossi, and Alex Ergasti. He has not been mentioned to have received major external grants, but his involvement in EU-funded projects (PReVENT, PEGASE, V-Charge) indicates significant collaborative funding. He teaches courses including Computer Science and Programming Laboratory, Fundamentals of Computer Vision, and Deep Learning for Engineering Applications. He is affiliated with the Computer Engineering Group and the IMP Lab at the University of Parma, contributing to a vibrant research environment focused on intelligent systems and autonomous technologies.

• Medical and Scientific Imaging
• Algorithm Design and Analysis
• Computational Complexity
• +1 more

Jim Cox is a Professor in the CIS Department at Brooklyn College. His research spans Medical and Scientific Imaging , Algorithm Design and Analysis , Computational Complexity , and Constraint Programming and Operations Research . Medical Imaging : Focused on anatomical modeling and volume data analysis. Algorithm Design : Contributions to digital Morse theory and surface reconstruction. Computational Complexity : Theoretical work on logic programming languages and complexity hierarchies. His publications highlight advancements in 3D image segmentation , digital geometry , and constraint programming , with key applications in biomedical computing and robotics. Awards and student advising details are not publicly listed in the provided materials.

• robotic manipulation
• robot learning
• motion planning
• +3 more

Dmitry Berenson is an Associate Professor in the Robotics Department at the University of Michigan, Ann Arbor. Previously, he served as an Assistant Professor at Worcester Polytechnic Institute from 2012 to 2016, following a postdoctoral position at the University of California, Berkeley in 2011-2012. His educational background includes: B.S. in electrical engineering from Cornell University (2005) Ph.D. in robotics from the Robotics Institute, Carnegie Mellon University (2011) Dr. Berenson's research focuses on advancing robotic manipulation capabilities, particularly in challenging scenarios involving deformable objects and complex environments. His work bridges the gap between theoretical motion planning algorithms and practical robotic applications, with emphasis on developing methods that can handle uncertainty, occlusion, and complex physical interactions. He has made significant contributions to contact-rich manipulation, deformable object handling, and the integration of learning with classical planning approaches. His research has important applications in manufacturing, agriculture, surgery, and everyday human environments where robots need to interact with complex objects. His recent publications demonstrate a strong trend toward integrating machine learning techniques (particularly diffusion models and other deep learning approaches) with traditional motion planning and manipulation methods. This hybrid approach allows for more robust handling of complex scenarios like deformable object manipulation, occluded environments, and long-horizon tasks that have traditionally challenged robotics systems. Among his notable achievements, Dr. Berenson has received the prestigious IEEE RAS Early Career Award and the NSF CAREER award, recognizing his significant contributions to robotics research and education. As an academic advisor, Dr. Berenson has mentored numerous students who have gone on to contribute to the field of robotics. His research has been supported by various grants that have enabled his team to pursue innovative approaches at the intersection of motion planning, manipulation, and machine learning. His work often involves interdisciplinary collaboration across computer science, mechanical engineering, and applied mathematics. Dr. Berenson leads a research group at the University of Michigan focused on robotic manipulation and motion planning. His lab develops algorithms that enable robots to perform complex manipulation tasks in unstructured environments, with particular expertise in handling deformable objects and reasoning about physical interactions. The group maintains strong connections with industry partners and other academic institutions, fostering a collaborative environment for advancing the state of the art in robotics.

Laurent O. JayView profile

Professor

• Numerical Analysis
• Scientific Computing
• Ordinary Differential Equations
• +16 more

Laurent O. Jay is a Professor in the Department of Mathematics at The University of Iowa, where he also serves as Director of the Interdisciplinary Graduate PhD Program in Applied Mathematical and Computational Sciences (AMCS). He is a faculty member of the College of Liberal Arts and Sciences, with research affiliations in numerical analysis and dynamical systems and differential equations. Department: Department of Mathematics School: College of Liberal Arts and Sciences University: The University of Iowa Position: Professor and Director of AMCS PhD Program His research focuses on numerical methods for differential equations, particularly Runge-Kutta and symplectic methods for ODEs and DAEs, with applications in mechanics and computational science. He has been actively publishing in top journals such as SIAM Journal on Numerical Analysis, BIT Numerical Mathematics, and Numerical Algorithms, with recent work on predictors for implicit methods, convergence analysis, and specialized integrators for constrained systems. His publications reveal a strong emphasis on structure-preserving algorithms, stiffness, and efficient implementations. Laurent O. Jay has received the Leslie Fox Prize in Numerical Analysis (Second Prize, 1995), a prestigious early-career award in the field. His research has been supported by the National Science Foundation under multiple grants. He advises several PhD students in the AMCS program and has mentored numerous former students who have pursued academic and industry careers. His teaching includes graduate courses such as Ordinary Differential Equations I and Introduction to Numerical Methods. He is also a proud member of the Runge-Kutta Club, reflecting his deep expertise in numerical integration methods.