Find Academic Supervisors

• Computer Science
• Virtual Reality
• Scientific Visualization
• +5 more

David H Laidlaw is a Professor of Computer Science at Brown University, specializing in virtual reality, scientific visualization, and medical imaging. His work spans interdisciplinary applications in neuroscience, biomedical research, and educational tools. Brown University Affiliation Department of Computer Science His research focuses on: Immersive visualization for complex data analysis Diffusion MRI and neuroimaging techniques Human-computer interaction in virtual environments 3D interaction methods for scientific exploration Collaborative visualization tools for multidisciplinary teams Recent trends in his publications highlight advancements in: Graph neural networks for biomedical data Memory-efficient segmentation algorithms Perceptual studies in VR environments Annotation and analysis of placental vasculature Technological innovations in foot dynamics research He teaches courses in virtual reality design and scientific visualization, including: CSCI 1370 - Virtual Reality Design for Science CSCI 1951S - Virtual Reality Software Review CSCI 1951T - Surveying VR Data Visualization Software CSCI 2370 - Interdisciplinary Scientific Visualization

• Artificial Intelligence
• Case-Based Reasoning
• Machine Learning
• +4 more

Prof. Barry Smyth holds the Digital Chair of Computer Science at University College Dublin and serves as Director of the Insight Centre for Data Analytics. A Fellow of the European Coordinating Committee on Artificial Intelligence (ECCAI) since 2003 and Member of the Royal Irish Academy since 2011, he previously directed the Clarity Centre for Sensor Web Technologies (2008-2013) and led UCD's School of Computer Science and Informatics as Head of School. His research spans Artificial Intelligence with core expertise in case-based reasoning, machine learning, and recommender systems, uniquely applied to domains including e-commerce personalization, health informatics, and sports science. Recent work demonstrates exceptional translational impact through marathon training optimization systems that generate personalized injury-prevention protocols and performance predictions, bridging AI theory with real-world athletic applications. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary AI applications: 60% focus on sports science (particularly marathon running), 25% on privacy-enhanced recommender systems, and 15% on financial time-series analysis. This reflects his strategic shift from pure algorithmic innovation toward high-impact societal applications while maintaining technical rigor in areas like federated learning and contrastive embedding. Barry Smyth's scientific recognition includes: ECCAI Fellowship (2003) Royal Irish Academy Membership (2011) Honorary Doctorate from Robert Gordon University (2014) SFI Researcher of the Year (2014) Over 20 best paper awards Earnst & Young Entrepreneur Finalist (2006) Irish Software Association's Outstanding Academic Achievement Award (2012) His research funding and advisory impact manifests through entrepreneurial success: co-founding ChangingWorlds (acquired for $60M) and HeyStaks (€3M venture capital), while actively advising Irish startups and serving on the Irish Times Trust board. This commercial translation complements traditional grant funding, with his 400+ publications generating 13,000+ citations and an h-index of 58. Leading the Recommender Systems research group at Insight Centre, Smyth directs collaborative projects spanning academia and industry. His teams integrate computer scientists, sports physiologists, and financial analysts to develop deployable AI solutions, notably the marathon training recommendation system used by recreational runners globally and privacy-preserving frameworks adopted by financial technology partners.

Yeonghyeon GuView profile

Assistant Professor

• Artificial Intelligence
• Meta Learning
• Transfer Learning
• +2 more

Yeonghyeon Gu serves as Assistant Professor in the Department of Artificial Intelligence Data Science at Sejong University, South Korea, a position held since 2022 after progressing from Principal Researcher (2014-2019) to Acting Professor (2019-2022). He maintains active affiliation with the university's AI Convergence Research Center and has produced 84 research outputs with 795 Scopus citations and an h-index of 14. His academic credentials include: B.A. from Sejong University (2004) M.A. from Sejong University (2006) Ph.D. from Sejong University (2014) Dr. Gu's research centers on Artificial Intelligence with specialization in Meta Learning, Transfer Learning, and Deep Learning methodologies. His work demonstrates strong interdisciplinary application across robotics, agricultural technology, energy systems, and meteorology. Key contributions include district heater load forecasting using parallel CNN-LSTM attention, image-based hot pepper disease diagnosis, and potato late blight prediction models. Analysis of his 2024-2025 publications reveals concentrated innovation in hybrid AI architectures, particularly combining graph networks with reinforcement learning for blockchain security and integrating physical models with deep learning for weather prediction. His work consistently addresses real-world engineering challenges through novel neural network applications while maintaining strong theoretical foundations in transfer learning frameworks. No scientific awards were documented in the source materials. While specific student advisees and grant details weren't listed, his extensive publication record (29 outputs in 2025 alone) and international collaborations suggest active mentorship and research funding. His work shows particular strength in cross-institutional projects with researchers from Turkey, Nigeria, Saudi Arabia, and South Korea. As a core member of Sejong University's AI Convergence Research Center, Dr. Gu contributes to institutional initiatives bridging AI theory with practical implementation across multiple sectors. The center's structure facilitates his interdisciplinary approach, connecting computer science with engineering, agriculture, and environmental science domains through shared computational infrastructure and collaborative research frameworks.

• Data Mining
• Machine Learning
• Relational Learning
• +12 more

Jennifer Neville is a Senior Principal Researcher at Microsoft Research Redmond and holds the Samuel Conte Chair Professor of Computer Science and Statistics at Purdue University. With over 100 publications and 10K citations, her research spans data mining, machine learning, and AI algorithms for relational and networked domains including social networks, epidemiology, and web analytics. Education: BS in Computer Science, University of Massachusetts Amherst (2000) MS in Computer Science, University of Massachusetts Amherst (2004) PhD in Computer Science, University of Massachusetts Amherst (2006) Her work focuses on relational learning techniques that exploit connections between entities to enhance pattern discovery. Recent research explores large language models (LLMs), emphasizing alignment with user intent through interaction at scale, while addressing statistical biases from graph structures. Selected scientific awards include the NSF Career Award (2012), ICDM Best Paper (2009), and IEEE’s 10 to Watch in AI (2008). She served on the AAAI Executive Council (2015-2018) and chaired multiple conferences including SIAM Data Mining (2019) and ACM Web Search (2016). Contact: neville@cs.purdue.edu jenneville@microsoft.com

Ben RaphaelView profile

Professor

• Bioinformatics
• Computational Biology
• Cancer Genomics
• +6 more

Ben Raphael is a Professor in the Department of Computer Science at Princeton University, with affiliations at the Lewis-Sigler Institute for Integrative Genomics, Omenn-Darling Bioengineering Institute, and Center for Statistics and Machine Learning. He is also an Affiliate Faculty member at the Rutgers Cancer Institute of New Jersey, Irving Institute for Cancer Dynamics at Columbia University, and New York Genome Center. His research focuses on computational methods for analyzing large-scale biological data, emphasizing cancer evolution, network/pathway analysis, and structural variation in genomes. Research Trends: His recent work spans cancer lineage trees, spatial transcriptomics, optimal transport for developmental models, and network analysis of mutations. Articles highlight applications in prostate cancer, pancreatic cancer, and single-cell genomics. Scientific Awards: 2024 ACM Fellow 2023 RECOMB Test of Time Award 2022 RECOMB Test of Time Runner-Up 2021 ISCB Innovator Award 2021 RECOMB Best Paper Runner-Up 2020 ISCB Fellow 2020 AACR Team Science Award 2011 NSF CAREER Award 2013 RECOMB Best Paper 2010-2012 Sloan Research Fellowship Advising: He has mentored numerous Ph.D. students and postdoctoral fellows, many of whom have transitioned to academic and industry roles. Current advisees include Uthsav Chitra, Gillian Chu, and Alexander Strzalkowski. Labs & Teams: Raphael leads the Raphael Lab at Princeton, developing tools like HotNet2, CHISEL, and HATCHet for cancer genomics and network analysis.

• Neuroimaging
• Alzheimer's Disease
• Neurodegenerative Disorders
• +5 more

Dr. Elisenda Bueichekú serves as a Research Scientist at the Yale School of Medicine within the Radiology & Biomedical Imaging Department . Her work focuses on neuroimaging applications in neurodegenerative disorders, particularly Alzheimer’s disease progression and memory systems analysis. She collaborates with leading researchers in the Mental Health PET Radioligand Development (MHPRD) Program and the PET Core facility. Primary Affiliation: Yale School of Medicine Department: Radiology & Biomedical Imaging Research Programs: MHPRD Program, PET Core Her research integrates multimodal neuroimaging techniques to investigate: Tau pathology propagation patterns Connectome-based disease modeling Cortical hub analysis in memory systems Cognitive resilience mechanisms Neuroimaging of anosognosia Functional network contributions to creativity Dr. Bueichekú contributes to translational research through: Advanced PET/MRI methodologies Spatiotemporal disease progression mapping Development of imaging-based biomarkers Recent publications demonstrate expertise in: Alzheimer’s disease neuroimaging Tau and amyloid co-accumulation Functional connectivity analysis Memory system characterization Scientific contributions include: 2025 Center for Brain & Mind Health Pilot Grant Key publications in top-tier journals (Nature Aging, Alzheimer's & Dementia, etc.)

• Information Theory
• Machine Learning
• Cybersecurity
• +3 more

Oliver Kosut is an Associate Professor at the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU), where he has worked since August 2012. He was promoted to Associate Professor in 2018 and received the NSF CAREER award in 2015. His research spans information theory, machine learning, cybersecurity, and power systems, with a focus on theoretical foundations and applications to privacy, security, and smart grid resilience. Education: B.S. in Electrical Engineering and Mathematics from MIT (2004), Ph.D. in Electrical and Computer Engineering from Cornell University (2010) His recent work explores differential privacy, adversarial robustness in decentralized networks, and information-theoretic approaches to cybersecurity. He advises graduate students with strong mathematical backgrounds, particularly those interested in fundamental theory for applied problems. Scientific accolades include the IEEE Information Theory Society Distinguished Lecturer (2023–2024) and NSF CAREER award. Key research areas: Information Theory, Privacy, Machine Learning, Power System Security Students: Obai Bahwal, Atefeh Gilani, Naima Tasnim (current); Nima Bazargani, Andrea Pinceti, Jingwen Liang, Zhigang Chu, Fatemeh Hosseinigoki, Nematollah Iri, Kousha Kalantari, Roozbeh Khodadadeh (former)

Sriraam NatarajanView profile

Professor

• Artificial Intelligence
• Machine Learning
• Healthcare Applications
• +5 more

Sriraam Natarajan is a Professor and Director of the Center for Machine Learning and StaRLing Lab at The University of Texas at Dallas (UTD), part of the Erik Jonsson School of Engineering & Computer Science. He holds additional roles as a hessian.AI Fellow at TU Darmstadt and an RBCDSAI Distinguished Faculty Fellow at IIT Madras. His expertise spans Artificial Intelligence, Machine Learning, and their applications in healthcare, with a focus on Relational Learning, Reinforcement Learning, and Graphical Models. He has been honored as an AAAI Fellow (2025), elected to the AAAI Executive Council, and recognized with the UTD Outstanding Graduate Teaching Award. Education: Completed his PhD in Computer Science at Oregon State University in 2007 under Prof. Prasad Tadepalli. Postdoctoral work at the University of Wisconsin-Madison with Professors Jude Shavlik and David Page. Previously served as faculty at Indiana University and Wake Forest School of Medicine. Research: Active in developing AI systems for healthcare, including predictive models for gestational diabetes and cardiac arrest in children. His work emphasizes integrating human knowledge into machine learning (e.g., Human-in-the-Loop systems) and advancing statistical relational AI frameworks like Markov Logic Networks and Probabilistic Circuits. Publications: Over 100 peer-reviewed articles, including notable works on causal learning, relational reinforcement learning, and knowledge graph construction. Recent focuses include explainable AI and scalable probabilistic models. Awards: AAAI Fellow, RBCDSAI Distinguished Fellowship, UTD Teaching Excellence Award. Advising and Collaboration: Mentored over 30 students, many now in academia and top institutions like IBM Research, Facebook, and Microsoft. Collaborates globally on projects like GLAD (Glocalized Anomaly Detection) and StaRLing Lab initiatives. Labs/Teams: Leads the StaRLing Lab, focusing on statistical relational AI, and directs UTD's Center for Machine Learning. Engaged in interdisciplinary projects with healthcare, robotics, and data science communities.

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +5 more

Dr. Kevin G. Jamieson is a faculty member at the University of Washington , School of Computer Science , with prior affiliations at the University of California, Berkeley (Department of Electrical Engineering and Computer Sciences) and the University of Wisconsin-Madison (Department of Electrical and Computer Engineering). His work spans machine learning, reinforcement learning, bandit algorithms, and robotics. Current university: University of Washington Academic rank: Professor His research focuses on: Bandit algorithms and sequential decision-making Optimization in non-stationary environments Reinforcement learning with real-world applications Multi-agent systems and game theory Efficient data selection for multimodal learning Human-in-the-loop AI systems Recent publications highlight his expertise in pure exploration strategies, robotic manipulation, and bridging simulation-to-reality gaps in RL. He has mentored numerous collaborators, though formal student advising details are not explicitly listed here. No scientific awards are mentioned in the provided data.

• Machine Learning
• Graph Neural Networks
• Bioinformatics
• +3 more

Tamara Drucks is a PreDoc Researcher at the Department of Machine Learning, Technische Universität Wien. She specializes in machine learning, with a focus on graph neural networks, bioinformatics, and optimization algorithms. Drucks teaches courses including 'Introduction to Machine Learning' and 'Theoretical Foundations and Research Topics in Machine Learning.' Her research explores expressive power of graph networks and applications in phylogenetic modeling. Key projects include the StruDL initiative (2023–2027) focusing on maximally expressive GNNs for outerplanar graphs. She has advised one PhD student, Martin Plattner, on optimization techniques in machine learning. Publications span theoretical advancements in GNNs and practical applications in computational biology. Drucks holds a Diploma in Technical Mathematics from TU Wien (2021) and is involved in interdisciplinary research at the intersection of AI and biological data analysis.

• Machine Learning
• Medical Image Analysis
• Deep Learning
• +5 more

Erik Bjørnager Dam is a Professor in the Machine Learning section at the Department of Computer Science, University of Copenhagen (UCPH). His research spans theoretical foundations of machine learning to practical applications in medical data analysis, sustainability, and materials science. His key research interests include: Small-scale and resource-efficient deep learning Medical image analysis and segmentation Sustainable and environmentally conscious AI development Graph neural networks for materials science Resource-constrained AI systems Professor Dam's recent publications demonstrate a strong focus on making AI more accessible and sustainable while maintaining high performance standards. His work on 'Performance Per Resource Unit' metrics addresses critical challenges in deploying AI in resource-limited environments, particularly in healthcare applications. His research bridges theoretical machine learning with practical implementations across multiple domains. His notable professional activities include: Co-founding Cerebriu A/S (since 2018) Co-founding Biomediq A/S (since 2008) Delivering lectures on AI's role in green transition (April 24, 2023) Media contributions on deep learning applications in plant research (September 13, 2018) With 74 documented research outputs, Professor Dam maintains an active research profile with significant contributions in 2023-2025 across medical imaging, sustainable AI, and materials science applications.

Abdullah MueenView profile

Professor

• Temporal Data Mining
• Blockchain Data Mining
• Social Media Mining
• +5 more

Abdullah Mueen is a Professor and Associate Chair in the Department of Computer Science at the University of New Mexico (UNM), where he has been since 2013. Previously, he worked as a Scientist in the Cloud and Information Sciences Lab at Microsoft Corporation. Research Interests : His work focuses on Temporal Data Mining , with emphasis on efficiency , interactivity , and interpretability . Key areas include Blockchain Data Mining (e.g., BitLink for Bitcoin cluster analysis), Seismic Data Mining (e.g., PAW for aftershock detection), and Social Media Mining (e.g., DeBot for Twitter bot detection). Article Trends : His recent publications span four domains: Seismology : Algorithms for earthquake data analysis (e.g., focal depth inference, aftershock classification). Blockchain : Temporal linkage of Bitcoin addresses (BitLink) and cryptocurrency fraud detection. Traffic Safety : Multi-LiDAR data fusion for real-time road safety monitoring. Time Series Methods : Innovations like MASS similarity search and DAMP anomaly detection for massive datasets. Scientific Awards : ACM SIGKDD Test-of-Time Award (2022) UNM Provost Research Leader Award UNM School of Engineering Junior Faculty Research Excellence Award KDD 2012 Doctoral Dissertation Contest Runner-Up KDD 2012 Best Paper Award Advising and Grants : He has mentored 11 PhD students now employed at institutions like Microsoft, Meta, and Lawrence Livermore National Lab. His research is funded by NSF , NIH , DARPA , AFRL , NEC , Exxon , Microsoft , and LANL .

Long NguyenView profile

Professor

• Nonparametric Bayesian Statistics
• Optimal Transport
• Machine Learning
• +4 more

Long Nguyen is a Professor of Statistics at the University of Michigan, Ann Arbor, with a courtesy appointment in Electrical Engineering and Computer Science. He is affiliated with the Michigan Institute for Data Science (MIDAS) and the Vietnam Institute for Advanced Study in Mathematics (VIASM). His research focuses on Bayesian nonparametrics, optimal transport, machine learning, and spatiotemporal data analysis. Nguyen holds a PhD in Computer Science from UC Berkeley and has held postdoctoral positions at Duke University and the Statistical and Applied Mathematical Institute. Education: B.Sc. in Computer Science from Pohang University of Science and Technology; M.Sc. in Mathematics from Arizona State University; Ph.D. in Computer Science from UC Berkeley (2007). Research Interests: Bayesian nonparametric methods, optimal transport theory, statistical inference for complex models, and applications in spatiotemporal data, functional data analysis, and hierarchical modeling. He emphasizes developing scalable algorithms and geometric approaches for statistical learning. Editorial Roles : Annals of Statistics Journal of Machine Learning Research SIAM Journal on Mathematics of Data Science Bayesian Analysis Awards : IMS Fellow, ASA Fellow NSF CAREER Award IEEE Signal Processing Young Author Award L. J. Savage Dissertation Award (via student Aritra Guha) Advising & Collaborations : Guided over 20 PhD students and postdocs, many now in academia and industry. Collaborates on projects in AI ethics, music theory, and environmental data science. Active in organizing summer schools in Vietnam on Bayesian statistics and machine learning. Labs & Teams : Co-leads the Statistical Machine Learning reading group at U-M and collaborates with the VIASM on advanced mathematical research in Hanoi.

Michael Qizhe ShiehView profile

Assistant Professor

• Large Language Models
• Deep Learning
• Natural Language Processing
• +4 more

Michael Qizhe Shieh is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), affiliated with the Tree and Rock AI Lab (TRAIL). He holds a PhD and Master's from Carnegie Mellon University (Machine Learning and Language Technologies) and a Bachelor's from Shanghai Jiao Tong University's ACM Class. His research focuses on Large Language Models, Deep Learning, and Natural Language Processing, with notable contributions to semi-supervised learning techniques like Noisy Student and UDA, and the RACE benchmark for reading comprehension. Education: PhD in Machine Learning, Carnegie Mellon University (2020) Master's in Language Technologies, Carnegie Mellon University (2018) Bachelor's in Computer Science, Shanghai Jiao Tong University (2016) His research explores robustness, safety, and scalability of AI systems. He has served as Area Chair for top conferences like NeurIPS, ICML, and ICLR. Current research directions include adversarial robustness, LLM self-evaluation, and alignment mechanisms. His lab, TRAIL, emphasizes foundational AI research. Selected contributions include: Developing UDA and Noisy Student techniques for semi-supervised learning Creating the RACE benchmark for exam-based reading comprehension Advancing methods for LLM safety and adversarial defense Prospective students are encouraged to apply to NUS's PhD program for collaborative research opportunities.

David WrightView profile

Professor

• Smart Home Technology
• Artificial Intelligence
• Usability Studies
• +3 more

Dr. David Wright is a Professor in the Department of English and Technical Communication at Missouri University of Science and Technology (Missouri S&T). He joined the faculty in 2007 after prior roles at NASA’s Education Project, Oklahoma state government, and the software industry. He holds a Ph.D. in Technical Communication (Oklahoma State University, 2007), an M.S. in Higher Education Administration (1996), and a B.S. in Organizational Psychology (1993), all from Oklahoma State University. His research focuses on smart home technology and artificial intelligence, particularly examining human-AI interaction through usability and user experience (UX) testing. He also explores technology diffusion, technical communication practices in emerging technologies, and educational methodologies for technical fields. His work integrates interdisciplinary approaches, blending engineering, sociology, and computer science. Recent publications highlight his contributions to IoT usability, smart home adoption challenges, and the intersection of AI ethics with virtual assistants. He has also authored studies on knowledge graph design, technical documentation in software development, and educational initiatives in computer science and healthcare. Dr. Wright teaches courses in technical writing, usability studies, and web-based communication. His academic service includes curriculum development and advising on technical communication pedagogy. While no specific awards are listed, his extensive publication record reflects sustained scholarly impact in his fields.