Find Academic Supervisors

• Federated Learning
• Cybersecurity
• Edge Computing
• +4 more

Dr. Meikang Qiu is a faculty member at Texas A&M University Commerce (Department of Computer Science) and Columbia University (Department of Electrical Engineering). He received his PhD in Computer Science from the University of Texas at Dallas in 2007. His research spans distributed intelligence systems with current focus areas including federated learning security, efficient AI for edge devices, and multimodal learning applications in healthcare and finance. Qiu's research integrates machine learning with systems engineering, emphasizing privacy-preserving AI , robust distributed systems , and resource-constrained optimization . His recent work explores: (1) Defenses against adversarial attacks in federated environments, (2) Quantum-classical hybrid computing frameworks, and (3) Efficient model compression techniques for large language models. Publication analysis (2024-2026) reveals three dominant themes: (1) Security enhancements for distributed learning systems, (2) Efficiency optimizations for edge AI deployment, and (3) Cross-modal applications in medical and financial domains. His work consistently addresses scalability challenges in real-world AI systems.

• Signal Processing
• Financial Signal Analysis
• Machine Learning
• +5 more

Ali Akansu is a Professor in the Department of Electrical and Computer Engineering at NJIT. His academic career spans multiple decades, with significant contributions to signal processing, financial engineering, and multimedia communications. He has led or co-led four major NSF-funded projects, including the Center for Digital Video & Media Research and the Laboratory for Multimedia Signal Processing and Communication. His research focuses on topics such as eigenanalysis for financial portfolios, machine learning in network analysis, and multimodal affective state assessment using EEG and fNIRS. Key research areas include financial signal processing, portfolio optimization, eigensubspace applications, and data hiding techniques. Notable projects involve FPGA-based real-time risk analysis systems and GPU implementations for eigenportfolio analysis. He has published over 190 peer-reviewed articles, with impactful work in journals like Journal of Investment Strategies and Brain Sciences . His recent work combines machine learning with signal processing to address challenges in network traffic forecasting and financial market dynamics. He has also contributed to biomedical signal processing through studies on affective state assessment using neuroimaging techniques. Awards and recognitions include a h-index of 27 and over 2,694 citations. Grants: National Science Foundation (1991-1993, 1997-2002): Real-Time Color Video Workstation, Multimedia Signal Processing Lab, and Digital Video Research Center Labs/Teams: Leadership in NJIT's Laboratory for Multimedia Signal Processing and Financial Signal Processing groups

• Machine Learning
• Deep Learning
• Computer Vision
• +3 more

Akshay Rangamani is an Assistant Professor in Data Science at the New Jersey Institute of Technology (NJIT). His research focuses on deep learning theory, neural network dynamics, generalization bounds, and optimization algorithms. He explores topics such as neural collapse, low-rank layers in neural networks, and the interplay between architecture design and performance. His work bridges theoretical insights with practical applications in signal processing, computer vision, and recurrent network analysis. Notable contributions include studies on skip connections enhancing associative memory capacity, weight decay effects on neural collapse, and generalization guarantees for interpolating kernel machines. Rangamani has published widely in top conferences and journals, with over 190 citations and an h-index of 19. His research has been highlighted in media outlets, emphasizing advancements in understanding deep classifier dynamics and neural network training mechanisms.

Xiang ChangView profile

Lecturer

• Robotics
• Control Systems
• Neural Networks
• +3 more

Xiang Chang is a Lecturer in the Engineering Unit at Aberystwyth University, within the Faculty of Business and Physical Sciences. His research focuses on robotics, control systems, and neural networks. He holds a Doctor of Philosophy (PhD) from Aberystwyth University, completing his thesis titled 'Robotic Imitation Learning from Videos: Boosting Autonomy and Transferability' in 2024. Research Interests: Robotics and Aerial Manipulation Control Systems and Nonlinear Dynamics Neural Networks and Deep Learning Image Processing and Computer Vision Reinforcement Learning and Optimization His recent work emphasizes advancements in unmanned aerial systems, neural network compression, and improving training efficiency in diffusion models. Key contributions include studies on convergence time control for aerial manipulators and techniques for long-tailed recognition in deep learning. Grants & Supervision: While no grants are explicitly listed, his thesis supervision indicates involvement in guiding research in robotic imitation learning and autonomous systems. He collaborates internationally, with research outputs often involving interdisciplinary teams. Labs/Teams: Active in the Engineering Unit's robotics and control systems research groups at Aberystwyth University.

Christine ZargesView profile

Senior Lecturer

• Artificial Intelligence
• Evolutionary Algorithms
• Optimization
• +3 more

Christine Zarges is a Senior Lecturer in the Department of Computer Science at Aberystwyth University. Her research focuses on evolutionary algorithms, artificial immune systems, and optimization techniques, with applications in data preprocessing, anomaly detection, and bioinformatics. She has contributed significantly to the theoretical foundations of immune-inspired randomized search heuristics and has explored their practical implementations in various domains. Her work often addresses challenges in combinatorial optimization, such as improving variable orderings in OBDDs and developing efficient algorithms for Lyndon factorization of biosequences. Recent projects include applications of adversarial diffusion for video anomaly detection and the use of rough set theory for big data preprocessing. She has also been involved in initiatives to address bias in physical science and engineering research. Christine has led and collaborated on externally funded projects, including the Horizon 2020-funded RoSTBiDFramework project and EPSRC grants focused on bias reduction in STEM fields. She has supervised graduate students, such as Yumna Zahid, whose work contributes to video anomaly detection. She actively contributes to the academic community by organizing conferences like EvoCOP and the Genetic and Evolutionary Computation Conference (GECCO), and serves on editorial boards of journals including Evolutionary Computation and IEEE Transactions on Evolutionary Computation .

• Artificial Intelligence
• Human-Computer Interaction
• Wearable Technology
• +4 more

Pattie Maes is a Professor affiliated with the Massachusetts Institute of Technology (MIT), known for pioneering work in human-computer interaction, wearable technology, and AI systems. Her research focuses on augmenting human capabilities through innovative interfaces and AI-driven solutions, emphasizing ethical and user-centered design. Her extensive academic contributions span over four decades, with publications in top-tier journals such as ACM Transactions on Computer-Human Interaction , Nature Machine Intelligence , and IEEE Transactions . She has collaborated with institutions globally, advancing fields like affective computing, virtual/augmented reality, and bio-integrated systems. Key research themes include: AI-generated companions and personalized learning systems Wearable devices for health monitoring and subconscious interaction Immersive technologies (VR/AR) for memory enhancement and social interaction Ethical implications of AI and human-AI collaboration Notable projects include: AttentivU: EEG-based learning engagement system ReLive: VR-based autobiographical memory tool Living Memories: AI-generated digital mementos Her work bridges computer science, cognitive science, and design, aiming to create technology that enhances human potential while addressing societal challenges like accessibility and mental well-being.

Mislav GrgicView profile

Professor

• Image Processing
• Biometrics
• Digital Video Communications
• +5 more

Mislav Grgic is a Full Professor at the University of Zagreb's Faculty of Electrical Engineering and Computing (FER), where he has been working since 1997. He currently serves as President of the Deans' Club - Association of Deans of Croatian University Faculties (2022-present) and as the 10th Grand Master of The Society of Brethren of the Croatian Dragon (2021-present). Previously, he was Dean of FER (2014-2018), the youngest ever elected to this position, and served as Special Advisor to the Rector of University of Zagreb (2015-2022). His educational background includes B.Sc. (1997), M.Sc. (1998), and Ph.D. (2000) degrees in electrical engineering, all from University of Zagreb's Faculty of Electrical Engineering and Computing. He was a visiting researcher at University of Essex, United Kingdom (1999-2000). Professor Grgic's research focuses on image processing, biometrics, digital video communications, and medical imaging applications. His work spans digital mammography for breast cancer detection, face recognition systems, image compression, and computer-aided diagnosis. He has published over 200 papers in books, journals and conference proceedings, with significant contributions to the field of image quality assessment and medical imaging analysis. His recent publications demonstrate continued leadership in image processing, with emphasis on image quality assessment, medical imaging applications, and educational outreach. His work bridges theoretical research with practical applications, particularly in healthcare technology and engineering education. Among his numerous honors and awards are: Croatian State Science Award for 2018 for popularization of technical sciences Order of the Croatian Star with the Effigy of Rudjer Boskovic IEEE Croatia Section Distinguished Service Award Fran Bosnjakovic Award - highest award in technical sciences of University of Zagreb Multiple Josip Loncar medals from FER Professor Grgic has been actively involved in professional service, including as Chair of IEEE Croatia Section (2013-2016) and as a full member of the Croatian Academy of Engineering since 2012. He has served as a guest editor for special issues in prestigious journals focusing on facial image processing and pattern recognition-based methods for biometric re-identification.

Marcus MagnorView profile

Professor

• Computer Science
• Computer Graphics
• Visual Computing
• +7 more

Prof. Dr.-Ing. Marcus Magnor is a full professor of Computer Science at Technische Universität Braunschweig (TU Braunschweig) and holds an adjunct professorship in the Department of Physics and Astronomy at the University of New Mexico, USA. He leads the Computer Graphics Lab at TU Braunschweig and has held roles such as Department Chair (2015–2019) and Vice Chair (2019–2023). His research focuses on visual computing, encompassing image processing, computer graphics, astrophysics visualization, and computational photography. Magnor has received accolades including the Wissenschaftspreis Niedersachsen and is an alumnus of the Alexander von Humboldt Foundation. Education: BA in Physics (1995, Würzburg University), MS in Physics (1997, University of New Mexico), PhD in Electrical Engineering (2000, Erlangen University), and habilitation in Computer Science (2005, Saarland University). He conducted postdoctoral research at Stanford University and the Max-Planck-Institut Informatik. Research emphasizes interdisciplinary applications, such as planetary nebulae visualization, flame dynamics, and garment motion capture. He has organized major conferences like Vision, Modeling, and Visualization (VMV) and served on editorial boards for journals like Computer Graphics Forum. His contributions span over 100 publications in top-tier venues, addressing topics like light field coding, free-viewpoint video, and 3D reconstruction. Awards and Honors: Fulbright Scholar (former), Elected Member of Braunschweigische Wissenschaftliche Gesellschaft, and Leadership roles in initiatives like EXC PhoenixD and the L3S Research Center. Professional Service: Technical Program Co-Chair for conferences, guest editorships, and leadership in university administrative roles. His work bridges academic research with applications in astrophysics, medical imaging, and visual entertainment technologies.

• Robotics
• Control Systems
• Machine Learning
• +3 more

Cong Wang is an Associate Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT). His research focuses on robotics, control systems, and mechatronics, with a particular emphasis on physical intelligence, robot learning, and human-robot interaction. He has led significant projects funded by the National Science Foundation (NSF), including the CAREER Award for Surrogate Learning in robotics and collaborative work on exoskeleton development for mobility restoration. Wang’s research interests span robotics manipulation, networked systems, and automation technologies. He has published extensively in top-tier conferences and journals, with notable contributions to topics like remote driving systems, video streaming codecs, and UAV-based manufacturing. His work integrates theoretical advancements with practical applications, such as assistive robotics for mobility assistance and teleoperated systems for intelligent transportation. Notable achievements include the NSF CAREER Award and collaborative grants involving interdisciplinary teams. His research has been highlighted in media coverage for innovations in robotic hand skills and exoskeleton technologies. Wang’s lab actively explores adaptive control strategies, crowdsourced learning methodologies, and the ethical implications of automation in labor markets.

• Systems
• Networking
• Security
• +3 more

Matthew Lentz is an Assistant Professor in the Department of Computer Science at Duke University, where he joined in Fall 2021. Prior to his faculty position at Duke, he was a Postdoctoral Researcher at VMware Research Group and continues to collaborate with them as an Affiliated Researcher. His academic journey includes a PhD in Computer Science from the University of Maryland, where he was advised by Bobby Bhattacharjee. Dr. Lentz's research focuses broadly at the intersection of systems, networking, and security. His current research directions include: (1) introducing new abstractions and tools for building secure, trustworthy software systems, and (2) building systems that improve performance for modern networking and machine learning applications. His work spans diverse areas including network performance measurement, security threat analytics, and privacy-preserving systems. His research often addresses practical challenges in real-world systems while maintaining strong theoretical foundations. As an educator, Dr. Lentz teaches courses including Operating Systems (CPS 510), Introduction to Operating Systems (CPS 310), and Secure Software Systems (CPS 585). He is actively involved in mentoring graduate students, currently advising three PhD candidates: Alexander Du (with Danyang Zhuo), Chenyang Liu (with Kartik Nayak), and Luka Duranovic. His recently graduated students include Yongji Wu (now a postdoc at UC Berkeley), Yalu Cai (at CertiK), Nathan Ostrowski (at Octane Security), and Andres Montoya-Aristizabal (at Two Sigma). Dr. Lentz has served on numerous program committees for top-tier conferences including IEEE S&P (2024-2026), MobiSys (2023-2024), MobiCom (2023-2024), CCS (2021-2022), EuroSys (2021), and HotMobile (2021). He also served as Proceedings Chair for SOSP 2023, one of the most prestigious conferences in systems research.

• Machine Learning
• Optimization Algorithms
• Data Security
• +4 more

Mahmoud Bekhit is a Lecturer at the Peter Faber Business School within the Faculty of Law and Business. His research focuses on machine learning applications in optimization, IoT systems, and data security. He specializes in predictive modeling for transportation systems, real-time video transmission, and network function virtualization. His work addresses challenges in healthcare technology, structural health monitoring, and financial sector innovations. Key research interests include optimization algorithms (e.g., genetic algorithms, jellyfish search), ensemble learning techniques, and transfer learning for cross-domain problems. He has contributed to projects such as bike-sharing demand forecasting, surgical tele-education systems, and energy-efficient IoT protocols. Bekhit has published 15+ peer-reviewed articles in journals like Mathematical Biosciences and Engineering and IEEE Access , with additional conference contributions. His work spans interdisciplinary domains including sports science (karate performance analysis), marine data prediction, and financial technology surveys. Notable collaborations involve teams addressing VNF resource allocation, hybrid cloud security for healthcare, and AI-driven fraud detection. His research emphasizes practical solutions for industry challenges through data-driven methodologies.

Russell C. HardieView profile

Professor

• Digital Signal Processing
• Image Processing
• Machine Learning
• +3 more

Russell C. Hardie is a full-time Professor at the University of Dayton , holding positions in the Department of Electrical and Computer Engineering with joint appointments in Electro-Optics and Photonics and Bioengineering . His academic journey began with a B.S. in Engineering Science from Loyola College (1988), followed by M.S. and Ph.D. in Electrical Engineering from the University of Delaware (1990, 1992). Prior to joining the University of Dayton in 1993, he served as a Senior Scientist at Earth Satellite Corporation (now MDA Federal). Research Interests : Digital signal/image processing, medical imaging, super-resolution techniques, hyperspectral/infrared imaging, pattern recognition Key Awards : 2006 Alumni Award in Teaching (University of Dayton) 1998 Rudolf Kingslake Medal (SPIE) 1999 School of Engineering Excellence in Teaching 2002 IEEE Professor of the Year 1997 Epsilon Delta Tau Engineering Professor of the Year Recent Work : Focuses on machine learning applications for medical imaging (lung segmentation, nodule detection), atmospheric turbulence mitigation, and hyperspectral data analysis. His 15 most recent publications span topics from zero-shot chest X-ray analysis to methane plume detection and turbulence-corrected imaging systems. Contact: rhardie1@udayton.edu

Eric BalsterView profile

Professor

• Signal Processing
• Image Processing
• Video Compression
• +2 more

Eric Balster is Professor and Department Chair of Electrical and Computer Engineering at the University of Dayton's School of Engineering. He graduated Magna Cum Laude with B.S. and M.S. degrees from Dayton and earned his Ph.D. from The Ohio State University. His research focuses on signal/image processing, video compression, embedded systems, and digital design, with applications in aerial surveillance. He previously worked at the Air Force Research Laboratory (AFRL), leading software development for persistent surveillance systems. Dr. Balster has received multiple awards including the Wohlleben-Hochwalt Research Award (2011) and Fred I. Diamond Award (2006). He teaches courses ranging from digital systems to signal processing and serves as IEEE faculty adviser.

• Computer Vision
• Machine Learning
• Signal Processing
• +6 more

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

• Multiple Sclerosis
• Neurology
• Neuroimmunology
• +2 more

Dr. Marios Pantziaris is a Visiting Associate Professor in Neurology at European University Cyprus's School of Medicine and Director of Neurology at The Cyprus Institute of Neurology and Genetics. He holds an MD and specialist neurology qualification from Aristotle University of Thessaloniki. His research focuses on multiple sclerosis biomarkers, neuroimaging techniques, and therapeutic interventions. Key investigations include autoimmune antibody profiles in MS progression, ultrasound assessment of carotid arteries, and telemedicine applications for neurological diagnostics. He leads the MS Clinic at Cyprus Institute of Neurology and Genetics. Research projects include studies on modifier genes affecting MS severity (2010-2012), microRNA profiles in interferon-resistant MS patients (2009-2011), and clinical trials of novel nutraceutical formulations for MS treatment (2013-2015). His publications demonstrate expertise in neuroimmunology and medical imaging applications in neurology.