Find Academic Supervisors

Lance E. ChampagneView profile

Associate Professor

• Operations Research
• Military Simulation and Modeling
• Machine Learning
• +3 more

Lance E. Champagne serves as an Associate Professor in the Graduate School of Engineering and Management at the Air Force Institute of Technology (AFIT), where he applies operations research and computational modeling to defense systems optimization. His work bridges military applications with advanced analytics, focusing on simulation-driven solutions for complex operational challenges. His academic foundation includes: Doctor of Philosophy (Ph.D.) in Operations Research from AFIT (2004) Master of Science (M.S.) in Operations Research from AFIT (1999) Bachelor of Science (B.S.) in Biomedical Engineering & Mathematics from Tulane University (1991) Champagne's research centers on military simulation, machine learning, and logistics optimization. He develops agent-based models for combat scenarios, UAV operations, and personnel training systems, while pioneering applications of deep learning in defense contexts. His work integrates Bayesian methods, digital twin frameworks, and quaternion neural networks to solve problems in threat avoidance, supply chain resilience, and real-time decision-making under cyber constraints. Recent publications reveal a pronounced shift toward AI-enhanced defense analytics, particularly in video/text classification for situational awareness and multi-objective optimization for wartime logistics. His 2022-2025 output shows consistent innovation in adapting commercial AI techniques (IndoBERT, GANs) to military-specific challenges while maintaining rigorous validation standards through probabilistic agreement metrics. His scholarly recognition includes: 2022-2023 Graduate School of Engineering and Management Dean's Distinguished Teaching Professor 2014 Winter AFIT/ENC Mathematics and Statistics Instructor of the Quarter As an advisor, Champagne mentors numerous graduate students evidenced by asterisk-marked co-authorships across 15+ publications annually. His research is supported by defense contracts focused on simulation validation, autonomous systems, and logistics optimization. He actively contributes to military operations research through book chapters on simulation history and validation methodologies, while maintaining operational relevance through direct collaboration with Air Force training and maintenance units.

Giacomo TarroniView profile

Lecturer

• Medical Image Analysis
• Machine Learning
• Artificial Intelligence
• +9 more

Dr. Giacomo Tarroni is a Lecturer in Artificial Intelligence at City St George's, University of London, where he is a member of the CitAI Research Centre within the Department of Computer Science. He has previously held research positions at Imperial College London (as a Research Fellow and Marie Skłodowska-Curie Fellow), University of Padova, and University of Bologna. His educational background includes a PhD in Medical Image Analysis from the University of Bologna (2009-2012), an M.S. in Electronic Engineering from the University of Bologna (2005-2008), and a B.S. in Electronic Engineering from the University of Bologna (2002-2005). During his PhD, he collaborated with the University of Chicago. Dr. Tarroni's research focuses on medical image analysis, with particular emphasis on unsupervised anomaly detection , self-supervised image classification/segmentation , and federated learning for medical applications. His work spans cardiovascular, brain, and fetal imaging, with significant contributions in cardiac MRI analysis. His research bridges computer vision and healthcare, developing AI solutions that address practical clinical challenges while respecting data privacy constraints through techniques like federated learning. His publication record demonstrates consistent high-impact contributions to medical imaging conferences (MICCAI, ISBI) and journals (IEEE Transactions on Medical Imaging, Medical Image Analysis). Recent work shows increasing focus on foundational AI techniques like diffusion models and self-supervised learning applied to medical imaging challenges. Fellow of Advance Higher Education (HEA), 2021 Marie Skłodowska-Curie Fellowship from the European Commission for the JUNO project (Principal Investigator) Board member of the UK Chapter of Marie Curie Alumni Association (MCAA-UK) since 2018 Associate Editor for IEEE ISBI 2019 conference Dr. Tarroni has secured significant research funding, generating over 183k€ through his Marie Skłodowska-Curie Fellowship. He has collaborated with major medical image analysis projects including SmartHeart (EPSRC, 5M£), iFind (Wellcome Trust and EPSRC, 10M£), and CHIRON (EU Artemis-JU, 18M€). As an active reviewer, he contributes to leading journals including IEEE Transactions on Medical Imaging and Medical Image Analysis, as well as top conferences like MICCAI and IEEE ISBI. His laboratory work centers around developing robust AI methods for medical imaging that can operate effectively with limited or distributed data sources.

Mahdi Maktabdar OghazView profile

Senior Lecturer

• Artificial Intelligence
• Deep Learning
• Generative AI
• +8 more

Dr. Mahdi Maktabdar Oghaz is a Senior Lecturer at the School of Computing and Information Science , Anglia Ruskin University . He leads the MSc Applied Data Science conversion course and teaches modules on Artificial Intelligence, Artificial Neural Networks, and Python programming. As the school's outreach lead, he coordinates engagement with partner colleges. B.E. in Software Engineering, AZAD University, Iran M.Sc. in Computer Science, University Technology Malaysia (UTM) PhD in Computer Science, UTM (2016) His research focuses on Artificial Intelligence applications in sustainability and healthcare , with expertise in remote sensing , aerial image analysis , computer vision , and Internet of Things (IoT) . He has published over 30 scientific articles across international journals and conferences. Selected research contributions include AI for urban sustainability , crowd safety , medical diagnostics , and climate monitoring . His work spans collaborations with institutions like Cyber Security Malaysia, Ministry of Higher Education Malaysia, and Kingston University London's ROVIT team. As a member of the Computing, Informatics and Applications Research Group , he contributes to interdisciplinary projects including the H2020 MONICA initiative for large-scale event security. His teaching emphasizes programming (Python, C++, Java), deep learning, and data mining fundamentals.

Christine Allen-BlanchetteView profile

Assistant Professor

• Control
• Robotics
• Dynamical Systems
• +3 more

Christine Allen-Blanchette is an Assistant Professor in the Department of Mechanical and Aerospace Engineering and affiliated with the Center for Statistics and Machine Learning at Princeton University. She also collaborates with Robotics at Princeton and previously held a Princeton Presidential Postdoctoral Fellowship. Education: PhD in Computer Science (2020), MSE in Robotics (2013) from the University of Pennsylvania; dual BS degrees in Mechanical Engineering and Computer Engineering (2011) from San Jose State University. Her research focuses on the intersection of deep learning, geometry, and dynamical systems. Key areas include control theory, robotics, and geometric deep learning, with applications to dexterous manipulation, 3D rotational dynamics, and equivariant neural architectures. Recent publications emphasize geometric algebra-based models for robotics, equivariant autoencoders for fluid dynamics, and physics-informed generative modeling. Her work integrates domain-specific constraints into neural architectures to enhance interpretability and performance. Scientific Awards: Princeton Presidential Postdoctoral Fellow Christine investigates connections between opinion dynamics and graph neural networks while advancing surrogate modeling and reward guidance methods in reinforcement learning. She contributes to robotics and machine learning communities through interdisciplinary research.

Sun-Yuan KungView profile

Professor

• Machine Learning
• Deep Learning
• Neural Networks
• +7 more

Sun-Yuan Kung is a Professor of Electrical and Computer Engineering at Princeton University specializing in high-performance learning networks and explainable AI. His research develops beyond back-propagation paradigms to create optimal neural architectures through structural learning and discriminant information metrics. His academic credentials include: Ph.D. in Electrical Engineering, Stanford University (1977) M.S. in Electrical Engineering, University of Rochester (1974) B.S. in Electrical Engineering, National Taiwan University (1971) Professor Kung pioneered Explainable Neural Networks (XNN) incorporating Internal Neuron's Learnability (INL) using internal teacher labels and discriminant information (DI) for node/layer ranking. This enables deep compression while enhancing model robustness and adaptability for real-time applications in image processing, privacy protection, and security-sensitive environments. His work directly addresses DARPA's Explainable AI (XAI) initiatives through end-user-adaptive labeling mechanisms. Recent publications (2023-2025) demonstrate sustained innovation in efficient deep learning architectures, with dominant themes in remote sensing applications (object detection, image captioning, change detection), quantum-inspired algorithms, and multimodal representation learning. Key trends include neural architecture search, model compression techniques, and reinforcement learning integration for specialized domains. His distinguished honors include: Life Fellow of IEEE (2016) IEEE Third Millennium Medal (2000) IEEE Signal Processing Society Best Paper Award (1996) IEEE Signal Processing Society Technical Achievement Award (1992) IEEE Fellow (1988) Professor Kung mentors graduate researchers including Katherine Shu-Min Li, Xiaxin Shen, and Zhuoqing Song, with funding supporting projects in deep compression, privacy-preserving ML, and XAI frameworks. His group develops foundational techniques for discriminant information-based network pruning and structural gradient methods. He leads a research team advancing the XNN framework for DARPA's Explainable AI initiatives, focusing on real-time decision support through internal neuron explainability and channel exploration for model optimization.

Julien MilleView profile

Associate Professor

• Computer Science
• Pattern Recognition
• Image Analysis
• +5 more

Julien Mille is an Associate Professor at INSA Centre Val de Loire and Ecole Polytechnique de l'Université de Tours , France. He conducts research with the RFAI team (Reconnaissance des Formes et Analyse d'Images) at the Laboratoire d'Informatique Fondamentale et Appliquée in Tours. Previously, he was an associate professor at Université Claude Bernard Lyon 1 (2009-2015), affiliated with the LIRIS laboratory and Imagine team . Key research areas: Pattern recognition, image analysis, active contours/surfaces, optimal transport, human activity recognition Technical contributions: Developed open-source tools for image labeling ( PixelLabeling ), DeepFlowCUDA optimization Scientific contributions span computer vision, biomedical imaging, and plant science applications. Notable work includes: Optimal transport methods for image restoration Hierarchical skeletonization for shape matching Pose-driven attention mechanisms for video analysis 3D segmentation of medical images His research has been published in leading journals like SIAM Journal on Imaging Sciences , International Journal of Computer Vision , and Computer Vision and Image Understanding , along with major conferences including CVPR , ECCV , and ICIP .

• Information Visualization
• Machine Learning
• Data Mining
• +5 more

Gilles Venturini is a Professor in Computer Science at the University of Tours, France, affiliated with PolytechTours and the Computer Science Lab. He has held this position since 1998 following his appointment as Assistant Professor from 1994-1998. His academic credentials include a Research Supervisor Degree (HdR) from the University of Tours (1997), a PhD in Computer Science from the University of the South of Paris (1994) supervised by Yves Kodratoff, and dual qualifications in Computer Science (MSc, 1990) and Electrical Engineering (ESIEE, 1990). Professor Venturini's research concentrates on information visualization, machine learning, and data mining with emphasis on genetic algorithms, neural networks, and computer vision. His work develops practical tools for visual data exploration, automatic dashboard generation, and analysis of large datasets, particularly addressing usability challenges for novice users in open data contexts. His 2023-2025 publications reveal sustained innovation in ternarization of vision-language models for edge devices, few-shot object detection, and backpropagation alternatives for binary neural networks. These contributions bridge theoretical machine learning advancements with applications in cultural heritage visualization and human resources analytics. No scientific awards were documented in the source materials. While specific grant details and student supervision records are absent from the provided text, his professorial role implies active involvement in research funding acquisition and academic mentoring. His work frequently intersects with interdisciplinary teams, particularly in digital humanities projects. He remains operationally active within the University of Tours' Computer Science Lab, driving research in visualization techniques and machine learning applications through this primary institutional affiliation.

Murat TahtaliView profile

Senior Lecturer

• Medical Imaging
• Light Field CT and SPECT
• Adaptive Optics
• +6 more

Dr. Murat Tahtali is a Senior Lecturer at UNSW Canberra within the School of Engineering and Information Technology at the University of New South Wales. His academic career spans multiple engineering disciplines with a strong focus on interdisciplinary research connecting engineering principles with medical applications. Dr. Tahtali's research interests span several interconnected fields including Medical Imaging , particularly Light Field CT and SPECT (L-SPECT), Adaptive Optics , Computer-Aided Design , Finite Element Analysis , Vibration Analysis , and Image Processing . His work bridges theoretical engineering concepts with practical medical applications, developing novel imaging techniques that can operate through challenging environments and media. His research has significant implications for both defense applications and medical diagnostics. An analysis of Dr. Tahtali's recent publications reveals a strong trend toward integrating machine learning techniques with traditional engineering approaches. His work spans medical imaging innovations, structural health monitoring using vibration analysis, computer vision for challenging environments, and deep learning applications. The interdisciplinary nature of his research connects mechanical engineering principles with computational methods and medical applications, demonstrating consistent innovation across multiple domains. Dr. Tahtali actively supervises research students and offers PhD scholarships ($35,000 per year) for high-achieving students in Computer Science, Electrical Engineering, or Mechanical Engineering. His research group works on cutting-edge problems at the intersection of engineering disciplines, with particular emphasis on medical imaging technologies and computational methods for challenging environments. His laboratory work focuses on developing novel imaging systems, particularly Light Field SPECT technology, as well as computational methods for image restoration through turbulent media. The research team combines experimental work with sophisticated computational modeling to advance imaging capabilities in both medical and defense contexts.

• Machine Learning
• Signal Processing
• Data Science
• +3 more

Salah Bourennane is a Lecturer and researcher in the Computer Science department at the Fresnel Institute , with a focus on advanced machine learning and multidimensional signal processing techniques. His research interests span: Machine Learning (deep learning, transfer learning, optimization algorithms) Signal Processing (hyperspectral analysis, tensor decomposition, noise reduction) Medical Imaging (diagnostic systems, brain mapping, cancer detection) Computer Vision (face recognition, surveillance systems, 3D verification) Security Applications (mask detection, concealed object classification, steganalysis) Recent publications highlight his work in: Tensor-driven methods for low-resolution face recognition Hybrid multilinear-linear architectures for camera network identification Deep learning frameworks for medical diagnostics (e.g., breast cancer segmentation, Alzheimer's disease mapping) Optimization algorithms (e.g., grey wolf optimizer variants) for sensor systems and image processing Real-time pandemic response technologies like social distance monitoring His work is implemented within the Fresnel Institute research laboratory, specializing in computer vision and data science applications.

• Machine Learning
• Computer Vision
• Distance Metric Learning
• +4 more

Xiaochen Yang is an Assistant Professor in the Department of Artificial Intelligence at the School of Computer Science and Control Engineering, University of Chinese Academy of Sciences. With a PhD from University College London completed in 2020, Dr. Yang has established a prolific research career spanning machine learning, computer vision, and computational mathematics. Their work bridges theoretical foundations with practical applications across multiple domains. Dr. Yang's research interests focus on advancing machine learning methodologies, particularly in distance metric learning, few-shot learning, and multimodal systems. Their work spans both theoretical aspects of numerical methods for stochastic differential equations and practical applications in computer vision, hyperspectral imaging, and security of large language models. This interdisciplinary approach has led to significant contributions in multiple fields. Analysis of Dr. Yang's recent publications reveals a strong trajectory in developing novel machine learning architectures, with particular emphasis on few-shot learning techniques, multimodal fusion approaches, and applications of diffusion models. Their work demonstrates increasing sophistication in handling complex data modalities while maintaining mathematical rigor, particularly evident in their publications from 2023-2025. Dr. Yang has established productive collaborations across institutions, as evidenced by their extensive co-author network spanning Chinese academic institutions and international partners. Their research has been published in top-tier venues including IEEE Transactions, Pattern Recognition, NeurIPS, and CVPR, reflecting the high impact of their contributions to the field.

Mona JarrahiView profile

Professor

• Terahertz Electronics
• Millimeter-wave Optoelectronics
• Microwave Photonics
• +3 more

Mona Jarrahi is a Professor in the Department of Electrical and Computer Engineering at the Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles (UCLA). Holding the Northrop Grumman Chair in Electrical Engineering, she leads groundbreaking research in terahertz/millimeter-wave electronics, microwave photonics, and AI-driven imaging systems. Terahertz Electronics Millimeter-wave Optoelectronics Microwave Photonics Imaging and Spectroscopy Systems AI-Driven Optical Imaging Her recent publications highlight advancements in terahertz plasmonic focal-plane arrays, single-pixel diffractive terahertz processors, and coherent terahertz synthesis. These works span keywords like Optoelectronics , Signal Processing , and Machine Learning , with sub-fields focusing on Plasmonics , Pixel-level Resolution , and Diffractive Neural Networks . Scientific Awards: Fellow of IEEE, AAAS, APS, Optica, SPIE, and IOP Guggenheim Fellowship SPIE Quantum Sensing Achievement Award IEEE Photonics Society Aron Kressel Award Moore Inventor Fellowship As head of the Terahertz Electronics Laboratory at UCLA, she drives innovations in terahertz technologies with applications in medical imaging, security, and deep-space exploration. Her work includes room-temperature terahertz sensors and photodetectors with 50 GHz speeds.

Art GleasonView profile

Research Professor

• Remote Sensing
• Marine Conservation
• Coral Reef Science
• +3 more

Art Gleason is a Research Professor at the University of Miami, jointly affiliated with the Department of Physics and Department of Marine Geosciences. His research integrates geophysics, biogeography, and engineering to develop remote sensing tools for environmental conservation. He holds a Master’s degree from the University of Maryland and leads interdisciplinary teams focused on marine technology innovation. His research spans three themes: 1) Satellite product validation (e.g., AVHRR orbital drift correction for net primary production calculations), 2) Marine habitat mapping using hyperspectral/acoustic classification, and 3) Underwater photomosaic technology for coral reef monitoring. His 2020 Pew Marine Fellowship supports advanced applications of photomosaics in coral restoration. Gleason’s publications emphasize coral reef dynamics, optical oceanography, and automated image analysis. Trends include high-resolution mapping, resilience assessment, and technology development for conservation. His collaborative network includes NOAA, Scripps Institute of Oceanography, The Nature Conservancy, and international research groups. Awards & Grants: Pew Marine Fellow (2020) – For advancing underwater mosaic technology in coral reef conservation He directs the Large Area Imaging (LAI) initiative, conducting biweekly office hours and courses on underwater data acquisition. His lab partners with 15+ organizations globally to deploy photomosaic technology for reef science.

Md Alimoor RezaView profile

Assistant Professor

• Computer Vision
• Robotics
• Machine Learning
• +4 more

Md Alimoor Reza serves as an Assistant Professor of Computer Science in the Department of Mathematics and Computer Sciences at Drake University's College of Arts & Sciences. He leads the Computer Vision and Robotics Lab and teaches courses including Introduction to Computer Science, Computer Vision, Machine Learning, and Robotics. His academic journey culminated with a Ph.D. from George Mason University under Professor Jana Kosecka. Ph.D. in Computer Science, George Mason University (Advisor: Jana Kosecka) Professor Reza specializes in computer vision, robotics, and machine learning with applications spanning cognitive science, biology, medical diagnosis, and engineering. His research bridges theoretical advances with practical implementations, particularly in 3D reconstruction, human-robot interaction, and assistive technologies. He has developed novel approaches for multi-view stereopsis, point cloud processing, and semi-automatic annotation tools that have advanced computer vision applications. His publication portfolio reveals a strong focus on solving core computer vision challenges while expanding into interdisciplinary applications. Recent work shows increasing emphasis on accessibility technologies, educational AI applications, and real-world deployment of vision systems. The trajectory demonstrates evolution from foundational geometric computer vision toward more applied, human-centered systems with societal impact. As an educator, Professor Reza has mentored numerous undergraduate and graduate students through research projects, many of whom have won awards at regional computing conferences. His teaching spans introductory programming to advanced machine learning and computer vision topics, with a documented course schedule showing consistent offerings of CS 167 (Machine Learning) and CS 195 (Computer Vision). He directs the Computer Vision and Robotics Research Group at Drake, which maintains active collaborations across disciplines and has produced significant student research output. The lab environment emphasizes both theoretical understanding and practical implementation skills, preparing students for advanced research and industry positions in computer vision fields.

• Dielectric Spectroscopy
• Recycling of Electronic Waste
• Liquid Crystal Characterization
• +5 more

Pierre-Alexandre Hébert is an Associate Professor at the University of the Littoral Opal Coast. His research spans interdisciplinary domains including dielectric material analysis , electronic waste recycling , and machine learning applications for environmental monitoring. Key research focus: Liquid Crystal recycling from E-waste using dielectric spectroscopy Contributions to phytoplankton detection via automated systems and clustering algorithms Collaborations across France, Turkey, Italy, and Ireland His recent publications (2021-2023) emphasize sustainable materials processing, particularly dielectric characterization of recycled liquid crystals for circular economy applications. Earlier work (2008-2018) showcases expertise in image processing , phytoplankton classification , and data clustering techniques. Collaborative projects include: JERICO NEXT (2018), ISyDMA'6 (2021), and SFGP (2022) conferences. Current affiliations involve laboratories at Université du Littoral Côte d’Opale (ULCO) and Université d’Artois (UA).

Mikael NilssonView profile

Senior Lecturer

• Machine Learning
• Computer Vision
• Deep Learning
• +4 more

Mikael Nilsson is a Senior Lecturer and key member of the Faculty of Engineering (LTH) at Lund University , Sweden. He is affiliated with the Centre for Mathematical Sciences and serves as Programme Director for the international master's program in Machine Learning, Systems and Control since 2019. Education : M.Sc.E.E. and Ph.D. in Applied Signal Processing (2007) from Blekinge Institute of Technology Research Focus : Developing novel Machine Learning models with emphasis on Deep Neural Network Designs Applications in Computer Vision (3D object estimation, traffic safety, radiation therapy) Applied research in Engineering Health and Natural & Artificial Cognition profile areas Recent Research Outputs (2022-2023): Medical imaging segmentation for cancer therapy (Radiation Oncology) Urban traffic surveillance systems (IEEE ITSC) 3D shape estimation from monocular vision Multi-view pose estimation via segmentation masks Yield forecasting using satellite-ground data fusion Key Collaborations : Swedish Government Agency for Innovation Systems (Vinnova) - DAIDESS project (2023-2026) Interdisciplinary work in Engineering Health and Medical Imaging Industrial collaboration with Axis Communications AB (2016-2019)