Find Academic Supervisors

• Computational Neuroscience
• Neural Networks
• Computer Hardware
• +3 more

Professor Liam McDaid serves as Interim Research Director at Ulster University's School of Computing, Engineering and Intelligent Systems (Magee Campus, Derry~Londonderry). His leadership spans computational neuroscience and neuromorphic engineering projects addressing global challenges through the UN Sustainable Development Goals framework. Research focuses on computational neuroscience and neural engineering , with expertise in spiking neural networks, astrocyte modeling, hardware implementations (FPGAs/Networks-on-Chip), and biomedical applications. His work integrates computer hardware with biological systems for fault-tolerant computing and medical diagnostics. Key publications reveal trends in neuromorphic hardware for real-time fault detection (RISC-V systems), medical AI for cardiac/adrenal diagnostics, and event-driven sensing . Research bridges computer engineering with neuroscience to solve healthcare infrastructure challenges. Prize 'Biotechnology' category in Northern Ireland Science Park 25K Award (2011) IgniteNI's global Accelerator Programme (2021) Life and Health Startup Company of the Year 2019 (InventNI) Prize Asthma (2019) Leads major projects including AI-EPOCMON (cardiac diagnostics), Nervous Systems (neural interfaces), and microwave thermal therapy for hypertension. Supervises PhD researchers in neural engineering while directing industry collaborations with healthcare technology focus. Maintains active partnerships across Europe in neuromorphic computing and medical device development. Directs research teams advancing spiking neural networks for hardware security and medical applications, with recent work on adrenal segmentation pipelines and RISC-V watchdog mechanisms. Future initiatives focus on expanding AI-driven point-of-care diagnostics and neuromorphic infrastructure monitoring systems.

• Machine Vision
• Polarization Imaging
• Composite Materials Analysis
• +3 more

Dr. Gary Atkinson is an Associate Professor in the Department of Engineering, Design and Mathematics at the University of the West of England (UWE). As a member of the Bristol Robotics Laboratory's Machine Vision group, his research focuses on polarization imaging, photometric stereo, and composite materials analysis. He has extensive experience in industrial inspection, security imaging, and 3D reconstruction techniques. Education: MSci in Physics, University of Nottingham (2003) PhD in Computer Science, University of York (2007) Postgraduate Certificate in Teaching and Learning in Higher Education, UWE His research explores polarization properties for surface reconstruction, with applications in composite material inspection, medical imaging, and underwater vision. Recent projects include deep learning-based defect detection in carbon fibre and polarization-driven surface normal estimation for seafloor imaging. Scientific Contributions: Fellow of the Higher Education Academy Principal Investigator for £189k DETI project (2020-2022) Co-investigator on multiple EPSRC/InnovateUK grants Delivered computer vision programs in China (University of Qiqihar) Co-convener of Biometrics Technology Conference (2010) As Programme Leader for BEng Robotics since 2023, he teaches computer vision, component inspection, and engineering mechanics. His work bridges academic research with industrial applications through partnerships like the National Composites Centre.

• Software Engineering
• Sustainability
• Human-Environment Interaction
• +12 more

Ana Leticia de Cerqueira Leite Duboc is a contracted professor at La Salle School of Engineering, affiliated with the Human-Environment Research department. Her work bridges Software Engineering and Sustainability , focusing on education, systems analysis, and cross-disciplinary applications. Key research areas: Software Engineering Sustainability & SDGs Animal Welfare via Acoustic Monitoring Cognitive Robotics ICT for Mental Health Recent projects include Mentaltic (ICT in mental health), CowTalk (cattle vocalization monitoring), and CMES (cross-modal robotic perception). Her collaborations span AGAUR and the Generalitat de Catalunya, with a focus on deep learning and sustainable systems.

Mohamed AbdelfattahView profile

Assistant Professor

• deep learning systems
• automated machine learning
• hardware-software codesign
• +7 more

Mohamed Abdelfattah is an Assistant Professor at Cornell Tech and the School of Electrical and Computer Engineering, Cornell University, specializing in machine-learning-centric computer systems for datacenters and mobile devices. Education: B.S., Electrical and Computer Engineering, German University in Cairo (2009) M.S., Electrical and Computer Engineering, University of Stuttgart (2011) Ph.D., Electrical and Computer Engineering, University of Toronto (2016) His research bridges computer architecture and machine learning with core focus areas: Deep learning systems optimization Hardware-aware automated machine learning Reconfigurable computing architectures FPGA-based acceleration He pioneers hardware-software co-design approaches to enhance ML efficiency across computing platforms. Awards: National Science Foundation Early CAREER Award (2024) Vanier Canada Graduate Scholarship Three best paper awards for embedded networks-on-chip for FPGAs His research is funded by the NSF CAREER grant, building on industrial collaborations from prior roles at Intel's Programmable Solutions Group and Samsung where he led hardware-aware ML research teams. His PhD work on FPGA architectures has been adopted by multiple semiconductor companies.

John R BenjaminView profile

Clinical Professor

• Trauma Medicine
• Expeditionary Medicine
• Malignant Hyperthermia
• +2 more

John R Benjamin serves as a Clinical Professor in the Department of Anesthesiology at Uniformed Services University of the Health Sciences (USUHS) School of Medicine. He is actively affiliated with Walter Reed National Military Medical Center in Bethesda, MD, where he practices as a Navy Commander. His primary appointment spans military medicine and academic anesthesia. His educational background includes a B.S. from Virginia Tech (1985-1989), M.Sc. from Miami University (1995-1998), M.D. from The Ohio State University College of Medicine (1998-2002), Surgery Internship at Naval Medical Center San Diego (2002-2003), Anesthesiology Residency at NCC (2006-2008), Chief Residency at NCC (2008-2009), and Anesthesia/Critical Care Fellowship at Massachusetts General Hospital (2010-2011). Benjamin's research focuses on Trauma and Expeditionary Medicine and Malignant Hyperthermia , with significant contributions to surgical intensive care unit mobility protocols and postoperative outcomes. His work bridges military medical needs with civilian critical care advancements, particularly in resource-limited environments. His publication trends reveal deep engagement with ICU mobility scoring systems Postoperative care optimization Medical device validation in field settings Interprofessional critical care collaboration spanning high-impact journals including The Lancet and Critical Care Medicine . Scientific recognition includes: Fellow of American Society of Anesthesiologists (F.A.S.A.) As a military-academic physician, Benjamin integrates operational medicine requirements with scholarly research. His grants and operational projects likely focus on expeditionary anesthesia protocols and trauma response systems for military personnel, though specific grants aren't detailed in the source text. He participates in multi-institutional initiatives like the International Early SOMS-guided Mobilization Research Initiative. His work occurs within the military medical ecosystem connecting USUHS, Walter Reed National Military Medical Center, and National Capital Consortium training programs, emphasizing practical applications for battlefield and disaster medicine.

• Functional Neurosurgery
• Neuroimaging
• Deep Brain Stimulation
• +7 more

Michael Hart is a Senior Lecturer in Neurosurgery at City St George's, University of London, specializing in functional neurosurgery and neuroimaging for movement disorders, pain, and epilepsy. He is affiliated with the Neuroscience & Cell Biology Research Institute and maintains an active clinical and academic practice focusing on deep brain stimulation and related technologies. His educational background includes a medical degree from the University of Edinburgh, specialist neurosurgery training in Cambridge, and a PhD on connectomics from the Department of Psychiatry. He completed fellowships in awake brain surgery (Montpellier), functional neurosurgery (Vancouver), and neuromodulation (St George's Hospital, London). Hart's research interests center on neuroimaging and functional brain mapping, with particular focus on connectomics, ultra-high field 7T MRI, and tractography. He leverages these technologies to understand disease mechanisms and improve treatment targets in functional neurosurgery. His work spans neuro-oncology, movement disorders, and epilepsy, with emphasis on translating technological advances to improved patient care through industry collaborations. His publications demonstrate expertise across neuroimaging techniques, deep brain stimulation accuracy, cognitive assessment in brain tumor patients, and glioma biology. The research shows a clear progression from neuro-oncology applications of connectomics to functional neurosurgery applications, with increasing focus on precision targeting and outcome measurement in neuromodulation procedures. Fellow of The Royal College of Surgeons of Edinburgh (FRCSEd) Associate of The Higher Education Academy (AHEA) European Association of Neurological Surgeons (EANS): Functional Committee British Society of Stereotactic and Functional Neurosurgery (BSSFN): Research & Governance lead Hart provides supervision for medical students, MSc/MRes students, MD(Res), and PhD candidates. He collaborates extensively with movement disorders, epilepsy, and pain teams at SGUL, including Prof Francesca Morgante, Dr Lucia Ricciardi, Prof Mark Edwards, and others. His external collaborations include researchers at Oxford University, University of British Columbia, and University of Seville. He has led industry collaborations with Novo Nordisk, uniQure, and Boston Scientific Inc, and co-founded tech startups including neuroXact for post-operative DBS lead appraisal.

Sarah RachutView profile

Researcher

• Digital Law
• E-Government
• Data Protection
• +7 more

Dr. Sarah Rachut is a Scientific Associate at the Technical University of Munich (TUM), working at the Chair of Digital Law and Security under Prof. Dr. Dirk Heckmann. She serves as Managing Director of the TUM Center for Digital Public Services research institute and is affiliated with the TUM School of Social Sciences and Technology in the Department of Governance. Her work bridges legal scholarship with practical digital governance solutions, focusing on fundamental constitutional issues in digital contexts. Dr. Rachut studied law with a European and international focus at the University of Passau and completed her legal traineeship at the Higher Regional Court of Munich. She earned her doctorate with a dissertation titled "Realization of Fundamental Rights in Digital Contexts," which received the Science Prize of the Society for Data Protection and Data Security in November 2024. She is also a member of the German Society for Law and Informatics (DGRI) and an associate member of the eSports Law Research Center at the University of Augsburg. Her research centers on e-government, e-health, and e-education law, with particular emphasis on how constitutional rights can be realized in digital environments. Dr. Rachut challenges the misconception that law hinders digitalization, arguing instead that legal frameworks enable balanced technological advancement that respects fundamental rights. She advocates for moving from application-based administration toward proactive government services that automatically recognize citizens' legitimate claims without requiring burdensome documentation. Dr. Rachut's extensive publication record demonstrates deep expertise in AI regulation, digital administrative procedures, and electronic examination systems. Her work uniquely combines theoretical legal scholarship with practical implementation experience, as evidenced by her pivotal role in developing Bavaria's Fernprüfungserprobungsverordnung (BayFEV) during the pandemic, which became a national model for legally secure remote examinations. Young Leader in Gov Tech Award by Handelsblatt and Possible (2024) Science Prize of the Society for Data Protection and Data Security (2024) Second place in Young Scientist of the Year award (2025) Talent in BayFiD program (Bavaria's Women in Digital Professions) (2020) Scholarship holder at Graduate School of Bavarian Research Institute for Digital Transformation (2022) As Managing Director of the TUM Center for Digital Public Services, Dr. Rachut mentors students and leads research initiatives focused on digital transformation in government. She has successfully guided student teams to multiple awards in the "Digital Future Challenge" competition. Her leadership extends to shaping policy through expert opinions for legislative bodies including the German Bundestag and various state parliaments, where she has advised on the Bavarian Digital Law and other key digital governance frameworks. Dr. Rachut plays a key role in the TUM Center for Digital Public Services, which serves as an interdisciplinary hub for digital government research. She also serves as spokesperson for the "E-Government Law" working group of the National E-Government Competence Center (NEGZ) and co-edits the journal "Ordnung der Wissenschaft" since October 2024.

• Reinforcement Learning
• Learning in Games
• Applied Probability
• +2 more

Zaiwei Chen is an Assistant Professor in the Edwardson School of Industrial Engineering at Purdue University. His research focuses on developing theoretical foundations for sequential decision-making under uncertainty, particularly in reinforcement learning, multi-agent systems, and optimization. He leads projects on data-efficient algorithms with applications in control and game theory. He holds a Ph.D. in Machine Learning and dual M.S. degrees in Mathematics and Operations Research from Georgia Institute of Technology, and a B.S. in Electrical Engineering from Zhejiang University. His research bridges applied probability, machine learning, and optimization to create robust algorithms for complex systems. Recent work emphasizes convergence guarantees in multi-agent reinforcement learning and scalable solutions for high-dimensional problems. Chen's publications demonstrate a consistent focus on theoretical guarantees for reinforcement learning algorithms, particularly in stochastic and game-theoretic settings. Key trends include Lyapunov stability frameworks, finite-sample analysis, and methods to overcome computational bottlenecks like the "curse of dimensionality." Awards include: Solberg Academic Excellence Scholarship PIMCO Postdoctoral Fellowship in Data Science (2022) Sigma Xi Best Ph.D. Thesis Award Runner-up for SIGMETRICS Doctoral Dissertation Award He actively recruits PhD students to work on reinforcement learning theory and maintains collaborations with Caltech and Georgia Tech. Future work includes scalable multi-agent learning frameworks and real-world applications in industrial systems.

• Machine Learning
• Human/AI Interaction
• Uncertainty Quantification
• +6 more

Douglas A. Talbert is a Professor of Computer Science at Tennessee Tech University. He holds a Ph.D. and M.S. in Computer Science from Vanderbilt University, and a B.S. in Mathematics from Tennessee Tech University. His research focuses on machine learning, human-AI interaction, uncertainty quantification, and healthcare informatics, with applications in clinical decision support systems. He has also contributed to software engineering and human-computer interaction in teaching. His research interests include developing explainable AI models, mitigating bias in machine learning systems, and applying AI to environmental monitoring and biomedical datasets. He has explored topics such as subgroup discovery in sequential databases, scalable reinforcement learning, and anomaly detection in industrial systems. His recent publications highlight advancements in uncertainty quantification for medical models, bias analysis in trauma triage, and semantic segmentation using multispectral satellite imagery. Notable collaborations include work on counterfactual explanations, drug repurposing for rare diseases, and trust-building techniques in biomedical machine learning. Talbert has co-authored numerous papers in conferences like FLAIRS and IEEE, often focusing on practical AI applications. His work bridges theoretical machine learning with real-world challenges in healthcare, cybersecurity, and environmental science.

Enjie LiuView profile

Professor

• Network Applications
• Healthcare Technology
• Big Data Management
• +4 more

Dr. Enjie Liu is a Professor of Network Applications at the University of Bedfordshire's School of Computer Science and Technology. She holds leadership roles as Portfolio Leader for the Undergraduate Computer Science and Technology Foundation and IRAC Postgraduate Research Lead. Her expertise spans networked applications in healthcare, big data, IoT, and wireless protocols. With a BSc from Northwest University and a PhD in Telecommunications from Queen Mary, University of London, she has over 100 peer-reviewed publications and led major EU/EPSRC projects like CARRE and Femtocell. Her research addresses healthcare monitoring, network management, and vehicular communication challenges. Enjie's work integrates medical knowledge retrieval, disease prediction, and efficient resource allocation in IoT/VANET systems. She has pioneered projects like MyHealthAvatar for patient-centric health visualization and co-developed reliable MAC protocols for safety-critical vehicular networks. Her industrial experience includes roles in China and the UK prior to joining Bedfordshire in 2003. Education BSc in Computer Science, Northwest University, China PhD in Telecommunications, Queen Mary, University of London Research Interests Dr. Liu focuses on: Healthcare IoT systems and telemedicine applications Big data analytics for disease progression modeling Wireless network optimization in vehicular and femtocell environments Secure and reliable communication protocols for critical infrastructure Her work bridges clinical needs with advanced networking technologies through projects like CARRE (EU FP7), which developed interactive visualization tools for health monitoring. Key Contributions Developed adaptive MAC protocols for VANET safety applications Designed spectrum allocation strategies for cognitive radio IoT systems Advanced femtocell network interference mitigation techniques Labs & Teams Affiliated with the Institute for Research in Applicable Computing (IRAC) and Centre for Computer Graphics and Visualisation (CCGV) . Collaborates across disciplines in health tech and telecommunications.

• Image Processing
• Signal Processing
• 3D Environments
• +4 more

Stuart Perry is a Professor at the University of Technology Sydney (UTS), serving as Head of Discipline for Signal Processing and Analytics in the School of Electrical and Data Engineering. He holds affiliations with UTS' Faculty of Engineering and Information Technology, the Global Big Data Technologies Centre, and the Visualization Institute. With over 20 years of experience, his career spans roles at DSTO and Canon Information Systems Research Australia (CiSRA), focusing on image processing, signal processing, and perceptual quality measurement. Perry co-directs the Perceptual Imaging Laboratory (PILab), researching 3D environments, light field technologies, and human perception in immersive realities. He actively contributes to international standards committees like ISO/TC42 and ISO/SC29/WG7, leading JPEG's point cloud coding efforts. His research emphasizes adaptive image processing, machine learning-driven object detection, and medical imaging applications. Perry has authored 60+ publications, two books, and 20 patents. Current projects include VR empathy case studies, point cloud compression, and disaster management digital transformation. Education: PhD in Engineering, University of Sydney (1999) Research Interests: His work bridges computational imaging and human perception, addressing challenges in augmented/virtual reality (AR/VR), 3D scanning, and immersive media. Key areas include light field and point cloud coding, psychophysics of visual perception, and medical diagnostics via machine learning. He explores how perceptual principles can optimize interactive technologies for education, healthcare, and entertainment. Recent Research Trends: Recent articles focus on 3D Gaussian splatting, glaucoma detection via deep learning, and JPEG Pleno standards for plenoptic imaging. His work balances technical innovation (e.g., efficient point cloud compression) with human-centric design (e.g., reducing VR motion sickness through display lag analysis). Awards & Recognition: Member of IEEE and founding SPINet participant. Over 60 refereed publications and 20 patents highlight his industry-academic impact. Grants & Leadership: Leads SmartSat CRC projects on yield estimation and Aus4innovation-funded disaster response tech. Manages grants totaling millions AUD. Editorial roles include Associate Editor of SPIE/IS&T Journal of Electronic Imaging. Labs & Collaborations: PILab collaborates internationally on perceptual imaging standards. Active in ISO committees shaping future media technologies.

• Computer Vision
• Medical Imaging
• Graph Cuts Algorithms
• +3 more

Ramin Zabih is a Professor at Cornell Tech with a joint appointment in Weill Cornell Radiology. His research focuses on computer vision, medical imaging, and advanced algorithms like graph cuts, which have garnered prestigious awards such as the Test of Time Award (ICCV 2011) and Koenderink Prize (ECCV 2012). He has held leadership roles, including Program Chair for CVPR 2007 and Editor-in-Chief of IEEE TPAMI (2009-2012). His work bridges medical imaging reconstruction and AI ethics, addressing challenges in MR image analysis and workflow optimization. Education details are not explicitly provided in the text, but his professional trajectory indicates a strong academic background in computer science and engineering. His research interests emphasize algorithmic innovation for medical applications and foundational computer vision problems. Key scientific contributions include advancements in graph cuts for inverse problems and adversarial methods for clinical text anonymization. His publications span medical imaging, machine learning, and vision, reflecting interdisciplinary impact. Awards highlight his transformative work in algorithmic efficiency and medical applications. Zabih has contributed to organizing major conferences (e.g., CVPR) and serves as President of the Computer Vision Foundation. His current work at Cornell Tech integrates computational methods with radiology, advancing both technical and clinical outcomes.

Aaron SteinfeldView profile

Research Professor

• Human-Robot Interaction
• Advanced Transportation Systems
• Assistive Robotics
• +4 more

Aaron Steinfeld is a Research Professor at Carnegie Mellon University 's Robotics Institute with a courtesy appointment in the Human-Computer Interaction Institute . His work bridges human-robot interaction and advanced transportation , focusing on inclusive design for users with disabilities. PhD in Industrial and Operations Engineering (1999) and MSE/BSE in the same field (1994/1993) from University of Michigan Postdoctoral work in Transportation Human Factors at UC Berkeley's California PATH program (2000) Research spans four key areas: Assistive Robotics : Developing inclusive technologies for visually impaired and disabled users Transportation Innovation : Redesigning transit systems through crowdsourcing and citizen science Human-Robot Group Dynamics : Studying social navigation and multi-robot coordination Trust & Communication : Creating frameworks for robot self-assessment and user trust alignment Recent publications emphasize social navigation benchmarks , pedestrian behavior modeling , and adaptive transit interfaces . Current projects include accessible delivery robots (NIDILRR 90IFDV0042) and FHWA-funded work on autonomous vehicle accessibility. Scientific Recognition NSF AI Institute (AI-CARING) co-PI ONR MURI grant recipient DARPA and multiple NSF grant collaborations Best Paper awards at CHI 2019 and IROS 2020 Advising both PhD and Master's students in human-robot interaction, his lab maintains partnerships with Georgia Tech, UMass Lowell, and Disney Research. He co-edited the book Accessible Public Transportation .

David MindellView profile

Professor

• Human-Machine Interaction
• Robotics
• Deep Ocean Robotics
• +4 more

David Mindell is the Frances and David Dibner Professor of the History of Engineering and Manufacturing at MIT, with a joint appointment in the Department of Aeronautics and Astronautics. He holds dual roles as an academic and entrepreneur, combining historical scholarship with engineering practice. His primary affiliations include MIT’s Program in Science, Technology, and Society, and he has served in leadership roles such as Director of the STS Program and Chair of MIT’s 150th Anniversary Steering Committee. Education: B.S. Electrical Engineering, Yale University (1988) B.A. Literature, Yale University (1988) Ph.D. History of Technology, MIT (1996) Research Interests: Mindell’s work spans human-machine collaboration, deep ocean robotics, and the history of aviation/spaceflight. He emphasizes the social implications of engineering through projects like undersea archaeological expeditions and autonomous aircraft systems. His recent focus includes reimagining industrialism and the ethical integration of automation. Awards & Recognition: Associate Fellow, American Institute of Aeronautics and Astronautics Senior Member, IEEE Fellow, Explorers Club and Massachusetts Historical Society Labs & Ventures: He founded Humatics Corporation to advance transparent autonomy technologies and serves as an Operating Partner at Blackhorn Ventures. His research aligns with affiliations like the Woods Hole Oceanographic Institution and NASA Historical Advisory Committee.

• Adaptive and personalized web systems
• Artificial Intelligence in Education
• Learning Analytics
• +7 more

Professor Alexandra Cristea is a Professor in the Department of Computer Science and Deputy Executive Dean (Postgraduate Research) at Durham University's Faculty of Science. She leads the Artificial Intelligence in Human Systems research group and the SCENE lab, focusing on adaptive systems, learning analytics, and gamification in education. Her work has influenced over 350 papers and includes high-impact contributions like frameworks for adaptive systems and gamification in education. She has led numerous projects funded by organizations like the Weizman Institute, British Council, and EU Minerva, and contributed to initiatives promoting women in STEM through mentoring and training programs. Her research spans educational technology, AI applications in learning, and social web systems. Notable roles include Alan Turing Academic Liaison for Durham, IEEE European Public Policy member, and leadership in international conferences like AIED 2022 and ECTEL 2025. Awards include BCS Fellowship and recognition as a top researcher in educational computing (Microsoft Research, 2015). Key projects include the JANET series in personalized science teaching, TechUP training for underrepresented groups in tech, and collaborations with media industries on predictive analytics. Her lab's work has been featured in global media including BBC News and phys.org. Professor Cristea advises on international education policies, chairs major academic conferences, and oversees grants totaling millions. Her labs (e.g., SCENE, Artificial Intelligence in Human Systems) drive innovation in VR education, neuro-enhanced learning, and ethical AI in education.