Find Academic Supervisors

• Signal Processing
• Computational Imaging
• Data Science
• +5 more

Professor Minh N. Do is the Thomas and Margaret Huang Endowed Professor in Signal Processing & Data Science at the University of Illinois at Urbana-Champaign (UIUC), with primary appointment in the Department of Electrical and Computer Engineering. He holds multiple affiliate appointments across campus including with the Coordinated Science Laboratory, Beckman Institute for Advanced Science and Technology, Department of Bioengineering, Department of Computer Science, Institute for Genomic Biology, College of Medicine, and School of Computing and Data Science. Additionally, he serves as Director of the joint VinUni-Illinois Smart Health Center and holds an Honorary Vice-Provost position at VinUniversity. Professor Do received his B.Eng. in Computer Engineering (First Class Honors) from the University of Canberra, Australia in 1997, followed by his Dr.Sci. in Communication Systems from the Swiss Federal Institute of Technology Lausanne (EPFL) in 2001. His educational journey was marked by exceptional achievement, earning the University Medal from the University of Canberra and a Silver Medal from the 32nd International Mathematical Olympiad. Professor Do's research focuses on developing new multidimensional signal processing tools with applications across several domains. His primary research interests include smart health, data science, computational imaging, and signal processing. His work spans biomedical imaging, machine learning, computer vision, and robotics, with particular emphasis on geometric image representations, integrating image formation and processing, and image processing from multiple sensors. His research bridges theoretical investigations with practical applications, creating impactful solutions in healthcare, diagnostics, and AI systems. His recent publications demonstrate a consistent trajectory toward multimodal AI systems, robust learning frameworks, and healthcare applications. Professor Do's work increasingly integrates signal processing with deep learning approaches to address challenges in medical imaging, cross-modal transfer, and real-world deployment of AI systems. His research shows strong emphasis on practical applications with societal impact, particularly in healthcare diagnostics and smart health technologies. Professor Do's scientific achievements have been recognized with numerous prestigious awards: Member of the National Academy of Artificial Intelligence (2025) Fellow of Asia-Pacific Artificial Intelligence Association (2023) Thomas and Margaret Huang Endowed Professor, UIUC (2020-present) Fellow of IEEE (2014) Young Author Best Paper Award, IEEE Signal Processing Society (2008) CAREER award from the National Science Foundation (2003) Best Doctoral Thesis Award from EPFL (2001) As an educator, Professor Do has taught numerous courses spanning digital signal processing, probability, data science, and image processing. His teaching excellence has been recognized with multiple "Teachers Ranked as Excellent" awards at UIUC. He also maintains active industry connections through tech-transfer efforts, having co-founded Personify and served as Chief Scientist of Misfit. His leadership extends to administrative roles, having served as Vice-Provost for VinUniversity during 2020-2021. Professor Do leads research initiatives at the intersection of signal processing and healthcare applications, with particular focus on the Smart Health Center collaboration between UIUC and VinUniversity. His lab develops innovative solutions for medical diagnostics, point-of-care testing, and neurological assessment using advanced signal processing and AI techniques.

SHEN LeiView profile

Researcher

• Multiscale Modeling and Simulation
• Materials Informatics
• Computational Physics
• +3 more

SHEN Lei is a researcher at the National University of Singapore (NUS), affiliated with the Department of Physics. With a PhD in Physics from NUS, he specializes in Multiscale Modeling and Simulation and Materials Informatics , leveraging machine learning and computational methods for advanced materials discovery. Research Focus: Density functional theory, molecular dynamics, finite element analysis, and data-driven design of materials. Teaching: Modules include Mechanics and Waves (PC1433), Applied Quantum Mechanics (PC2130B), and Mechanical Properties of Materials (ESP2109). His work spans spintronics, ferroelectricity, and energy storage materials, with recent publications on interatomic potentials, sliding heterostructures, and battery anodes. He has received the Teaching Commendation Award and declined the Lee Kuan Yew Postdoctoral Fellowship . Notable Trends: Recent articles emphasize machine learning in materials science, van der Waals heterostructures, quantum transport, and medical image analysis. Subfields include Rashba spin-orbit coupling, piezoelectric tensor modeling, and defect-informed neural networks. Scientific Awards: Teaching Commendation Award (AY15/16; AY16/17) Lee Kuan Yew Postdoctoral Fellowship (2014) (declined)

Katie BoumanView profile

Associate Professor

• Computational Imaging
• Astrophysics
• Machine Learning
• +3 more

Katie Bouman is an Associate Professor of Computing and Mathematical Sciences (CMS), with courtesy appointments in Electrical Engineering and Astronomy at the California Institute of Technology (Caltech). She is also a Rosenberg Scholar and an Investigator at the Heritage Medical Research Institute. Her research focuses on computational imaging, integrating algorithm and sensor design to observe phenomena traditionally inaccessible through conventional methods. She leads the Computational Cameras Group, which develops systems for scientific discovery and technological innovation in fields such as astrophysics, medical imaging, and computer vision. Bouman holds a B.S.E. in Electrical Engineering from the University of Michigan (2011), an S.M. and Ph.D. in Electrical Engineering and Computer Science from MIT (2013 and 2017). Before joining Caltech, she was a postdoctoral fellow with the Event Horizon Telescope (EHT), contributing to the groundbreaking first image of a black hole in 2019. Her research interests span signal processing, machine learning, and physics-driven imaging solutions. Notable projects include 3D black hole gas reconstruction, MRI acceleration, and neural field-based tomography. She emphasizes interdisciplinary collaboration, merging computational methods with domain-specific challenges in astronomy and biomedical engineering. Awards: PECASE Award, Sloan Fellowship, NSF CAREER Award, Royal Photographic Society Progress Medal, Breakthrough Prize (co-recipient). Grants: Okawa Research Grant, HMRI Investigator funding. Bouman advises students in CMS and collaborates with industry through the Caltech Center for Sensing to Intelligence (S2I). Her lab fosters diversity and inclusion, prioritizing equitable participation in STEM fields.

David G StorkView profile

Lecturer

• Computational Imaging
• Art Analysis
• Metasurface Optics
• +3 more

David G. Stork serves as an Adjunct Lecturer in Electrical Engineering at Stanford University's School of Engineering. His multidisciplinary research spans computational image analysis of fine art, metasurface optical elements for computational sensing, and applications of computer algebra systems to scientific problems. With graduate degrees in Physics from MIT and the University of Maryland, complemented by Art History studies at Wellesley College, Stork brings interdisciplinary depth to his work. His professional background includes executive roles as Chief Scientist at Ricoh Innovations and Rambus Fellow at Rambus, Inc. He has held faculty appointments across diverse fields including computer science, mathematics, neuroscience, psychology, and art history at institutions such as Stanford, Wellesley, and Swarthmore. Stork has received significant recognition through fellowships in nine professional societies including IEEE, OSA, and SPIE, and was named a 2023 Leonardo@Djerassi Fellow. An accomplished inventor, he holds 64 US patents and has authored over 220 peer-reviewed publications alongside nine scholarly books on topics ranging from pattern recognition to computer-assisted art analysis.

• Machine Learning
• Deep Learning
• Video Classification
• +3 more

Reza Bosagh Zadeh is an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning, deep learning, and their applications in video classification, healthcare analytics, and distributed algorithms. He specializes in developing scalable computational methods for real-time data processing and has contributed to advancements in neural networks and optimization techniques. Reza's work spans theoretical and applied domains, with notable contributions to TensorFlow frameworks, video summarization systems, and medical imaging analysis. His research often integrates interdisciplinary approaches, leveraging both academic and industrial collaborations. Notable projects include developing machine learning models for glaucoma detection and creating efficient algorithms for large-scale data processing in environments like Apache Spark. His publications emphasize real-time video stream analysis, distributed computing architectures, and practical implementations of deep learning. Reza holds a strong presence in both academic and tech sectors, with contributions to platforms like Twitter's Who-to-Follow system and innovations in edge computing for video surveillance.

Shweta JainView profile

Professor

• Cybersecurity
• Blockchain Technology
• Wireless Networks
• +6 more

Shweta Jain is a Professor in the Department of Mathematics and Computer Science at John Jay College of Criminal Justice, part of the City University of New York (CUNY). She holds dual roles as Graduate Faculty in the Digital Forensics and Cyber Security program and Doctoral Faculty in Computer Science at CUNY's Graduate Center. With a Ph.D. in Computer Science from Stony Brook University (2007), her expertise spans Cybersecurity, Blockchain, Wireless Networks, and Software Development. Education Background: Ph.D. Computer Science, Stony Brook University, 2007 M.S. Computer Science, Stony Brook University, 2005 B.E. Electronics and Telecommunication Engineering, Indian Institute of Engineering Science and Technology (IIEST) Shibpur, 2005 Research Interests: Cybersecurity frameworks and digital forensics Blockchain applications in social systems Wireless network protocols and security Perceptual hashing for image authentication Network vulnerability analysis Notable Achievements: Recipient of 2014 IEEE Region-1 Award for Outstanding Teaching Senior Member of IEEE Over 30 peer-reviewed publications and patents in networks, forensics, and distributed systems Advising & Grants: Guided multiple student research projects in network security and forensics Developed innovative tools like E-Witness for digital evidence preservation Contributed to NSF-funded projects on wireless simulation realism Labs & Teams: Director of the Cybersecurity Research Lab at John Jay College Collaborates with WINLAB at Rutgers University on wireless protocols

Sai GuView profile

Professor

• Bioenergy
• Carbon Capture
• Catalytic Processes
• +3 more

Professor Sai Gu is a distinguished academic and leader in Chemical Engineering, currently serving as Deputy Pro-Vice-Chancellor (East and South East Asia) at the University of Warwick. He holds a Professorship in Chemical Engineering and has held senior roles at institutions including the University of Surrey, Cranfield University, and Southampton. His expertise spans bioenergy, carbon capture, materials science, and digital transformation in industry. Prof Gu earned his PhD in Material Modelling from the University of Nottingham and has led over £100 million in EPSRC/EU-funded projects, pioneering innovations like Industry 6th Sense technologies and catalytic biomass conversion. Research interests include biomass fast pyrolysis, CO2 capture via novel solvents, thermal spray coatings, and AI-driven manufacturing. He established the Centre for Bioenergy and Resource Management and leads the EPSRC-funded DigitalMetal CDT to advance metals industry digitization. Prof Gu has published 200+ papers (20,000+ citations) and is a Fellow of the Royal Academy of Engineering. His work bridges academia and industry, fostering global collaborations in clean energy and sustainable technologies. Education: PhD in Material Modelling (University of Nottingham), Postdoc at University of Cambridge Awards: FREng (Royal Academy of Engineering) Key Projects: EPSRC CDTs in NetZero and Digital Metals, 'Stepping towards Industrial 6th Sense' Labs: Centre for Connected Plants of the Future (UoS), Centre for Bioenergy (Cranfield)

Chris WarnerView profile

Researcher

• Atmospheric Science
• Climate Modeling
• Geophysical Fluid Dynamics
• +3 more

Chris Warner is a Researcher at the Department of Applied Mathematics and Theoretical Physics (DAMTP) within the Faculty of Mathematics at the University of Cambridge. His research focuses on atmosphere-ocean dynamics, particularly gravity wave parameterization, stratospheric dynamics, and climate modeling. He has contributed to advancements in general circulation models (GCMs) and the Met Office Unified Model, emphasizing realistic simulations of atmospheric processes such as the Quasi-Biennial Oscillation (QBO). His work integrates satellite data (e.g., CRISTA) with theoretical models to understand gravity wave momentum flux and spectral characteristics. Key areas include filtering mechanisms for nonorographic gravity waves and the development of ultra-simple parameterization schemes to improve climate model accuracy. Collaborations involve institutions like NASA and the Met Office, addressing challenges in mesopause physics and stratospheric-tropopause coupling. Publications span over two decades, demonstrating sustained contributions to both atmospheric science and instrumentation (e.g., microelectronic probe techniques). No scientific awards are explicitly mentioned in the provided materials. His research group is part of DAMTP’s Atmosphere-Ocean Dynamics section, contributing to theoretical and applied studies in geophysical fluid dynamics.

Luis Jose Fuentes MeleroView profile

Professor

• Cognitive Neuroscience
• Attention Disorders
• Differential Outcomes Procedure
• +3 more

Luis Jose Fuentes Melero is a Full Professor of Basic Psychology at the University of Murcia, Spain, affiliated with the Faculty of Psychology and Speech Therapy. His career includes leadership roles in research groups such as the Cognitive Neuroscience Laboratory and the former Neuropsychology of Attention group at the University of Almería (1988–2004). He holds a PhD in Psychology from the University of Granada (1987), supervised by Dr. Pío Tudela Garmendia. Research Focus: Cognitive neuroscience, attention mechanisms, differential outcomes procedure, neurodegenerative diseases, and educational psychology. Affiliations: Principal investigator in projects funded by the Spanish Ministry of Science and Innovation (Consolider Ingenio 2010, Plan Nacional I+D+i) and Fundación Séneca. His work combines experimental psychology with neuroimaging, focusing on attention deficits, aging, and applied behavioral interventions. He has supervised 17 doctoral theses and contributed to over 200 publications, with an h-index of 32. Collaborations include institutions like the Virgen de la Arrixaca Hospital and Oxford University. Notable achievements include developing the Differential Outcomes Procedure (DOP) to enhance learning and memory, and pioneering studies on electrical brain stimulation for attention and memory improvement (e.g., PID2021-125408NB-I00 project).

Marco FarinaView profile

Professor

• Electromagnetic Fields
• Scanning Probe Microscopy
• Nanotechnology
• +4 more

Marco Farina is a Full Professor in Electromagnetics at the Department of Information Engineering, College of Engineering, Polytechnic University of Marche, Italy. His research spans electromagnetic modeling, scanning microwave microscopy, and nanotechnology, with applications in 2D materials, biosensors, and advanced measurement systems. He is a Senior Member of IEEE and actively contributes to Technical Committees on RF Nanotechnology. Laurea and Ph.D. in Electronic Engineering from University of Ancona Research interests focus on quantitative scanning microwave microscopy, electromagnetic analysis of active/passive components, and nanoscale characterization techniques. His work bridges theoretical modeling with practical implementation, including the development of the EM3DS software suite and novel inverted SMM systems. Recent publications highlight interdisciplinary applications in biomedical analysis and semiconductor physics. Scientific recognition includes the 3M-Nano Best Conference Paper Award and grants from US Army Research Laboratory and US Air Force Office of Scientific Research. He holds an ESA-funded patent for VNA calibration and has co-authored a book on planar structure analysis. Collaborative projects emphasize RF device optimization and biological imaging.

Lasse HeikkinenView profile

Senior Lecturer

• Physics
• Applied Physics
• Process Tomography
• +5 more

Lasse Heikkinen is a Senior Lecturer at the Department of Technical Physics, Faculty of Science, Forestry and Technology, University of Eastern Finland. His work spans applied physics, process tomography, and educational technology, with a focus on innovative teaching methods like flipped classrooms during the pandemic. Current Role: Deputy Head of Department, Senior University Lecturer Research Themes: Electrical impedance tomography for industrial processes, flipped teaching frameworks, and physics education adaptation to remote learning. Recent publications highlight his dual expertise in process tomography (gas-solid flows, pharmaceutical monitoring) and pedagogical innovation (learning analytics, flipped classrooms). He has contributed to educational manuals and toolkits for teacher training. Key Projects: Technology education infrastructure development (2015–2024), pandemic-era teaching adjustments (2022). Collaborations: Active in international process tomography conferences and multidisciplinary teams like the Ameba project.

Defang ChenView profile

Researcher

• Machine Learning
• Deep Learning
• Computer Vision
• +6 more

Defang Chen is a Researcher and Postdoctoral Associate in the Department of Computer Science and Engineering at the University at Buffalo, working under the supervision of Siwei Lyu. He is affiliated with the School of Engineering and Applied Sciences and is based at 301A Davis Hall in Buffalo, NY. His research focuses on advanced machine learning techniques, including knowledge distillation, diffusion models, graph neural networks, and domain generalization. Defang's work emphasizes improving model efficiency through distillation methods, exploring generative models like diffusion processes, and enhancing cross-domain adaptability. His contributions span both theoretical advancements and practical applications, such as accelerating diffusion sampling and optimizing neural network architectures. His publications between 2023–2025 highlight trends in model compression, generative AI, and graph-based learning. Notable themes include refining knowledge transfer between models, improving adversarial robustness, and developing scalable sampling techniques. Defang’s research has implications for computer vision, natural language processing, and semantic segmentation. No academic awards or grants are explicitly listed in the provided information. While no formal advisees are noted, his role as a postdoctoral researcher suggests collaborative involvement in academic projects and teams. Defang’s work is accessible via his Google Scholar profile .

Jin LuView profile

Assistant Professor

• Machine Learning
• Optimization
• Bio-informatics
• +6 more

Jin Lu is an Assistant Professor at the University of Georgia's School of Computing, part of the Franklin College of Arts & Sciences. He earned his Ph.D. (2019) and M.S. (2019) in Computer Science and Engineering from the University of Connecticut. Prior to his current role, he served as an Assistant Professor at the University of Michigan–Dearborn (2019–2023). Educational Background: Ph.D. in Computer Science and Engineering, University of Connecticut, 2019 M.S. in Computer Science and Engineering, University of Connecticut, 2019 Research Interests: Dr. Lu focuses on machine learning, optimization, bio-informatics, and smart mobility. His work spans federated learning, healthcare applications (e.g., depression and BMI monitoring), IoT systems, and computer vision. Recent projects explore AGI's potential in medical and educational contexts, leveraging models like CycleGAN and reinforcement learning. Grants & Funding: Develop digital brains to advance portable diagnosis of neurological conditions (Google, 2025) Lab/Teams: While specific lab affiliations are not explicitly stated, his research involves collaborations in interdisciplinary areas such as health informatics and smart mobility.

Maxime Ferreira Da CostaView profile

Associate Professor

• Signal Processing
• Data Science
• Inverse Problems
• +6 more

Maxime Ferreira Da Costa is an Associate Professor at CentraleSupélec, Université Paris-Saclay, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on theoretical and algorithmic foundations of data science, particularly in inverse problems, structured signal processing, and physical layer security. Key research areas include super-resolution, system calibration, and privacy-enhancing communication schemes. He holds a Ph.D. from Imperial College London (2018), and previously worked at USC and Carnegie Mellon University. Notable achievements include an ANR Young Researcher Grant (2023) for projects on data science and physical layer security. He actively contributes to conferences and workshops, with recent presentations at Institut Henri Poincaré and Université Paris-Saclay. Education: Ph.D. in Electrical Engineering, Imperial College London (2018) M.Sc. Electrical Engineering, Imperial College London (2012) Engineer Diploma, CentraleSupélec (2012) Research interests span continuous inverse problems, off-the-grid methods, wireless security, and sensing systems. Current projects explore goal-oriented resource allocation, fake path injection for privacy, and preconditioned optimization techniques. His work bridges signal processing theory with practical applications in imaging, telecommunications, and sensing.

• Biomedical Engineering
• Machine Learning
• Computer Vision
• +3 more

Aggelos K. Katsaggelos is a Professor in the Department of Electrical Engineering and Computer Science at Northwestern University's McCormick School of Engineering. His research focuses on biomedical imaging, machine learning, and computer vision applications in healthcare. He has collaborated extensively with interdisciplinary teams, including clinicians and engineers, to develop advanced algorithms for medical diagnosis and image analysis. Key research interests include medical image processing, deep learning for diagnostics, and computational methods in cardiology. His work spans applications such as MRI and ultrasound analysis, automated pathology detection, and multimodal sensing for health monitoring. Recent articles highlight contributions to myocardial scar quantification, lung ultrasound scoring, and AI-driven cough detection. His methodologies often combine domain-specific physics with modern machine learning techniques to solve real-world clinical challenges. Notable collaborations include projects with institutions like the University of Chicago and international teams in astrophysics and cognitive science. His work emphasizes translating algorithmic advancements into practical clinical tools.