جست‌وجوی استاد و پژوهشگر

Mingyao Liمشاهده پروفایل

دانشیار

• Bioinformatics
• Biostatistics
• Genetics
• +۲ مورد دیگر

Mingyao Li, PhD, is an Associate Professor of Biostatistics whose scholarship bridges high-dimensional statistics with modern genomics and spatial biology. Her work is recognised for advancing computational methods that dissect complex tissue architecture and disease heterogeneity at single-cell resolution. Research Interests Bioinformatics and computational biology Statistical methods for high-dimensional genomic data Single-cell and spatial multi-omics integration Genetics and genomics of cardiovascular and cancer systems Systems biology approaches to disease mechanisms Across more than one hundred peer-reviewed publications, Li’s research exhibits a clear trajectory from developing rigorous statistical frameworks—such as hypothesis testing in stochastic block models—to large-scale applications in human disease. A dominant theme is the fusion of spatial transcriptomics, single-cell multi-omics and AI-driven image analysis to uncover cell-type heterogeneity, lineage relationships and microenvironmental crosstalk in atherosclerosis, lung adenocarcinoma, pancreatic cancer and neurodegenerative disorders. Scientific Awards & Recognition While specific awards are not listed in the provided text, the consistent appearance of Dr Li as senior or corresponding author on high-impact papers in Nature family journals and the development of widely-used computational tools (e.g., MISO, iSCALE) attest to significant scholarly recognition. Research Support & Teams Granting agencies and collaborative networks are not explicitly detailed, but the scale and scope of the projects—from whole-organ spatial atlases to primate genome-editing studies—imply substantial multi-institutional funding and interdisciplinary teams integrating biostatisticians, wet-lab scientists and clinician-scientists.

• Computational Methods
• Numerical Analysis
• Control Theory
• +۹ مورد دیگر

Boris Kramer is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California San Diego , affiliated with the Jacobs School of Engineering . He leads research in computational methods for control , optimization , and uncertainty quantification of complex systems, with applications in space weather modeling , systems biology , and soft robotics . Center for Extreme Events Research (CEER) Center for Computational Mathematics (CCoM) Air Force Center of Excellence Multi-Fidelity Modeling His work focuses on reduced-order modeling (ROM) and data-driven methods that preserve physical structures (e.g., energy conservation in Hamiltonian systems). Recent projects include collaborations with Samsung Electronics for semiconductor manufacturing optimization and leadership in DOE's PSAAP IV program for radiation resilience modeling. Kramer's research has been featured in Nature Computational Science and Science News , with grants from NSF , DoD , and AFOSR . His group has advised students like Opal Issan (published in Journal of Computational Physics) and Nate Linden (Nature Communications). Scientific awards include: NSF CAREER Award (2022) DoD Newton Award for Transformative Ideas (2020) Outstanding PhD Student Award (2024-2025, UCSD MAE) MURI funding for Digital Twins (2023) Outreach efforts include participation in the Barrio Logan Science & Art Expo and the Southeast San Diego STEM Ecosystem , emphasizing science communication for K-12 audiences.

Brian Clearyمشاهده پروفایل

استادیار

• Machine Learning
• Computational Biology
• Bioinformatics
• +۴ مورد دیگر

Brian Cleary serves as an Assistant Professor in Boston University's Faculty of Computing & Data Sciences (CDS), with cross-appointments in Biology and Biomedical Engineering departments. He is a core faculty member in the Bioinformatics Program and the Biological Design Center at the Rajen Kilachand Center for Integrated Life Sciences & Engineering, conducting interdisciplinary research at the intersection of computer science and biology. His educational trajectory includes dual undergraduate degrees in Biology and Business, Economics, and Management from Caltech, followed by 8 years developing trading algorithms in finance before returning to academia. He completed his PhD in Computational and Systems Biology at MIT in 2019. Cleary's research pioneers computational approaches to decipher spatial gene expression patterns in tissues, focusing on theoretical frameworks that transform cellular and tissue physiology understanding. His lab implements paired computational-experimental methodologies to study organ development (particularly brain and ovary), disease progression mechanisms, and tissue organization principles through machine learning and statistical innovations. Analysis of his recent publications reveals dominant themes in compressed sensing techniques for high-throughput biological interrogation, spatial transcriptomics optimization, and scalable genetic screening methods. His work consistently bridges algorithmic innovation with biological discovery across reproductive biology, cardiovascular disease, and infectious disease diagnostics. Scientific recognition includes: Independent Broad Fellow at the Broad Institute of MIT and Harvard Cleary actively recruits PhD students and postdocs for his Algorithmic Lens on Biology Laboratory, leveraging both computational and wet-lab approaches. His research program emphasizes experimental design informed by statistical learning theory to overcome scalability limitations in biological measurement systems. The Algorithmic Lens on Biology Laboratory operates across two physical locations: the Center for Computing and Data Sciences (15th floor) and the Biological Design Center (6th floor in CILSE), employing random composite experiments and low-dimensional feature learning to study cellular pathways and tissue organization at unprecedented scales.

Eric Laberمشاهده پروفایل

استاد

• Statistical Reinforcement Learning
• Precision Medicine
• Dynamic Treatment Regimes
• +۳ مورد دیگر

Eric Laber serves as James B. Duke Distinguished Professor of Statistical Science at Duke University, with cross-appointments as Professor of Biostatistics & Bioinformatics in the School of Medicine and Research Professor of Global Health. He co-directs the Duke Computing Initiative and maintains active roles in the Department of Statistical Science. His educational background includes: Ph.D. in Statistics, University of Michigan, Ann Arbor (2011) M.A. in Statistics, University of Michigan, Ann Arbor (2007) Laber's research pioneers statistical reinforcement learning for dynamic decision-making in complex health environments. He specializes in developing methodologies for Sequential Multiple Assignment Randomized Trials (SMARTs) to optimize personalized treatment sequences in precision medicine. His work bridges machine learning with clinical applications, focusing on cancer therapeutics, mental health interventions, and resource-constrained global health settings. Recent innovations address network interference in policy learning and risk-sensitive optimization for digital health platforms. Analysis of his 15 most recent publications reveals strong trends in adaptive clinical trial design, with increasing emphasis on mHealth applications (40% of recent work), PTSD management in cancer survivors (25%), and network-based reinforcement learning (20%). Key methodological threads include Thompson sampling variants (33%), functional data analysis for truncated outcomes (15%), and causal inference under interference (25%). His scientific recognition includes: James B. Duke Distinguished Professorship (premier faculty honor at Duke) Laber secures substantial grant funding for high-impact health research, currently leading 10 active projects totaling over $15M. Major awards include NIH/NCI funding for next-generation SMARTs in cancer therapeutics (2023-2028), NSF support for risk-sensitive statistical learning (2024-2027), and multiple NIH trials for digital mental health interventions. His collaborative approach spans oncology, psychiatry, and global health teams, with special focus on vulnerable populations including cancer survivors and adolescents. As Co-Director of the Duke Computing Initiative, he leads interdisciplinary teams developing scalable computational frameworks for precision medicine. Current efforts integrate reinforcement learning with electronic health records systems to create real-time clinical decision support tools, particularly for resource-limited settings in infectious disease management and chronic pain treatment.

Purnamrita Sarkarمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Network Analysis
• Asymptotic Theory
• +۲ مورد دیگر

Purnamrita Sarkar serves as Associate Professor in the Department of Statistics and Data Sciences at The University of Texas at Austin, where she has held faculty positions since 2014 after completing a postdoctoral fellowship at UC Berkeley with joint appointments in Electrical Engineering & Computer Sciences and Statistics. Her research specializes in large-scale statistical machine learning , with core contributions to: Statistical modeling for complex network structures Asymptotic theory development for high-dimensional data Scalable inference algorithms for massive network datasets Professor Sarkar co-leads the NSF TRIPODS grant establishing UT Austin's Foundations of Data Science institute, driving transdisciplinary collaboration across campus. This initiative represents significant research infrastructure development in theoretical data science principles. Her work maintains strong industry relevance through algorithmic innovations for real-world network analysis while advancing fundamental statistical theory. Current research directions focus on extending methodological frameworks to emerging data modalities and improving computational efficiency for next-generation network datasets.

Ramya Korlakai Vinayakمشاهده پروفایل

استادیار

• Machine Learning
• Statistical Inference
• Crowdsourcing
• +۳ مورد دیگر

Ramya Korlakai Vinayak is the Dugald C. Jackson Assistant Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison's College of Engineering. Her research bridges theoretical machine learning with human-centered AI applications. Education: Postdoctoral Researcher, Paul G. Allen School of Computer Science and Engineering, University of Washington Ph.D. and M.S. in Electrical Engineering, California Institute of Technology (Caltech) B.Tech. in Electrical Engineering, Indian Institute of Technology Madras (IIT M) Research Focus: Her work centers on machine learning foundations with emphasis on statistical inference, crowdsourcing, and human-AI collaboration. Key themes include modeling heterogeneous human preferences, developing robust out-of-distribution detection systems, and creating culturally-aware generative models. She pioneers methods for pluralistic alignment where AI systems accommodate diverse human values through ideal point modeling and adaptive feedback mechanisms. Publication Trends: Recent publications (2023-2025) reveal three converging trajectories: (1) Human-in-the-loop frameworks for reducing false positives in safety-critical systems, (2) Metric learning innovations using limited preference data, and (3) Critical examinations of cultural limitations in generative AI. Her work consistently addresses real-world challenges like cognitive overload in crowdsourcing and biases in face generation systems. Research Infrastructure: She leads an active research group with a dedicated lab website (https://ramyakv.github.io/) focusing on developing theoretically-grounded yet practically-deployable AI systems that respect human plurality.

Claudia Solís-Lemusمشاهده پروفایل

استادیار

• Statistical Modeling
• Phylogenetics
• Microbiome Analysis
• +۳ مورد دیگر

Claudia Solís-Lemus is an Assistant Professor in the Department of Plant Pathology at the University of Wisconsin-Madison, where she develops statistical and machine learning methods to solve complex biological problems. Her work bridges computational statistics with plant pathology and evolutionary biology, focusing on network-based approaches to genomic and microbiome data. Educational background: PhD in Statistics, University of Wisconsin–Madison Her research centers on phylogenetic network inference , microbiome analysis , and high-dimensional statistical modeling . She creates open-source tools like CMiNet and MiNAA to empower biologists with robust network analysis capabilities. Her lab tackles challenges in biodiversity research, agricultural disease prediction, and microbial ecology through innovative computational frameworks that handle massive biological datasets. Analysis of her 2024-2025 publications reveals a cohesive focus on scalable network inference methods across phylogenetics and microbiome studies. She integrates Bayesian statistics, regularization techniques (e.g., spike-and-slab LASSO), and high-performance computing to address data complexity. Her work consistently emphasizes practical software implementation (R packages, Shiny apps, Julia tools) for real-world biological applications including potato disease prediction, hornwort evolution, and freshwater ecosystem dynamics. Scientific recognition: NSF CAREER Award (2022) for "Towards Scalable and Robust Inference of Phylogenetic Networks" Dr. Solís-Lemus leads an interdisciplinary research group at the Wisconsin Institute for Discovery, securing competitive grants to advance phylogenetic network methodology. Her CAREER project combines algorithmic innovation with educational outreach to train next-generation computational biologists. Current efforts focus on improving network inference for polyploid genomes and developing consensus methods for microbiome data integration across diverse environmental conditions. The Solís-Lemus Lab operates within UW-Madison's Wisconsin Institute for Discovery ecosystem, fostering collaborations between statisticians, computer scientists, and biologists. Her team actively develops user-friendly software to lower computational barriers for life scientists studying evolutionary processes and microbial communities.

Yang Liمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Bayesian Nonparametrics
• Scalable Optimization
• +۵ مورد دیگر

Yang Li serves as Associate Professor of Marketing and Associate Dean for the MBA Program at Cheung Kong Graduate School of Business (CKGSB). Holding a PhD in Marketing from Columbia Business School alongside dual master's and bachelor's degrees from Columbia and Peking University respectively, he bridges advanced statistical methodologies with practical business applications. His research centers on statistical machine learning and Bayesian nonparametrics applied to consumer behavior analysis, with specialization in online personalization, text mining, and choice modeling. Recent work demonstrates significant focus on fragmented attention economies, ethical AI frameworks, and NFT network dynamics, reflecting contemporary digital market challenges. Management Science Marketing Science Journal of Marketing Research Journal of Consumer Research Harvard Business Review Professor Li's publications reveal evolving expertise from foundational pricing elasticity studies toward cutting-edge AI applications in consumer contexts. His work increasingly integrates generative models and graph neural networks to decode complex consumer collection behaviors and digital ecosystem dynamics. Scientific recognition includes being a Finalist for the 2021 Paul E. Green Best Paper Award. Industry impact is demonstrated through executive education programs and strategic consultancies with Tencent, Haier, and Tmall. As Associate Dean for MBA Programs, he oversees curriculum development while maintaining active corporate governance roles on boards of publicly traded companies across China and Hong Kong, directly applying his research insights to strategic decision-making in digital transformation initiatives.

Matthias H. Hennigمشاهده پروفایل

استاد

• Computational Neuroscience
• Large-Scale Neural Recordings
• Neural Population Activity Modeling
• +۴ مورد دیگر

Matthias H. Hennig is a Professor of Computational Neuroscience at the University of Edinburgh, School of Informatics, Institute for Adaptive and Neural Computation. He leads the Hennig Lab, which focuses on the intersection of neuroscience and computational methods. His research spans three main areas: Development of tools for analysis of large-scale neural recordings, including the SpikeInterface project and Herding Spikes software Modeling neural population activity using data-driven approaches like Targeted Neural Dynamical Modeling Studying retinal development and function, particularly spontaneous network activity patterns like retinal waves The lab has produced significant methodological contributions to neural data analysis, with software tools used internationally. Recent publications (2019-2022) show consistent output in high-impact journals across computational neuroscience, neural data analysis methods, and biological studies of neural circuits. The research demonstrates a strong focus on developing reproducible analysis pipelines for next-generation electrophysiology data. Professor Hennig currently supervises six PhD students across multiple CDT programs and has an impressive track record of student placements in both academic and industry positions. His international collaborations span institutions in Europe, North America, and India, reflecting the global impact of his work.

• Machine Learning
• Statistics
• Artificial Intelligence
• +۶ مورد دیگر

Michael Irwin Jordan is a Professor at the University of California, Berkeley, ranked #1 worldwide in Machine Learning and Distributed Computing, #36 in Computer Science, and #9 in Statistics according to Academic Influence rankings. His interdisciplinary research bridges theoretical foundations with practical applications across machine learning, statistics, and artificial intelligence. His educational background includes a PhD in Computer Science from the University of California, San Diego, a Master's in Mathematics from Arizona State University, and a Bachelor's in Psychology from Louisiana State University. This diverse foundation has shaped his approach to integrating perspectives from computer science, statistics, neuroscience, and psychology. Professor Jordan's research spans several decades, beginning with foundational work in neural networks and connectionist models. He made seminal contributions to probabilistic graphical models, variational inference, and Bayesian nonparametrics. His 2001 paper 'Latent Dirichlet Allocation' has received over 34,000 citations, becoming one of the most influential papers in machine learning. More recently, his work has focused on applications to biological data analysis, particularly single-cell transcriptomics, where he has developed probabilistic models and software tools like scvi-tools. Member of the National Academy of Engineering (2010) Ranked #1 worldwide in Machine Learning Ranked #1 worldwide in Distributed Computing Ranked #36 worldwide in Computer Science Ranked #9 worldwide in Statistics As an advisor, Professor Jordan has mentored numerous PhD students who have become leaders in academia and industry. His recent work through 2022 shows continued innovation across multiple domains, including uncertainty quantification, theoretical analysis of transfer learning, and scalable frameworks for reinforcement learning. He also contributes significantly to data science education, having helped develop Berkeley's undergraduate data science curriculum.