Find Academic Supervisors

Zhou Zhi-HuaView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +3 more

Zhou Zhi-Hua is a Professor at Nanjing University's Department of Computer Science & Technology, serving as Standing Deputy Director of the National Key Lab for Novel Software Technology and Founding Director of LAMDA (Institute of Machine Learning and Data Mining). He holds simultaneous fellowships from ACM, AAAI, AAAS, IEEE, IAPR, IET/IEE, and CCF, reflecting his exceptional contributions to computational intelligence. His educational background includes: B.Sc. in Computer Science from Nanjing University (1996) M.Sc. in Computer Science from Nanjing University (1998) Ph.D. in Computer Science from Nanjing University (2000) Zhou's research pioneers fundamental advances in machine learning theory and applications. His seminal work on ensemble methods established new frameworks for classifier combination, while innovations in multi-label learning and anomaly detection addressed critical challenges in complex data analysis. His research bridges theoretical rigor with practical implementations across diverse domains including biometrics, data mining, and computer vision, resulting in over 150 publications and 18 patents. His textbooks "Ensemble Methods" (2012) and "Machine Learning" (2016) have become standard references in the field. Analysis of his publication trajectory reveals sustained leadership in core machine learning challenges: evolving from neural network ensembles (2002) through semi-supervised learning breakthroughs (2005) to foundational work on multi-instance learning (2012) and theoretical margin analysis (2013). His recent focus demonstrates increasing sophistication in handling complex data structures while maintaining theoretical soundness. His scientific excellence is recognized through: National Natural Science Award of China (2013) PAKDD Distinguished Contribution Award (2016) IEEE ICDM Outstanding Service Award (2016) IEEE CIS Outstanding Early Career Award (2013) Microsoft Professorship Award (2006) Simultaneous fellowships from 7 major international societies Zhou provides extraordinary service to the academic community as Executive Editor-in-Chief of Frontiers of Computer Science and Associate Editor-in-Chief of Science China Information Science. He founded the ACML conference and has chaired premier events including ICDM'16 and PAKDD'14. His leadership extends to serving as General Chair for ICDM'16, Program Chair for IJCAI'15 Machine Learning Track, and Area Chair for multiple top conferences. The available text does not specify student advising details or research grants. He directs LAMDA research group at Nanjing University, which has established itself as a global powerhouse in machine learning research, and contributes significantly to the National Key Lab for Novel Software Technology's mission of developing next-generation intelligent systems.

Shivani AgarwalView profile

Associate Professor

• Machine Learning
• Data Science
• Statistical Learning Theory
• +3 more

Shivani Agarwal is an Associate Professor of Computer and Information Science and (by courtesy) Statistics and Data Science at the University of Pennsylvania. Her research focuses on computational, mathematical, and statistical foundations of machine learning, including algorithm design, theory, and applications in life sciences. She holds leadership roles in initiatives like the NSF-funded Penn Institute for Foundations of Data Science (PIFODS) and the Penn Research in Machine Learning (PRiML) forum. Previously, she was a Radcliffe Fellow at Harvard, and held academic positions at MIT, Indian Institute of Science, and the University of Illinois at Urbana-Champaign. Education: PhD in Computer Science from the University of Illinois, Urbana-Champaign. Prior roles include Assistant Professor (Ramanujan Fellow) at IISc, postdoctoral lecturer at MIT, and Radcliffe Fellow at Harvard. Research interests span machine learning theory, ranking systems, bandit algorithms, noisy label learning, and interdisciplinary applications in economics, operations research, and psychology. She has organized numerous conferences and workshops, including COLT 2020 and NIPS workshops on ranking and learning. Key professional activities include leadership in Indo-US research collaborations and editorial roles for the Journal of Machine Learning Research and Harvard Data Science Review.

Andrew YonelinasView profile

Professor

• Human Memory
• Cognitive Neuroscience
• Memory Processes
• +5 more

Andrew Yonelinas is a Professor in the Department of Psychology at the University of California, Davis, where he directs the Human Memory Lab. He holds additional leadership roles as Associate Director of the Center for Mind and Brain and is an affiliated faculty member with the UC Davis Center for Neuroscience. His research bridges cognitive psychology and neuroscience to investigate fundamental memory mechanisms and their neural substrates. His educational background includes a Ph.D. in Experimental Psychology from McMaster University (1995) and a B.S. in Cognitive Science from the University of Toronto (1990). These foundational studies established his expertise in experimental methodologies and cognitive theory. Yonelinas specializes in dual-process models of memory, distinguishing between recollection (detailed contextual retrieval) and familiarity (vague recognition). His lab employs process dissociation, remember/know procedures, and ROC modeling alongside neuroimaging (fMRI, ERP) and clinical studies with amnesic and Alzheimer's patients. Recent work expands into auditory working memory, multisensory integration, and the impact of mental illness on cognitive processes, revealing hippocampal roles across memory systems. His research consistently addresses how memory fails in clinical conditions while developing unified theoretical frameworks. Analysis of his 2024-2025 publications shows a strong focus on memory mechanisms across sensory modalities, with increasing emphasis on clinical applications. Key trends include hippocampal contributions to visual/auditory working memory, EEG-based biomarkers for mental illness, and the interplay between schema knowledge and memory distortion in aging populations. His work demonstrates methodological innovation through model-based EEG phenotyping and multisite clinical collaborations. His scientific recognition includes: American Psychological Society’s Shahin Hashtroudi Memorial Award University of California Chancellor’s Fellow Award European Brain and Behavior Society International Lecture Award Yonelinas actively shapes his field through editorial roles at top journals including Proceedings of the National Academy of Sciences and Journal of Experimental Psychology, while serving as a grant reviewer for NIH, NSF, and international funding bodies. His Human Memory Lab trains next-generation researchers in memory theory and methodology, with recent projects examining stress effects on memory precision and neural mechanisms of action slips. The Human Memory Lab operates within UC Davis's neuroscience ecosystem, collaborating closely with the Center for Mind and Brain on projects involving clinical populations and neuroimaging. Current initiatives include the CNTRACS Consortium for EEG standardization in mental illness and investigations into how stress modulates memory binding through hippocampal mechanisms.

Michael EvansView profile

Professor

• Statistical Computing
• Theoretical Statistics
• Statistical Inference
• +3 more

Professor Michael Evans is a faculty member at the University of Toronto , affiliated with the Department of Statistical Sciences (St. George campus) and the Department of Computer and Mathematical Sciences at the Scarborough Campus . His research focuses on statistical inference , Bayesian methods , and measuring statistical evidence through his work on the relative belief ratio . He has contributed to ROC analysis , linear models , and stochastic processes , with recent work addressing biases in statistical reasoning and connections to frequentist criteria. Research Themes : Defining and measuring statistical evidence Bayesian inference and prior-data conflict resolution Monte Carlo methods and stochastic process theory Recent Publications explore statistical evidence in scientific practice, medical diagnostics, and Bayesian frameworks. His 2024 Encyclopedia article critiques frequentist criteria, while the 2022 Entropy paper introduces relative belief in ROC analysis. Awards : ASA Fellow (American Statistical Association) Teaching : He teaches advanced courses like STAC62 (Probability and Stochastic Processes) and STAC63 (Probability and Stochastic Processes II), emphasizing theoretical rigor and applications in fields like mathematical finance and machine learning .

Marie RochView profile

Professor

• Bioacoustics
• Machine Learning
• Signal Processing
• +3 more

Professor Marie Roch is a distinguished faculty member in the Department of Computer Science at San Diego State University within the College of Sciences . Her groundbreaking research bridges Bioacoustics and Machine Learning , focusing on advanced algorithms for automated detection, classification, and analysis of marine mammal vocalizations using passive acoustic monitoring. Core research in marine bioacoustic signal processing and deep learning applications for echolocation click detection Published extensively in Journal of the Acoustical Society of America , Biological Reviews , and IEEE Transactions Developed deep learning frameworks for whale whistle extraction without human annotation Created open-source tools like Silbido Profundo for automated marine mammal call analysis Marie's work has been supported by over $3 million in grants from the DOD Office of Naval Research , Bureau of Ocean Energy Management , and Human Frontier Science Program . She actively mentors graduate students and serves on numerous thesis committees, with recent advisees working on deep learning for baleen whale calls and terrestrial animal recognition . Her Marine Acoustic Research Lab (MAR Lab) leads in developing the Tethys metadata workbench for ocean acoustic data management.

S. Marshal IsaacsView profile

Professor

• Emergency Medicine
• Prehospital Care
• Clinical Trials
• +5 more

Dr. S. Marshal Isaacs is a distinguished Professor of Emergency Medicine at the University of Texas Southwestern Medical School and a senior faculty member at Parkland Hospital's Emergency-Trauma Center. As Medical Director of the Dallas Fire-Rescue Department (DFR), he oversees one of the nation's busiest EMS systems (500 daily incidents, 400+ paramedics). His career includes 12 years as Medical Director for the San Francisco Fire Department, renowned for advancing prehospital care, AED programs, and clinical trials. He collaborates closely with EMS leaders like Drs. Ray Fowler and Kathy Rinnert to enhance Dallas' BioTel System, a 15-municipality EMS federation. Dr. Isaacs' academic roles include NIH-funded research through the ROC consortium and teaching EM residents. His clinical expertise spans otology/neurotology, surgical innovation (e.g., endoscopic ear surgery), and radiation oncology for skull base tumors. He co-authored key NAEMSP texts and pioneered bacteriophage therapy for mastoiditis. Awards include the prestigious Michael K. Copass Award for EMS excellence. Education: Union College, SUNY Stony Brook Medical School (1988), residency at Allegheny General Hospital (Pittsburgh), and a Stanford University EMS fellowship. He served as SFGH's Chief Flight Physician and UCSF faculty before joining UTSW. Current initiatives include the Dallas EMS Vision Process and interdisciplinary NIH projects.

• Biostatistics
• Epidemiologic Methods
• Longitudinal Data Analysis
• +3 more

Laurence S. Magder, PhD, serves as Professor in the Department of Epidemiology and Public Health at the University of Maryland School of Medicine, where he has held continuous faculty positions since 1994 after progressing from Assistant to Associate to full Professor. With over 30 years of biostatistical expertise, he has contributed to nearly 200 biomedical publications through collaborative research across diverse health domains. Educational background includes: PhD in Biostatistics, Johns Hopkins University (1994) Master of Public Health, University of Michigan (1983) His research program centers on developing accessible statistical methodologies for real-world biomedical challenges. Key specialties include longitudinal data analysis, handling misclassified/missing data, transmission probability modeling, and systemic lupus erythematosus applications. Magder actively promotes a paradigm shift in statistical practice—advocating for evidence quantification over rigid hypothesis testing frameworks, which he argues renders traditional concerns like one-sided tests and multiple comparisons adjustments largely obsolete in scientific decision-making. Publication analysis reveals consistent methodological innovation across infectious disease modeling, diagnostic test evaluation, and missing data solutions. His work prioritizes practical applicability, translating complex statistical theory into tools usable by non-statisticians while maintaining rigorous evidence standards. Recurring themes include simplification of analytical approaches and contextual interpretation of statistical evidence within broader scientific judgment. As a collaborative biostatistician, Magder has supported numerous biomedical research projects throughout his career, though specific advising relationships and grant details remain undocumented in available sources. His role exemplifies the critical contribution of statistical expertise to advancing medical and public health research through both methodological development and direct project consultation.

Abolfazl SimorghView profile

Researcher

• Aircraft Trajectory Optimization
• Climate Change Mitigation in Aviation
• Robust Control
• +3 more

Abolfazl Simorgh is a researcher at Charles III University of Madrid's Department of Aerospace Engineering, specializing in climate-optimized aviation systems. His work bridges mathematical control theory with practical climate impact mitigation, focusing on robust trajectory optimization under environmental and operational uncertainties. He leads development of open-source tools for sustainable flight planning while contributing to major European aviation initiatives. Education: B.Sc. in Control Engineering (2017) M.Sc. in Control Engineering (2020) Ph.D. in Aerospace Engineering from Charles III University of Madrid Dr. Simorgh's research centers on developing mathematical frameworks that reconcile aircraft trajectory optimization with climate impact reduction. His expertise spans robust control systems, optimization under uncertainty, and climate modeling integration, with particular emphasis on non-CO₂ emissions. His methodology addresses both CO₂ and non-CO₂ climate forcing mechanisms through computationally efficient algorithms that account for weather variability and climate metric uncertainties. This work directly supports aviation's decarbonization by providing operational strategies that reduce environmental footprint without prohibitive cost increases. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on operationalizing climate-optimal flight planning. His work consistently integrates climate science with aerospace engineering through robust optimization frameworks, demonstrating particular innovation in handling multiple uncertainty sources (weather, climate models, emissions). The publications cluster around three interconnected themes: 1) Development of open-source computational tools (ROOST, CLIMaCCF), 2) Network-scale implementation of climate-aware air traffic management, and 3) Risk analysis of climate mitigation strategies. This body of work establishes new methodological standards for quantifying and minimizing aviation's total climate impact. Scientific Awards: Luis Azcárraga Aeronautical Innovation Award for collaborative research impact Best Paper Award (2022) from a high-impact aerospace journal Dr. Simorgh secures significant research funding through European Commission projects including FlyATM4E (climate-optimized flight planning), ALARM (aviation emissions reduction), and RefMAP (sustainable aviation pathways). His grant portfolio emphasizes practical implementation of climate mitigation strategies, with strong industry-academia collaboration. He mentors junior researchers through project teams and has developed three major open-source Python libraries (CLIMaCCF, ROOST, ROC) that have become community standards for climate impact assessment in aviation research. His current work focuses on scaling climate-optimized trajectories to continental airspace while addressing operational constraints and economic viability. He leads a research group focused on climate-aware air traffic management, developing the ROOST simulation framework for GPU-accelerated trajectory optimization and the CLIMaCCF library for standardized climate metric calculations. His team collaborates with European air navigation service providers and aircraft manufacturers to transition research into operational practice, with current projects emphasizing real-time implementation and regulatory compliance frameworks.

Hua HeView profile

Professor

• Mixture population modeling
• Causal inference
• Longitudinal data analysis
• +5 more

Hua He is a Professor in the Department of Biostatistics and Data Science at Tulane University's School of Public Health and Tropical Medicine, specializing in advanced statistical methodologies for public health research. Her work bridges theoretical biostatistics with practical applications in infectious disease diagnostics and causal inference frameworks. Her academic credentials include: PhD in Statistics, University of Rochester MA in Statistics, University of Rochester BS in Mathematics, Southwest Normal University, China Dr. He's research focuses on mixture population modeling, causal inference, longitudinal data analysis, and ROC analysis, with significant applications in tuberculosis diagnostics and molecular epidemiology. She develops novel statistical approaches for social media data analysis, drinking outcome modeling, and zero-modified datasets, emphasizing methodological rigor in public health contexts. Her textbook Applied Categorical and Count Data Analysis and edited volume Statistical Causal Inferences and Their Applications in Public Health Research establish her as a leading methodological authority. Recent publications (2023-2025) demonstrate a concentrated focus on CRISPR-based diagnostic technologies and next-generation sequencing for tuberculosis detection, with strong emphasis on point-of-care applications, diagnostic accuracy validation, and low-resource setting adaptations. These works integrate biostatistical innovation with molecular diagnostics to address critical gaps in infectious disease control. Her scientific recognition includes: Teaching Excellent Award (2020) Dr. He directs the Methodology/Biostatistics Unit at Tulane University Translational Science Institute (TUTSI) and serves as Principal Investigator for an NIH R01 grant Moving beyond description: statistical and causal inference for social media data , alongside multiple pilot studies and foundation-funded projects. She has contributed biostatistical expertise to dozens of NIH-funded investigations, particularly in tuberculosis and infectious disease research. As Co-director of TUTSI's Clinical Research Core and Statistical Deputy Editor for the American Journal of Public Health , she leads institutional research infrastructure while advancing methodological standards in public health science.

Steven SternView profile

Professor

• Data Science
• Operational Risk Management
• Sports Analytics
• +3 more

Steven Stern is a Professor of Data Science at Bond Business School and Professor at the Centre for Data Analytics, Bond University. He holds a PhD in Mathematical Statistics from Stanford University. Previously, he served as Professor and Australian Bureau of Statistics Chair at Queensland University of Technology (2014–2016), Associate Professor at Australian National University (1994–2013), and Lecturer at Stanford University (1993–1994). His research focuses on data-driven methodologies applied to healthcare, sports analytics, financial risk management, and medical imaging. Notable contributions include the Duckworth-Lewis-Stern method for cricket scoring, MRI acceleration techniques, and operational risk modeling in financial institutions. Recent work emphasizes optimizing diagnostic imaging (e.g., knee/shoulder MRI), predicting athlete performance, and enhancing financial literacy studies. He actively supervises PhD students in multivariate ROC analysis and sports team ratings. Professional activities include membership on Bond University’s Research Committee (2025–2025) and invited talks on interdisciplinary data science applications.

Ruitao LinView profile

Assistant Professor

• Bayesian adaptive design
• Robust Bayesian methods
• Meta-analytic trial design
• +6 more

Dr. Ruitao Lin is an Assistant Professor in the Department of Biostatistics at The University of Texas MD Anderson Cancer Center and an Adjunct Assistant Professor at Rice University. He holds a PhD in Biostatistics from The University of Hong Kong (2012–2016) and completed postdoctoral training at the University of Washington and MD Anderson. His research focuses on Bayesian adaptive designs, robust statistical methods, and clinical trial methodologies, particularly in oncology and precision medicine. He leads the development of novel Bayesian approaches for dose-finding in phase I/II trials, meta-analytic trial designs, and high-dimensional data analysis. Dr. Lin is an Associate Editor for Contemporary Clinical Trials , Biometrical Journal , and Pharmaceutical Statistics , and has contributed to software tools like the BOIN design suite and TITE-BOIN12. His work bridges statistical innovation with practical clinical challenges, including late-onset toxicities in immunotherapy and heterogeneous treatment effects. He has secured NIH funding (e.g., R01CA261978) and collaborated with academic and industry partners to advance trial design and analysis. His software platforms, including R packages and Shiny apps, enable clinicians to implement cutting-edge statistical methods in real-world settings. Education : PhD, Biostatistics, The University of Hong Kong (2016); Postdocs at University of Washington (2016–2017) and MD Anderson (2017–2019). Grants : NIH/NCI R01CA261978 (2021–2025), focusing on Bayesian methods for precision oncology trials. Teaching : Teaches clinical trial design courses at Rice University and UTHealth. Labs/Software : Developed the BOIN design suite, TITE-BOIN12, and other tools for dose-finding and phase I/II trials.

• Biophotonics
• Cell Mechanics
• Optical Methods
• +3 more

Felix Jünger is an academic researcher at the Chair of Bio- and Nanophotonics, University of Freiburg, within the Faculty of Engineering. He holds a diploma in experimental physics from the Technical University of Kaiserslautern (2010) and completed his doctorate at the same chair, investigating cellular mechanics during phagocytosis using optical methods. Currently serving as an assistant at the Chair, his research focuses on live-cell microscopy, optical trapping, particle tracking, and ROCS microscopy. Education: Diploma in Physics (TU Kaiserslautern, 2010) Doctorate: 2011–2014, University of Freiburg Research Interests: His work bridges physics and biology, emphasizing optical techniques to study cellular processes. Key areas include the mechanics of living cells, advanced microscopy approaches, and light scattering analysis for biological systems.

Robert Gilmore Pontius JrView profile

Professor

• Geographic Information Science (GIS)
• Land Change Science
• Simulation Modeling
• +3 more

Robert Gilmore Pontius Jr. is a Professor at Clark University's Graduate School of Geography specializing in Geographic Information Science with expertise in Land Change Science , Simulation Modeling , and Statistical Analysis . He develops quantitative methods for spatial data analysis that are implemented in the TerrSet software suite. B.S. Mathematics & Economics , University of Pittsburgh (1984) M.S. Applied Statistics , Ohio State University (1989) Ph.D. Environmental Science , SUNY College of Environmental Science and Forestry (1994) His research focuses on map comparison methodology and land change modeling , particularly addressing quantity disagreement and allocation disagreement in spatial data. He pioneered the Total Operating Characteristic (TOC) framework and advanced techniques for accuracy assessment in remote sensing . Recent publications analyze land category transitions , urban risk modeling , and multi-resolution map comparison . His work has received 19,000+ citations and been funded by NSF , NASA , and Edna Bailey Sussman Fund for research in Plum Island Ecosystems and Brazilian Cerrado Biome . Michael Breheny Prize (2005) Fulbright Scholar of Brazil Clark Labs research affiliate Scientific Advisory Board member, MapBiomas He teaches GIS & Land Change Models and GIS & Map Comparison , with student-created tutorials viewed internationally. He also performs as Doctor Stardust , a professional juggler who won the International Jugglers Association's People's Choice Award .

Danielle ToumaView profile

Assistant Professor

• Climate Variability
• Climate Change
• Extreme Climate Events
• +2 more

Danielle Touma is a Research Assistant Professor at The University of Texas at Austin, affiliated with the Institute for Geophysics within the Jackson School of Geosciences. She holds an office at ROC and can be reached at danielle.touma@utexas.edu . Her research focuses on climate variability, extreme climate events, hydroclimatology, and natural hazards. She is based at 2305 Speedway Stop C1160, Austin, TX 78712-1692, and can be contacted at (512) 471-6048. Research Interests : Dr. Touma’s work examines the interplay between climate change and extreme weather phenomena, including wildfire risk amplification, hydroclimatic volatility, and the impacts of vegetation dynamics on precipitation regimes. She also explores attribution methods for linking extreme events to anthropogenic climate drivers, with a focus on nonstationary climate systems and multi-dimensional risk assessment. Key Contributions : Her research has highlighted the growing frequency of extreme rainfall events and their compounding effects on post-wildfire landscapes, as well as the role of stratospheric aerosol injection in fire weather mitigation. She has also contributed to understanding tropical cyclone precipitation patterns and the regional impacts of greenhouse gas emissions. Affiliations and Labs : As part of the Institute for Geophysics, she collaborates on projects addressing climate variability and natural hazards, leveraging advanced modeling and observational data to inform environmental policy and disaster preparedness.

Carlos José Díaz BasoView profile

Research Fellow

• Solar Physics
• Bayesian Statistics
• Deep Learning
• +3 more

Carlos José Díaz Baso is a Research Fellow at the Rosseland Centre for Solar Physics (RoCS), part of the Institute of Theoretical Astrophysics at the University of Oslo. His research focuses on solar chromospheric phenomena, Bayesian statistics, and deep learning applications in solar physics. Education: Ph.D. in Astrophysics (2014-2018, Universidad de La Laguna, Spain), followed by postdoctoral positions at Stockholm's Institute for Solar Physics (2018–2022) and currently at RoCS (2022–present). Research emphasizes analyzing solar spectra and magnetic field dynamics using advanced statistical and machine learning techniques. Key projects include the ISSRESS initiative studying small-scale solar reconnection events. Recent publications explore spectral resolution impacts, coronal oscillations, and sunspot light bridges. Active in international collaborations using instruments like SST/CRISP and SolO/EUI. Engaged in developing observational strategies for solar telescopes and improving data analysis methodologies.