Find Academic Supervisors

Hong HuView profile

Assistant Professor

• Statistical Signal Processing
• Machine Learning
• High-Dimensional Data Analysis

Hong Hu is an Assistant Professor in the Department of Statistics & Data Science and the Department of Electrical & Systems Engineering at Washington University in St. Louis, within the McKelvey School of Engineering. He holds a PhD from Harvard University. Hong Hu earned his PhD from Harvard University. His research focuses on statistical signal processing and machine learning, with an emphasis on the theoretical foundations of algorithms for extracting information from high-dimensional data. He investigates fundamental properties of high-dimensional random systems, such as phase transitions and universality, to understand and improve high-dimensional information processing algorithms. His work also aims to apply these principles to real-world applications. His recent publications explore topics including heterogeneous computation resource allocation using data-driven methods and the application of deep learning in multilingual sentiment analysis, reflecting his interdisciplinary approach to machine learning and data science challenges. Hong Hu’s advising and grant activities are not detailed in the provided information.

William FitzgeraldView profile

Lecturer

• Probability theory
• Random matrix theory
• Random growth models
• +3 more

Dr. William Fitzgerald is a Lecturer in Probability at The University of Manchester, focusing on probability theory and mathematical physics models. His research includes random growth models, interacting particle systems, and random matrices. He holds a PhD from the University of Warwick (2019) and previously served as a Postdoctoral Research Fellow at the University of Sussex (2019–2021). Research interests span determinantal/Pfaffian point processes, non-colliding stochastic processes, and Brownian motion applications. His work combines rigorous mathematical analysis with probabilistic models from physics. Recent articles explore polynuclear growth dynamics, ordered exponential random walks, and Fredholm Pfaffians in interacting systems. He actively supervises PhD students and currently offers funded projects in probability theory. No scientific awards are explicitly mentioned in the profile. Academic history includes postdoctoral research at Sussex University and doctoral training at Warwick. Collaborations with researchers like Denisov, Tribe, and Zaboronski are evident in co-authored works. His research bridges theoretical probability with applications in particle systems and random matrix theory.

Rosario B. Jaime-LaraView profile

Assistant Professor

• Nutrition
• Neurophysiology
• Obesity Research
• +5 more

Rosario B. Jaime-Lara is an Assistant Professor at the University of California, Los Angeles (UCLA) School of Nursing. She holds advanced degrees including a PhD in Nursing from the University of Pennsylvania, MSN from Columbia University, and dual BS degrees in Nursing and Biological Sciences from University of Pennsylvania and UC Davis. Her research focuses on neurophysiological mechanisms of eating behavior, nutritional disparities in Mexican-American communities, and chemosensory science. Key themes include obesity research, sensory neuroscience, and translational clinical studies. She employs rodent models and clinical research methodologies to explore taste/smell physiology and metabolic health. Jaime-Lara has received over 17 honors including the 2022 Hommer Memorial Award and multiple diversity fellowships. Her work spans interdisciplinary collaborations in genomics, microbiome research, and health IT interventions for chronic disease management. Recent publications emphasize olfactory dysfunction in coronaviruses, fat taste mediators, and metabolic profiling. Her research bridges basic science and clinical practice, with particular attention to underserved populations.

• Optical Physics
• Applied Mathematics
• Artificial Intelligence and Image Processing
• +3 more

T V Raziman is a Researcher in the Department of Mathematics at Imperial College London within the Faculty of Natural Sciences. He specializes in nanophotonics, focusing on light-matter interactions through theoretical models and simulations. His academic journey includes postdoctoral roles at Eindhoven University of Technology (2017–2021) and École Polytechnique Fédérale de Lausanne (2016–2017), and holds a PhD from EPFL and an Integrated MSc in Physics from IIT Kanpur. Raziman's research spans Optical Physics , Applied Mathematics , and Artificial Intelligence , with a strong emphasis on Nanophotonics . He explores cutting-edge topics like synthetic optical motion, retinomorphic machine vision, and topological optimization of nanostructures. His work bridges theoretical frameworks with practical applications in photonics and materials science. Key contributions include studies on semiconductor network lasers, surface lattice resonance lasers, and neural network-driven laser mode control. He collaborates within the Complex Nanophotonics Group , advancing interdisciplinary projects at the intersection of photonics and machine learning. No scientific awards are explicitly mentioned, but his prolific publication record highlights sustained innovation in photonics research. He has advised no recorded students, focusing primarily on independent and collaborative research efforts.

Haiyan HuangView profile

Professor

• Computational Biology
• Applied Statistics
• High Dimensional Data Analysis
• +3 more

Haiyan Huang is a Professor and Chair of the Department of Statistics at the University of California, Berkeley. She is affiliated with the Center for Computational Biology and the Graduate Group in Biostatistics. Her research focuses on computational biology, applied statistics, and high-dimensional genomic data analysis, with an emphasis on network modeling and translational bioinformatics. Education details are not explicitly listed, but her academic background includes a PhD in Statistics. She has advised numerous graduate students and postdocs, contributing to impactful research in statistical methods for genomics and bioinformatics. Her work spans statistical methodology development, including sparse canonical correlation analysis, biclustering, and single-cell RNA sequencing analysis. Notable projects include predicting tumor heterogeneity, designing random heteropolymers, and linking protein expression to cell motility. Publications highlight her contributions to gene network inference, reproducibility in high-throughput experiments, and integrating multi-platform genomic data. Awards include recognition by Faculty of 1000 Biology for her 2010 PNAS work. Huang teaches advanced courses in computational biology, statistical theory, and consulting. Her lab collaborates with experts in bioengineering, chemistry, and pediatrics, fostering interdisciplinary research. Current projects include precision medicine, deep learning applications, and probabilistic modeling of biological systems. Her team uses variational inference, hidden Markov models, and machine learning to address challenges in single-cell analysis and pharmacogenomics. The lab’s work has been published in top journals like Nature, PNAS, and Bioinformatics, reflecting her leadership in computational genomics.

Ruan ElliottView profile

Senior Lecturer

• Nutritional Metabolism
• DNA Damage and Repair
• Nutrigenomics
• +4 more

Dr Ruan Elliott is Dean of the Doctoral College and Senior Lecturer in Nutrition at the University of Surrey's School of Biosciences. He holds a PhD in Nutritional Metabolism (2017) and BA in Natural Sciences from Cambridge (1988). His research focuses on nutritional modulation of DNA repair mechanisms, functional genomics in nutrigenomics, and micronutrient homeostasis of iron, copper, selenium, and zinc. He has led studies on vitamin D efficacy, food bioavailability, and oxidative stress biomarkers. Education: PhD (University of Surrey, 1992), BA (University of Cambridge, 1988) Previous Roles: Senior Scientist (Institute of Food Research, 1994-2009), Postdoctoral Fellow (University of British Columbia, 1992-1994) His research integrates molecular biology and nutrition to address health impacts of dietary components. Recent work explores oat-based milk alternatives' sensory profiles, vitamin D supplementation efficacy, and DNA repair pathways in cardiovascular disease. He collaborates internationally on projects like the D2-D3 Study and NuGO initiatives. Publications emphasize translational research, including systematic reviews on insect protein efficacy and meta-analyses of vitamin D supplementation. His work bridges lab-based models (e.g., Caco-2 cells for iron studies) and clinical applications. No scientific awards were explicitly listed in the provided texts. He advises on doctoral training programs and has contributed to grant-funded projects like the University of Surrey's food and infection biosciences initiative (2024). Collaborations include studies on probiotics' immune effects and transcriptomic analysis of metabolic conditions.

Mark MeckesView profile

Professor

• Geometry of Metric Spaces
• High Dimensional Probability
• Random Matrix Theory
• +3 more

Mark Meckes is a Professor at Case Western Reserve University, affiliated with the Department of Mathematics, Applied Mathematics, and Statistics within the College of Arts and Sciences. His research focuses on Geometry of Metric Spaces and High-Dimensional Probability, with contributions to topics like metric magnitude, convex bodies, and random matrix theory. He is reachable via mark.meckes@case.edu . His research interests delve into the geometric and probabilistic structures of metric spaces, including intrinsic volumes, spectral analysis, and applications to quantum mechanics and ecology. Recent work explores extremal metric spaces, fluctuations in random matrix ensembles, and quenched limits in stochastic models. Publications since 2015 highlight trends in random matrix theory, geometric measure theory, and interdisciplinary applications. Notable topics include the circular law for complex Ginibre ensembles, self-similarity in unitary ensembles, and biodiversity optimization. No scientific awards are explicitly listed in the provided material. Advising and grant details are not specified, though his work suggests active participation in academic mentorship. No lab or team affiliations are noted here.

• Econometrics
• Labor Economics
• Public Policy
• +3 more

Gerard J. van den Berg is a Professor at the University of Groningen, holding dual affiliations in the Faculty of Economics and Business (Department of Economics, Econometrics and Finance) and the Faculty of Medical Sciences (Department of Epidemiology). His research focuses on labor economics, public policy, health economics, and econometrics. He has contributed to studies on unemployment dynamics, labor market policies, and transgenerational health effects. Notably, he has led randomized controlled trials evaluating integration agreements for the unemployed and examined the impact of economic conditions on health outcomes. He is a Fellow of the Econometric Society and a member of the Royal Netherlands Academy of Arts and Sciences. His work spans interdisciplinary areas including epidemiology, development economics, and behavioral economics. Education details are not explicitly provided in the text, but his roles suggest advanced academic training in economics and epidemiology. He has been involved in numerous collaborative projects across institutions such as the Institute for Employment Research (IAB), IZA, and J-PAL. His recent publications explore topics like minimum wage effects, biomarker-driven health studies, and the long-term impacts of early-life environments. Awards: Royal Netherlands Academy membership, Fellow of Econometric Society. Grants/Projects: Evaluation of labor market programs (IZA), research on economic determinants of diabetes (Health Economics). Labs/Teams: Affiliated with multiple institutions including CEPR, ZEW, and IFAU.

Catherine CarrView profile

Senior Lecturer

• Music Therapy
• Mental Health
• Arts Therapies
• +3 more

Dr Catherine Carr is a Senior Clinical Lecturer in Music Therapy at Queen Mary University of London, affiliated with the Centre for Psychiatry and Mental Health. She also serves as a Music Therapist at East London NHS Foundation Trust and teaches/supervises MA Music Therapy students at the Guildhall School of Music and Drama. Her roles include representing the British Association for Music Therapy on the Council for Allied Health Professions Research (CAHPR) and editorial board involvement with Music & Science . She co-founded the Alliance for Recovery Research in Music Therapy (ARRIMT) and the International Music Therapy Clinical Trials Network (IMTCT-Net). Dr Carr holds multiple NIHR awards, including a Clinical Doctoral Research Fellowship and NIHR Clinical Lectureship. Her research focuses on evaluating music therapy processes/outcomes, developing interventions for mental health populations, and improving patient engagement in clinical trials. She leads the multicenter ERA study (funded by NIHR HTA) and previously led the SYNCHRONY trial on group songwriting for chronic depression. Her clinical work involves music therapy for older adults with mental health needs, combined with staff psychological supervision. She completed advanced training in the Bonny Method of Guided Imagery and Music Imagery techniques. Dr Carr supervises PhD students exploring arts therapies and mental health interventions funded by Barts Charity. Key achievements include NIHR Clinical Trials Fellowship (2019) and developing shared therapeutic models across art/dance/music therapies. She actively contributes to policy development through her network affiliations and has published extensively on music therapy efficacy and methodology.

Do Young EunView profile

Professor

• Distributed Optimization
• Machine Learning
• Network Modeling
• +5 more

Do Young Eun is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University (NC State), with affiliations in Computer Science and Operations Research. He holds a Ph.D. from Purdue University and M.S./B.S. degrees from KAIST, Korea. His research focuses on distributed optimization for machine learning, network modeling, and algorithms for social/wireless networks, with applications in epidemic analysis and graph analytics. Education: Ph.D. in Electrical and Computer Engineering, Purdue University (2003) M.S. in Electrical Engineering, KAIST (1997) B.S. in Electrical Engineering, KAIST (1995) Research Interests: Distributed optimization and machine learning Network modeling and performance analysis Epidemic modeling and control Graph analytics and social network analysis Stochastic processes and algorithms Highlighted Awards: NSF CAREER Award (2006) Outstanding Paper Award, ICML 2023 Best Paper Awards at IEEE ICCCN (2005), IPCCC (2006), NetSciCom (2015) Best Student Paper Award, ACM MobiCom 2007 Advising & Grants: Current advisees include Jie Hu, Yi-Ting Ma, and Feiya Xiang NSF Grant (2024–2027): 'Toward Maximally Efficient Sampling and Optimization for Decentralized Learning' Supervised over 10 Ph.D. students, many in academic or industry leadership roles Labs/Teams: His research group focuses on interdisciplinary projects at the intersection of networking, machine learning, and stochastic systems, with collaborations in academia and industry.

Chao TianView profile

Associate Professor

• Information Theory
• Distributed Data Storage
• Coding Theory
• +6 more

Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.

Peter ChinView profile

Professor

• Machine Learning
• Artificial Intelligence
• Differential Geometry
• +8 more

Professor Peter Chin is a Professor of Engineering at Dartmouth College and Director of the Learning, Intelligence + Signal Processing (LISP) Lab. He holds affiliations with the Thayer School of Engineering and serves as Associate Editor of IEEE Transactions on Computational Social Systems. His research bridges signal processing, machine learning, game theory, and differential geometry, with applications in cybersecurity, healthcare, and network analysis. Education: Bachelor of Science in Electrical Engineering, Computer Science, and Mathematics from Duke University (1993) Doctor of Philosophy in Mathematics from MIT (1998) Research Interests: Chin’s work focuses on fundamental questions at the intersection of machine learning, game theory, and signal processing. His lab explores topics like adversarial defense mechanisms, topological machine learning, and computational neuroscience. Recent projects include cybersecurity resilience modeling, medical imaging enhancements via GANs, and multi-agent reinforcement learning frameworks. Publications Trends: His most recent articles address cutting-edge challenges in cybersecurity (e.g., autonomous defense systems), medical AI (e.g., Alzheimer’s classification), and adversarial robustness. A notable 2025 focus is on quantitative resilience modeling for cyber defense, reflecting growing demand for AI-driven security solutions. Awards: Faculty Scholar Award, Duke University George Sherred III Award, Duke University Julia Dale Memorial Award, Duke University Grants & Leadership: Recipient of DARPA cybersecurity research grants Co-chair for SPIE/DSS Cyber Sensing Conference (2013–2020) Developed novel compressive sensing microscope for biological imaging LISP Lab: This interdisciplinary lab pioneers projects like nFlip (multiplayer security game models) and topological machine learning frameworks, emphasizing practical applications of theoretical advancements.

Holden LeeView profile

Assistant Professor

• Machine Learning
• Probability
• Theoretical Computer Science
• +3 more

Holden Lee is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University, where he joined in 2022. His research focuses on the theoretical foundations of machine learning, probability, and their intersections with theoretical computer science. He explores probabilistic methods in modern machine learning, including deep learning-based generative models and convergence guarantees for sampling algorithms like Markov Chain Monte Carlo. Prior to JHU, he was a postdoc at Duke University and a Simons Fellow at UC Berkeley. He holds a PhD in Mathematics from Princeton University and degrees from MIT and the University of Cambridge. Education: PhD in Mathematics, Princeton University, 2019 MASt in Pure Mathematics, University of Cambridge, 2014 BSc in Mathematics, MIT, 2013 Research Interests: Machine Learning Theory Probabilistic Sampling Methods Generative Models Statistical Learning Theory His work emphasizes theoretical rigor, particularly in understanding the success and limitations of deep learning algorithms and designing efficient sampling techniques beyond classical log-concave settings. Articles Trends: Lee’s recent publications (2022–2024) focus on advancing sampling algorithms for complex distributions, analyzing generative models, and improving convergence guarantees for methods like MCMC and score-based diffusion. His work bridges theory and practice, with applications in multimodal data, text generation, and dynamical systems. Awards: Simons Fellow at UC Berkeley (2021) Advising & Grants: Lee has contributed to projects at NeurIPS and collaborates on research in AI efficiency and theoretical guarantees. He teaches courses on probability and applied mathematics at JHU and Duke. Labs/Teams: His research group focuses on theoretical machine learning and probabilistic methods, with ongoing projects on scalable sampling algorithms and generative model analysis.

Agnieszka RoginskaView profile

Professor

• Immersive Audio
• 3D Audio Technology
• Spatial Sound
• +5 more

Agnieszka Roginska is a Professor of Music Technology at the Steinhardt School, New York University, specializing in immersive audio, spatial sound, and auditory displays. Her work integrates acoustic science with virtual reality and medical applications, including postural stability studies for vestibular rehabilitation. She holds leadership roles as AES President-Elect and co-edited the authoritative book 'Immersive Sound'. Education: B.A. in Piano Performance and Computer Applications in Music (McGill University, 1996); M.M. in Music Technology (NYU, 1998); Ph.D. in Music Technology (Northwestern University, 2004). Research focuses on 3D audio technologies, auditory displays for virtual environments, and sensory integration studies. She leads NYU's Music and Audio Research Lab (MARL) and advises the Society for Women in TeCHnology (SWiTCH) at NYU. Awards include AES Fellowship and leadership in international audio engineering societies. Recent publications emphasize audio's role in postural control, distributed music performance frameworks (Holodeck), and VR sound design. Her work bridges technical innovation with artistic applications, including collaborative music systems in mixed reality.

Hege Bjøkne WathneView profile

Associate Professor

• eHealth Interventions
• Chronic Illness Management
• Post-Hospital Care
• +3 more

Hege Bjøkne Wathne is an Associate Professor in Intensive Care Nursing at the Department of Care and Ethics, University of Agder. She is affiliated with the NFR project "eHealth from Hospital 2 Home" and the research program area "HevHelse". Her primary research focuses on eHealth interventions to improve health services post-hospital discharge, particularly for patients with chronic illnesses and non-communicable diseases. She holds a PhD candidate status in Health Sciences (2020–), a Master in Health Sciences (2011–2013), and certifications in Intensive Care Nursing (2006–2007) and Nursing Education (1996–1999). Her work emphasizes nurse-assisted remote monitoring systems, usability of digital health tools, and patient self-management strategies. Key publications explore eHealth feasibility in colorectal cancer care, heart failure management, and chronic illness recruitment strategies. She has presented at conferences like the Nordic Conference in Nursing Research and participated in interdisciplinary collaborations to design patient-centered digital health solutions. Current projects aim to bridge gaps between hospital and home care through innovative technology. Wathne’s contributions include protocol development for randomized controlled trials and co-creation methodologies involving patients in eHealth design. Her research aligns with broader goals of enhancing healthcare accessibility and continuity through technology-driven interventions.