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Hooman SamaniView profile

Associate Professor

• Creative Robotics
• AI-Driven Social Service Robotics
• Cognitive Robotics
• +3 more

Hooman Samani is a Reader and Course Leader of Creative Robotics at the University of the Arts London's Creative Computing Institute, and a Visiting Lecturer at the University of Hertfordshire. He holds a PhD in Robotics from the National University of Singapore and is a Fellow of the Higher Education Academy (FHEA). His research spans AI-driven social robotics, creative robotics, and robotics for healthcare, with notable contributions to ethical AI, human-robot interaction, and pandemic response systems. He has led projects at institutions like Philips, Fraunhofer, and the Keio-NUS CUTE Centre. Education: PhD in Robotics (National University of Singapore), prior roles at National Taipei University (founding AIART Lab), and industry experience at Posco and Philips. Research Interests: Focus on interdisciplinary applications of robotics, including cognitive robotics, cultural robotics, and AI in mental health. He advocates for ethical design, emphasizing trustworthiness and societal impact. Publications: Over 98 articles, books like Creative Robotics and Robotics for Pandemics . Recent trends include memory-driven AI, agentic systems, and ethical multi-robot collaboration. Awards: FHEA, multiple RoboCup competition wins. Active in media, featured on BBC, CNN, and Discovery Channel. Labs/Teams: Founded AIART Lab, involved in collaborative projects with Keio-NUS CUTE Centre. Current focus on creative robotics in theater and education.

• Stochastic Simulation
• Optimization
• Machine Learning
• +5 more

Giulia Pedrielli is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence, with affiliations as a Senior Global Futures Scientist. She holds a Ph.D., M.Sc., and B.S. in Mechanical and Industrial Engineering from Politecnico di Milano, Italy. Her research focuses on stochastic simulation, optimization, and machine learning applications in biomanufacturing, digital twins, and cyber-physical systems. She has contributed to frameworks for pandemic modeling (PySIRTEM), RNA design, and high-dimensional optimization. Pedrielli has collaborated with institutions like NUS and UC Berkeley, and her work spans biomedical engineering, supply chain resilience, and security of critical infrastructure. Recent articles emphasize co-simulation tools, AI-driven logic translation, and falsification methods for CPS security. Her teaching includes advanced stochastic simulation courses and thesis supervision across multiple disciplines. Education: Ph.D. Mechanical Engineering, Politecnico di Milano, Italy M.Sc. Industrial Engineering, Politecnico di Milano, Italy B.S. Industrial Engineering, Politecnico di Milano, Italy Research interests integrate simulation-based optimization with machine learning, particularly in safety-critical systems like healthcare and smart infrastructure. She develops digital twin methodologies for real-time control under uncertainty, leveraging Gaussian processes and Bayesian approaches. Pedrielli's work bridges theoretical advancements (e.g., BASSO optimization algorithm) with practical applications in biomanufacturing and pandemic response.

Xianzhi LiView profile

Associate Professor

• 3D Vision
• Computer Graphics
• Point Cloud Processing
• +3 more

Xianzhi Li is an Associate Professor at the School of Computer Science and Technology, Huazhong University of Science and Technology (HUST). Before joining HUST, he was a postdoctoral fellow at The Chinese University of Hong Kong (CUHK) and the Hong Kong Center for Logistics Robotics. He earned his Ph.D. in Computer Science and Engineering from CUHK under Professors Pheng-Ann Heng and Chi-Wing Fu, preceded by an M.Sc. in Biomedical Engineering (CUHK, 2015) and a B.Eng. in Biomedical Engineering from Sichuan University (2014). His research focuses on 3D vision, computer graphics, point cloud processing, and deep learning, with a specific emphasis on designing machine learning algorithms for 3D data analysis. Recent work includes advancements in 3D point cloud analysis, semantic segmentation, and sim-to-real robotics applications. Key research trends include the integration of large language models with 3D data (e.g., MiniGPT-3D), open-world semantic segmentation (PDF), and joint 2D-3D learning frameworks (Joint-MAE). His contributions span robotics, computer vision, and graphics, with notable applications in industrial bin picking and 3D reconstruction. Awards: Teaching Assistant of Merit (2018), Biomedical Engineering Scholarship (2015), and National Scholarship in China (2013). Teaching: Courses include Computer Vision (Spring 2022), Advanced Topics in Computer Graphics and Visualization, and Principles of Computer Graphics.

Shuvra S. BhattacharyyaView profile

Professor

• Signal Processing
• Embedded Systems
• Biomedical Engineering
• +3 more

Shuvra S. Bhattacharyya is a Professor at the University of Maryland, College Park (UMD), Department of Electrical and Computer Engineering, with a joint appointment in UMIACS. He is also a part-time visiting professor at Tampere University (Finland) and INSA Rennes (France). His research focuses on signal processing systems, embedded systems, biomedical engineering, and machine learning. He has authored 7 books and over 300 papers, and directs the Maryland DSPCAD Research Group. He holds an NSF Career Award and is an IEEE Fellow. His honors include the Fulbright Senior Specialist and FiDiPro Professorship. He advises graduate students and leads projects in neural decoding, cyber-physical systems, and criminal justice AI. Education: B.S., University of Wisconsin-Madison Ph.D., University of California, Berkeley Research Interests: Signal processing architectures, biomedical circuits, embedded software, hardware/software co-design, and AI for healthcare and criminal justice. His work emphasizes real-time systems, energy efficiency, and interdisciplinary applications. Grants & Awards: NIH Grant for real-time neural decoding (2023) IEEE Fellow (2011) Jimmy Lin Entrepreneurship Award (2012) Labs & Teams: Maryland DSPCAD Research Group, collaborating on dynamic data-driven systems, neural interface technologies, and AI ethics in criminal justice. His work integrates academic and industrial partnerships, including roles at Hitachi America and Kuck & Associates.

• Robotics
• Artificial Intelligence
• Autonomous Systems
• +4 more

Nikolaos Tsiogkas is an Assistant Professor in the Declarative Languages and Artificial Intelligence (DTAI) group at KU Leuven's Faculty of Engineering Technology, Department of Computer Science. His research focuses on robotics, artificial intelligence, and autonomous systems, with particular emphasis on cognitive reasoning, knowledge representation, and sensor fusion for robotic navigation. Projects: Promotor of initiatives like 'Harnessing Robotics for Safe Agriculture' (2024-2028) and 'ROSANA: Robust Semantic Navigation in Orchards' (2022-2027); co-promotor in demining robotics and multi-arm manipulation research. Research: Combines symbolic AI with robotics, exploring knowledge graphs for explainable navigation, reinforcement learning frameworks, and computationally efficient free-space detection algorithms.

Ralf SalomonView profile

Professor

• Embedded Systems
• Localization and Positioning
• Time Interval Measurement
• +12 more

Ralf Salomon is a Professor at the Faculty of Computer Science and Electrical Engineering , University of Rostock , Germany. He holds the title of Prof. Dr.-Ing. habil. and is actively involved in research and teaching in embedded systems, localization, and signal processing. University: University of Rostock School: Faculty of Computer Science and Electrical Engineering Position: Full Professor Contact: Room 201, Tel: +49 381 498 7260 His research focuses on high-precision time measurement , ambient assisted living , industrial embedded systems , and localization technologies . He applies bio-inspired principles, particularly from the barn owl auditory system, to develop low-cost, high-accuracy systems. His work spans health monitoring (e.g., fall detection, cochlear implants), sports technology, and animal experimentation systems. The recent publications (2020–2025) show a strong trend in sensor systems , embedded vision , and real-time measurement , with applications in sports, healthcare, and automation. His work combines theoretical innovation with practical implementation, often using FPGAs and microcontrollers. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: He advises students such as Theo Gabloffsky , who co-authors multiple publications and assists in teaching. He has led multiple funded projects including Welisa (DFG Graduate College) , iHeal , CurlDat , and BOUNCE . Many of his projects focus on health, localization, and embedded intelligence. Labs and Teams: He collaborates closely with researchers like Ralf Joost , Matthias Hinkfoth , Gerald Bieber , and Marian Lüder . His team develops FPGA-based systems, embedded sensors, and intelligent algorithms for real-world applications in health, industry, and sports.

Nabil J. SarhanView profile

Associate Professor

• Computer Systems
• Deep Learning
• Video Streaming
• +5 more

Dr. Nabil J. Sarhan is an Associate Professor of Electrical and Computer Engineering at Wayne State University , where he directs the Computer Systems and Deep Learning Research Laboratory and the interdisciplinary M.S. Program in Artificial Intelligence (Systems and Hardware Track) . He previously served as Graduate Program Director and College of Engineering Faculty Assembly Chair. Education : Ph.D. and M.S. in Computer Science and Engineering from Pennsylvania State University (GPA 4.0), B.S. in Electrical Engineering from Jordan University of Science and Technology Internationally recognized for expertise in computer systems , deep learning , video streaming , and AI hardware accelerators , Dr. Sarhan's research bridges theoretical and applied domains in automated video surveillance and medical AI systems . His work includes novel approaches to epileptic seizure prediction , energy-efficient neural network accelerators , and mixed-signal circuit design for edge computing. Recent grant highlights include a $418,907 National Science Foundation award for reconfigurable AI accelerator development. His patent (US Patent 9,313,463) on automated video surveillance systems demonstrates practical technology impact. Scientific Awards IEEE SEM Outstanding Professional of the Year Wayne State President's Excellence in Teaching Award College of Engineering Excellence in Teaching Award WSU Academy of Teachers (2022) IEEE Senior Member (2016) As an educational reform leader , he has chaired over 20 ABET accreditation panels in Saudi Arabia and Palestine, evaluating programs in electrical engineering, computer science, and software engineering. His intellectual property expertise spans significant patent infringement cases including ACQIS v. ASUSTek and VideoShare v. Google .

Rishabh DabralView profile

Researcher

• 3D Computer Vision
• Computer Graphics
• Multi-Modal Interactions
• +6 more

Rishabh Dabral is a Research Group Leader at the Max Planck Institute for Informatics since August 2024, leading the "3D Visual Intelligence" group. He is also affiliated with the Research Training Group on Neuro-Explicit Models of Language, Vision, and Action at Saarland University. Expertise: 3D computer vision, computer graphics, human-object interaction modeling, and motion synthesis. Leadership: Conducts cutting-edge research on 3D human performance capture and physical plausibility in motion. His research focuses on: 3D human pose estimation under gravity constraints Multi-modal gesture synthesis using neural architectures Quantum auto-encoding for 3D representations Wearable robotics informed by human behavior Temporal dynamics in human-object interaction Recent publications at top venues like SIGGRAPH , CVPR , and ICCV demonstrate his work on: Music-driven motion synthesis Egocentric motion capture systems Reactive two-person interaction models Diffusion-based gesture generation Object-aware motion prediction Wearable robotic limb design

Rich WhittleView profile

Assistant Professor

• Human-Systems Engineering
• Space Physiology
• Aerospace Biomechanics
• +4 more

Dr. Rich Whittle is an Assistant Professor in Mechanical and Aerospace Engineering at the University of California, Davis, leading the Bioastronautics and eXploration Systems (BXS) Laboratory within the Center for Space Flight Research (CSFR). His work focuses on understanding human physiological responses to space environments and developing technologies to enhance spaceflight safety and efficiency. Education: PhD in Aerospace Engineering, Texas A&M University MSc in Astronautics and Space Engineering, Cranfield University PGDip in Strategic Management and Leadership, Stratford Business School MA/MEng in Engineering, University of Cambridge Research interests include space physiology, human-systems engineering, and exploration systems design. Key themes involve cardiovascular responses to gravity, virtual reality applications for astronaut training, and countermeasures for long-duration missions. His lab investigates ocular perfusion, metabolic modeling in altered gravity, and lunar habitat safety concepts. Recent publications analyze ocular pressure dynamics, gravitational stress impacts on cardiovascular systems, and virtual reality's role in mission resilience. His work bridges clinical data, engineering systems, and mission-critical design. Awards: Fellow of the British Interplanetary Society (BIS) Advising and grants: While specific student names are unlisted, his lab actively collaborates on NASA-funded projects. Research focuses include military recruit injury prevention and pandemic epidemiology modeling (e.g., NYC's 2020 data). Dr. Whittle leads the BXS Lab and contributes to the CSFR, fostering interdisciplinary teams in space systems engineering and human factors. His prior military service (2009–2023) as a British Army officer informs his focus on operational readiness and mission-critical decision-making in extreme environments.

• Affective Computing
• Emotion Recognition
• Brain-Computer Interfaces
• +3 more

Guillaume Chanel is a researcher at the University of Geneva, affiliated with the Department of Computer Science under the Faculty of Sciences, and the Centre interfacultaire en sciences affectives. His work spans affective computing, emotion recognition, and physiological signal analysis in human-computer interaction contexts. Affective Computing Emotion Recognition Brain-Computer Interfaces Multimodal Interaction Physiological Computing Human-Computer Interaction Chanel's research focuses on integrating physiological signals (e.g., EEG, electrodermal activity) with computational models to enhance emotion detection and adaptive systems. Recent work includes attention mechanisms in U-Net for medical imaging, variational autoencoders for PPG denoising, and biofeedback-driven gaming systems. His publications emphasize multimodal fusion, transfer learning, and latent representation techniques. His 15 most recent articles (2025–2020) analyze advanced deep learning architectures for emotion and impression recognition, physiological signal processing, and adaptive game design. Key themes include attention mechanisms, variational autoencoders, multimodal synchronization, and biofeedback applications. Chanel collaborates with the Computer Vision and Multimedia Laboratory and the Centre universitaire d'informatique, contributing to open datasets and tools like the Toolbox for Emotional feAture extraction from Physiological signals (TEAP).

Thomas ByrdView profile

Assistant Professor

• Artificial Intelligence in Healthcare
• Patient Safety and Deterioration
• Clinical Informatics
• +3 more

Thomas Byrd, MD, MS, serves as Assistant Professor in the Division of Hospital Medicine within the Department of Medicine at the University of Minnesota School of Medicine. His academic work bridges clinical practice with cutting-edge informatics research, focusing on improving hospital-based care through technological innovation and data-driven approaches. Dr. Byrd's research program centers on artificial intelligence applications in surgical and critical care settings, patient deterioration detection systems, and secure clinical communication technologies. His investigations span validation of deterioration index models, barriers to secure messaging adoption, AI-driven radiology follow-up systems, and earlier work in medical imaging and biomedical engineering. This multidisciplinary focus aims to enhance patient safety through real-time monitoring, workflow optimization, and predictive analytics in hospital environments. Analysis of his 15 most recent publications (2014-2024) reveals an evolving research trajectory from biomedical engineering foundations toward AI-driven clinical solutions. Key thematic clusters include: (1) AI applications in surgery and critical care triage; (2) patient deterioration monitoring systems; (3) secure mobile communication adoption; and (4) earlier biomedical engineering work in medical imaging and drug delivery systems. The publications demonstrate increasing focus on translational informatics with strong emphasis on real-world implementation challenges. Scientific Awards: No scientific awards documented in available source material Advising and Grants: While Dr. Byrd's role as faculty suggests potential advising responsibilities, no student names or grant funding details appear in the provided documentation. His research output indicates active investigation in clinical informatics but specific mentoring activities or funded projects remain unspecified in the source text. Laboratory and Team Affiliations: Dr. Byrd is institutionally anchored in the Division of Hospital Medicine at the University of Minnesota, though specific laboratory facilities or dedicated research teams are not described. His collaborative work appears cross-disciplinary, spanning departments of medicine, surgery, radiology, and biomedical engineering as evidenced by publication topics.

James A. Grant-JacobView profile

Academic

• High Harmonic Generation
• Phase Retrieval
• Laser-Induced Forward Transfer (LIFT)
• +7 more

Dr James A. Grant-Jacob is a Senior Research Fellow at the Optoelectronics Research Centre (ORC) , University of Southampton. His work spans high harmonic generation, phase retrieval, laser fabrication, and artificial intelligence applications in photonics. PhD (2011): Table-Top XUV Nanoscope , University of Southampton MSc in Physics: Strings and branes in exotic space-times His research integrates deep learning with laser-based processes for applications in medical imaging, environmental monitoring, and manufacturing . Recent projects include Lasers that Learn and PhototheRapy Enabled Via Artificially-Intelligent Lasers (PREVAIL) . Key article trends include AI-driven laser optimization (2025: 8/10 papers), biomedical applications (psoriasis treatment, pollen sensing), and low-cost optical systems (Raspberry Pi-based imaging). Collaborations with NVIDIA, Dyson, and Southampton General Hospital highlight his translational focus. Students mentored include: Fedor Chernikov (PhD ORC) Yuchen Liu (PhD ORC) Grants from NVIDIA and projects like Beam-shaping for laser-based manufacturing underscore his innovation in smart lasers . Active in the Smart Lasers and Special Fibres research group, he continues advancing interdisciplinary laser technologies.

• Computer Vision
• Pattern Recognition
• Facial Analysis
• +5 more

Jorge Manuel M. C. Pereira Batista is an Associate Professor at the Department of Electrical and Computer Engineering , University of Coimbra, Portugal. He serves as a senior researcher at the Institute for Systems and Robotics (ISR-UC) and leads the Computer Vision Group. His career spans academic roles, research coordination, and industry collaboration. Academic Affiliation: University of Coimbra (Electrical & Computer Engineering Department) Research Institute: Institute for Systems and Robotics (ISR-UC) His research focuses on Computer Vision , Pattern Recognition , and applications of Differential Geometry in these fields. Key subdomains include facial analysis, visual surveillance, real-time vision systems, and machine learning integration. From 2010–2023, his recent publications highlight advancements in probabilistic models , transformer architectures , multi-branch learning , and biomimetic robotics . He has coordinated multiple funded projects such as: STORK : Avian protection systems NeuroCity : Intelligent street lighting 4D Facial Dynamics : Identity recognition iTRAFFIC : BRISA highway traffic monitoring His work bridges theoretical innovation (e.g., Riemannian manifold applications) with practical deployments in transportation, energy, and healthcare sectors.

Abdelhak M. ZoubirView profile

Professor

• Statistical Signal Processing
• Cooperative Sensor Communications
• Radar Imaging
• +5 more

Abdelhak M. Zoubir is a Professor of Signal Processing and Head of the Signal Processing Group at Technische Universität Darmstadt, Germany. He has held leadership roles including Head of the Department of Electrical Engineering and Information Technology (2012–2014 and 2020–2022), and President of the European Association for Signal Processing (EURASIP, 2017–2018). His research focuses on statistical signal processing with applications in radar imaging, biomedical engineering, and automotive systems. Zoubir has authored over 500 publications and is a Fellow of IEEE and EURASIP. He currently leads projects on radar communication integration, robust signal processing algorithms, and radiation-hardened sensor development. Education: Dipl.-Ing. (BSc/MSc) from Fachhochschule Niederrhein and Ruhr-Universität Bochum, followed by a Dr.-Ing. (PhD) in Electrical Engineering from Ruhr-Universität Bochum (1992). Research Interests: Bootstrap techniques, robust detection/estimation, cooperative sensor networks, radar for landmine detection, and automotive safety systems. He has pioneered methods in robust statistical signal processing, including low-rank matrix completion and sparsity-aware algorithms. Recognition: Recipient of the IEEE Meritorious Service Award (2018), IEEE Signal Processing Magazine Best Paper Award (2017), and the M. Barry Carlton Award (2014). He has been a keynote speaker at major conferences such as ICASSP and EUSIPCO, and served as Editor-in-Chief of the IEEE Signal Processing Magazine (2012–2014). Current Projects: Focus on automotive radar signal processing, radiation-hardened sensors (MALTA), and distributed learning robustness. His work bridges theoretical advancements with practical applications in defense, healthcare, and automotive industries.

Donald E. BrownView profile

Professor

• Data Fusion
• Knowledge Discovery
• Predictive Modeling
• +5 more

Donald E. Brown is the W.S. Calcott Professor in the Systems and Information Engineering Department at the University of Virginia, serving as Founding Director of the Data Science Institute and Co-Director of the Translational Health Institute of Virginia. He holds a B.S. from the United States Military Academy (1973), M.S. and M.E. from UC Berkeley (1979), and a Ph.D. from the University of Michigan (1985). His research focuses on data fusion, knowledge discovery, and predictive modeling with applications in healthcare, security, and safety. Dr. Brown leads over 90 federal/state/private research projects, publishes extensively (120+ papers, 2 books), and is a Fellow of the IEEE. He has received prestigious awards including the Norbert Wiener Award and IEEE Millennium Medal. His work bridges academia and industry through Commonwealth Computer Research, Inc., providing data analysis services. He advises on national committees including the National Research Council and the NRC Committee on Transportation Security. His teaching excellence was recognized by students three times as 'best undergraduate teacher' (2001–2003). Research Interests: Data Fusion, Knowledge Discovery, Simulation Optimization, Machine Learning, Predictive Analytics Publications: Focus on healthcare analytics (e.g., Long COVID, tuberculosis, histopathology), AI-driven medical imaging (capsule endoscopy, eosinophil segmentation), and cybersecurity applications. Awards: IEEE Joseph Wohl Career Achievement Award (2017), Governor's Technology Award (1999), Norbert Wiener Award (2002). Grants/Projects: Over 90 funded projects on data science, healthcare tech, and security systems. He leads interdisciplinary initiatives like the iTHRIV Commons for health data sharing and develops AI tools for medical diagnostics at UVA. Current work includes AI in cardiovascular disease prediction, perioperative data digitization for LMICs, and real-time anomaly detection in healthcare systems.