Find Academic Supervisors

Yuedong WangView profile

Professor

• Biostatistics
• Computational Statistics
• Machine Learning
• +7 more

Yuedong Wang is a Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara (UCSB), part of the College of Letters and Science. He holds a PhD from the University of Wisconsin-Madison and has prior experience at the University of Michigan. His research focuses on statistical methodologies, including nonparametric and semiparametric models, smoothing splines, machine learning, and biostatistical applications in renal diseases and human circadian rhythms. He is a Fellow of the ASA, IMS, ISI, and RSS, and a member of IBS and ICSA. His work spans over 150 publications, with recent emphasis on modeling dialysis patient outcomes, SARS-CoV-2 transmission in healthcare settings, and statistical computing. Key collaborations include the MONDO initiative for dialysis outcomes research. He has developed R packages like ASSIST for nonlinear mixed-effects models and contributed to smoothing spline theory. His book *Smoothing Splines: Methods and Applications* is a seminal text in the field. Education: BS in Mathematics (USTC), MS in Operations Research (Chinese Academy of Sciences), PhD in Statistics (UW-Madison). Research interests: Machine learning applications, functional data analysis, longitudinal data modeling, and biostatistical methods in nephrology. His articles address topics like tensor product RKHS analysis, nonparametric mixture models for clinical data, and AI-driven predictive analytics for dialysis patients. Awards include recognition for contributions to statistical methodology and its biomedical applications.

Sinan KockaraView profile

Lecturer

• Medical Image Processing
• Virtual Reality (VR) Surgical Simulation
• Arthroscopic Surgery Training
• +3 more

Sinan Kockara is a Lecturer in the Department of Computer Science at Rice University. He holds adjunct roles at the University of Central Arkansas (Computer Science and Engineering) and the University of Arkansas at Little Rock. With a Ph.D. in Applied Computing and a BS in Computer Engineering, his expertise spans medical image processing, virtual reality (VR) surgical simulation, and high-performance computing. His research focuses on dermatology imaging and arthroscopic surgery training systems, with $2.4+ million in funding from NIH, NSF, and industry partners. He has authored 40+ publications and holds over 640 citations. Education: BS, Computer Engineering, Dokuz Eylul University (Izmir, Turkey) PhD, Applied Computing, University of Arkansas at Little Rock Research Interests: Dr. Kockara develops VR-based surgical simulators for orthopedic training, including fluid dynamics simulations and bone drilling procedures. His work integrates machine learning for skill assessment and medical image analysis for dermatology applications. His lab (Biomedical Image Processing & HPC Lab) emphasizes real-time visualization and HPC optimization for medical systems. Grants & Validation: He secured major grants to validate surgical training platforms like ViRCAST and VATDEP. His studies compare novice/expert surgeon performance metrics and employ systematic validation frameworks for medical simulations. Labs & Future Work: Leads the Biomedical Image Processing & High-Performance Computing Lab, advancing real-time medical visualization and GPU-accelerated algorithms. Ongoing projects include cloud-based surgical simulators and AI-driven medical education tools.

Yu-Wing TaiView profile

Associate Professor

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

Yu-Wing Tai is an Associate Professor of Computer Science at Dartmouth College, one of the Ivy League institutions. His research focuses on Computer Vision, Deep Learning, and Multimodalities Generative AI. He holds a PhD from the National University of Singapore and has served as an Associate Editor for IEEE TPAMI and as an Area Chair for major conferences like CVPR, ICCV, and NeurIPS. He is a Senior Member of the IEEE. Previously, he held roles as a Senior Research Director at Kuaishou Technology (2020-2023), Adjunct Professor at HKUST (2017-2023), and Research Director at Tencent (2017-2020). His work spans low-level vision, image/video editing, 3D reconstruction, and applications of large generative models. His research highlights include Motion-Agent for human motion generation, ChatCam for camera control, and Gear-NeRF for 3D reconstruction. He advises a team of PhD students and has mentored alumni now in academia and industry worldwide. His teaching includes courses on Multi-modalities Generative AI and Computer Graphics.

Joshua ClaytonView profile

Clinical Professor

• Computer Science
• Digital Media
• Interactive Design
• +3 more

Joshua Clayton is a Clinical Professor of Computer Science at New York University's Department of Computer Science, part of the College of Arts and Science. He holds an MPS in Interactive Telecommunications from NYU (2011) and a BFA in Graphic Design from Western Michigan University (2001). His work bridges art and technology, focusing on computational creativity, web-based art, and interactive media. Education: MPS, Interactive Telecommunications, NYU (2011) BFA, Graphic Design, Western Michigan University (2001) Research Interests: Digital media, generative art, spatiotemporal data visualization, and the expressive potential of code in web design. His creative projects explore intersections between computational logic and artistic expression. His artistic works include Codex Lucida (spatiotemporal metadata interfaces), Polychromatics (procedural color composites), and Locative Waves (GPS-driven sound installations). Clayton has taught extensively since 2011, covering introductory programming, web design, and specialized topics like Drawing on the Web . While no formal awards are listed, his contributions to teaching and digital art practice are central to his profile. He advises via his course development and creative projects rather than traditional academic supervision.

Søren HøjsgaardView profile

Associate Professor

• Statistics
• Graphical Models
• Bayesian Networks
• +7 more

Søren Højsgaard is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His work bridges statistical theory, computational methods, and pedagogical innovation, with a strong focus on graphical models, Bayesian networks, and computer algebra systems in R. His research interests lie at the intersection of statistics, machine learning, and software development. He is particularly known for developing R packages such as caracas , gRbase , and sparta , which facilitate symbolic computation and efficient inference in probabilistic models. His work supports both advanced research and accessible teaching in data science. The recent publications highlight a consistent trend toward integrating symbolic mathematics with statistical computing, improving scalability in Bayesian network predictions, and enhancing statistics education through tools like Quarto and R. His contributions span theoretical algorithms, software implementation, and educational applications. Active contributor to open-source statistical software Focus on model-based prediction and symbolic computation Emphasis on teaching innovation using computational tools He has been involved in academic outreach through conference presentations and media engagement, discussing topics ranging from R programming to workplace safety modeling. His leadership role in the Department of Mathematical Sciences was recently highlighted in university communications. Søren Højsgaard leads and contributes to projects that combine rigorous statistical methodology with practical implementation, supporting both research and education in modern data science.

• Graph Grammars
• Artificial Intelligence
• Algorithms and Data Structures
• +3 more

Dr. Anna Paszyńska is a Lecturer at Jagiellonian University, specializing in computational science and graph-based algorithms. Her research focuses on graph grammars, artificial intelligence, and numerical methods for solving complex engineering problems. She has contributed to advanced simulations in environmental modeling, tsunami prediction, and adaptive finite element methods. Her work integrates graph theory with computational techniques to optimize solver performance and address real-world challenges like pollution tracking and disaster simulations. Affiliation: Jagiellonian University Key Research Areas: Graph grammars, AI-driven simulations, computational fluid dynamics, and numerical algorithms. Her recent publications highlight innovations in physics-informed neural networks (PINNs) for solving PDEs and optimizing tsunami and pollution models. She actively collaborates on interdisciplinary projects involving environmental science, engineering, and computer science.

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

Xin Zhong is an Assistant Professor of Computer Science at the University of Nebraska at Omaha (UNO), affiliated with the College of Information Science & Technology (IS&T). He specializes in deep learning, machine learning, computer vision, and image processing, with a focus on robust watermarking techniques, adversarial training, and multimedia security. His work integrates computational intelligence into practical applications like medical imaging, disaster risk assessment, and smart city technologies. Dr. Zhong serves as an advisor for PhD, MS, and undergraduate researchers and contributes to academic governance as a member of the Artificial Intelligence concentration committee and the Computer Science Department’s graduate program committee. He also reviews for journals, conferences, and NSF grants, emphasizing interdisciplinary collaboration. His research interests span image watermarking robustness, AI ethics, and interpretable machine learning. Notable contributions include innovations in cross-attention mechanisms, noise-invariant domain learning, and deep morphological neural networks. His lab focuses on advancing cybersecurity, healthcare diagnostics, and urban infrastructure safety through AI-driven solutions. Xin Zhong’s advising and service roles reflect his commitment to advancing both technical research and educational initiatives. His projects often involve partnerships with industry and government agencies to address real-world challenges in automated systems and cyber-physical infrastructure.

Wenke LeeView profile

Professor

• Network Security
• Botnet Detection
• Malware Analysis
• +7 more

Wenke Lee is the USG Regents Professor and John P. Imlay Jr. Chair in Software at Georgia Tech's College of Computing, where he has been a professor since 2001. He currently serves as Executive Director of the Institute for Information Security & Privacy (IISP), leading Georgia Tech's global cybersecurity initiatives. His academic roles also include leadership in the Georgia Tech Information Security Center (GTISC) from 2012 to 2015. Dr. Lee holds a Ph.D. in Computer Science from Columbia University (1999). His research focuses on systems and network security, including botnet detection, malware analysis, and secure software development. Notable contributions include co-founding Damballa, Inc. (2006), a cybersecurity firm targeting botnet mitigation. His research interests span anomaly detection, adversarial machine learning, and privacy-preserving technologies. Recent work emphasizes AI-driven security solutions, IoT protection, and cyber-physical system resilience. Lee’s projects are funded by NSF, DHS, DoD, and industry partners. Key awards include the USG Regents Professorship and the John P. Imlay Jr. Chair. He has authored over 100 publications, with many highly cited in cybersecurity domains. His affiliations include the School of Cybersecurity and Privacy, CERCS, and the Online Master of Science in Computer Science (OMSCS) program.

• Computer Vision
• Machine Learning
• Pattern Recognition
• +8 more

Sergio Escalera is a Professor at the Department of Mathematics and Informatics, Universitat de Barcelona, and leads the Human Behavior Analysis Group (HuPBA). He holds affiliations at Aalborg University (Distinguished Professor), Computer Vision Center (UAB), and Mathematics Institute of Barcelona. His roles include editorships at journals like TPAMI and Data-centric Machine Learning. He co-created the Codalab platform and co-founded NeurIPS competitions. His research focuses on human-centric AI, including visual and multimodal data analysis, and he has pioneered challenges like ChaLearn Looking at People. Education: Doctorate in Computer Science (Universitat de Barcelona). Research spans computer vision, machine learning, and topological deep learning. He has published over 560 papers and holds patents in AI and biometrics. Research Interests: Inclusive human analysis, transparent AI, affective computing, and sports analytics. Notable projects include SoccerNet for sports video understanding and MyoPS for cardiac MRI analysis. His work bridges theory (e.g., Cellular Transformers) and applications (e.g., mental health monitoring). Awards: ICREA Academia, ELLIS Fellow, AAIA Fellow, multiple best-paper nominations. He has advised 20+ PhD/Master students and led grants totaling millions in funding. Key labs include HuPBA and collaborations with institutions like NVIDIA Jetson Research. Current Projects: MetrikaMind (mental health AI platform), SoccerNet 2024 challenges, and TopoX (topological machine learning software). Active in 3D human motion generation, unlearning algorithms, and robust OOD detection.

Xiaodong LiView profile

Associate Professor

• High-Dimensional Statistics
• Statistical Learning
• Matrix Completion
• +7 more

Xiaodong Li is an Associate Professor in the Department of Statistics at the University of California, Davis, within the College of Letters and Science. His research lies at the intersection of high-dimensional statistics, statistical learning, and nonconvex optimization, with applications in matrix completion, phase retrieval, and network analysis. His research interests focus on developing and analyzing statistical methodologies for modern data science challenges. He investigates high-dimensional inference , nonconvex optimization algorithms , and robust statistical methods for complex data structures. His work often bridges theoretical guarantees with practical algorithmic implementation, particularly in unsupervised learning and signal processing. The recent publications highlight a strong trend toward theoretical analysis of nonconvex methods in matrix estimation, community detection in networks , and high-dimensional signal recovery . His work frequently appears in top-tier journals such as the Annals of Statistics , IEEE Transactions on Information Theory , and Journal of Machine Learning Research , reflecting consistent contributions to both statistical theory and machine learning. Scientific Awards: No scientific awards mentioned in the provided text. Xiaodong Li has actively advised multiple PhD students, including Ji Chen (2020), Xingmei Lou (2022), Yucheng Liu (2022), and Xiaohan Hu (2024), with Zhentao Li currently under supervision. His research is supported by publications rather than explicitly mentioned grants, indicating a strong publication-driven academic profile. He teaches a wide range of courses from undergraduate probability and regression to graduate-level mathematical statistics and longitudinal data analysis. While no specific lab or research group name is mentioned, his collaborative work with researchers such as Emmanuel J. Candes, T. Tony Cai, and Yudong Chen suggests active participation in the broader statistical learning and high-dimensional data analysis community. His recent work on polytree models and community detection indicates ongoing exploration into structured high-dimensional models and network inference.

Vishal PatelView profile

Associate Professor

• Computer Vision
• Machine Learning
• Image Processing
• +3 more

Vishal Patel is an Associate Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), affiliated with the Vision & Image Understanding (VIU) Lab. He previously held the position of A. Walter Tyson Assistant Professor at Rutgers University and was part of the research faculty at the University of Maryland Institute for Advanced Computer Studies (UMIACS). He earned his Ph.D. in Electrical Engineering from the University of Maryland, College Park in 2010. His research focuses on computer vision, machine learning, biometrics, medical image analysis, and signal/image processing. Key areas include cross-spectrum face synthesis, domain adaptation, open-set recognition, and medical image restoration. He leads the VIU Lab, advancing AI-driven visual reasoning and applications in healthcare, autonomous systems, and security. Patel serves as an Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence and holds leadership roles in the IEEE Biometrics Council and Signal Processing Society. He has received numerous awards, including the 2021 IEEE SPS Early Career Award, NSF CAREER Award, and IAPR Young Investigator Award. His work has been recognized through Best Paper Awards at IEEE AVSS, BTAS, and ICPR, and he became an IAPR Fellow in 2024. Recent research highlights include developing novel diffusion models for medical video generation (e.g., GenVOG, EyePose), universal image restoration frameworks (UniRes), and multimodal AI tools for surgical skill assessment and maritime surveillance. His lab’s collaborations span bioengineering, robotics, and defense applications, emphasizing both theoretical advances and real-world impact.

Tracy A HeathView profile

Associate Professor

• Evolutionary Biology
• Phylogenetics
• Computational Biology
• +5 more

Dr. Tracy A. Heath is an Associate Professor at Iowa State University, specializing in evolutionary biology and computational phylogenetics. Her research focuses on developing Bayesian statistical methods and software tools like RevBayes to analyze molecular, morphological, and fossil data. She leads the Heath Lab, advancing phylogenetic inference and integrating paleontological evidence into evolutionary studies. Tracy holds a B.A. in Biology from Boston University (2000) and a Ph.D. in Ecology, Evolution, and Behavior from the University of Texas at Austin (2008). She actively contributes to open-source initiatives such as the Taming the BEAST educational platform and GitHub repositories for phylogenetic software development. Her work bridges theoretical frameworks with applied computational methods, emphasizing the importance of fossil data and model-based approaches in understanding biodiversity patterns and evolutionary processes. Key Contributions: RevBayes: Bayesian phylogenetic inference software Taming the BEAST: Community-driven BEAST 2 tutorial resource Fossil integration methodologies Phylogenetic network simulation (SiPhyNetwork) Her research spans topics including adaptive evolution in waterfowl, genomic studies of penguin adaptation, and resolving phylogenetic relationships in diverse taxonomic groups. Tracy advocates for inclusive educational practices, integrating museum collections and computational training to enhance biodiversity science accessibility.

Gordon CarlsonView profile

Associate Professor

• New Media Communication
• Virtual Reality
• Educational Technology
• +3 more

Dr. Gordon Carlson is an Associate Professor in the Department of Communication Studies at Fort Hays State University (FHSU), Kansas. He holds a doctorate in New Media Communication from the University of Illinois at Chicago and has taught at institutions in Oregon, Chicago, Hawaii, and Kansas City. His work focuses on new media’s intersection with rhetoric, emphasizing interactive, collaborative, and virtual technologies. Education: Bachelor's and Master's in Communication, Oregon State University Doctorate in New Media Communication, University of Illinois at Chicago Research Interests: His research explores how new media disrupt and converge with rhetorical frameworks. He leads the Institute for New Media Studies , which develops transdisciplinary projects like 3D visualization tools for education (e.g., livestock anatomy, human brain structures) and immersive platforms for emergency training, campus navigation, and mascot-driven outreach. Projects include Tiger Range (3D campus modeling), Virtual Library (immersive library exploration), and Virtual Victor (synthetic mascot communication). Grants & Projects: Modular Smart Classroom (IMLS grant, $61,889, 2018) A-Star (robot concierge for library navigation) Labs/Teams: He directs the Institute for New Media Studies, which collaborates across disciplines to create innovative educational and communication technologies. The institute’s work spans VR applications, AI-driven avatars, and pedagogical tool development.

Wasim AhmadView profile

Senior Lecturer

• Signal Processing
• Machine Learning
• Data Analytics
• +9 more

Wasim Ahmad is a Senior Lecturer (Associate Professor) in Signal Processing and Machine Learning at the School of Engineering, University of Glasgow. He earned his PhD from the University of Surrey (2011) and held prior academic positions at the University of Hull (2013–2015) and the University of Surrey (2011–2013). He is part of the Communication, Sensing and Imaging (CSI) research group at Glasgow, focusing on applications of signal processing and machine learning in sound modeling, robot audition, healthcare technologies, and IoT-based systems. Education: PhD in Engineering from the University of Surrey (2011), followed by postdoctoral roles at Hull and Surrey before joining Glasgow in 2015. Research Interests: His work spans signal processing for assistive technologies, teleoperation systems, activity recognition, and IoT-enabled healthcare monitoring. He has pioneered projects like radar-based indoor navigation for visually impaired individuals and emotion recognition for human-robot interaction. Publications: Over 40 peer-reviewed articles, with recent focus on edge computing optimization, immersive VR education, and teleoperation interfaces. His work bridges theoretical advancements with practical applications in robotics, healthcare, and education. Grants: Recipient of the University of Glasgow Learning and Teaching Development Fund (2017–2018) for student-staff partnership in assessment innovation. Teaching: Leads courses on real-time systems, microelectronics, and programming, emphasizing hands-on engineering projects. Supervised MSc/PhD students in telerobotics and edge computing. Labs/Teams: Active contributor to the CSI group, collaborating on projects involving sensor networks and AI-driven systems.

Peter SzabóView profile

Associate Professor

• Mathematics
• Linear Algebra
• Computer Algebra
• +5 more

Peter Szabó is an Associate Professor at the Department of Aviation Technical Training , Faculty of Aeronautics , Technical University of Košice , Slovakia. He is actively involved in research at the intersection of mathematics, computer algebra, and aerospace engineering. Academic Affiliation: Technical University of Košice, Faculty of Aeronautics, Department of Aviation Technical Training Research Interests include: Mathematical modeling in aviation (Free Route Airspace, complex networks) Linear algebra and numerical methods with MATLAB/SageMath Calculus education and computational techniques Graph theory applications to air traffic systems Recent Article Trends highlight: Integration of calculus and numerical analysis for educational tools (2025) Mathematical axioms for airspace modeling (2022) Cloud computing in aviation research (2022) Linear algebra textbooks and computational exercises (2019) Advising : Mentored students on topics ranging from satellite stabilization to air traffic optimization.