Find Academic Supervisors

• Computational Imaging
• Computational Photography
• Neuroimaging
• +7 more

Nick Antipa is an Assistant Professor at the University of California, San Diego, affiliated with the Jacobs School of Engineering and the Department of Electrical and Computer Engineering. His work focuses on computational imaging systems that integrate optics, sensors, and algorithms to enable novel imaging modalities. PhD in Electrical Engineering from UC Berkeley Former optical metrology engineer at Lawrence Livermore National Lab Research interests span computational imaging , lensless camera design , and single-shot high-dimensional optical signal capture . His lab develops systems like DiffuserCam for compressive 3D imaging and Miniscope3D for miniature fluorescence microscopy. Recent publications address differentiable wave optics, high-speed video reconstruction, and marine imaging applications. Awards include Best Paper at ICCP 2016/2019 and Best Demo at ICCP 2017. His lab explores machine learning-driven optical design and differentiable rendering frameworks for end-to-end optimization of imaging systems. Current projects include oceanographic imaging, computational photography, and infrared spectroscopy acceleration.

Olga KurasovaView profile

Professor

• Machine Learning
• Data Visualization
• Cybersecurity
• +3 more

Dr. Olga Kurasova is a Professor and Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies, where she leads research in the Cognitive Computing Group. Her work focuses on developing advanced computational methods for real-world applications. Her primary research explores machine learning paradigms including deep learning for cybersecurity (keystroke dynamics, adversarial attacks), medical image analysis (pancreatic cancer detection), industrial monitoring, and explainable AI. She maintains strong collaborations across disciplines, particularly in healthcare and security domains. Analysis of her recent publications (2023-2025) reveals three dominant themes: (1) Advanced biometric authentication systems using behavioral analysis and deep learning, (2) Medical AI applications focusing on pancreatic cancer detection through CT image analysis, and (3) Theoretical advancements in explainable AI methodologies for high-stakes domains. Significant scientific recognition includes: 2021 Lithuanian Science Prize for the cycle 'From Data Science to Artificial Intelligence Technologies' 2024 Vilnius University Rector's Science Prize She has led multiple national research projects, including a 2024-2027 LMT-funded initiative on 'adversarial machine learning for cybersecurity' and coordinated interdisciplinary teams for projects on cognitive computing capabilities and optimal data mining solutions. She directs research within the Cognitive Computing Group, focusing on developing intelligent systems for data analysis, visualization, and decision support across healthcare, cybersecurity, and industrial applications.

Bo HuView profile

Professor

• Biostatistics
• Bioinformatics
• Neurosurgery
• +7 more

Bo Hu is Professor of Biostatistics & Bioinformatics and Professor of Neurosurgery at Duke University, where he leads methodological and collaborative research at the intersection of biostatistics, bioinformatics, and clinical neurosciences. His dual appointments situate him within the Division of Biostatistics in the Department of Biostatistics & Bioinformatics and within the neurosurgical faculty. Education: Ph.D. in Biostatistics, University of Wisconsin–Madison, 2006 Research Interests: Professor Hu’s methodological work centers on advanced biostatistical and machine-learning techniques for high-dimensional biomedical data, including generative AI, synthetic data generation, and predictive analytics in medicine. Clinically, he collaborates on precision-medicine trials in oncology, neurodegeneration (Alzheimer’s disease), metabolic disease (type 2 diabetes and bariatric surgery), and treatment-resistant depression. His neuroimaging genetics portfolio explores structural brain endophenotypes in bipolar disorder and epilepsy using single-cell transcriptomic integration. Complementing his medical research, he maintains a vigorous program in remote-sensing informatics, developing deep-learning solutions for object detection, domain adaptation, and energy-infrastructure mapping from overhead imagery. Recent Grant Portfolio: Empagliflozin to Improve Right Ventricular Function in Pulmonary Arterial Hypertension – Cleveland Clinic Lerner College of Medicine (2025-2030) Gender and Asthma – Mayo Clinic Hospital-Arizona (2025-2027) Engaging Patients in Prenatal Genetic Testing Decisions – Cleveland Clinic Lerner College of Medicine (2025-2027) Laboratory & Collaborative Networks: Professor Hu leads interdisciplinary teams that bridge Duke’s Department of Biostatistics & Bioinformatics with clinical departments (Neurosurgery, Psychiatry, Medicine) and external partners such as Cleveland Clinic, Mayo Clinic, and multiple NIH consortia. These collaborations support large-scale clinical trials, multi-omics neuroimaging studies, and AI-driven remote-sensing analytics.

• Biostatistics
• Cancer Genomics
• Statistical Methods for High-Dimensional Data
• +4 more

Yuan Huang is an Assistant Professor in the Department of Biostatistics at the Yale School of Public Health . Her research focuses on statistical methods for high-dimensional data, motivated by challenges in cancer genomics and neurodegenerative diseases. She develops approaches for biomarker identification, network structure estimation, and gene-environment interaction analysis, with applications in Alzheimer’s, Huntington’s, and Parkinson’s diseases. Key Affiliations: Yale Cancer Center, Center for Brain & Mind Health, Yale Center for Analytical Sciences (YCAS) Her methodological work emphasizes integrative analysis across multiple datasets to improve reproducibility and discovery. Recent collaborations span clinical trials, genetics, and epidemiology, with a focus on addressing heterogeneity and nonlinearity in complex biomedical data. Her publications include Bayesian finite mixture models, precision matrix estimation, and advanced techniques for high-dimensional causal mediation analysis. She actively engages in translational research, linking statistical innovation to clinical and public health challenges.

Chuanping SunView profile

Lecturer

• Empirical Asset Pricing
• Financial Econometrics
• Machine Learning in Finance
• +5 more

Chuanping Sun is a Lecturer in Finance at Bayes Business School, City, University of London. He earned his PhD in Economics from Queen Mary University of London and has held visiting positions at New York University and FGV Sao Paulo. His research bridges machine learning and empirical finance, with a focus on robust factor selection in asset pricing models. His research interests include: Empirical Asset Pricing Financial Econometrics Machine Learning in Finance Portfolio Choice Cross-Sectional Asset Returns Factor Models His recent work investigates the impact of factor correlations on model robustness, proposing a correlation-robust machine learning approach (using OWL shrinkage) to identify key drivers of asset returns, including the market, liquidity, momentum, and profitability factors. His research shows that traditional methods like LASSO and Fama-MacBeth often fail to detect the market factor due to high correlations, while his approach maintains robustness and delivers superior out-of-sample portfolio performance. His two recent publications in the Journal of Empirical Finance (2024, 2022) demonstrate his focus on methodological innovation in high-dimensional financial datasets, particularly in handling correlated factors and exploiting stock return correlations. He has received internal research funding from City University of London's Pump Priming Fund, supporting his ongoing projects in machine learning applications in finance. While no formal advisees are listed, his role as a lecturer and research supervisor (as indicated by his CRediT contribution) suggests involvement in student mentorship. Chuanping Sun is affiliated with the Finance department at Bayes Business School, where he contributes to research and teaching in quantitative finance and econometrics.

• Artificial Intelligence
• Robotics
• Data Science
• +4 more

Cecilio Angulo Bahón is a full Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Barcelona School of Industrial Engineering (ETSEIB) and the Department of Systems, Automatics and Industrial Informatics Engineering . He leads research in Artificial Intelligence and Robotics , with significant contributions to healthcare data analytics, digital twins, and human-robot collaboration. His research spans machine learning for medical data harmonization, generative adversarial networks in health informatics, and evolutionary algorithms for control systems. Recent publications focus on synthetic healthcare data generation, climate-resilient agriculture , and UMAP-based data analysis . His work bridges AI theory with practical applications in industrial and healthcare domains. Scientific awards include the Sant Jordi 2023 Digital Polytechnic Initiative Award . He has supervised doctoral candidates like Carlos Flores-Vázquez and N. Raya, with key collaborations at the IDEAI-UPC Intelligent Data Science and AI Research Group and the Institute of Robotics and Industrial Informatics (CSIC-UPC).

Gregory WheelerView profile

Professor

• Formal Epistemology
• Philosophy of Science
• Probability Theory
• +4 more

Gregory Wheeler is a Professor of Philosophy and Computer Science at the Frankfurt School of Finance and Management, where he serves as Head of the Computational Science & Philosophy Department and Academic Director of the Master of Applied Data Science program. He received a joint PhD in Philosophy and Computer Science from the University of Rochester in 2002 and has held faculty positions at LMU Munich, Carnegie Mellon University, and Nova University Lisbon, with visiting roles at TU Dresden AI Institute and Max Planck Institute for Human Cognition in Berlin. His research focuses on Formal Epistemology , Bayesian Statistics , and Machine Learning , particularly Imprecise Probability Theory and Probabilistic Models of Coherence . His recent work explores intersections between logic, probability, and computational methods in philosophical inquiry. From his publications, key trends emerge in Uncertainty Quantification , Probabilistic Reasoning , and AI Applications across philosophy and statistics. Topics include peer disagreement resolution, causal modeling, and foundational issues in imprecise probability. Gregory co-founded Exaloan AG in 2019, serving as CTO and Head of AI & Machine Learning. He has contributed to academic leadership through editorial roles and organizing symposia, including the International Symposium on Imprecise Probabilities.

Wenlong MouView profile

Assistant Professor

• Statistical Learning
• Reinforcement Learning
• Optimization
• +8 more

Wenlong Mou is an Assistant Professor at the University of Toronto's Department of Statistical Sciences, with additional affiliation at the Vector Institute for AI. His research develops optimal statistical methods and efficient algorithms for data-driven decision-making, focusing on reinforcement learning, stochastic approximation, and causal estimation. He teaches advanced courses in theoretical statistics (STA3000) and stochastic processes (STA447/2006). Education: Ph.D. in EECS, University of California, Berkeley (2023) B.Sc. in Computer Science and Economics, Peking University Research Focus: Mou's work bridges statistical theory with machine learning practice. Key areas include: Theoretical foundations of reinforcement learning (e.g., Bellman equations, continuous-time systems) Efficient algorithms for semi-parametric estimation and debiasing Non-asymptotic analysis of stochastic optimization and MCMC methods High-dimensional statistical inference and causal modeling Publication Trends: Recent articles (2023–2025) emphasize reinforcement learning theory (policy evaluation, adaptive interpolation), causal inference (debiased estimators, propensity scores), and statistical computing (diffusion processes, Langevin algorithms). Methodological rigor and non-asymptotic guarantees characterize his work. Awards: INFORMS APS Student Paper Competition Finalist (2022) Advising and Labs: Actively recruiting PhD students with backgrounds in mathematics or deep learning. Students access GPU clusters via the Vector Institute. Grants unspecified in sources.

Guo-Cheng YuanView profile

Professor

• Genomics
• Epigenetics
• Gene Expression
• +4 more

Guo-Cheng Yuan is a Senior Faculty member in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. His research focuses on genomics, epigenetics, and computational biology , with particular emphasis on single-cell analysis, chromatin regulation, and cancer immunology. Institution: Icahn School of Medicine at Mount Sinai School: Graduate School of Biomedical Sciences Department: Genetics and Genomic Sciences Research Interests Dr. Yuan's work spans multiple domains including: Single-cell RNA sequencing applications in brain development and cancer Chromatin structure analysis in differentiation processes Spatial transcriptomics for tissue microenvironment characterization Computational methods for multiomic data integration Enhancer-promoter interaction dynamics Development of robust bioinformatics pipelines Scientific Trends Analysis His recent publications (2021-2025) demonstrate a focus on single-cell and spatial omics methodologies applied to diverse biological contexts ranging from cancer immunology to neurodevelopment . Notable trends include: Development of trajectory analysis algorithms Multiomic integration for regulatory network mapping Epigenetic mechanisms in cell differentiation Computational approaches for data robustness Three-dimensional genome organization studies Applications in both developmental biology and oncology

Mathias MunschauerView profile

Researcher

• Molecular Virology
• RNA Biology
• Host-Pathogen Interactions
• +7 more

Mathias Munschauer leads the Department of Molecular Virology at Heidelberg University's Faculty of Medicine, within the Center for Infectious Diseases. His research group focuses on unraveling RNA regulatory mechanisms that govern viral infection outcomes, with emphasis on HCV, HBV, and Dengue virus. His research interests lie at the intersection of RNA biology and virology, particularly in understanding how viral RNA molecules interact with host cell components. The lab employs cutting-edge methodologies including RAP-MS and SHIFTR for RNA interactomics, integrated with functional genomics, single-cell transcriptomics, and AI-driven analysis of high-dimensional data. This systems-level approach enables the identification of host factors and regulatory pathways critical for viral replication and immune evasion. The recent publications highlight a strong trend toward spatially and temporally resolved analysis of RNA-protein interactions across diverse RNA viruses. There is a consistent focus on developing and applying innovative technologies to map host-virus interfaces, with applications in identifying antiviral targets and understanding infection mechanisms. The work spans molecular, cellular, and systems biology, with increasing integration of computational and machine learning approaches. Systems virology RNA-protein interactomics Host-pathogen interactions CRISPR screening Single-cell analysis Antiviral strategies Dr. Munschauer mentors a research team and contributes to the doctoral program in Infectious Diseases. His lab develops and shares novel reagents and methods, fostering collaborative science. While specific grants are not listed, the technological sophistication suggests substantial funding support. The lab operates within a vibrant research environment alongside other virology groups such as AG Bartenschlager and AG Ruggieri. The Munschauer Lab is part of a larger virology and infectious disease research ecosystem at Heidelberg University, collaborating across disciplines to advance understanding of viral pathogenesis. The team actively develops and applies innovative tools for RNA-centric discovery, positioning the group at the forefront of molecular virology.

Linda ValeriView profile

Assistant Professor

• Biostatistics
• Epidemiology
• Causal Mediation Analysis
• +9 more

Linda Valeri is an Assistant Professor of Biostatistics at the Mailman School of Public Health, Columbia University, and an Adjunct Assistant Professor in Epidemiology at the Harvard T.H. Chan School of Public Health. Education: M.Sc. in Economics and Social Sciences from Bocconi University Ph.D. in Biostatistics from Harvard University Her research focuses on causal mediation analysis, environmental health, and the development of machine learning approaches under Bayesian frameworks to estimate complex health outcomes. She has contributed to software development for mediation analysis in SPSS, SAS, STATA, and R. Dr. Valeri leads NIH-funded studies on causal inference in mHealth and mechanisms underlying Alzheimer’s disease. She serves as Associate Editor for the International Journal of Biostatistics and as Statistical Editor for JAMA Psychiatry and JAMA Network Open .

• Statistics
• Machine Learning
• Functional Data Analysis
• +3 more

Anton Rask Lundborg is a Postdoctoral Research Fellow at the Department of Mathematical Sciences, University of Copenhagen. He works in the areas of Statistics, Machine Learning, and Functional Data Analysis. Key research areas: Statistics, Machine Learning, Functional Data Analysis, Causal Inference, Bioinformatics, Nonparametric Statistics Research groups: SPT (Statistical Learning Theory), CoCaLa (Collaborative Causal Learning) His recent work focuses on causal feature selection, variable significance testing, and functional data analysis applications. Publications span journals like Journal of the American Statistical Association , Briefings in Bioinformatics , and Annals of Statistics . Methodological innovations include the Projected Covariance Measure and conditional independence testing in Hilbert spaces. Explore his full research profile and publications via ORCID or the University of Copenhagen's Mathematical Sciences website .

Sebastian KaiserView profile

Professor

• Optical Measurement Techniques
• Reactive Flows
• Combustion
• +4 more

Prof. Sebastian Kaiser is a full professor at the University of Duisburg-Essen's Institute for Combustion and Gas Dynamics, where he leads research on reactive fluid dynamics since 2011. His academic background includes a Bachelor's from Dartmouth College, Diplomingenieur from RWTH Aachen, and PhD from Yale University, followed by postdoctoral work at Sandia National Laboratories. Research Focus: Kaiser specializes in optical diagnostics for reactive systems with emphases on: High-speed imaging of combustion processes Nanoparticle synthesis via spray-flame techniques Tribology and fluid-structure interactions Engine diagnostics using laser-based methods His work bridges experimental techniques and simulation development for energy and propulsion systems. Publication Trends: Recent articles (2023-2025) demonstrate consistent focus on advanced optical diagnostics applied to combustion systems, nanoparticle synthesis, and engine research. Key methodologies include laser-induced fluorescence, high-speed imaging, and machine learning for fluid dynamics analysis. Awards & Honors: Harding-Bliss Prize for Engineering Excellence (Yale, 2005) SAE Excellence in Oral Presentation Award (2008) NRW Returning Scientists Grant (2010) Professional Affiliations: Member of Society of Automotive Engineers (SAE) and The Combustion Institute, with extensive experimental facilities for reactive flow characterization.

Bin HanView profile

Professor

• Computational Mathematics
• Numerical Partial Differential Equations (PDEs)
• Applied Harmonic Analysis
• +4 more

Bin Han is a Professor of Mathematics at the Department of Mathematical and Statistical Sciences , University of Alberta, Canada. He holds a PhD (1998), MSc (1994), and BSc (1991) in Mathematics from the University of Alberta, Chinese Academy of Sciences, and Fudan University, respectively. Research Interests: Computational Mathematics: High-order finite difference methods, numerical solutions of PDEs (Helmholtz, elliptic interface, Burgers' equations), and Fourier/wavelet-based algorithms. Applied Harmonic Analysis: Framelets/wavelets with applications in image processing, data sciences, and deep learning, focusing on directional and quasi-tight properties. Wavelet Theory: Construction of wavelets on bounded intervals for boundary value problems, Gibbs phenomenon analysis, and stability of refinable functions. Computer Aided Geometric Design (CAGD): Subdivision schemes, spline approximation, and isogemetric analysis. Article Trends: His recent work (2021-2022) emphasizes high-order finite difference methods for Helmholtz and interface problems, directional tensor product complex tight framelets for image processing, and quasi-tight framelets with balancing orders for robustness and sparsity. Scientific Awards: NSERC Postdoctoral Fellowship (1999-2000) Advising & Grants: He has supervised PhD students Qiwei Feng, Michelle Michelle, Ran Lu, and Chenzhe Diao. His research is supported by NSERC, Westgrid, Compute Canada, and MITACS.

Robin CarpentierView profile

Research Fellow

• Data Privacy
• Information Management
• Hardware Security
• +3 more

Robin Carpentier is a Research Fellow at the School of Computing, Macquarie University . His work focuses on Data Privacy , Information Management , and Hardware Security , particularly in developing secure personal data management systems using Trusted Execution Environments (TEE) and SGX technology. Research Interests : Secure data processing with third-party code Privacy-preserving computation frameworks Hardware-based security for databases Dimensionality challenges in text privacy Resource-constrained privacy-preserving methods Recent Publications Trends : Robin's research over the past decade has explored data leakage mitigation, TEE-optimized database operations, and privacy-preserving mechanisms for large-scale data applications. His 2024 work extends these principles to secure AI/LLM interactions and advanced text privacy techniques.