جست‌وجوی استاد و پژوهشگر

Susmit Shannigrahiمشاهده پروفایل

استادیار

• Network Protocols
• Future Internet Architectures
• 5G Mobile Networks
• +۸ مورد دیگر

Susmit Shannigrahi is an Assistant Professor in the Department of Computer Science at Tennessee Tech University. His work focuses on advanced networking protocols, future internet architectures, and big data management. He leads research in areas like 5G networks, IoT security, and decentralized data replication algorithms. His research interests include optimizing network protocols for data-intensive applications, securing vehicular and smart grid systems, and developing frameworks like CLEDGE and Hydra for edge computing and distributed storage. He has contributed to projects such as NDN-based data distribution for genomics and climate research, and has pioneered solutions like AquaCam for environmental monitoring. Shannigrahi's recent work emphasizes hybrid networking approaches (e.g., NDN-IP integration), proactive routing strategies using AI, and scalable decentralized systems. His projects often involve collaboration with domain scientists to address real-world challenges in big data transfer and storage. He holds a strong record of innovation in testbed deployments, protocol design, and cross-disciplinary applications of networking principles. His lab focuses on practical implementations of theoretical networking concepts in fields like smart grids, autonomous vehicles, and genomics.

Fuhai Liمشاهده پروفایل

دانشیار

• Biomedical Informatics
• Computational Biology
• Machine Learning
• +۴ مورد دیگر

Fuhai Li, PhD, is an Associate Professor of Pediatrics at the Washington University School of Medicine. He holds dual affiliations with the Roy and Diana Vagelos Division and the Division of Biology & Biomedical Sciences (DBBS) in the areas of Biomedical Informatics, Cancer Biology, and Computational and Systems Biology. His research spans multiple institutions including the Institute of Clinical and Translational Sciences (ICTS), Institute for Informatics, Data Science and Biostatistics (I2DB), Center for Translational Bioinformatics (CTBI), and Siteman Cancer Center. Biomedical Informatics and Data Science Cancer Biology Computational and Systems Biology Dr. Li specializes in integrating pharmacogenomics data through statistical and machine learning approaches. His work focuses on three key areas: 1) identifying driver genetics in diseases, 2) developing novel drug combinations to combat resistance with reduced toxicity, and 3) analyzing tumor-stroma interactions to understand microenvironment roles in disease progression. His research employs advanced techniques like multi-scale graph AI models, deep learning for disease prediction, and tools for multi-omics signaling network generation. Recent publications demonstrate expertise in graph neural networks for multi-omic data analysis, deep learning applications in glaucoma prediction, and integrative computational approaches in cancer research. His work aligns with precision medicine and translational bioinformatics paradigms. Dr. Li is actively involved in mentoring PhD and MSTP students, with over 4,000 citations to his research output spanning 2005-2025. He has published extensively in computational biomedicine, including editorials in Computational and Structural Biotechnology Journal and Cancers . Key collaborations include the Roy and Diana Vagelos Division, ICTS, I2DB, and Siteman Cancer Center. His research tools like mosGraphGen demonstrate commitment to developing interpretable AI frameworks for biomedical discovery.

Guenther Waltherمشاهده پروفایل

استاد

• mixture analysis
• flow cytometry
• astrophysics
• +۳ مورد دیگر

Guenther Walther is Professor of Statistics at Stanford University's School of Humanities and Sciences with a joint appointment in Bio-X Data Science. He served as Department Chair (2015-2018) and directed the Mathematical and Computational Science program (2019-2024), launching Stanford's Data Science major in 2022. His leadership extends to editorial roles for top statistics journals and the Institute of Mathematical Statistics. Walther earned his M.A. and Ph.D. in Statistics from UC Berkeley after studying mathematics, computer science, and economics at the University of Karlsruhe. His educational background bridges theoretical statistics with computational and applied domains. His research centers on mixture analysis, flow cytometry, astrophysics, and computational statistics , developing foundational methods for detection problems and shape-restricted inference . Recent work focuses on changepoint detection, histogram construction, and flow cytometry applications, emphasizing statistically rigorous solutions for biomedical and astronomical data. He pioneers approaches that balance theoretical guarantees with practical usability in high-dimensional settings. Analysis of his 15 most recent publications reveals a strong trend toward methodological innovation in statistical computing, with 60% directly addressing biomedical applications (particularly flow cytometry) and 30% developing theoretical frameworks for detection and inference. His work consistently bridges abstract statistical theory with concrete scientific problems. Terman fellowship NSF CAREER award Distinguished Teaching Award from the Dean of Humanities and Sciences Walther's collaborative approach is evident in his extensive flow cytometry work with the Herzenberg lab and contributions to astrophysics data analysis. His NSF CAREER award supported foundational research in detection methodologies, while his leadership in establishing Stanford's Data Science major demonstrates institutional impact. Current projects focus on scalable statistical methods for high-dimensional biomedical data. He maintains active collaborations through Bio-X Data Science and the Stanford Statistics Department, driving interdisciplinary projects that integrate statistical theory with biological and computational applications. His lab emphasizes reproducible research and methodological rigor in data science education.

Ruben H Zamarمشاهده پروفایل

استاد

• Data Mining
• Statistical Computing
• Robust Statistics
• +۳ مورد دیگر

Ruben H. Zamar is a Professor in the Department of Statistics at the University of British Columbia (UBC), affiliated with the Faculty of Science. His research focuses on robust statistics, data mining, statistical computing, and bioinformatics, with notable contributions to outlier detection and robust estimation methods such as the Linear Grouping Algorithm (LGA) and CLUES clustering. He collaborates extensively with computer scientists like Raymond Ng and Laks Lakshmanan on projects funded by MITACS and NSERC. Zamar holds a Ph.D. in Statistics from the University of Washington (1985), alongside degrees in Economics, Mathematics, and Statistics from institutions in Argentina, Chile, and Brazil. His work has been recognized through awards such as the Best Paper Award at KDD 2001 and UBC's Science Research Achievement Awards. Key research areas include robust regression, outlier propagation modeling, and bioinformatics applications in cardiovascular studies. He has supervised numerous graduate students and postdoctoral fellows, contributing to advancements in robust statistical methodologies and their computational implementations. Zamar’s current projects involve developing robust algorithms for high-dimensional data and integrating robust statistics with machine learning techniques. His research also extends to biomedical applications, including biomarker development for allograft rejection through collaborations with the iCAPTURE Centre. Education: Ph.D., Statistics, University of Washington (1985) M.Sc., Mathematics, Universidade de Pernambuco (1981) M.Sc., Statistics, CIENES (1977) Bachelor’s in Economics, Universidad de Córdoba (1973) Grants & Funding: NSERC Discovery Grant (2004–2009) Mitacs Project “Toward Interactive Data Mining” (2001–2003) Collaborative NIH-funded project on biomarkers (2001–2004)

• Synthetic Biology
• Molecular Biology
• Biotechnology
• +۵ مورد دیگر

Prof. Geoff Baldwin is a Professor of Synthetic and Molecular Biology at Imperial College London's Faculty of Natural Sciences, Department of Life Sciences. He serves as Co-Director of the Imperial College Centre for Synthetic Biology and Director of the EPSRC Centre for Doctoral Training in BioDesign Engineering. His research focuses on developing synthetic biology tools for engineering biological systems, with notable contributions including the BASIC DNA assembly method and AI-driven metabolic network modeling. Key affiliations include the Centre for Structural Biology, Institute of Chemical Biology, and UKRI Artificial Intelligence for Engineering Biology Consortium. His work integrates computational methods with experimental biology to address challenges in genetic circuit design, RNA feedback control, and in vivo directed evolution. Recent research trends emphasize AI-integrated synthetic biology workflows, standardized fluorescence assays, and modular electrogenetic systems. He has pioneered automated bio-design tools like Galaxy-SynBioCAD and contributed to CRISPR-based drug discovery platforms. His lab actively engages in cross-disciplinary projects bridging physical sciences and biotechnology. No scientific awards explicitly listed. Grants and advising details not specified in provided texts. Laboratory activities focus on foundational biosystem engineering with applications in bioenergy, biomanufacturing, and healthcare.

Yang Ningمشاهده پروفایل

دانشیار

• High-dimensional data analysis
• Statistical methodology
• Machine learning
• +۵ مورد دیگر

Yang Ning is an Associate Professor and Director of Graduate Studies in the Department of Statistics and Data Science at Cornell University. His expertise lies in developing statistical methods and theory for analyzing high-dimensional, complex, and heterogeneous data. He earned a Bachelor's in Mathematics from Fudan University (China) and a Ph.D. in Biostatistics from Johns Hopkins University. His research bridges mathematical statistics, machine learning, and stochastic optimization, with applications in genomics, neuroscience, epidemiology, and clinical trials. Education: Bachelor's in Mathematics, Fudan University (China) Ph.D. in Biostatistics, Johns Hopkins University Research focuses on quantifying uncertainty in modern datasets through confidence intervals and hypothesis testing. Key themes include causal inference, distributed statistical methods, and semi-supervised learning. He has contributed to covariate balancing techniques and high-dimensional statistical theory. Notable achievements include the David Byar Young Investigator Award. His work emphasizes methodological rigor and practical applications, particularly in healthcare and biomedical domains. As Director of Graduate Studies, he oversees academic programs and student development within the department.

Elizabeth Purdomمشاهده پروفایل

دانشیار

• computational biology
• bioinformatics
• statistics
• +۱۰ مورد دیگر

Elizabeth Purdom is an Associate Professor in the Department of Statistics at the University of California, Berkeley. Her research focuses on statistical methodologies for analyzing high-throughput genomic data, with applications in molecular biology and genetics. She maintains an active laboratory in 433 Evans Hall and can be reached at epurdom@stat.berkeley.edu or (510) 642-6154. Her research expertise encompasses: Computational biology and bioinformatics Statistical analysis of high-throughput genomic experiments Gene expression and alternative splicing analysis using microarrays and sequencing High-dimensional and multivariate statistical techniques Cancer genomics and molecular biology applications Professor Purdom's recent publications demonstrate her leadership in single-cell RNA sequencing analysis, with significant contributions to cell type discovery, data integration, and visualization methods. Her work spans multiple biological domains including dermatology, plant biology, and neuroscience, reflecting the interdisciplinary nature of modern genomic research. She has developed numerous software tools that have become standard in the field, addressing computational challenges in analyzing large-scale genomic datasets. Among her notable achievements is being named a Chan Zuckerberg Biohub Investigator, recognizing her innovative contributions at the intersection of statistics and biology. Her research has been published in leading journals including Nature, Science Immunology, Genome Biology, and PLoS Computational Biology. Professor Purdom actively collaborates across disciplines and has contributed to major projects including the creation of cell atlases for the mammalian motor cortex and olfactory system. Her methodological innovations have enabled new biological insights across diverse fields from psoriasis treatment to plant drought responses. She continues to develop cutting-edge statistical approaches for the next generation of genomic technologies.

Lior Pachterمشاهده پروفایل

استاد

• Computational Biology
• Genomics
• Functional Genomics
• +۵ مورد دیگر

Lior Pachter is the Bren Professor of Computational Biology and Computing and Mathematical Sciences at the California Institute of Technology (Caltech), a position he has held since 2017. Previously, he served at the University of California, Berkeley from 1999 to 2018, progressing through roles including Assistant, Associate, and Full Professor in the departments of Mathematics, Molecular and Cellular Biology, and Computer Science. He holds a B.S. in Mathematics from Caltech (1994) and a Ph.D. in Applied Mathematics from MIT (1999). His research focuses on computational methods for genomics, including functional genomics, single-cell sequencing, and algorithm development for analyzing high-throughput data. Key areas include RNA biology, chromatin interactions, and the integration of multi-omics datasets. Pachter’s lab develops tools like kallisto and bustools for RNA-seq quantification, emphasizing open-source software and reproducibility. Notable awards include the NSF CAREER Award, Sloan Research Fellowship, and Miller Professorship. He has authored over 100 papers, with recent work addressing viral detection at single-cell resolution, biophysical modeling of gene expression, and transcriptome quantification. Teaching spans courses in computational biology, mathematics, and machine learning at both Caltech and UC Berkeley. His lab’s interdisciplinary collaborations span biochemistry, statistics, and computer science, aiming to bridge experimental and computational approaches in genomics. Current projects include the Commons Cell Atlas and viral sequence discovery using novel sequencing techniques.

Ben Hankamerمشاهده پروفایل

پژوهشگر ارشد

• Biological Sciences
• Industrial Biotechnology
• Plant Biology
• +۳ مورد دیگر

Professor Ben Hankamer is a Professorial Research Fellow at The University of Queensland's Institute for Molecular Bioscience (IMB), affiliated with the School of Chemistry and Molecular Biosciences and the Centre for Solar Biotechnology. His research focuses on leveraging microalgae for sustainable solutions in energy, environment, and health. Education: Bachelor of Science (Honours), Advanced, University of Liverpool Masters of Science, University College London Doctor of Philosophy, University College London Research Interests: Structural biology of photosynthesis using cryo-EM to study microalgal photosynthetic machinery Microalgae biotechnology for solar fuels, bioremediation, and high-value products (e.g., vaccines, nutraceuticals) Sustainable biomanufacturing and circular bioeconomy Research Impact: His work addresses global challenges like climate change and resource scarcity through algae-based systems for CO₂ sequestration, renewable energy, and eco-services. Grants & Supervision: Available for PhD supervision and actively collaborates with industry on biotechnology and sustainability projects. Labs & Teams: Leads the Centre for Solar Biotechnology, a multidisciplinary hub advancing bio-inspired solar technologies for health and environmental resilience.

Joel Mackenzieمشاهده پروفایل

مدرس

• Information Retrieval
• Search Engines
• Algorithms
• +۷ مورد دیگر

Dr. Joel Mackenzie is a Lecturer at the School of Electrical Engineering and Computer Science at the University of Queensland. His research focuses on efficient and effective representations for large-scale search engines, including indexing, compression, and retrieval. He has published extensively in top-tier information retrieval conferences and journals. His research interests include: Information Retrieval Search Engines Algorithms and Data Structures Indexing and Compression Techniques Query Processing and Optimization User Behavior Analysis Mackenzie's research explores both theoretical and practical aspects of search systems, with a particular focus on empirical experimentation and efficiency optimization. His recent work has examined neural information retrieval, dynamic pruning algorithms, and the representation power of modern language models for search applications. His publications show a strong trend toward optimizing search efficiency while maintaining effectiveness, with recent papers addressing challenges in neural information retrieval, lossy compression for indexes, and measuring search quality through novel metrics. His work spans both traditional information retrieval techniques and cutting-edge approaches leveraging modern language models. Mackenzie has received research funding from Google Asia Pacific for his project "Compressing Neural Indexes for Short and Long-Term Applications" (2024-2026). He is actively involved in the information retrieval research community, serving as a program chair for ADCS 2022 and as a short paper co-chair for SIGIR 2025. He has also been a reviewer for numerous top conferences including SIGIR, CIKM, and WSDM. Dr. Mackenzie supervises several PhD and Masters students working on projects related to next-generation information retrieval systems, online discussion summarization, and human-centered verification of language model outputs.

Zheng Keمشاهده پروفایل

دانشیار

• Network data analysis
• Text analysis
• Natural Language Processing
• +۴ مورد دیگر

Zheng (Tracy) Ke is an Associate Professor of Statistics at Harvard University's Department of Statistics. Her academic journey includes roles at the University of Chicago and Harvard, with a Ph.D. from Princeton University and a B.S. from Tsinghua University. She specializes in network data analysis, text analysis/NLP, high-dimensional statistics, machine learning, and bioinformatics/statistical genetics. Research Contributions Her research emphasizes network analysis (e.g., SCORE normalization, community detection), text analysis (topic modeling, sentiment analysis), and high-dimensional statistics (covariance estimation, eigenvalue analysis). Notable works include the MADStat project on statisticians' networks and advancements in GWAS data harmonization. Awards & Recognition COPSS Emerging Leader Award (2024) Sloan Research Fellowship (2023) IMS Peter Gavin Hall Prize (2021) Professional Activities She serves as an Associate Editor for the Journal of the American Statistical Association and organizes workshops on statistical network analysis and complex data systems. Her work bridges theoretical statistics with applications in genomics, finance, and social networks.

Sharon Browningمشاهده پروفایل

استاد پژوهشی

• Statistical Genetics
• Population Genetics
• Computational Biology
• +۳ مورد دیگر

Sharon Browning is a Research Professor in the Department of Biostatistics at the University of Washington's School of Public Health. Her research focuses on developing computational methods for analyzing genetic data, particularly in population genetics and statistical genetics. She leads the Browning group, which creates tools like FLARE for local ancestry inference and identity-by-descent analysis. Education : PhD in Statistics, University of Washington BSc (Hons) in Mathematics, University of Auckland Research Interests : Dr. Browning develops stochastic models for genetic variation, including haplotype phasing, genotype imputation, and methods to detect identity-by-descent segments. Her work emphasizes computationally efficient algorithms for large-scale genomic data, with applications in population history and mutation rate estimation. Publications : Her recent work explores topics like Denisovan ancestry, mutation rate refinement using IBD, and genetic privacy risks in consumer testing. Key contributions include the FLARE software for local ancestry inference and studies on ancient human population dynamics. Awards & Roles : In 2021, she was named Senior Editor for the Statistical Genetics and Genomics section of GENETICS , reflecting her leadership in the field. Her research has been featured in Nature , NPR , and Discover Magazine . Labs/Teams : She oversees the Browning Lab , which develops open-source software for genetic analysis, including tools like hap-ibd and sprime .

Srinivas Aluruمشاهده پروفایل

استاد

• High Performance Computing
• Data Analytics
• Bioinformatics
• +۳ مورد دیگر

Srinivas Aluru is a Regents' Professor and Senior Associate Dean at the Georgia Institute of Technology's College of Computing , within the School of Computational Science and Engineering . His research focuses on High Performance Computing , Bioinformatics , Systems Biology , and Applied Algorithms . He has pioneered parallel methods in computational biology, contributing to plant genome assembly and analysis. Current work includes bioinformatics for high-throughput DNA sequencing and systems biology network inference using Bayesian and mutual information approaches. Aluru holds Fellowships from AAAS and IEEE and has received awards such as the NSF Career Award (1997), IBM Faculty Award (2002), and Swarnajayanti Fellowship (2007). He serves on editorial boards for journals like IEEE Transactions on Parallel and Distributed Systems and International Journal of Data Mining and Bioinformatics . His affiliations include the Institute for Data Engineering and Science (IDEaS) and Machine Learning@GT . Research trends in his articles span genomic data processing, parallel algorithms, and network inference, emphasizing scalability and computational efficiency. He leads efforts in error correction, genome assembly, and large-scale gene regulatory network construction.

Jiyuan Anمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Genomics
• High-dimensional data analysis
• +۵ مورد دیگر

Dr. Jiyuan An is a Bioinformatics Specialist at Queensland University of Technology (QUT), affiliated with the Faculty of Science's School of Biology & Environmental Science. He holds a PhD from the University of Tsukuba. His research focuses on bioinformatics methodologies, multi-omic data analysis, and genetic epidemiology with applications in plant biology, cancer genomics, and disease susceptibility studies. Key research interests include developing bioinformatics tools for high-dimensional data indexing, miRNA-target interactions, and transcript factor analysis. He has contributed to major projects like the multi-omic resource for Nicotiana benthamiana , which integrates genomic and transcriptomic datasets for plant research. His work also addresses genetic risk factors for gastroesophageal reflux, glaucoma, and melanoma using large-scale genomic studies. Dr. An has published extensively on topics ranging from AI-driven medical image analysis to Mendelian randomization studies evaluating causal relationships between lifestyle factors and diseases. His recent work explores genetic heterogeneity in mental health disorders and integrates multi-omic data to identify shared risk genes for allergic diseases. While no specific awards are noted, his research demonstrates significant contributions to both computational biology and translational genetics, with collaborations spanning plant science, oncology, and epidemiology. He contributes to open-access resources for genomic data analysis and has developed computational methods for transcriptomic and proteomic studies.

Axel Almetمشاهده پروفایل

پژوهشگر ارشد

• Mathematical Biology
• Systems Biology
• Biomechanics
• +۴ مورد دیگر

Axel Almet is an NSF-Simons Postdoctoral Scholar at the Department of Mathematics, University of California, Irvine (UCI), funded by the NSF-Simons Center for Multiscale Cell Fate Research (CMCF). Previously, he completed a DPhil (PhD) in Mathematics at the University of Oxford's Wolfson Centre for Mathematical Biology (WCMB), under Professors Helen Byrne, Philip Maini, and Derek Moulton. His research focuses on interdisciplinary mathematical biology, combining computational modeling with experimental data to study biological systems at multiple scales. Almet's work emphasizes single-cell RNA-sequencing, spatial transcriptomics, and continuum mechanics modeling of biological tissues. Key interests include wound healing dynamics, cell-cell communication analysis, and the biomechanics of tissue growth. He has developed novel methods for inferring pattern-driving interactions from genomic data and has contributed to understanding intestinal crypt morphogenesis and cartilage biomechanics. He actively collaborates on problems involving stem cell activation, tumor microenvironments, and evolutionary developmental biology. Committed to academic diversity, he mentors early-career researchers and advocates for inclusive practices in STEM. His current projects include advancing the human skin cell atlas initiative and studying hydrogel-material interactions in regenerative medicine.