جست‌وجوی استاد و پژوهشگر

WONG Lim Soonمشاهده پروفایل

استاد

• Computational Biology
• Algorithms & Theory
• Database
• +۶ مورد دیگر

WONG Lim Soon is the KITHCT Chair Professor at the School of Computing , National University of Singapore (NUS). He also served as Deputy Dean of the NUS Graduate School and currently directs the Integrative Sciences and Engineering Programme . His career spans significant contributions to database theory, computational biology, and knowledge discovery technologies. Education: B.Sc. (Engineering) in Computing Science, First Class Honours, Imperial College London Ph.D. in Computer & Information Science, University of Pennsylvania His research interests intersect computational biology , database theory , and knowledge discovery . He focuses on enhancing proteomic profile analysis, addressing confounders in omics data, predicting transcription factor interactions, and rethinking synchronized iteration in programming languages. His work aims to improve biomarker reproducibility in proteomics and design robust statistical methods for omics analysis. His recent publications highlight advancements in genomic data processing , proteomics , and database algorithms . Key themes include non-equijoin efficiency , batch effect normalization , and protein family prediction in low-similarity regimes. Scientific Awards & Honours: Fellow of the ACM, 2013 ICDT 2014 Test of Time Award (with Peter Buneman and Val Tannen) FEER Asian Innovation Gold Award, 2003 (with Allen Yeoh, Huiqing Liu, and Jinyan Li) Singapore Youth Award Medal of Commendation, 2006 Limsoon co-founded Molecular Connections in India and has chaired the company for over a decade, growing it into a 2,000-person organization. His research is supported by grants from Singapore’s National Research Foundation and Ministry of Education Academic Research Fund (Tier-1 and Tier-2). He also develops open-source tools like Synchrony DBModel and GMQL implementations in Scala/Python.

• Biochemistry and Cell Biology
• Evolutionary Biology
• Microbiology
• +۴ مورد دیگر

Dr. John Pinney is a Senior Teaching Fellow and Early Career Researcher at Imperial College London's Central Faculty. As the Data Science Skills Leader within the Graduate School, he designs and delivers training programs in programming, statistics, and machine learning for graduate students. His research focuses on computational systems biology, integrating macromolecular sequences, protein structures, and biological networks to study biological systems' evolution and function. His research interests span biochemistry, evolutionary biology, microbiology, and computational tools development. He is affiliated with the Industrial Biotechnology Hub (BIO) and contributes to interdisciplinary projects. His publications emphasize viral evolution (e.g., herpesviruses), host-pathogen interactions, and metabolic network analysis. He has developed tools like PathwayBooster and metaSHARK for metabolic pathway curation and network reconstruction. No scientific awards are explicitly mentioned in the text. He advises no listed students but collaborates widely on grants and training initiatives. His work supports the Graduate School's mission to enhance data science skills for modern research. He is part of the Industrial Biotechnology Hub, contributing to applied systems biology and biotechnology research.

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.

Manoj M Wagleمشاهده پروفایل

مربی

• Computational Biology
• Artificial Intelligence
• Deep Learning
• +۴ مورد دیگر

Manoj M Wagle is a PhD candidate in the School of Mathematics and Statistics at The University of Sydney and the Children's Medical Research Institute. He holds part-time academic roles as a Tutor and Demonstrator in Data Science and Statistics. His research focuses on applying deep learning and computational methods to single-cell omics, neuroscience, and precision medicine. Education: PhD Candidate (Computational Biology/Deep Learning), University of Sydney (2023–Present) MSc (Health Engineering), Université Grenoble Alpes (2021–2022) MSc (Bioinformatics), Manipal Academy of Higher Education (2020–2022) B.Pharmacy, Manipal Academy of Higher Education (2016–2020) Research Interests: Interdisciplinary applications of AI in biology, single-cell omics analysis, precision medicine, and bioinformatics tool development. Awards: Philipp Hofflin International Research Travel Scholarship (2024) EMBLA Short-term Travel Grant (2023) Children’s Medical Research Institute PhD Scholarship (2023) Teaching & Outreach: Led courses like DATA1001 and STAT5003, and engaged in science outreach activities for high school students.

Daniela Wittenمشاهده پروفایل

استاد

• High-Dimensional Data Analysis
• Statistical Learning
• Genomics
• +۴ مورد دیگر

Daniela Witten is a Professor in the Departments of Biostatistics and Statistics at the University of Washington and holds the Dorothy Gilford Endowed Chair of Mathematical Statistics. Her research focuses on high-dimensional data analysis, statistical learning, genomics, and computational biology. She co-authored the influential textbook Introduction to Statistical Learning , bridging theory and application in data science. Education: BS in Biological Sciences and Mathematics (Stanford University) MS and PhD in Statistics (Stanford University) Research Interests: Her work spans statistical methods for biomedical data, including genomics, neuroscience, and clinical applications. She emphasizes interpretable machine learning and collaborates across disciplines to address complex biological questions. Recent projects include methods for calcium imaging data analysis and multi-omics integration. Awards and Recognition: 2022 COPSS Presidents’ Award (prestigious early/mid-career statistician honor) 2022 Elected Fellow of the Institute of Mathematical Statistics 2018 Simons Investigator Award 2019 Mortimer Spiegelman Award (best paper in public health statistics) Advancing the Field: She advocates for diversity in STEM, mentors over 30 PhD students, and actively contributes to statistical education and policy. Her leadership includes roles on the IMS Council and advocacy for equitable award nomination practices.

• Microbial Informatics
• Bioinformatics
• Microbial Ecology
• +۹ مورد دیگر

Dr. Umer Zeeshan Ijaz is a Research Fellow at the University of Glasgow's College of Medical, Veterinary & Life Sciences, where he leads groundbreaking research in microbial informatics and bioinformatics. With over 26 years of experience since 1999, he has established himself as an international leader in microbial ecology, inverse problems, and medical imaging. His work spans multiple disciplines including sustainable agriculture, aquaculture, water treatment, and human health, with a strong commitment to addressing global challenges through the United Nations Sustainable Development Goals framework. Dr. Ijaz's research interests focus on understanding microbial communities through advanced computational approaches. He has pioneered tools like CONCOCT, SEQENV, and microbiomeSEQ that have transformed how researchers analyze microbial data. His work bridges engineering principles with biological insights, particularly in understanding how microbial communities assemble, adapt, and function in diverse environments from human guts to wastewater treatment systems. He has made significant contributions to sustainable agriculture through microbial solutions for cotton leaf curl disease and potato blackleg, while also advancing aquaculture through innovative gut microbiome research on Atlantic cod and salmon. His research portfolio demonstrates remarkable breadth, connecting microbial ecology with practical applications across multiple sectors. Dr. Ijaz has secured over £22 million in research funding, including a notable £10.5 million from UK Research and Innovation. His work has resulted in more than 250 publications, with a NERC Independent Research Fellowship alone generating 134 publications over 60 months. He has successfully supervised or co-supervised 47 PhD students to completion and currently mentors numerous early career researchers. Best Researcher of the Year (2007) Best Researcher of the Year (2008) LG Electronics - Excellent Achievement Award (The Second Prize) (2006) Dr. Ijaz's leadership extends to international collaborations with institutions across Europe, Asia, and North America. He established the Environmental'Omics Lab, which houses the Orion computing cluster for advanced microbial data analysis. His research group actively engages with industry partners including MoyPark, James Hutton Institute, and Forman Christian College to translate scientific discoveries into practical solutions. Current projects focus on ovarian cancer research through microbial analysis, sustainable agricultural practices, and innovative water treatment technologies that address climate change challenges.

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.

Qing Nieمشاهده پروفایل

استاد

• Computational Systems Biology
• Mathematical Biology
• Single-cell RNA-seq Analysis
• +۴ مورد دیگر

Qing Nie is a Distinguished Professor of Mathematics and Developmental & Cell Biology at the University of California, Irvine (UCI), with affiliation to the Biomedical Engineering Department. He directs the NSF-Simons Center for Multiscale Cell Fate Research, focusing on computational systems biology, cell fate dynamics, and single-cell RNA-seq analysis. His work integrates mathematical modeling, computational tools, and experimental data to study developmental processes, tissue regeneration, and disease mechanisms. Affiliations: UCI Departments of Mathematics, Developmental & Cell Biology, and Biomedical Engineering Key Expertise: Multiscale biological systems, spatial transcriptomics, cell-cell communication analysis, and computational methods for omics data Research interests span mathematical modeling of cellular processes, development of analytical tools for single-cell and spatial transcriptomics, and understanding how signaling pathways regulate cell fate decisions. Notable contributions include CellChat (a systematic analysis framework for cell-cell communication) and spatial transition tensor methods for analyzing spatial transcriptomics data. His work bridges computational and experimental biology, with applications in cancer, wound healing, and developmental biology. Recent publications highlight advancements in spatial transcriptomics analysis, tumor microenvironment modeling, and evolutionary developmental biology. Collaborations with experimental labs (e.g., M. Plikus, Ricardo Mallarino) underscore his translational approach. Awards include recognition as a top scholar in transcriptomics technologies (ScholarGPS).

Hao Fengمشاهده پروفایل

استادیار

• Biostatistics
• Bioinformatics
• Computational Biology
• +۳ مورد دیگر

Hao Feng, PhD, is an Assistant Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University’s School of Medicine. He is also a member of the Population and Cancer Prevention Program at the Case Comprehensive Cancer Center. His research focuses on developing biostatistical and bioinformatics methods to analyze high-throughput omics data, particularly in epigenomics. He has created multiple open-source software tools (e.g., ISLET, magpie, NeuCA) widely used in disease studies, with over 10,000 annual downloads. Dr. Feng collaborates with clinical and wet-lab researchers to interpret cancer genomics data. Education: PhD in Biostatistics, Emory University (2019) MS in Biostatistics, Emory University (2018) MSPH in Biostatistics, Emory University (2013) BS in Biosciences, University of Science and Technology of China (2011) Research Interests: Dr. Feng specializes in statistical methods development for omics data, including epigenomics and transcriptomics. His work emphasizes practical applications in computational biology, such as identifying cancer subtypes via epigenetic biomarkers and improving cell-type-level inference through personalized genomics deconvolution. Recent projects include developing tools for power analysis in MeRIP-seq studies and neural network-based cell-type annotation. Grants & Funding: He leads the NIH/NIGMS Maximizing Investigators' Research Award (MIRA) grant (1R35GM154862), totaling $1.9M, focused on personalized genomics deconvolution. This work aims to enhance cell-type-level inference accuracy in complex biological systems. Labs & Teams: His lab (hfenglab.org) focuses on integrating statistical and computational approaches to solve challenges in precision medicine and cancer research. Collaborations span clinical oncology, neurobiology, and bioinformatics.

Zhijun Yinمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Health Informatics
• +۴ مورد دیگر

Zhijun Yin is an Assistant Professor in the Department of Biomedical Informatics and Computer Science at Vanderbilt University's School of Engineering. His research focuses on developing machine learning and data mining techniques to analyze health behaviors through online data sources like social media and electronic health records (EHRs). He holds dual appointments in Biomedical Informatics and Computer Science, reflecting his interdisciplinary work in health informatics and computational methods. Yin earned a Ph.D. in Computer Science and an M.S. in Biostatistics from Vanderbilt University. His research spans ethical AI applications in healthcare, predictive modeling for disease recurrence, and understanding caregiver dynamics in online communities. He has published extensively on topics including AI ethics, health data privacy, and leveraging social media for medical insights. Notable areas of focus include optimizing word embeddings for small medical datasets, improving risk prediction models using EHR longitudinal data, and addressing algorithmic bias in healthcare AI systems. His work often bridges biomedical data analysis with computational methodologies, aiming to enhance patient care through technology-driven solutions.

Eran Mukamelمشاهده پروفایل

استاد

• Computational Neuroscience
• Epigenomics
• Neural Data Analysis
• +۳ مورد دیگر

Eran Mukamel is a Professor in the Department of Cognitive Science at the University of California San Diego (UCSD). His research focuses on computational analysis of neural data, electrophysiology of sleep and general anesthesia, and epigenomic dynamics in brain cells. He leads the Computational Neural DNA Dynamics (CNDD) Lab, affiliated with UCSD’s Halicioğlu Data Science Institute, the Kavli Institute for Brain and Mind, and the Neurosciences Graduate Program. His work integrates genomic, transcriptomic, and electrophysiological approaches to study brain function and disease. Research interests include neuronal epigenomics, single-cell methylome analysis, and systems neuroscience. He has pioneered methods for studying DNA methylation patterns in brain cells, linking epigenetic signatures to neuronal function and disease states. Recent projects involve investigating how environmental factors (e.g., maternal immune activation) impact brain development and epigenetic programming. Eran’s lab develops computational tools like SnapATAC for analyzing single-cell ATAC-seq data and collaborations span neurology, psychiatry, and computational biology. His contributions bridge molecular neuroscience with clinical applications, particularly in understanding neurodegenerative disorders like Alzheimer’s and ALS.

Federico Mariniمشاهده پروفایل

استاد

• Chemometrics
• Analytical Chemistry
• Food Chemistry
• +۳ مورد دیگر

Federico Marini is a Full Professor at the Department of Chemistry, University of Rome La Sapienza. He holds editorial roles including Editor-in-Chief of *Chemometrics and Intelligent Laboratory Systems* and membership in multiple journal boards. His research focuses on chemometrics, particularly method development and application in food chemistry, metabolomics, and multi-block data analysis. He has authored over 300 publications and edited books like *Chemometrics in Food Chemistry* (Elsevier 2013). Education: PhD in Analytical Chemistry (University of Rome La Sapienza, 2004) with Marie Curie Fellowship (2000–2004). Postdoctoral roles included visiting positions at universities in Copenhagen, Stellenbosch, and Lille. Research interests span advanced chemometric methods for food authentication, omics data processing, and non-linear modeling. Notable contributions include SO-PLS(-LDA) algorithms and multi-block fusion strategies. He coordinates the Chemometrics Task Group of DAC-EuCheMS and serves as Vice President of the Italian Society of NIR Spectroscopy. Awards include the 2006 Young Researcher Award (Italian Chemical Society) and 2012 Chemometrics Award. His work bridges analytical chemistry with data science, addressing real-world problems in agriculture, food safety, and biomedicine through innovative spectroscopic and statistical techniques.

Cees Haringaمشاهده پروفایل

استادیار

• Industrial fermentation
• Computational fluid dynamics
• Bioprocess scale-down
• +۴ مورد دیگر

Dr. Cees Haringa is an Assistant Professor in the Department of Biotechnology at Delft University of Technology , where he leads the Cees Haringa Group within the Bioprocess Engineering section. His research is centered on understanding and optimizing large-scale industrial fermentation processes from the microbial perspective, integrating computational fluid dynamics (CFD), metabolic modeling, and scale-down strategies. Education: PhD (with honors) in Chemical Engineering, Delft University of Technology – Dissertation on CFD simulation of industrial fermentation systems MSc in Chemical Engineering, Delft University of Technology BSc in Chemical Engineering, Delft University of Technology Research Interests: Haringa’s group explores how microorganisms perceive and respond to environmental fluctuations in large-scale bioreactors. His team develops digital twins of industrial fermentations using CFD , coarse-grained models , and experimental scale-down simulators . Key themes include: Microbial lifelines and gradient response CFD-based metabolic modeling Scale-down and scale-up strategies Data-driven bioprocess optimization Sustainable bioprocess engineering Scientific Contributions: His recent publications (2022–2025) span a wide range of topics including syngas fermentation , Penicillium chrysogenum metabolism under oxygen fluctuations, dynamic compartment modeling , and machine learning for bioreactor regime classification . These works emphasize bridging mechanistic understanding with industrial application. Awards and Recognition: DECHEMA Industrial Bioprocess Award – Inaugural recipient for PhD research Teaching and Mentorship: Dr. Haringa is actively involved in curriculum development and teaching at TU Delft, offering courses such as: Modelling and Computation for Life Science & Technology Thermodynamics of Bioprocesses Artificial Intelligence in (Bio-)Chemical Engineering Python Programming for Life Sciences He currently supervises 7 PhD students and has mentored numerous MSc and BSc students in bioprocess modeling and fermentation research. Collaborations and Outreach: Haringa actively seeks both academic and industrial collaborations to address real-world challenges in bioprocess development. His group is open to joint research, student projects, and knowledge exchange in sustainability, digital twins, and bioprocess innovation.

Arvind Merمشاهده پروفایل

استادیار

• Computational Biology
• Bioinformatics
• Machine Learning
• +۸ مورد دیگر

Dr. Arvind Mer is an Assistant Professor in the Department of Biochemistry, Microbiology and Immunology at the University of Ottawa. He holds a B.Tech from Dr. APJAK Technical University (2007), M.Tech from Jawaharlal Nehru University (2009), and a PhD from the Max Delbrück Center for Molecular Medicine and Freie Universität Berlin (2014). His postdoctoral training included stints at the Karolinska Institute (Sweden) and University Health Network (Toronto). He joined the University of Ottawa in 2021. His research focuses on leveraging machine learning and artificial intelligence to analyze high-throughput omics data for advancing personalized medicine. Key areas include: Developing computational methods for single-cell sequencing analysis Identifying biomarkers for cancer drug response prediction Exploring drug repurposing strategies using pharmacogenomic data Understanding therapeutic resistance mechanisms in leukemia and breast cancer Dr. Mer's lab integrates multi-omics datasets with clinical outcomes to improve treatment precision. His work has produced open-source tools like PharmacoDB 2.0 and contributed to novel insights into AML subtypes and drug-tolerant persister cells. He has advised multiple postdoctoral fellows and students interested in computational biology and bioinformatics. His research has been published in journals such as Nature Communications, Cancer Research, and Scientific Reports.

Tarek Zikryمشاهده پروفایل

استادیار

• Precision Medicine
• Computational Biology
• Statistical Machine Learning
• +۶ مورد دیگر

Tarek Zikry is an Assistant Professor at the University of North Carolina at Chapel Hill (UNC-Chapel Hill), holding primary appointment in the School of Data Science and Society (SDSS). He is also Core Faculty in the Curriculum in Bioinformatics & Computational Biology and holds an adjunct appointment in the Department of Statistics and Operations Research. His research focuses on developing statistical methodologies for precision medicine, computational biology, and public health applications. Dr. Zikry earned his Ph.D. in Biostatistics from UNC-Chapel Hill under the mentorship of Michael Kosorok (Biostatistics) and Jeremy Purvis (Genetics). Before joining SDSS in 2025, he completed a postdoctoral fellowship at Columbia University with Genevera Allen, collaborating with the Zuckerman Institute and Irving Institute for Cancer Dynamics. Research interests include dynamic treatment regimes leveraging reinforcement learning, manifold learning for high-dimensional biological data, and single-cell omics analysis for cancer biomarkers. His work bridges statistical theory with practical healthcare applications, emphasizing stability and interpretability in machine learning models. The Zikry Lab at UNC will begin active operations in 2025-26, seeking motivated students from SDSS, STOR, and BCB programs. Key collaborations include interdisciplinary projects across biostatistics, genetics, and computational neuroscience. His research portfolio spans cancer therapeutics, neurodegenerative disease modeling, and public health interventions, with a focus on translating data science innovations into clinical and societal impact.