Find Academic Supervisors

• Computer Vision
• Applied Machine Learning
• Computer Graphics
• +3 more

Fahim Hasan Khan is an Assistant Professor in the Computer Science and Software Engineering Department at California Polytechnic State University, San Luis Obispo (Cal Poly). His research focuses on computer vision, applied machine learning, and citizen science applications, with a special emphasis on environmental monitoring and education. He holds a PhD in Computer Science and Engineering from UC Santa Cruz, where he was advised by Professors Alex Pang and James Davis, and a Master's in Computer Science from the University of Calgary. Key research contributions include real-time rip current detection systems (RipFinder, RipScout), mobile citizen science platforms (SmartCS), and educational tools to engage high school students in STEM research. His work has received media attention for innovations in drowning prevention and environmental safety. Notable awards include the Best Poster Presentation Award at ICIAR 2019 and the Best of the Baskin School of Engineering Award at UC Santa Cruz in 2022. Dr. Khan collaborates extensively with industry and academic partners to develop practical solutions for challenges in marine safety, autonomous systems, and healthcare diagnostics. He actively mentors students and seeks to democratize access to machine learning tools through no-code platforms.

Truong NghiemView profile

Associate Professor

• Intelligent cyber-physical systems
• Digital twins
• Scientific and physics-informed machine learning
• +5 more

Truong Nghiem is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Central Florida (UCF), part of the College of Engineering and Computer Science. He holds a Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania and previously served as an Assistant and Associate Professor at Northern Arizona University (2018–2024). His research focuses on intelligent cyber-physical systems, digital twins, physics-informed machine learning, and distributed optimization/control, with applications in smart buildings, autonomous vehicles, and robotics. He leads the intelligent Cyber-Physical Systems (iCPS) Lab, advancing foundational research in these areas. Notable awards include the NSF CAREER Award (2023) for composite physics-informed learning and the NSF ERI Award (2022) for building HVAC system research. He is a Senior Member of the IEEE and a member of the ACM. Recent work emphasizes safe machine learning for control systems, communication-efficient optimization algorithms, and physics-constrained motion prediction for autonomous systems. His publications span journals like IEEE Robotics and Automation Letters and conferences such as the American Control Conference. Grants and research initiatives include projects on HVAC system modeling, distributed trajectory planning for multi-vehicle systems, and adaptive sampling strategies for mobile sensor networks. Collaborations involve industry and academic partners in energy systems, robotics, and control engineering. The iCPS Lab’s work integrates theory, simulation, and real-world validation to address complex cyber-physical challenges.

• Additive Manufacturing
• Computer-Integrated Manufacturing
• Intelligent Modeling and Optimization
• +3 more

Souran Manoochehri is a Professor and Chair of the Department of Mechanical Engineering at Stevens Institute of Technology, within the Charles V. Schaefer, Jr. School of Engineering and Science. He joined Stevens in 1989 as an Assistant Professor, advancing to Associate Dean for Research and Technology (2004-2009) and Director of the Design and Manufacturing Institute (1990-2004). He holds a PhD (1986), MS (1983), and BS (1981) in Mechanical Engineering from the University of Wisconsin-Madison and Illinois Institute of Technology, respectively. Education: PhD, MS, and BS in Mechanical Engineering Roles: Department Chair, former Associate Dean, and co-founder of the Design and Manufacturing Institute Research: Focuses on additive manufacturing, computer-integrated design, and intelligent optimization His research integrates mathematical modeling, machine learning, and experimental studies to ensure product and process quality in manufacturing. Over his career, he has secured $30M+ in grants, authored 130+ publications, and supervised 30+ graduate students and 12 postdoctoral fellows. He is an ASME Fellow and recipient of awards including the ASME Design Engineering Division Award. Key research trends in his articles include real-time monitoring of additive manufacturing processes (e.g., melt pool analysis, acoustic emission sensors), machine learning applications in quality control, and optimization of manufacturing systems. His work addresses challenges in precision, defect detection, and process automation across 3D printing and microfluidics. Scientific Awards: ASME Fellow, ASME IDETC Award, DMC Best Presentation Grants: Over 50 contracts totaling $30M+; advising over 30 graduate students Labs/Teams: Co-founded the Design and Manufacturing Institute (DMI) at Stevens His contributions span academic leadership and industry-relevant innovation, emphasizing interdisciplinary solutions in advanced manufacturing.

• Control Systems
• Biomechanics
• Neuroprosthetics
• +4 more

Raviraj Nataraj is an Associate Professor in the Department of Biomedical Engineering at Stevens Institute of Technology, leading the Movement Control Rehabilitation (MOCORE) Laboratory. He holds a PhD from Case Western Reserve University (2010) and an MS from Stanford University (2003). His research focuses on integrating control systems with assistive technologies to enhance motor function recovery for individuals with neurotrauma, including spinal cord injury, stroke, and amputation. Key areas include wearable sensors, virtual reality environments, and neuroprosthetics. Education: PhD in Biomedical Engineering, Case Western Reserve University (2010) MS in Mechanical Engineering, Stanford University (2003) Research interests emphasize sensory feedback mechanisms, cognitive factors in motor rehabilitation, and the development of personalized computerized interfaces. His lab creates instrumented wearables and VR tools to improve functional movement. Recent work includes optimizing feedback modalities for upper-limb rehabilitation and studying neural responses to altered visual feedback. Notable awards include the NSF CAREER Award (2023) and Stevens’ Harvey Davis Distinguished Teaching Award. He has secured grants from NSF, Department of Veterans Affairs, and New Jersey Health Foundation for projects like ‘Personalizing sensory-driven interfaces for motor rehabilitation’. Professional roles include NIH Review Panelist and Associate Editor for Frontiers of Medical Engineering. He chairs Stevens’ Institutional Review Board and oversees BME faculty searches. The MOCORE Lab (www.mocorelab.com) collaborates on clinical solutions like the ‘Cognition Glove’ and muscle-training braces.

Nirwan AnsariView profile

Professor

• 6G Networks
• Wireless Charging
• Machine Learning
• +5 more

Nirwan Ansari is a Distinguished Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT). His research focuses on cutting-edge advancements in 6G networks , wireless charging , machine learning , and Internet of Things (IoT) . He has contributed extensively to AI-driven network optimization and sustainable energy solutions for next-generation communication systems. Ph.D., Electrical Engineering, Purdue University (1988) M.S., Electrical Engineering, University of Michigan-Ann Arbor (1983) B.S., Electrical Engineering, NJIT (1982) His recent work explores AI-native network slicing , UAV-assisted edge computing , and holographic communication . Publications highlight synergies between terrestrial and non-terrestrial networks , digital twin integration, and energy-efficient IoT systems . He serves as an Honorary Chair and contributes to major conferences like IEEE INFOCOM and IWCMC.

• Autonomous Driving
• Machine Learning
• Wireless Communication
• +11 more

Dr. Tao (Kevin) Huang is a researcher at James Cook University's College of Science and Engineering, with expertise spanning autonomous driving, wireless communication systems, and medical imaging applications. His work integrates machine learning, sensor fusion, and multimodal data analysis to address complex challenges in vehicular networks, environmental monitoring, and healthcare technology. Research Interests: Dr. Huang's research focuses on Autonomous driving perception systems IoT-enabled vehicular networks AI for medical diagnostics and environmental sensing Signal processing and privacy-preserving communication protocols Recent Publications: His 2025 work emphasizes advancements in V2X cooperative perception, radar-LiDAR-camera fusion, and diffusion models for medical imaging. Key trends include cross-modal robustness, real-time processing for autonomous systems, and AI applications in sustainability.

Daniel McKenzieView profile

Assistant Professor

• Derivative-free optimization
• Implicit neural networks
• Geometric methods in data science
• +3 more

Daniel McKenzie is an Assistant Professor in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. His research focuses on derivative-free optimization, implicit neural networks, and geometric methods in data science. He holds a B.Sc.(hons) and M.Sc. in Mathematics from the University of Cape Town (2010, 2014) and a PhD in Mathematics from the University of Georgia (2019). B.Sc.(hons): Mathematics and Applied Mathematics, University of Cape Town, 2010 M.Sc.: Mathematics, University of Cape Town, 2014 PhD: Mathematics, University of Georgia, 2019 His research explores the intersection of optimization theory and machine learning, with applications in spatial data modeling, geometric data analysis, and high-dimensional clustering. Recent work emphasizes curvature-aware algorithms, comparison-based optimization, and implicit network architectures like LatticeVision. His methods address challenges in non-stationary spatial data and convex game equilibria prediction. Key contributions include Fermat distance metrics for clustering, Jacobian-Free Backpropagation (JFB) for implicit networks, and zeroth-order algorithms for black-box optimization. While no scientific awards are listed, his publications reflect a strong focus on advancing optimization techniques for modern data science problems. No specific grants or advising roles are detailed in the provided text. His work bridges computational mathematics and applied AI, with potential applications in robotics, spatial statistics, and algorithmic game theory.

Kevin KellyView profile

Professor

• Ockham's Razor
• Epistemology
• Formal Learning Theory
• +7 more

Kevin Kelly is a Professor of Philosophy at Carnegie Mellon University and the Director of the Center for Formal Epistemology. His work bridges formal epistemology, computational learning theory, and philosophy of science, with a focus on Ockham's razor, belief revision, and the topology of inquiry. Key Research Areas: Ockham's Razor, Epistemology, Formal Learning Theory, Modal Epistemic Logic, and Interdisciplinary Applications of Topology. Grants: John Templeton Foundation grant for research on truth-finding efficiency and scientific simplicity. Scientific Awards: John Templeton Foundation grant (2018–2021) Kelly's publications emphasize connections between probabilistic reasoning and qualitative belief, solutions to the lottery paradox, and computational models of knowledge acquisition. His recent work explores lighting design, human-centric ergonomics, and machine learning epistemology, reflecting a deep interdisciplinary engagement with technology and science.

Angela YaoView profile

Associate Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +3 more

Angela Yao is a Dean's Chair Associate Professor and Assistant Dean of Research at the National University of Singapore's School of Computing, Department of Computer Science. She leads the Computer Vision and Machine Learning Group and specializes in visual perception of people, focusing on both high-level semantics of human actions and lower-level physical modeling. Her research interests span Computer Vision , Machine Learning , and Artificial Intelligence , with specific expertise in human action recognition, 3D human modeling, video understanding, and small data AI. Dr. Yao's work bridges theoretical advances with practical applications, particularly in activity anticipation and human-computer interaction. Dr. Yao's publication trends reveal a strong focus on zero-shot learning for activity anticipation, 3D human modeling, and techniques for working with limited training data. Her research has evolved from foundational work in 3D pose estimation to more recent innovations in diffusion models and cross-modal learning, demonstrating consistent contributions to advancing computer vision capabilities. NRF Fellowship for Artificial Intelligence (2019) German Pattern Recognition (DAGM) Award (2018) Dr. Yao has successfully mentored PhD students including Fadime Sener and secured significant research funding including the NRF Fellowship. Her research group focuses on developing AI systems capable of understanding and anticipating human activities with applications in robotics and human-computer interaction. She teaches CS4243 Computer Vision and Pattern Recognition and leads the Computer Vision and Machine Learning Group at NUS Computing.

Christopher GilliamView profile

Assistant Professor

• Signal Processing
• Radar Systems
• Sampling Theory
• +5 more

Dr. Christopher Gilliam is an Assistant Professor in Applied Signal Processing at the University of Birmingham's Department of Electronic, Electrical and Systems Engineering. He holds an MEng (1st Class Hons) in Electrical & Electronic Engineering (2008) and a Ph.D. in Signal Processing (2013), both from Imperial College London. Prior to joining Birmingham in 2022, he was a Postdoctoral Fellow at The Chinese University of Hong Kong (2013–2017) and a Research Fellow at RMIT University, Australia (2017–2022). Research Interests: Sensor signal processing, radar imaging, sampling theory, motion estimation, quantum navigation, and medical imaging. Labs: Microwave Integrated Systems Laboratory (MISL). Committees: Member of IEEE Signal Processing Society and APSIPA Technical Committees. His work focuses on advancing signal processing techniques for radar systems, navigation, and medical imaging. Recent research highlights include drone-based SAR imaging, motion correction in MRI, and fusion of classical/quantum sensors for inertial navigation. He is actively supervising PhD students and contributes to projects sponsored by DSTG. Publications span radar SLAM, probabilistic navigation algorithms, and deep learning-driven medical imaging solutions. His research bridges theoretical signal processing with practical applications in autonomous systems and healthcare.

Melvin LeokView profile

Professor

• Numerical Differential Equations
• Numerical Analysis
• Computational Geometric Control Theory
• +3 more

Melvin Leok is a Professor of Mathematics at the University of California, San Diego (UCSD). He directs the Computational Geometric Mechanics group, affiliated with the Center for Computational Mathematics and the Computational Science, Mathematics, and Engineering (CSME) Program. His research focuses on computational geometric mechanics, combining differential geometry and numerical analysis to develop stable and robust methods for modeling and controlling engineering systems. Leok holds a Ph.D. in Control and Dynamical Systems from Caltech (2004). Before joining UCSD in 2009, he was an assistant professor at Purdue University and a visiting researcher at Caltech and the University of Michigan. He has received prestigious awards, including the Simons Fellowship, DoD Newton Award, and NSF CAREER Award. His research interests include numerical differential equations, geometric control theory, and computational methods for interconnected systems. He has authored over 100 publications and serves on editorial boards for journals like Journal of Nonlinear Science . Leok teaches advanced courses such as optimization on manifolds and numerical analysis, emphasizing geometric principles. Key achievements include co-authoring the monograph Global Formulations of Lagrangian and Hamiltonian Dynamics on Manifolds , developing variational integrators for mechanical systems, and leading projects in geometric uncertainty propagation and structure-preserving algorithms for plasma physics. He actively collaborates on NSF-funded initiatives like the TILOS AI Research Institute. Leok advises doctoral students, including Brian Tran, who won the Chancellor's Dissertation Medal. He also mentors postdoctoral researchers through the Alexander von Humboldt Foundation's Feodor Lynen Program.

Plamen AngelovView profile

Professor

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +5 more

Prof. Plamen Angelov holds a Chair in Intelligent Systems and is Director of Research at Lancaster University's School of Computing and Communications. He leads the Lancaster Intelligent, Robotic, and Autonomous Systems (LIRA) Centre, uniting 70+ faculty across 15 departments. With a PhD (1993) and DSc (2015), he is a Fellow of IEEE, IET, ELLIS, and AAIA. His research focuses on explainable AI, evolving systems, and computational intelligence, with 400+ publications (h-index 65) in top venues like TPAMI and IEEE Transactions. Notable achievements include the Dennis Gabor Award (2020) and ranking in Stanford's Top 0.2% AI researchers (2024). He leads projects funded by UK Research Councils, ESA, DSTL, and industry. As Editor-in-Chief of Springer's Evolving Systems , he drives standards in explainable AI through IEEE's P2976 Working Group. His work spans autonomous systems, cybersecurity, and biomedical applications through initiatives like AI4EO and H-UNIQUE.

Steve MaddockView profile

Senior Lecturer

• Facial Modelling and Animation
• Augmented Reality
• Virtual Reality
• +3 more

Dr Steve Maddock is a Senior Lecturer in Computer Graphics and Acting Head of the Visual Computing research group at the University of Sheffield's School of Computer Science. He holds a Class I Degree in Computer Science (University of Sheffield), a PGCE in Mathematics (11-18), and a PhD in computer graphics modeling and animation, all from the University of Sheffield. With over 30 years of experience in computer graphics software development, he has contributed to the computer games industry through a six-month secondment at Gremlin/Infogrames. His research focuses on facial modeling and animation, augmented/virtual/mixed reality applications, and sketch-based interfaces. Key areas include 3D computer graphics, real-time rendering, and human-robot collaboration systems. Maddock has led and co-led several grants, including projects on game software engineering, rail network surveillance, and heritage visualization using immersive technologies. He is a member of INSIGNEO, Sheffield Robotics, and the Cultural Industries Research Network. Publications highlight contributions to facial analysis for medical diagnostics, style transfer techniques for games, and safety zone visualization in robotics. His work integrates interdisciplinary approaches, combining computer science with fields like biology and robotics. Maddock's Visual Computing research group explores cutting-edge solutions in graphics, virtual environments, and computational tools for real-world applications.

Ferrante NeriView profile

Professor

• Machine Learning
• Artificial Intelligence
• Optimization
• +5 more

Professor Ferrante Neri is a faculty member at the University of Surrey, holding the positions of Professor of Machine Learning and Artificial Intelligence and Associate Dean (International) for the Faculty of Engineering and Physical Sciences (FEPS). He is affiliated with the Nature Inspired Computing and Engineering Research Group, Surrey Institute for People-Centred AI (PAI), and the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on optimization, explainable AI, and machine learning, with contributions to memetic computing and differential evolution. Since 2010, he has chaired the IEEE Task Force on Memetic Computing. He advises PhD students in topics like dynamic multi-objective optimization and AI-driven applications. His teaching expertise includes mathematical foundations for computer science. He has supervised students such as Aisha E S E Saeid and Pengjin Wu. Notable research areas include evolutionary algorithms, neural architecture search, and applications in robotics and environmental monitoring. Labs and teams include the Nature Inspired Computing group, which explores AI-driven solutions for complex problems. His work bridges theoretical advancements and practical applications in fields like autonomous systems and deep learning.

Mikkel Lønborg FriisView profile

Associate Clinical Professor

• Surgical Simulation
• Medical Education
• Ultrasound Training
• +7 more

Mikkel Lønborg Friis is a Clinical Associate Professor at Aalborg University, affiliated with the Department of Clinical Medicine under The Faculty of Medicine. He also serves as a senior consultant ( Ledende overlæge ) at Aalborg University Hospital, where he leads simulation-based training initiatives at NordSim – Centre for Skills Training and Simulation. His dual academic and clinical roles position him at the intersection of advanced surgical practice and innovative medical education. Clinical Associate Professor, Aalborg University Ledende overlæge (Chief Physician), Aalborg University Hospital Member, NordSim – Centre for Skills Training and Simulation His research interests focus on enhancing surgical and diagnostic competencies through technology-driven education. Key areas include simulation-based training, artificial intelligence in fetal and surgical ultrasound, robotic surgery assessment using deep learning, and curriculum development for cross-specialty ultrasound education. He actively contributes to improving clinical outcomes in pilonidal sinus disease and advancing AI integration in medical imaging. The recent publications highlight a strong trend toward interdisciplinary innovation, particularly in blending AI, simulation, and medical education. His work spans clinical surgery, educational methodology, and computational analysis of surgical performance. A significant portion of his research involves designing and evaluating training protocols, developing datasets for skill assessment, and exploring ethical and practical implications of AI in clinical settings. Mikkel Friis has not been publicly recognized with scientific awards in the provided text, but his leadership in simulation and curriculum design suggests significant institutional impact. He is involved in mentoring and advising through collaborative research projects, particularly in simulation and ultrasound education. While formal students are not listed, his role in study protocols and dataset creation implies supervision of junior researchers and medical trainees. He participates in funded or institutionally supported research activities related to medical education innovation and surgical technology. His involvement in press and media coverage further underscores his role as a thought leader in clinical simulation careers. Friis is deeply embedded in NordSim – Centre for Skills Training and Simulation, where he contributes to developing and implementing simulation-based assessment tools, particularly for abdominal ultrasound and surgical skills. His team collaborates across departments and institutions, focusing on creating standardized, scalable training models that integrate emerging technologies like AI and virtual reality.