جست‌وجوی استاد و پژوهشگر

Ralf Herwigمشاهده پروفایل

پژوهشگر

• Computational Biology
• Bioinformatics
• Systems Biology
• +۵ مورد دیگر

Dr. Ralf Herwig is a computational biologist at the Max Planck Institute for Molecular Genetics in Berlin, Germany. His work focuses on statistical methods for integrative analysis of gene expression, proteomics, and metabolomics data to model biological processes in human diseases like cancer and diabetes. He develops tools such as ConsensusPathDB for molecular interaction networks and IsoTools for long-read RNA-seq analysis. Education: Diploma in mathematics (Free University of Berlin), PhD in mathematics/statistics (FU Berlin) on clustering algorithms and information-theoretic methods. Research Interests include computational network biology, multi-omics data integration, machine learning for cancer survival predictions, and alternative splicing analysis. His group pioneered network propagation frameworks to explain drug toxicity and black-box ML models. Key Publications cover deep learning in drug combinations, long-read sequencing for cancer isoforms, and systems biology approaches to metabolic disorders. Tools developed by Herwig's lab are widely cited (~2,500 citations for ConsensusPathDB). Labs & Collaborations: Leads the Herwig Lab, collaborating on projects involving cancer, diabetes, and cardiotoxicity. The lab maintains critical computational resources for the biomedical community.

Qian Liuمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Applied Statistics
• +۵ مورد دیگر

Dr. Qian Liu is an Assistant Professor in the Department of Applied Computer Science at the University of Winnipeg . She holds a PhD in Individual Interdisciplinary Studies, integrating Computer Science, Statistics, and Medical Genetics. Her research focuses on advanced data science techniques for healthcare and material science, including multi-modal data integration and interpretable AI. Education : 2023: Postdoc, Western University, Canada 2023: PhD, University of Manitoba, Canada 2019: MSc, University of Manitoba, Canada Research Interests : Machine learning, deep learning, bioinformatics, computational biology, medical imaging, and their applications in healthcare and material informatics. Her lab develops tools for genomic integration, radiogenomics, and AI-driven biomarker discovery. Recent Contributions : Her work spans AI in nanomaterial classification, breast cancer biomarker discovery, and emotion recognition. Articles highlight innovations in graph neural networks, diffusion models, and interpretable deep learning. Awards : 2024 University of Manitoba Distinguished Dissertation Award 2019 Best Oral Paper at IEEE Bioinformatics Conference Grants : NSERC 2024 Discovery Grant (Principal Investigator) CIHR 2023 Project Grant (Co-Applicant) Labs/Teams : Active in interdisciplinary collaborations across computer science, statistics, and biomedical domains. Lab members include postdocs, PhD/MS students, and undergraduate researchers.

Inga Prokopenkoمشاهده پروفایل

استاد

• Genetic Epidemiology
• Multi-Omics Analysis
• Type 2 Diabetes
• +۵ مورد دیگر

Professor Inga Prokopenko is a faculty member at the University of Surrey's School of Biosciences (Faculty of Health and Medical Sciences), specializing in e-One Health and leading the Statistical Multi-Omics group. Her research focuses on genetic epidemiology, multi-omics approaches, and the comorbidity between metabolic disorders like type 2 diabetes and conditions such as cardiovascular disease, cancer, and mental health disorders. She leads studies on genetic determinants of blood glucose regulation, obesity-related risks, and shared pathophysiological pathways between diseases. Key research areas include: Genetic links between type 2 diabetes and cancers (breast, pancreatic, etc.) Mechanisms of GLP-1 receptor signaling in diabetes treatment Causal relationships between depression and diabetes using Mendelian randomization Role of abdominal obesity in cancer susceptibility She supervises PhD/MSc students in biosciences and medicine programs and develops computational tools like comorbidPRS for polygenic risk score analysis. Her work integrates large-scale genomic data from cohorts like the UK Biobank and EPIC studies to uncover shared genetic mechanisms across diseases. Recent work highlights include: Systematic reviews on flavan-3-ols and cardiovascular health Meta-analyses of trans-ethnic diabetes GWAS data Epigenetic studies linking blood metabolites to cancer risk Her research bridges statistical genetics with clinical applications, emphasizing precision medicine strategies for complex diseases.

Yuè Liمشاهده پروفایل

استاد

• Computational Biology
• Machine Learning
• Population Genetics
• +۴ مورد دیگر

Yuè Li is a Professor in the Department of Computer Science at McGill University, where he leads the Li Lab focused on machine learning applications in genomics and healthcare. His research develops computational methods for analyzing electronic health records (EHR), single-cell multi-omics data, and population genetics. Dr. Li teaches core courses including Applied Machine Learning (COMP 551), Machine Learning in Genomics and Healthcare (COMP 565), and Computer Programming for Life Sciences (COMP 204). His research interests span: AI methods for computational biology and translational healthcare Multi-modal EHR integration and clinical topic modeling Time-series health forecasting and trajectory analysis Single-cell transcriptomics and epigenomics Polygenic risk modeling and causal variant inference Regulatory genomics and functional annotation integration Publications demonstrate strong focus on transformer architectures for healthcare forecasting, Bayesian methods for genomic inference, and neural topic models for clinical phenotyping. Recent work emphasizes foundation models for single-cell data and federated learning for EHR analysis. Scientific Awards: KDD HealthDay2022 Best Paper Award for seed-guided topic modeling Dr. Li mentors graduate students and postdoctoral researchers working on machine learning applications in biomedical domains. Current lab members include Master's students Bo-Hong Wang, Claris Gu, Neda Esfehani, and Ruilin Wang, along with postdoctoral researcher Dr. Jun Bai. The Li Lab operates within McGill's School of Computer Science, developing computational frameworks to integrate heterogeneous biomedical data for improved disease understanding and clinical decision support.

• Data Mining
• Bioinformatics
• Machine Learning
• +۳ مورد دیگر

Domingo Savio Rodríguez Baena is a Professor at Pablo de Olavide University, affiliated with the Department of Computer Languages and Systems. His research focuses on data mining, bioinformatics, and computational biology, with a particular emphasis on biclustering algorithms, gene co-expression networks, and high-performance computing applications. PhD in Engineering, Data Science, and Bioinformatics (2012) from Pablo de Olavide University His work spans interdisciplinary domains, including recommender systems , livestock behavior analysis , and biological data interpretation . Recent articles highlight his contributions to multi-GPU optimization , ensemble learning , and historical database construction . Key collaborations include the DATAi Intelligent Data Analysis and DASE Data Analytics Science & Engineering research groups. He has developed tools like the CyEnGNet–App for gene network visualization and BIGO for gene enrichment analysis. Contact: dsrodbae@upo.es

• Machine Learning
• Artificial Intelligence
• Data Analysis
• +۷ مورد دیگر

Jonathan Voersaa Wenshøj is an academic researcher at the Department of Computer Science, University of Copenhagen. He contributes to the Machine Learning section's activities spanning theoretical foundations and applications in diverse domains like information retrieval, medical data analysis, remote sensing, sustainability, and biological modeling. The section participates in the SCIENCE AI Centre and collaborates with initiatives like TreeSense for global tree resource analysis. His research intersects machine learning with quantum computing, medical informatics, and sustainability. Recent publications highlight applications in environmental monitoring, healthcare diagnostics, and energy-efficient AI systems. The department provides advanced compute resources including a powerful cluster for intensive machine learning tasks. This researcher's work appears in diverse machine learning domains, with recent publications addressing quantum-inspired architectures, explainable AI in medical imaging, and sustainable computing practices. The section actively hosts events including seminars, conferences, and PhD defences related to machine learning advancements.

Grant Montgomeryمشاهده پروفایل

استاد

• Genetics
• Genomics
• Reproductive Disorders
• +۴ مورد دیگر

Professor Grant Montgomery is a leading academic at the University of Queensland (UQ), serving as Director of the UQ Genome Innovation Hub and Group Leader of the Genomics of Reproductive Disorders at the Institute for Molecular Bioscience (IMB). His primary focus is on understanding the genetic and epigenetic basis of reproductive disorders, particularly endometriosis, through large-scale genomic studies and functional analyses. His work integrates multi-omics data to identify disease mechanisms and develop translational solutions. Research Interests: Genetic and epigenetic regulation of endometrial function Systems genetics approaches to study complex diseases Functional validation of genomic risk variants Development of clinical tools like eQTL/mQTL browsers Multi-omics integration for disease biomarker discovery Recent Articles: Highlighting endometriosis genetics, comorbidity mechanisms, and novel diagnostic approaches. Key themes include genetic risk stratification, epigenetic dysregulation, and translational applications of genomic data. Collaborations: Active partnerships with institutions globally, including University of California, San Francisco, Oxford Endometriosis CaRe Centre, and Monash University. Major consortia include International Endometriosis Genetics Consortium and Reprogen Consortium. Advising & Training: Supervises PhD students focusing on endometrial cell biology, epigenetic regulation, and computational genomics. Provides training opportunities in functional genomics and systems biology. Labs/Teams: Leads a multidisciplinary team with expertise in molecular biology, bioinformatics, and clinical research. Maintains cell culture facilities and single-cell sequencing capabilities.

Andrea Sottorivaمشاهده پروفایل

استاد

• Cancer Evolution
• Computational Biology
• Genomics
• +۴ مورد دیگر

Andrea Sottoriva is the Head of the Computational Biology Research Centre at Human Technopole , Milan, Italy, and holds the title of Professor of Cancer Genomics and Evolution . His work bridges computational biology, evolutionary theory, and clinical oncology to predict cancer progression and design adaptive treatment strategies. University of Bologna – BSc in Computer Science (2006) University of Amsterdam – MSc in Computational Sciences (2008) University of Cambridge – PhD in Computational Biology (2012) Dr. Sottoriva's research focuses on cancer evolution , applying machine learning and population genetics to decode tumor heterogeneity through multi-omics data. His lab integrates patient-derived organoids and spatial genomics to understand how genetic and epigenetic factors drive cancer progression. The 15 most recent publications demonstrate a consistent emphasis on subclonal dynamics (7/15), computational methods (6/15), and evolutionary modeling (5/15). Key themes include adaptive mutability in colorectal cancer , immune editing in post-transplantation relapses, and deep learning applications for tumor heterogeneity. Scientific recognition includes: Cancer Research UK Future Leaders in Cancer Research Prize (2016) His lab currently trains PhD students and postdoctoral researchers in computational oncology, maintaining a living biobank of patient-derived models while pioneering AI-mechanistic hybrid models for clinical translation.

• Statistics
• Machine Learning
• Bioinformatics
• +۴ مورد دیگر

Ping Ma is a Professor of Statistics with a courtesy appointment in Computer Science at the University of Georgia. His research focuses on developing innovative statistical and machine learning methodologies for complex high-dimensional data, with applications spanning bioinformatics, computational biology, social network analysis, and anomaly detection in power systems. Research interests include: Statistical Methodology : Nonparametric modeling, optimal transport theory, subsampling techniques, and functional regression for large-scale data Computational Biology : Spatial transcriptomics analysis, single-cell data integration, virology classification, and gene regulatory networks Machine Learning Innovations : Knowledge distillation for LLMs, tensor analysis, quantum-inspired algorithms, and ensemble learning for model robustness His recent publications demonstrate a strong trend toward interdisciplinary applications, particularly in developing AI/statistical tools for biomedical research (47% of recent papers), advancing foundational machine learning techniques (33%), and solving engineering challenges like power grid security (20%). Methodologically, 67% focus on novel algorithm development while 33% refine existing techniques for scalability.

Maxwell J. Smithمشاهده پروفایل

استادیار

• Public Health Ethics
• Infectious Disease Ethics
• Health Equity
• +۳ مورد دیگر

Maxwell J. Smith is an Assistant Professor at the School of Health Studies within the Faculty of Health Sciences at Western University . He holds multiple appointments across the Department of Philosophy , Schulich Interfaculty Program in Public Health , Department of Epidemiology and Biostatistics , and other research centers. As the Western Research Chair in Public Health Ethics , his work bridges bioethics, public health policy, and social justice. Research Domains: Public health ethics, infectious disease ethics, health equity, moral philosophy Collaborations: Social epidemiologists, global health organizations, interdisciplinary ethics institutes Key Focus: Ethical frameworks for pandemic response, health equity in policy-making His recent publications explore the intersection of ethics and epidemiology in health equity modeling, pandemic treaties, workplace vaccination mandates, and resource allocation during crises. He has contributed to high-impact journals like New England Journal of Medicine and The Lancet Infectious Diseases . Notable contributions include: Developing empirical ethics studies for public health policy Advocating for intersectoral action on health inequities Addressing systems thinking in ethical frameworks Global governance of pandemics Equity-focused vaccine monitoring strategies Dr. Smith’s work emphasizes translating philosophical ethics into practical public health solutions, with a strong commitment to social justice and interdisciplinary collaboration.

Sonit Singhمشاهده پروفایل

مدرس

• Artificial Intelligence
• Computer Vision
• Natural Language Processing
• +۷ مورد دیگر

Dr. Sonit Singh is a Lecturer at the School of Computer Science and Engineering at the University of New South Wales (UNSW), Sydney, Australia. Prior to this role, he served as a Postdoctoral Research Fellow at the same institution. He holds a PhD from Macquarie University, completed in collaboration with Macquarie. His research focuses on artificial intelligence, computer vision, natural language processing, and their applications in healthcare, particularly in medical imaging and diagnostic systems. Dr. Singh's academic background includes a strong emphasis on biomedical imaging, intelligent robotics, and computing education. His work bridges machine learning advancements with real-world clinical challenges, such as tumor segmentation, radiology report generation, and disease detection through imaging modalities like CT scans and ultrasound. His recent publications highlight innovations in 3D medical image localization, robust tumor segmentation networks, and clinical context-aware radiology report systems. These contributions underscore his expertise in integrating deep learning techniques with healthcare workflows. Dr. Singh collaborates with multidisciplinary teams, including medical professionals, to develop solutions for challenges like placenta segmentation and dietary intake monitoring using AI. While no specific awards or grants are explicitly listed, his extensive publication record reflects sustained academic engagement in high-impact areas. His research often addresses practical clinical needs, such as improving diagnostic accuracy and patient care through advanced computational methods.

Maureen A. Sartorمشاهده پروفایل

استاد

• Bioinformatics
• Epigenomics
• Cancer Research
• +۴ مورد دیگر

Maureen A. Sartor is a Professor in the Department of Computational Medicine and Bioinformatics at the University of Michigan Medical School, with a joint appointment in Biostatistics at the School of Public Health. She serves as Co-Director of the Bioinformatics Graduate Program and leads an active research laboratory focused on computational biology. Education: PhD in Biostatistics, University of Cincinnati (2007) MS in Biomathematics, North Carolina State University (2000) BS in Mathematics with minors in Biology and Computer Science, Xavier University (1998) Research Focus: Dr. Sartor develops bioinformatics methods for analyzing high-throughput regulatory and epigenomic data. Her primary research examines cancer epigenomics and biomarker discovery in oral squamous cell carcinomas, with additional projects on ALS pathogenesis and computational methods for predicting chemical exposure-gene interactions. Her lab specializes in multi-omics analyses and tool development for genomic data interpretation. Publication Trends: Her recent work (2016-2022) demonstrates strong emphasis on HPV-related head/neck cancer biology, epigenetic regulation mechanisms (DNA methylation/hydroxymethylation), and development of bioinformatics tools for genomic region annotation and enrichment analysis. Publications consistently integrate computational method development with translational cancer research applications. Student Advising: Actively mentors graduate students across bioinformatics, biostatistics, and cancer biology programs. Current advisees include 8 PhD candidates and 1 MS student. Previously advised 6 PhD graduates now working in academia and industry. Laboratory Leadership: Directs the Sartor Lab emphasizing collaborative science and Michigan Medicine's core values (Caring, Integrity, Teamwork, Innovation). Research activities focus on cancer epigenomics, HPV oncology, and bioinformatics tool development for the research community.

Zhicheng Jiمشاهده پروفایل

استادیار

• Biostatistics
• Bioinformatics
• Single-cell RNA sequencing
• +۵ مورد دیگر

Zhicheng Ji is an Assistant Professor of Biostatistics & Bioinformatics at Duke University, affiliated with the Division of Integrative Genomics. His research focuses on developing computational methods for single-cell RNA-seq , spatial transcriptomics , and genomic data analysis . He teaches courses like BIOSTAT 824: Case Studies in Biomedical Data Science . Research Highlights : Statistical modeling of microbiome data Machine learning for cell segmentation T cell immunology in cancer Multi-omics data integration Spatially variable gene detection Foundation models for epigenetics Scientific Awards : NIGMS Grant (2024-2029) for spatial transcriptomics NIEHS Grant (2023-2028) on PFAS exposure NSF ERC PreMiEr (2022-2027) NCI Grant (2022-2027) on ferroptosis

Zhangjun Feiمشاهده پروفایل

استاد مدعو

• Genomics
• Systems Biology
• Bioinformatics
• +۴ مورد دیگر

Zhangjun Fei is an Adjunct Professor at the School of Integrative Plant Science, Cornell University, and a faculty member at the Boyce Thompson Institute (BTI). His research spans Developing computational tools for omics data analysis Creating genomic databases for crop improvement Applying next-generation sequencing (NGS) to understand crop evolution Inferring gene regulatory networks Research interests include: Genomics and systems biology of economically important crops (tomato, cucurbits, sweetpotato, fruit trees) Integration of genome, transcriptome, proteome, and metabolome datasets Development of bioinformatics resources like Plant MetGenMAP, iAssembler, iTAK, and VirusDetect Exploration of gene regulatory networks via multi-omics data fusion Recent publications highlight trends in Super-pangenome analysis for structural variation Gene discovery in stress tolerance and domestication Molecular mechanisms of fruit quality and disease resistance Evolutionary insights across cucurbit and fruit crops Labs and teams include the Fei Lab at BTI , which has led initiatives like: Pan-African Sweet Potato Virome and Chinese Tomato Virome projects Collaborations with USDA, Meiogenix, and the Foundation for Food & Agriculture Research (FFAR) Training programs through BTI Summer Internships and Cornell graduate fields

• High-performance computing
• Graph algorithms
• Graph machine learning
• +۱ مورد دیگر

Ariful Azad serves as an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, where he leads research at the intersection of high-performance computing and graph analytics. His work focuses on developing scalable algorithms for graph machine learning with applications in bioinformatics and security informatics. Educational Background: Ph.D. in Computer Science, Purdue University (2014) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2006) Research Focus: Dr. Azad specializes in high-performance graph algorithms , particularly for distributed-memory systems. His pioneering work includes the Combinatorial BLAS library and novel approaches for graph neural networks (GNNs), with emphasis on explainability through Shapley values and optimization of sparse matrix operations. His bioinformatics research tackles large-scale metagenomics challenges through projects like Exabiome. Publication Trends: Recent publications (2023-2025) reveal three dominant themes: (1) Scalable GNN explanation frameworks using distributed Shapley values, (2) High-performance sparse linear algebra for graph embeddings and knowledge graphs, and (3) Bioinformatics applications in metagenomics and network alignment. His work consistently bridges theoretical algorithm development with practical implementations for exascale systems. Scientific Recognition: NSF CAREER Award (2024) for foundational contributions to scalable graph algorithms Indiana University Trustee's Teaching Award (2024) U.S. Department of Energy Early Career Award (2021) Research Leadership: As principal investigator for multiple federal grants, Dr. Azad directs projects advancing graph analytics at extreme scales. His work on Weapons of Mass Destruction knowledge graphs demonstrates applied security research, while Exabiome represents significant contributions to computational biology. He actively develops open-source tools like PLANETALIGN for network analysis benchmarking, fostering reproducibility in computational science.