Find Academic Supervisors

Guanghao QiView profile

Assistant Professor

• Statistical Genetics
• Genomics
• Single-cell RNA Sequencing Analysis
• +3 more

Guanghao Qi is an Assistant Professor in the Department of Biostatistics at the University of Washington. His research focuses on developing statistical and machine learning methods for multi-omics approaches in genetic studies, particularly integrating single-cell RNA-seq, GWAS, and functional genomic data. Key areas include single-cell eQTL analysis, Mendelian randomization, and multi-trait genetic association analyses. Education: PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health (2020), BS in Mathematics from Fudan University (2015). Research interests emphasize high-dimensional data analysis, allele-specific expression in single cells, and causal inference using genetic variants. Notable achievements include a 2025 NIH K01 award for developing methods to integrate single-cell eQTL and GWAS data, and the development of the TWiST method for single-cell transcriptome-wide association studies. Recent work highlights advancements in computational tools like SURGE for context-specific genetic regulation analysis, and evaluations of Mendelian randomization methods in studies of type 2 diabetes and cardiovascular disease. His work often bridges computational biology and statistical theory to address challenges in interpreting large-scale genomic datasets. Awards: NIH K01 Award (2025) Key Contributions: TWiST method (2025), SURGE framework (2024), HIPO power optimization (2018) Labs/Teams: Active collaborations in genomic epidemiology and statistical genetics, with a focus on single-cell multi-omics integration and causal inference methodologies.

Jiebo LuoView profile

Professor

• Computer Vision
• Natural Language Processing
• Machine Learning
• +7 more

Jiebo Luo is the Albert Arendt Hopeman Professor of Engineering and Professor of Computer Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD and has been affiliated with the Department of Computer Science since 2011, following a 15-year career at Kodak Research. His research spans computer vision, natural language processing, machine learning, data mining, computational social science, and digital health. Luo is an ACM Fellow, AAAI Fellow, IEEE Fellow, SPIE Fellow, and IAPR Fellow. Education: PhD (specific discipline not explicitly stated in text). Research interests include computer vision, machine learning, data mining, social media analysis, biomedical informatics, human-computer interaction, and ubiquitous computing. He co-authored the book Deep Neural Network for Medical Image Computing: Principles and Applications (Elsevier, 2022). His work has led to nearly 600 technical papers and 90+ U.S. patents. Research Trends: Recent work focuses on large language models (LLMs), multimodal systems, AI-driven social media analysis, and healthcare applications. Key areas include bias analysis in political simulations, video understanding, and benchmark development for AI-generated content evaluation. His research bridges theoretical advancements with practical applications in healthcare, social sciences, and multimedia systems. Awards: ACM SIGMM Technical Achievement Award (2021), IEEE Region 1 Technological Innovation Award (2018), Eastman Innovation Award (2004), and multiple best-paper recognitions at top conferences. Service & Leadership: Served as program co-chair for ACM Multimedia 2010, IEEE CVPR 2012, ACM ICMR 2016, and IEEE ICIP 2017. Currently Editor-in-Chief of IEEE Transactions on Multimedia (2020–2022). Editorial board roles include several IEEE Transactions journals and conferences. Labs & Teams: Leads research groups in computer vision and multimodal computing at the University of Rochester, collaborating on projects like UroSAM (kidney stone classification) and computational social science initiatives.

Karim SabraView profile

Professor

• Acoustics
• Structural Health Monitoring
• Biomechanics
• +7 more

Dr. Karim Sabra is a Professor at the George W. Woodruff School of Mechanical Engineering , Georgia Institute of Technology, specializing in Acoustics and Dynamics . He holds a Ph.D. from the University of Michigan (2003) and joined Georgia Tech in 2007 as an Assistant Professor. His research integrates theoretical and experimental approaches to study wave propagation in diverse fields including structural health monitoring, biomechanics, and ocean acoustics. Key areas of focus include passive imaging techniques using ambient noise and diffuse wave fields, with applications in non-invasive monitoring of mechanical systems and seismoacoustic environments. Education: Ph.D., University of Michigan, 2003 M.S., University of Michigan, 2000 M.Sc., École Nationale Supérieure de Techniques Avancées (France), 2000 Research Interests: Dr. Sabra’s work spans acoustics, structural health monitoring, biomechanical systems evaluation, underwater acoustics, and geophysics . Recent projects include developing passive elastography techniques for soft tissues using physiological vibrations and exploring ambient noise-based tomography for ocean environments. His interdisciplinary approach bridges multi-scale engineering challenges with multi-wave tools (acoustical, electrical, optical). Publications: His work focuses on advanced acoustic technologies, including underwater communication systems, passive acoustic identification tags, and ray-based tomography methods. Themes include seamount effects on sound propagation, machine learning for acoustic modeling, and environmental sensing using shipping noise. Awards: R. Bruce Lindsay Award (2011) Fellow of the Acoustical Society of America (2007) Institute of Acoustics A.B. Wood Medal (2009) Advising & Grants: Dr. Sabra mentors graduate students in acoustics and wave phenomena, emphasizing interdisciplinary collaboration. His research is supported by grants focused on underwater acoustics, environmental sensing, and biomedical applications. Labs/Teams: His research group develops novel sensors and algorithms for oceanographic and biomedical applications, collaborating with industry and academic partners.

Chengcheng HuangView profile

Assistant Professor

• Mathematical Biology
• Neuroscience
• Computational Neuroscience
• +3 more

Dr. Chengcheng Huang is an Assistant Professor in the Department of Neuroscience and Mathematics at the University of Pittsburgh. She holds affiliations with the Center for Neuroscience, the Mathematics Graduate Program, and the Program of Neural Computation at the Center for the Basis of Neural Cognition. Her research focuses on computational modeling of neural networks, exploring circuit dynamics and information processing in sensory systems, particularly the role of neural variability in context-dependent responses. Huang's work is supported by grants from the NIH BRAIN Initiative, NSF CAREER Award, Simons Foundation, and PITT Momentum Scaling Award. Education: PhD in Neuroscience from New York University (2015), B.Sc. in Mathematics from Nanjing University (2010). Postdoctoral training included roles at leading institutions. She teaches courses such as Applied Differential Equations and Introduction to Computational Neuroscience. Her lab, the Huang Lab for Neural Dynamics and Computation, investigates neural variability in network models and its implications for neural coding. Current projects include studying functional roles of interneuron subtypes, attentional modulation, and synaptic plasticity. The lab actively recruits postdoctoral fellows, graduate students, and undergraduates. Awards and recognition include prestigious grants and fellowships, reflecting her impactful contributions to computational neuroscience and mathematical biology.

Jinhan KimView profile

Researcher

• Software Engineering
• Mutation Testing
• Fault Localization
• +4 more

Jinhan Kim is a Postdoctoral Researcher at the Università della Svizzera italiana (USI) in the Faculty of Informatics, working in the TAU lab under Prof. Paolo Tonella. He earned his Ph.D. from KAIST under Prof. Shin Yoo, focusing on software engineering research in mutation testing, fault localization, and deep learning system testing. His work bridges traditional software engineering techniques with AI-driven methodologies, emphasizing AI4SE and SE4AI paradigms. Education: Ph.D. in Software Engineering, KAIST, 2023 Research Interests: Mutation Testing Deep Learning System Testing Autonomous Systems Testing Adversarial Attack Detection Empirical Software Engineering Service and Leadership: Organized SBFT 2026 and DeepTest 2026 (co-located with ICSE 2026) Program Committee Member for ASE, ISSTA, Mutation, and DeMeSSAI Board of Distinguished Reviewers for TOSEM (2024–2025) Labs and Teams: Active contributor to the TAU Lab at USI, focusing on advanced software testing and AI integration.

• Semiparametric Estimation
• Nonparametric Regression
• Robust Statistics
• +3 more

Dr. Serhan Sadikoglu is an Assistant Professor at Tilburg University's Tilburg School of Economics and Management (TiSEM), Department of Econometrics and Operations Research. He earned his PhD in Econometric Theory from Tilburg University in 2019. His research focuses on econometric theory, semiparametric estimation, robust statistics, and panel data analysis. He has published in prestigious journals such as Econometric Reviews and Econometrics Journal , with recent work addressing nonseparable panel models and robust estimation techniques. Sadikoglu teaches courses including Quantitative Methods, New Venture Creation and Growth, and Statistics for Econometrics. His research emphasizes methodological contributions to econometrics, particularly in handling complex data structures and improving estimation robustness.

Diego DonzisView profile

Professor

• Fluid Dynamics
• Turbulence
• Turbulent Mixing
• +3 more

Diego Donzis is a Professor in the Department of Aerospace Engineering at Texas A&M University, affiliated with the College of Engineering. He holds the Presidential Impact Fellow title. His work focuses on high-performance computing for fluid dynamics, particularly compressible turbulence, turbulent mixing, and shock-turbulence interactions. Donzis earned his Ph.D. and M.S. in Aerospace Engineering from the Georgia Institute of Technology. Research interests include large-scale simulations of turbulent flows, thermal boundary condition effects on turbulence, and the development of advanced numerical methods like Selected-Eddy Simulations (SES) for extreme-scale computing. His studies explore universality in turbulence scaling, energy spectra dynamics, and the interplay between compressibility and fluid mixing. Publications emphasize turbulence decay laws, shock-turbulence interactions, and the role of thermal non-equilibrium in turbulent flows. Notable contributions include advancing asynchronous algorithms for exascale CFD and analyzing density gradient statistics in compressible turbulence. Awards include the Presidential Impact Fellow distinction. Donzis collaborates on grants such as the Frontera Travel Grant for compressible turbulence research. His work bridges computational methods with fundamental fluid dynamics, addressing challenges in both numerical accuracy and physical modeling.

Frederi G. ViensView profile

Professor

• Probability Theory
• Stochastic Processes
• Quantitative Finance
• +7 more

Frederi G. Viens is a Professor of Statistics at Rice University, where he leads research in probability theory, stochastic processes, and their applications to finance, climate science, and agro-ecology. Previously, he was a full professor at Michigan State University (2016–2022) and Purdue University (2000–2015), serving as Department Chair and Director of Actuarial Science. His work bridges theoretical mathematics with practical problems in agriculture, economics, and nuclear physics. Education: Ph.D. Mathematics, University of California, Irvine (1996) M.S. Mathematics, University of California, Irvine (1991) Maîtrise de Mathématiques Pures, Université de Paris VII, France (1991) Research Interests: Probability Theory & Stochastic Analysis Quantitative Finance & Actuarial Science Climate Science & Bayesian Statistics Agro-ecology & Agricultural Economics His collaborative projects include climate modeling, nuclear physics simulations, and sustainable crop diversity initiatives like the DRIVES network. Awards & Honors: Fellow of the Institute of Mathematical Statistics (2012) Franklin Fellow, U.S. State Department (2010) Purdue College of Science Research Award (2013) Grants & Collaborations: Funded by the NSF, USDA, and private donors, Viens has organized major conferences and serves on editorial boards for journals like Annals of Finance . He advises transnational research groups, including Sustainability Lake Chad , addressing agrarian sustainability in West Africa. Labs & Initiatives: Founding member of the DRIVES agro-ecology collaborative and moderator of the Seminar on Stochastic Processes.

Fatemeh MollaeiView profile

Lecturer

• Motor Speech Disorders
• Parkinson’s Disease
• Neuroimaging
• +4 more

Fatemeh Mollaei is a Lecturer in Clinical Language Sciences at the University of Reading, affiliated with the School of Psychology and Clinical Language Sciences. She is a core member of the Centre for Integrative Neuroscience and Neurodynamics (CINN), focusing on neurophysiological mechanisms of speech disorders. Her work integrates behavioral, electrophysiological, and neuroimaging techniques to study Parkinson’s disease (PD) and develops neuro-rehabilitation methods using non-invasive brain stimulation (e.g., TMS, tDCS). Her research explores how sensory and motor systems interact during speech production in PD, employing tools like EEG, fMRI, and MEG. She investigates microstructural white matter changes and auditory processing deficits linked to speech impairments, aiming to translate findings into clinical interventions. Her studies also address broader topics like sensorimotor adaptation and stuttering, emphasizing translational neuroscience. Publications highlight trends in neuroimaging of speech disorders, auditory feedback mechanisms, and neuroplasticity in PD. While no formal awards are listed, her contributions to understanding speech motor control and rehabilitation practices are significant. Advising and grant details are not explicitly provided, but her involvement in interdisciplinary CINN projects suggests active collaborative research. Labs/Teams: Active contributor to the Centre for Integrative Neuroscience and Neurodynamics (CINN) at the University of Reading, collaborating on translational neuroscience projects.

Chung-Lin Martin YangView profile

Assistant Professor

• Psycho- and Neurolinguistics
• Speech Perception/Production
• Word Recognition
• +3 more

Chung-Lin Martin Yang is an Assistant Professor (Instructional Track) in the Department of Brain and Cognitive Sciences at the University of Rochester, School of Arts & Sciences. His research focuses on psycho- and neurolinguistics, speech perception/production, word recognition, word learning, evolution of human cognition, and second language acquisition. His work investigates how orthography influences second language phonological encoding, the role of Broca’s area in non-linguistic sequence learning, and neural correlates of emotional processing in early childhood. He has published extensively on topics such as bilingual phonological processing, individual differences in language learning, and the impact of orthographic depth on lexical representation. Notable research themes include the interplay between orthographic cues and phonological encoding in Mandarin and English, as well as neurodevelopmental aspects of emotional and linguistic processing in children. His contributions bridge cognitive neuroscience, psycholinguistics, and educational linguistics. Dr. Yang holds a PhD in Cognitive Science and teaches courses related to language acquisition and cognitive mechanisms. His lab explores experimental methods to understand how linguistic and cognitive systems interact during learning and perception.

Chris AdrianView profile

Senior Lecturer

• Corporate Governance
• Financial Accounting
• Auditing
• +3 more

Chris Adrian is a Senior Lecturer in the Department of Accounting at Monash University, specializing in corporate governance, financial accounting, and auditing. His research explores intersections between environmental factors (e.g., climate change, natural disasters) and corporate financial practices, alongside political influences on capital markets. Prior to Monash (since 2017), he held a Lecturer position at Macquarie University. Chris earned his PhD in 2015, focusing on corporate governance stakeholder perceptions. His work has been published in journals like Journal of Business Ethics and International Journal of Auditing , with recent studies examining audit fee dynamics during natural disasters, political connections in audit committees, and pandemic resilience through sustainability disclosures. Education: PhD (2015) Professional Affiliations: American Accounting Association (AAA), European Accounting Association (EAA), Accounting and Finance Association of Australia and New Zealand (AFAANZ) His research projects include applying large language models to sustainability reporting standards and analyzing charity performance during climate disasters. He is currently accepting PhD students and serves as an ad-hoc reviewer for multiple accounting journals. Key themes in his articles include audit risk mitigation during political shifts, disaster-driven corporate tax behaviors, and pandemic impacts on firm valuation. His work consistently bridges financial reporting practices with broader socio-environmental challenges.

Regan HarrellView profile

Assistant Professor

• Vestibular Disorders
• Traumatic Brain Injury (TBI)
• Neurological Rehabilitation
• +3 more

Regan Harrell is an Assistant Professor in the Department of Physical Therapy at the University of Pittsburgh's School of Health and Rehabilitation Sciences (SHRS). She is concurrently completing her PhD in rehabilitation science while serving as a faculty member and co-coordinator of the Advanced Vestibular Physical Therapist Certificate Program at SHRS. Her research focuses on vestibular disorders in moderate-severe traumatic brain injury (TBI) and technology development for diagnostic and treatment applications. Education includes a Bachelor of Science and Doctor of Physical Therapy (DPT) from Duquesne University. She holds certifications as a Board-Certified Specialist in Neurologic Physical Therapy (NCS) and has been recognized with awards such as the Promotion for Doctoral Studies II Scholarship (2024) and Second Place at the 2023 American Physical Therapy Association Combined Sections Meeting for her work on TBI and BPPV laterality. Her professional affiliations include the Academy of Neurologic Physical Therapy and the International Neurologic Physiotherapy Association. Her research interests emphasize advancing clinical understanding of BPPV, improving rehabilitation protocols for vestibular dysfunction in TBI patients, and adapting telehealth strategies during crises like the pandemic. She has contributed to global discussions on telehealth delivery for vestibular care and published extensively on TBI-related vestibular disorders and clinical outcomes. Notable contributions include a case report on vestibular rehabilitation for cerebellar ataxia with neuropathy and vestibular areflexia syndrome (CANVAS) and a scoping review on vestibular disorder prevalence in TBI patients. She was part of the University of Pittsburgh Physical Therapy delegation to the 2024 Bárány Society Meeting, highlighting her engagement in international vestibular research networks.

Angela KashubaView profile

Professor

• HIV Pharmacology
• Antiretroviral Drug Distribution
• Clinical Pharmacology
• +4 more

Angela Kashuba, Ph.D., is the John A. and Margaret P. McNeill, Sr. Distinguished Professor and Chair of the Division of Pharmacotherapy and Experimental Therapeutics at the UNC Eshelman School of Pharmacy. She serves as Director of the Clinical Pharmacology and Analytical Chemistry Core at the UNC Center for AIDS Research. Her research focuses on optimizing HIV treatment, prevention, and cure strategies through clinical pharmacology and analytical chemistry, with an emphasis on antiretroviral drug distribution in tissues and adherence monitoring. Education: Bachelor of Science in Pharmacy, University of Toronto (Canada) General Practice Residency, Women’s College Hospital (Toronto) Critical Care Pharmacy Practice, Mount Sinai Hospital (Toronto) Pharm.D., State University of New York at Buffalo Postdoctoral Pharmacology Training, Bassett Healthcare Clinical Pharmacology Research Center Research Interests: HIV drug pharmacokinetics and tissue distribution Development of novel adherence monitoring techniques (e.g., mass spectrometry imaging of hair) Optimizing dosing strategies for HIV prevention and cure interventions Impact of sex, gender, and comorbidities on drug efficacy Long-acting drug delivery systems (e.g., implants, biodegradable formulations) Grants & Projects: NIH P30-AI50410: UNC Center for AIDS Research Core NIH R01AI111891: Multi-Species Mechanisms of Drug Bio-distribution in HIV Tissue Reservoirs NIH/NIAID UM1AI126619: Collaboratory of AIDS Researchers for Eradication (CARE) Industry partnerships (e.g., Chimerix, TaiMed Biologics) Awards: Rawls-Palmer Progress in Medicine Award (ASCPT, 2023) John A. and Margaret P. McNeill, Sr. Distinguished Professorship (UNC, 2013) Labs & Collaborations: Director of the Clinical Pharmacology & Analytical Chemistry Laboratory, pioneering mass spectrometry imaging and drug reservoir analysis. Future Work: Advancing ultra-long-acting HIV PrEP implants Exploring sex-specific pharmacology in transgender populations Global HIV prevention initiatives via data sharing (HIV Pharmacology Data Repository)

Katrin VorkampView profile

Professor

• Environmental Chemistry
• Toxicology
• Arctic Environmental Science
• +3 more

Professor Katrin Vorkamp holds a position at the Department of Environmental Science, specializing in Environmental Chemistry and Toxicology at Aarhus University. Her research focuses on understanding the transport, exposure pathways, and impacts of persistent pollutants such as PFAS, POPs, and mercury in Arctic and Antarctic ecosystems, with a particular emphasis on wildlife and human health implications. She has contributed to projects addressing climate change effects on contaminant dynamics, policy-oriented monitoring frameworks (e.g., Water Framework Directive), and innovative methods like passive sampling and non-target screening. Her work spans interdisciplinary collaborations, including the AMAP Core 2025-2027 project tracking pollution in Greenland biota and the ArcSolution initiative exploring a One Health perspective in Arctic pollution. She also leads the NAMMINE project on non-target analysis in Nordic marine mammals. Key themes in her research include understanding local vs. long-range pollutant sources, climate-contaminant interactions, and developing early warning systems for emerging chemicals. Publications highlight her expertise in biochar applications for waste valorization, contaminant trends in Adélie penguin eggs, and computational tools for risk assessment. Her projects often bridge environmental science with policy, aiming to inform sustainable solutions for pollution challenges in sensitive ecosystems.

Aryeh KontorovichView profile

Professor

• Theoretical Machine Learning
• Probability
• Statistics
• +4 more

Aryeh Kontorovich is a Professor in the Computer Science Department at Ben-Gurion University. His research primarily focuses on theoretical machine learning, with expertise in probability, statistics, Markov chains, and metric spaces. His research interests span theoretical machine learning, with particular emphasis on: Probability theory and concentration inequalities Statistical learning theory Markov chains and mixing time estimation Metric space learning Kernel methods Sample compression schemes Professor Kontorovich's recent publications (2021-2025) demonstrate a continued focus on theoretical foundations of machine learning. His work shows strong trends in statistical estimation for Markov processes, distribution learning, metric space analysis, and sample compression. Many papers explore the intersection of probability theory and machine learning, particularly examining concentration inequalities, minimax optimality, and theoretical guarantees for learning algorithms. His research consistently bridges abstract mathematical theory with practical machine learning applications. Scientific awards and recognitions: Distinguished contribution award at MLG 2007 for "A Universal Kernel for Learning Regular Languages" Professor Kontorovich has advised numerous students and collaborated extensively with researchers in theoretical machine learning. His work spans both theoretical foundations and practical applications, with significant contributions to understanding the mathematical limits of learning algorithms. While specific grant information isn't provided in the source material, his extensive publication record in top venues suggests successful funding for his research programs. He maintains active collaborations with researchers worldwide, including prominent names like L. Gottlieb, D. Berend, and S. Hanneke.