جستجوی استاد راهنمای دانشگاهی

Auguste Genovesioمشاهده پروفایل

پژوهشگر

• Computational Bioimaging
• Bioinformatics
• Machine Learning
• +۷ مورد دیگر

Auguste Genovesio is a Research Director (DR INSERM) leading the Computational Bioimaging and Bioinformatics team at the Centre for Computational Biology within the École Normale Supérieure (ENS) in Paris. His work focuses on large-scale cellular morphology analysis, integrating machine learning, microscopy, and computational modeling to study cellular responses to perturbations. His team develops algorithms for analyzing high-dimensional biological data, with applications in drug discovery, functional genomics, and neuroscience. Education and Affiliations: Genovesio’s research is anchored at ENS and collaborates with institutions like Institut Curie, Collège de France, and ESPCI. His lab develops open-source tools such as PySpacell and ALFA , advancing spatial analysis and genomic data processing. Research Interests: His group combines deep learning, bioinformatics, and experimental biology to tackle challenges in cellular dynamics, morphological heterogeneity, and predictive modeling. Recent work includes applying diffusion models to reveal subtle phenotypes and optimizing microscopy image analysis pipelines. Key Projects: Cross-modal knowledge distillation for transcriptomics, latent diffusion models for small datasets, and super-resolution microscopy via StyleGAN regularization. Applications: Collaborations in drug screening, neurobiology (e.g., Drosophila memory studies), and cancer cell analysis. Publications: Over 50 peer-reviewed articles since 2007, including work in Nature Communications , Developmental Cell , and NeurIPS . Recent focus on generative AI for biological image analysis and self-supervised learning biases. Grants & Awards: While specific grants aren’t listed, his lab’s cutting-edge research suggests significant institutional and collaborative support. No explicit awards mentioned in texts. Labs/Teams: Director of the Computational Bioimaging group, part of the Functional Genomics section at ENS. Supervises PhD students and postdocs in AI-driven biology and computational microscopy.

Jiaming Liangمشاهده پروفایل

استادیار

• Mathematical Optimization
• Sampling Algorithms
• Stochastic Programming
• +۳ مورد دیگر

Jiaming Liang is an Assistant Professor in the Department of Computer Science and Data Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. His research focuses on designing efficient algorithms for optimization and sampling in data science, with expertise in convex/nonconvex optimization, stochastic programming, and algorithmic game theory. He holds a PhD in Operations Research from Georgia Tech and completed a postdoctoral fellowship at Yale University under Andre Wibisono. Education PhD in Operations Research, Georgia Institute of Technology Postdoctoral Researcher, Yale University (Yale CS Department) Research Interests Liang's work bridges theoretical optimization and practical algorithm design. He develops scalable methods for nonsmooth, stochastic, and high-dimensional problems, with applications in machine learning and data analysis. Key areas include: Proximal bundle methods for hybrid optimization Sampling algorithms under non-smooth conditions Accelerated gradient techniques Algorithmic game theory applications Publications His recent work emphasizes algorithmic unification (e.g., combining accelerated gradients with proximal bundle methods) and variance reduction strategies. He explores stochastic optimization without smoothness assumptions and develops sampling methods for nonconvex potentials. Labs/Teams While specific lab affiliations aren't detailed here, his research aligns with computational optimization and data science groups at the Hajim School.

Carlo Cavicchiaمشاهده پروفایل

استادیار

• Statistics
• Data Science
• Machine Learning
• +۶ مورد دیگر

Dr. Carlo Cavicchia is an Assistant Professor of Statistics at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. He holds a PhD in Methodological Statistics from La Sapienza University of Rome and has held roles such as Research Fellow at UnitelmaSapienza University and Consultant for NGOs in Zanzibar. His research focuses on latent variable models, composite indicators, and unsupervised classification, with applications in environmental policy, sports analytics, and teacher job satisfaction. Cavicchia teaches statistics and data science courses at undergraduate and graduate levels and actively contributes to academic communities through journal reviewing, conference organizing, and editorial roles. Education: PhD in Methodological Statistics (La Sapienza University of Rome, 2020) MSc in Statistics and Decision Sciences (La Sapienza University of Rome, 2016) BSc in Statistics (La Sapienza University of Rome, 2013) Dutch University Teaching Qualification (BKO, 2022) Research Interests: Cavicchia’s work emphasizes hierarchical models, non-parametric statistics, and data science applications. He develops methodologies for composite indicators, including ultrametric Gaussian mixture models and disjoint principal component analysis. His research bridges theoretical advancements with real-world problems, such as waste management in Italian municipalities and ranking European football teams using composite metrics. Grants & Awards: 2024: IFCS Chikio Hayashi Award 2023: ESE Starter Grant (€300,000) 2017: Research Grant for Junior Researchers (€1,270) 2016: PhD Scholarship, La Sapienza University Academic Engagement: Cavicchia serves as IASC Data Analysis Competition Officer (2023–2025), co-edits the ISI Magazine , and organizes conferences like DSSV 2020 and DSSV-ECDA 2021. He is an elected member of the International Statistical Institute and contributes to SVQS’s Sustainability initiatives. Labs & Teams: He co-organizes the Econometrics internal seminars at Erasmus University and collaborates with researchers at University of Naples Federico II on hierarchical models and convex clustering.

Mattias Villaniمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• Computational Methods
• +۳ مورد دیگر

Mattias Villani is Professor of Statistics at Stockholm University, specializing in Bayesian statistics and machine learning. He obtained his PhD in Statistics from Stockholm University in 2000 and has held positions at Sveriges Riksbank and Linköping University. Villani develops computationally efficient Bayesian methods for inference, prediction and decision-making with flexible probabilistic models. Research Interests: His work spans Bayesian computation (MCMC, HMC, variational inference), machine learning (Gaussian processes, mixture models), and applications in neuroimaging, transportation, and econometrics. Research focuses on scalable Bayesian methods for large datasets and complex models. Publication Focus: Recent articles concentrate on Bayesian neuroimaging analysis, transportation network modeling, and efficient MCMC algorithms. Methodological innovations in subsampling techniques for large-scale Bayesian computation represent a significant research trend. Student Advising: Supervises PhD students in statistical methodology development and applications. Current research groups focus on spatiotemporal modeling, locally stationary processes, and neuroimaging statistics.

Anirban Mondalمشاهده پروفایل

دانشیار

• Bayesian Inference
• Markov Chain Monte Carlo Methods
• Spatial Statistics
• +۵ مورد دیگر

Anirban Mondal is an Associate Professor and Director of Graduate Studies at Case Western Reserve University's Department of Mathematics, Applied Mathematics and Statistics, specializing in Bayesian Inference, Markov Chain Monte Carlo Methods, and Uncertainty Quantification. Holding a Ph.D. in Statistics from Texas A&M University, his research spans spatial statistics, inverse problems, and data mining applications across biomedical, materials science, and public health domains. Education: Ph.D. in Statistics, Texas A&M University His recent publications (2022-2024) demonstrate interdisciplinary applications including heart disease prediction via optimized machine learning, additive manufacturing defect analysis, and pandemic transmission modeling. While primarily focused on Bayesian frameworks and computational statistics, his work extends to geomechanics, remote sensing, and environmental risk assessment. Current research explores advanced sampling algorithms, functional data emulation, and multiscale hierarchical modeling for complex systems. Key trends include uncertainty quantification in machine learning systems (2024), Bayesian calibration methods (2023), and pandemic modeling (2022). His work balances methodological innovation with real-world applications in medical diagnostics, materials science, and climate science. Contact: anirban.mondal@case.edu

Dora Erdosمشاهده پروفایل

مدرس ارشد

• Algorithm Design
• Data Mining
• Combinatorial Optimization
• +۳ مورد دیگر

Dora Erdos is a Senior Lecturer and Director of Undergraduate Studies in the Department of Computer Science at Boston University. She specializes in algorithmic challenges, data mining, and combinatorial optimization with a focus on network-based problems. Her work bridges theoretical computer science and practical applications in education technology and network analysis. Education: PhD in Computer Science, Boston University (2015) MSc in Pure Mathematics, Eotvos University (Advisor: Andras Frank) Postdoctoral Research at Brown University's Raphael Lab (Advisor: Ben Raphael) Research Interests: Erdos investigates algorithms for network analysis, including centrality measures, graph reconstruction, and optimization problems in educational systems. She develops scalable methods for tensor factorization and content placement in navigational networks. Her work often integrates combinatorial approaches with real-world applications. Professional Roles: As Director of Undergraduate Studies, Erdos oversees academic advising and curriculum development. She emphasizes student accessibility, maintaining office hours and encouraging direct communication via email (edori@bu.edu). Recent Research Trends: Her publications (2011–2017) focus on network-centric problems such as centrality evaluation frameworks, boolean tensor decomposition, and team formation algorithms for educational scheduling. These contributions highlight her dual expertise in theoretical algorithm design and applied educational technology.

Steve Marronمشاهده پروفایل

استاد

• Statistics
• Data Science
• Machine Learning
• +۴ مورد دیگر

Steve Marron is the Amos Hawley Distinguished Professor of Statistics and Operations Research at the University of North Carolina at Chapel Hill (UNC-CH). He holds a joint appointment in the School of Data Science and Society and is a professor in the Department of Biostatistics at the Gillings School of Global Public Health. Additionally, he serves as an adjunct professor in the Department of Computer Science within the College of Arts & Sciences. His research focuses on statistics, data science, and machine learning, with a particular emphasis on integrating diverse data types such as genomics, imaging, and demographic data. Education: Marron earned an AA from Orange Coast College (1974), BS from University of California, Davis (1977), and PhD from UCLA (1982). He has held faculty positions at UNC-CH since 1982 and Cornell University (2001-2002). His honors include Fellowships from the Institute of Mathematical Statistics and American Statistical Association, and he is a top-cited mathematician (1991-2001). Research interests include object-oriented data analysis, high-dimensional data methods (HDLSS), visualization techniques like SiZer, and statistical methodology for imaging and genomics. Notable contributions include Distance-Weighted Discrimination (DWD), Principal Nested Spheres, and JIVE for data integration. His work has applications in cancer genomics, medical imaging, and bioinformatics. Awards and recognitions include the Amos Hawley Professorship, S. N. Roy Memorial Lectureship, and Saw Swee Hock Visiting Professorship. Marron has advised numerous students and collaborated on NIH-funded grants, including studies on cancer genomics and imaging. His lab focuses on developing statistical tools for complex, multi-source data analysis.

Arno Siebesمشاهده پروفایل

استاد

• Data Mining
• Pattern Mining
• Minimum Description Length Principle
• +۲ مورد دیگر

Arno Siebes is Professor of Algorithmic Data Analysis in the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His research focuses on data mining methodologies, particularly pattern mining and Minimum Description Length (MDL) principles. Key research areas include: Developing efficient algorithms for pattern discovery Applying MDL to data characterization Creating interpretable models for complex datasets Addressing challenges in data science education Recent publications demonstrate applications in diverse domains including mobility analysis, genomic screening, and pandemic response. His work combines theoretical foundations with practical implementations for knowledge discovery.

• Mathematical Statistics
• Non-Euclidean Data Analysis
• Algebraic Statistics
• +۳ مورد دیگر

Andrew McCormack is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on mathematical statistics, with a particular emphasis on non-Euclidean data analysis, algebraic statistics, and information geometry. His research interests include: Non-Euclidean Data Analysis: Developing methodologies for data on manifolds and metric spaces. Algebraic Statistics: Applying nonlinear algebra to understand statistical models, especially graphical models. Tensor Decompositions: Exploring low-dimensional representations of high-dimensional data using matrices and tensors. Information Geometry: Studying statistical models through geometric lenses, incorporating ideas from optimal transport. Statistical Decision Theory: Investigating foundational approaches to evaluating statistical methods from both classical and Bayesian perspectives. Mccormack's recent work spans robust statistical methods, information geometry in covariance modeling, and the application of algebraic techniques to statistical problems. His contributions often bridge theory and computation, addressing challenges in high-dimensional and geometric data analysis. No scientific awards listed. No advisees or grants mentioned. Labs or teams are not specified in the provided information.

Anthony Constantinouمشاهده پروفایل

مدرس ارشد

• Causal Machine Learning
• Decision Systems
• Bayesian Networks
• +۶ مورد دیگر

Anthony Constantinou is a Senior Lecturer at Queen Mary University of London, part of the School of Electronic Engineering and Computer Science. He leads the MInDS Research Group and the Bayesian AI Lab, focusing on causal machine learning for decision-making under uncertainty. His research spans healthcare, defense, sports, economics, and gaming, with a strong emphasis on Bayesian networks and causal inference. Research Interests: Causal machine learning, decision systems, Bayesian networks, uncertainty quantification, and their applications in healthcare, military defense, sports analytics, and economics. He collaborates with academia and industry to advance AI-driven decision-making systems. Grants: Noted for securing funding such as the Engineering and Physical Sciences Research Council grant (EP/S001646/1) for Bayesian AI research. His work emphasizes practical applications in diverse fields through collaborative projects. Labs & Teams: Head of the MInDS Research Group and Bayesian AI Lab, fostering interdisciplinary research in AI and decision systems.

Yi-Zhuang Youمشاهده پروفایل

دانشیار

• Condensed Matter Physics
• Quantum Entanglement Dynamics
• Topological Phases
• +۴ مورد دیگر

Yi-Zhuang You is an Associate Professor in the Department of Physics at the University of California, San Diego (UCSD). He holds a Ph.D. from Tsinghua University (2013). His research focuses on theoretical investigations of correlated topological phases, quantum entanglement dynamics, and machine learning applications in many-body systems. Key areas include deconfined quantum criticality, symmetry-protected topological (SPT) phases, and the interplay between topology and quantum matter. His work bridges condensed matter physics and high-energy physics, exploring topics such as topological responses in gauge theories, entanglement holography, and quantum machine learning. Recent studies involve machine learning-driven approaches to quantum state preparation, symmetry discovery, and tomographic reconstruction of quantum systems. His contributions span theoretical frameworks for understanding topological transitions, fractionalization in lattice models, and the role of symmetry in quantum critical phenomena. Notable research highlights include the study of symmetric mass generation as a deconfined quantum criticality mechanism, the application of classical shadow tomography for efficient quantum state estimation, and the development of algorithms for self-similar dynamics modeling. His publications frequently intersect with experimental proposals for observing topological phases in materials like graphene and iridates. Dr. You’s affiliations include the UCSD Physics Department, with collaborations extending to institutions globally. His research is supported by grants focusing on quantum information, topological materials, and machine learning applications in physics. While no specific awards are listed, his work has been widely cited in high-impact journals across condensed matter and theoretical physics.

Antonio Pellegrinoمشاهده پروفایل

مدرس

• Mechanical Engineering
• Materials Science
• High Strain Rate Mechanics
• +۴ مورد دیگر

Antonio Pellegrino is a Lecturer in the Department of Mechanical Engineering at the University of Bath. His research focuses on high strain rate mechanics of materials, including lightweight polymers, titanium alloys, and biological materials. He develops novel experimental apparatus like the combined tension-torsion Hopkinson Bar. Key areas include AI in materials modelling, failure stress loci of syntactic foams, and dynamic fracture analysis. He holds a PhD in Mechanical Engineering from the University of Catania (2012). Recent projects include an EPSRC-funded study on syntactic foams using in-situ microscale tomography and X-ray scattering. Collaborations span institutions globally, with emphasis on advanced materials testing and data-driven modelling. Awards include election to the Governing Board of DYMAT (2024). He supervises doctoral students and serves as a peer reviewer for journals like International Journal of Mechanical Sciences and Engineering Fracture Mechanics . Research highlights include: development of a tension-torsion Hopkinson Bar; studies on strain rate-dependent behavior of composites and metals; biomechanical investigations of eye lens mechanics. His work contributes to UN SDGs through sustainable materials research and education initiatives. Education: PhD Mechanical Engineering (University of Catania, 2012) Key Skills: High-strain rate testing, data-driven modelling, experimental apparatus design Recent Activities: 16 peer review and committee roles (2023-2025)

Milica Orlandicمشاهده پروفایل

دانشیار

• Hyperspectral Imaging
• Remote Sensing
• Digital Design
• +۷ مورد دیگر

Milica Orlandic is an Associate Professor in the Department of Electronic Systems at NTNU. She holds an MSc from the University of Montenegro (2009) and a PhD from NTNU (2015). Her research focuses on hyperspectral imaging, remote sensing, FPGA-based systems, and embedded computing for aerospace applications. She is actively involved in the HYPSO CubeSat mission, developing onboard processing systems for Earth observation. Education: MSc in Electrical Engineering, University of Montenegro (2009) PhD in Electronics, NTNU (2015) Research Interests: Her work spans hyperspectral data processing , including compression, anomaly detection, and onboard computing for satellites. She also explores reconfigurable hardware (FPGAs) for real-time signal processing, cyber-physical systems, and spaceborne sensor systems. Publications Trends: Recent work emphasizes lightweight machine learning for anomaly detection, FPGA acceleration of hyperspectral compression (CCSDS 123), and algorithm co-design for CubeSat missions. Key contributions include robust onboard processing frameworks for HYPSO-1 and adaptive hardware-software systems. Advising & Teams: She supervises a dynamic team of over 40 PhD and MSc students working on FPGA implementations, satellite systems, and hyperspectral algorithms. Notable collaborations include the HYPSO CubeSat project, which aims to deliver high-resolution Earth observation data with low latency. Labs & Infrastructure: Her research leverages NTNU’s facilities for embedded systems prototyping, FPGA development, and CubeSat payload testing. The HYPSO mission integrates her team’s hardware-software co-design innovations for space applications.

Quan Zhouمشاهده پروفایل

استادیار

• Markov chain Monte Carlo sampling
• Bayesian methodology
• Variable selection
• +۷ مورد دیگر

Quan Zhou is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing advanced sampling methods, particularly Markov chain Monte Carlo (MCMC) algorithms, with applications in Bayesian methodology, variable selection, stochastic optimization, and statistical genetics. He holds a BS from Fudan University and a PhD from Baylor College of Medicine, followed by a postdoctoral fellowship at Rice University. He teaches courses such as Mathematical Probability, Multivariate Analysis, and Advanced Stochastic Processes. Notable contributions include work on informed MCMC samplers, Schrödinger bridge theory, and high-dimensional structure learning. He advises PhD students like Hyunwoong Chang (now at UT Dallas) and Guanxun Li (Beijing Normal University). Active in academic service, he served as President of the Southeastern Texas Chapter of the American Statistical Association (SETCASA).

Katherine B. Ensorمشاهده پروفایل

استاد

• Data Science
• Environmental Statistics
• Computational Finance
• +۹ مورد دیگر

Katherine B. Ensor is the Noah G. Harding Professor of Statistics at Rice University's George R. Brown School of Engineering and Computing. She serves as Director of the Center for Computational Finance and Economic Systems (CoFES) and previously led the Kinder Institute Urban Data Platform (UDP). Her expertise spans statistical methods for complex problems in public health, environmental science, and finance. Ensor has a 35-year academic career at Rice, including 14 years as Department Chair. She pioneered wastewater surveillance for pathogens, expanding to monitor 29 communicable illnesses through Houston Wastewater Epidemiology. Education: Ph.D. in Statistics (Texas A&M University), M.S. and B.S.E. in Mathematics (Arkansas State University). Research focuses on high-dimensional time series, spatial processes, Bayesian methods, and computational finance. She leads interdisciplinary initiatives, such as the UDP providing urban data for Greater Houston studies. Research interests include data science applications in public health, environmental analytics, and quantitative finance. Her work bridges academia and community, addressing issues like flood impacts via the Texas Flood Registry and asthma surveillance through collaborations with Houston Health Department. Ensor chairs the National Academies committee on Statistics in Science and Engineering and holds leadership roles in AAAS and ASA. Awards include the ASA Founders Award (2024), AAAS Fellowship (2013), and Texas A&M's Distinguished Former Students honor. She advises on national boards, including NSF's IPAM and ABET's CSAB. Her grants and collaborations drive innovations in statistical methods for societal challenges. Labs/Teams: Co-directs Houston Wastewater Epidemiology and Rice Environmental Statistics & Health (RESH) group with Dr. Loren Hopkins. Active in CoFES, UDP, and the Kinder Institute's urban data initiatives.