جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Biomedical Informatics
• Drug Design
• +۳ مورد دیگر

Marinka Zitnik is an Associate Professor of Biomedical Informatics at Harvard Medical School, Associate Faculty at the Kempner Institute for the Study of Natural and Artificial Intelligence, and Associate Member at the Broad Institute of MIT and Harvard. Her research focuses on advancing artificial intelligence for medicine and science, including AI-driven drug design, genomic medicine, and multimodal data fusion. She leads initiatives such as the Therapeutics Data Commons and the International AI4Science initiative, emphasizing AI's role in scientific discovery and personalized healthcare. Research Interests: Dr. Zitnik's work integrates AI with biomedical challenges, developing geometric deep learning and multi-scale models to address therapeutic design, disease prediction, and precision medicine. Her lab pioneers approaches for contextualized AI systems and large pretrained models to analyze genetic, cellular, and molecular data for clinical insights. Labs/Teams: Zitnik Lab (focusing on AI in Biomedicine) and collaborations across institutions like the Broad Institute and Harvard Data Science Initiative. She also contributes to global initiatives like the AI4Science network.

Hilary Ann Collerمشاهده پروفایل

استاد

• Cellular Quiescence
• Cancer Biology
• Metabolism
• +۴ مورد دیگر

Hilary Ann Coller is a Professor at the University of California, Los Angeles (UCLA), jointly appointed in the Department of Molecular, Cell and Developmental Biology and the Department of Biological Chemistry within the College of Letters and Science. She leads a research laboratory focusing on cellular quiescence, metabolism, and their implications in cancer and disease progression. Her research integrates molecular biology, epigenetics, and systems approaches to study: Transcriptional and epigenetic control of quiescence Metabolic adaptations in tumor microenvironments RNA biology and gene regulation networks Development of biomedical technologies (e.g., biosensors) Recent publications (2022–2025) highlight trends in: Cellular quiescence mechanisms and transcription factor dynamics Cancer epigenetics and metabolic reprogramming Innovative tools for metabolomics and drug monitoring Educational research on remote STEM training programs She directs multiple NIH-funded projects, including R01CA221296 (stromal autophagy in melanoma) and R01GM086465 (microRNA mechanisms). Her lab mentors graduate students and postdocs, and she co-leads UCLA’s Bruins-in-Genomics summer initiative for undergraduate computational biology training.

Julia Rileyمشاهده پروفایل

استادیار

• Amphibian Ecology
• Reptile Behavior
• Evolutionary Biology
• +۳ مورد دیگر

Dr. Julia Riley is an Assistant Professor in the Department of Biology at Mount Allison University. Her research focuses on amphibian and reptile behavior, evolution, and ecology, with a strong emphasis on conservation. She leads the Riley Integrative Ecology Lab, which investigates topics such as social behavior in lizards, developmental plasticity in reptiles, and the impacts of urbanization on species adaptation. Dr. Riley collaborates with institutions across Canada and globally, including Dalhousie University and the New Brunswick Museum. Her work integrates behavioral ecology, evolutionary biology, and conservation science, addressing questions like how environmental factors shape animal traits and how urbanization affects ecological interactions. She actively promotes conservation strategies, such as evaluating protective measures for turtle nests and mitigating road mortality for amphibians and reptiles. Research Themes: Amphibian/Reptile Conservation, Behavioral Ecology, Urban Ecology, Evolutionary Adaptation Key Collaborations: Dr. Joshua Baxter-Gilbert, Dr. Mike Whiting, Dr. Liese Schwanz Funding Partners: NSERC, New Brunswick Innovation Fund, Nature Conservancy of Canada Dr. Riley’s lab emphasizes interdisciplinary approaches, combining fieldwork with quantitative analysis. She mentors students in MSc/PhD programs through her adjunct appointments and prioritizes training in grant writing, field techniques, and ethical research practices.

Qiongshi Luمشاهده پروفایل

دانشیار

• Biostatistics
• Genetic Epidemiology
• Human Genetics
• +۳ مورد دیگر

Qiongshi Lu is an Associate Professor at the University of Wisconsin–Madison, affiliated with the Department of Biostatistics and Medical Informatics within the School of Medicine and Public Health. He also holds affiliate faculty positions in the Department of Statistics, Computer, Data & Information Sciences at the College of Letters and Science. His research focuses on developing statistical methods for human genetics, including genome-wide association studies (GWAS), gene-environment interactions, and genetic risk prediction. Lu’s work bridges computational biology, epidemiology, and clinical genetics, with a particular emphasis on understanding complex trait variability and resilience mechanisms in neurodegenerative diseases like Alzheimer’s. His lab (qlu-lab.org) explores innovative statistical frameworks such as PIGEON for gene-environment interaction analysis and has contributed to tools like the R package 'ipd' for predictive data inference. Recent studies highlight his interdisciplinary approach, addressing topics from congenital heart disease genetics to socioeconomic health gradients using large-scale genomic datasets. While no formal advisees are listed, his research collaborations span multiple institutions and disciplines.

Xianghong Jasmine Zhouمشاهده پروفایل

استاد

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

Dr. Xianghong Jasmine Zhou is a Professor in the Department of Pathology and Laboratory Medicine at the University of California, Los Angeles (UCLA) School of Medicine . Her work focuses on integrating genomic , epigenetic , and bioinformatic approaches for non-invasive cancer detection and monitoring through cell-free DNA analysis . Principal Investigator for multiple NIH grants (R01CA246329, U01CA230705, etc.) Developed software tools ( cfSNV , cfTools , CancerDetector ) for cell-free DNA mutation and methylation analysis Co-developed CancerLocator and HCC EV ECG score for tumor-of-origin prediction and hepatocellular carcinoma monitoring Research Interests: Specializes in 3D genome modeling , epigenetic regulation , and machine learning for liquid biopsy applications. Her work bridges computational biology with clinical translation , particularly in cancer diagnostics and treatment response assessment . Scientific Contributions: She has pioneered deep learning and population-based modeling approaches for cell-free DNA deconvolution , isoform-level functional annotation , and multi-omics integration . Her 2016 Cell paper on alternative splicing has been highly influential, with over 266 mentions.

Sheheeda Mariam Manakkaduمشاهده پروفایل

دانشیار

• Robotics
• Data Analytics
• Machine Learning
• +۴ مورد دیگر

Sheheeda Mariam Manakkadu is an Associate Professor in the Department of Computer Science at Southern Illinois University Carbondale. She teaches graduate courses in Data Structures, Object-Oriented Programming, Data Mining, Text Mining, and Cloud Architecture, along with undergraduate courses in Operating Systems and Data Analytics. Ph.D., Computer Engineering M.E., Biomedical Engineering Her research spans robotics, data analytics, and parallel computing. Key areas include adaptive control of robotic manipulators, big data processing via MapReduce, IoT resource allocation, and computational bioinformatics for protein networks. Recent publications focus on neuro-sliding mode control, cloud architecture, and scalable recommender systems. She actively participates in academic service as a committee member for graduate courses and the IEEE Erie Section. Her work integrates machine learning, optimization algorithms, and distributed systems across diverse domains.

Fabien Duveauمشاهده پروفایل

پژوهشگر

• Evolutionary Biology
• Genetics
• Molecular Biology
• +۵ مورد دیگر

Fabien Duveau is a CNRS Researcher at the Laboratory of Biology and Modelling of the Cell (LBMC) at École Normale Supérieure de Lyon. He leads the eGRIDE project, funded by an ERC Consolidator Grant, investigating evolutionary mechanisms of gene regulation in dynamic environments. His work focuses on understanding how gene expression plasticity and mutational effects shape organismal adaptation. Duveau holds a PhD from Université Paris 7/CNRS (2011), followed by postdoctoral research at the University of Michigan and Université Paris 7. He joined LBMC in 2019 as part of Gaël Yvert’s team, supported by an ANR Young Researchers grant. His research integrates experimental and computational approaches, including high-throughput RNA sequencing and genetic mapping in yeast ( Saccharomyces cerevisiae ), to study how regulatory evolution balances mutational randomness and environmental selection pressures. Key areas include gene expression noise, cis/trans regulatory variation, and fitness outcomes under environmental stress. Recognitions include the prestigious ERC Consolidator Grant (2024), awarded for innovative projects advancing fundamental biological understanding. Duveau collaborates across disciplines, combining evolutionary genetics with systems biology to predict regulatory evolution in natural and pathological contexts.

Lenka Zdeborováمشاهده پروفایل

دانشیار

• Statistical Physics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Lenka Zdeborová is an Associate Professor at EPFL, jointly affiliated with the School of Basic Sciences and School of Computer and Communication Sciences. She leads the Laboratory of Statistical Physics of Computational Systems, where her research bridges statistical physics, machine learning, and computational biology. Education: PhD in Physics, Université Paris-Cité (2012) MSc in Fundamental Physics, École Normale Supérieure (2009) BSc in Physics, École Normale Supérieure de Lyon (2007) Her work focuses on phase transitions in learning algorithms, high-dimensional statistics, and neural network theory. Current projects investigate fundamental limits of machine learning, dynamics of graph neural networks, and applications to biological systems. Recent publications explore attention mechanisms in transformers, neural network depth advantages, and Bayes-optimal learning. Methodological innovations include cavity methods for hypergraphs and analysis of high-dimensional inference problems. Supervises doctoral students researching statistical physics approaches to machine learning and optimization. Teaches graduate courses in data science and machine learning for physicists.

Karol Nowicki-Osuchمشاهده پروفایل

پژوهشگر ارشد

• Cancer Biology
• Molecular Oncology
• Gastrointestinal Cancers
• +۳ مورد دیگر

Dr. Karol Nowicki-Osuch is a Group Leader at the German Cancer Research Center (DKFZ) , leading a research group focused on tumorigenesis and molecular cancer prevention. His work emphasizes understanding early stages of gastrointestinal tumor development using multimodal techniques like single-cell RNA sequencing, organoid models, and spatial transcriptomics. Key research areas include the molecular mechanisms underlying Barrett's esophagus, gastric intestinal metaplasia, and the transition to adenocarcinoma. Collaborations span institutions in Germany, Poland, the UK, and the US. His team investigates how normal epithelial cells lose molecular identity and progress to precancerous states. Notable achievements include demonstrating that esophageal adenocarcinoma originates from gastric tissue and identifying novel mutational signatures using the mutREAD method. Current projects aim to develop early cancer detection strategies and in vivo models of tumor progression. Dr. Nowicki-Osuch supervises PhD students and employs cutting-edge technologies such as spatial transcriptomics and organoid platforms. His lab integrates computational biology with experimental approaches to address gaps in understanding clonal evolution and somatic mutations in gastroesophageal cancers.

Zihang Luمشاهده پروفایل

استادیار

• Biostatistics
• Epidemiological Research
• Bayesian Analysis
• +۴ مورد دیگر

Dr. Zihang Lu is an Assistant Professor in the Department of Public Health Sciences at Queen’s University, affiliated with the School of Medicine and Faculty of Health Sciences. He holds a PhD and MSc in Biostatistics from the University of Toronto (2020 and 2013) and a BSc in Biostatistics from Southern Medical University (2012). His research focuses on developing novel statistical and machine learning methods for complex data analysis, with applications in clinical and epidemiological studies, particularly in asthma, obesity, sleep, pain, and cancer research. He collaborates with clinicians to address statistical challenges in longitudinal and functional data analysis, Bayesian modeling, and integrative clustering. Education : PhD in Biostatistics, University of Toronto (2020) MSc in Biostatistics, University of Toronto (2013) BSc in Biostatistics, Southern Medical University (2012) Research Interests : Bayesian data analysis and variable selection Longitudinal and functional data clustering High-dimensional data integration Statistical methods for disease subtyping Design and analysis of observational studies Advising & Grants : Current advisees: Caroline Lee (MSc 2022), Veronique Rowley (2021), Zhiwen Tan (PhD 2022), Mojtaba Ahmadiankalati (MSc 2021) Recruiting CANSSI Distinguished Post-Doctoral Fellow for Bayesian methods in longitudinal health data analysis Accepting MSc/PhD students for 2023 Labs & Teams : His team focuses on statistical methodology development and clinical collaborations, with active projects in pediatric lung function (e.g., the CHILD Cohort Study) and chronic disease management.

Yalong Yangمشاهده پروفایل

استادیار

• Immersive Analytics
• Virtual Reality
• Data Visualization
• +۳ مورد دیگر

Yalong Yang is an Assistant Professor at the School of Interactive Computing at Georgia Institute of Technology. His research focuses on immersive analytics, virtual reality (VR), and augmented reality (AR) interfaces, with a particular emphasis on spatial interaction, hybrid user interfaces, and data visualization techniques. He explores how embodied interactions and immersive environments enhance understanding in domains like education, sports analytics, and collaborative decision-making. Key research themes include hybrid immersive systems (combining VR/AR with physical devices), asymmetric collaboration in mixed environments, and AI-driven tools for programming education. His work spans both theoretical frameworks and applied systems, such as SPHERE for scalable personalized feedback in coding classrooms and VizGroup for collaborative learning analytics. Yang’s recent publications highlight trends in spatial hybrid interfaces, navigation in immersive environments, and the integration of generative AI in educational technologies. His projects often involve evaluating interaction techniques (e.g., label placement in AR) and comparing performance across desktop and VR platforms. Notable contributions include the SportsXR initiative for immersive analytics in sports, and systems like CompositingVis for creating complex visualizations in 3D spaces. His work addresses challenges in situated analytics, wearable technologies for outdoor activities, and audience analysis in VR exhibitions.

Vernon Chinchilliمشاهده پروفایل

استاد

• Biostatistics
• Clinical Trials
• Multivariate Analysis
• +۴ مورد دیگر

Vernon M. Chinchilli is a Distinguished Professor of Public Health Sciences and Professor of Statistics at The Pennsylvania State University. He holds appointments in the Department of Public Health Sciences (Division of Biostatistics and Bioinformatics) and the Department of Statistics. His primary affiliation is with the College of Medicine. Dr. Chinchilli received his PhD in Statistics from the University of North Carolina at Chapel Hill (1979) and a BS in Mathematics from Penn State (1974). His research focuses on biostatistical methodology for multi-center clinical trials, multivariate analysis of longitudinal data, and statistical methods for crossover designs. He has led NIH-funded Data Coordinating Centers for studies on lung disease, kidney disease, and acute kidney injury (AKI). Notable roles include Principal Investigator for the Natural History of AKI Consortium and the Biostatistics Research Center for Impaired Awareness of Hypoglycemia. Dr. Chinchilli teaches doctoral courses in multivariate analysis (PHS 523) and statistical methods in public health (PHS 554/555). He has advised 35 PhD and 40 MS students. His honors include Fellow of the American Statistical Association (1997) and extensive contributions to methodological advancements in clinical trial design and analysis. Key areas of investigation include: 1) statistical methods for agreement assessment and equivalence testing in clinical trials, 2) biomarker development for kidney function decline, and 3) application of machine learning in predicting post-hospitalization outcomes. He collaborates internationally on projects like the ENICTO cancer treatment outcomes initiative and the S-AKI sepsis biomarker study. Recent work explores: cognitive change measurement in aging populations, opioid alternative therapies for chronic pain, and vaccine efficacy in immunocompromised patients. His research spans 541 peer-reviewed publications and 15 active NIH grants, with particular emphasis on translating statistical innovations into clinical practice.

Samah Fodehمشاهده پروفایل

دانشیار

• Artificial Intelligence in Healthcare
• Machine Learning
• Biomedical Informatics
• +۷ مورد دیگر

Samah Fodeh is an Associate Professor in the Department of Emergency Medicine at Yale School of Medicine, with a secondary appointment in Biostatistics and affiliation with the VA Connecticut Healthcare System. Her research focuses on leveraging AI and machine learning to improve patient-centered care, particularly in healthcare communication, opioid addiction, HIV outcomes, and pandemic response. She leads the Fodeh Lab and collaborates on NIH-funded projects like the Bridge2AI Program. Education: PhD in Biomedical Informatics from Michigan State University (2010). Research Interests: Her work integrates AI techniques to analyze patient-generated and clinical data, including social media and electronic health records. Key areas include detecting patient preferences, improving pain care quality, and addressing public health crises like opioid misuse and suicide risk through Twitter-based analyses. She also develops predictive models for fall risks in aging populations with HIV. Publications: Over 39 peer-reviewed articles, emphasizing AI-driven solutions in healthcare. Recent work includes risk models for HIV patients, opioid misuse detection on social media, and pandemic-related sentiment analysis. Labs/Teams: Director of the Fodeh Lab, part of Yale’s Data Mining and Health Informatics initiatives. Collaborates with interdisciplinary teams at the Yale School of Public Health and computational biology programs.

• Genetics
• Bioinformatics
• Immunology
• +۳ مورد دیگر

Overview Francesc Lopez-Giraldez is a Research Scientist in Genetics at the Yale School of Medicine, with affiliations in the Yale Center for Genome Analysis (YCGA) and the Center for Biomedical Data Science. His research focuses on integrating genomics, bioinformatics, and molecular biology to address complex biological and clinical questions in areas such as cancer, neurodevelopmental disorders, and immune-related diseases. Education PhD in Evolutionary Genetics, Autonomous University of Barcelona (2006) MSc in Health and Life Sciences, Pompeu Fabra University (2004) BS in Biology, University of Girona (2000) Research Interests Lopez-Giraldez’s work spans genetics, immunology, and computational biology. Key areas include: Genetic mechanisms underlying congenital heart diseases and neurodevelopmental disorders Bioinformatic analysis of cancer genomics and immune checkpoint resistance Role of mTOR signaling in vascular and neurological pathologies Spatial transcriptomics to study tumor microenvironments and autoimmune diseases Key Contributions Recent publications highlight his interdisciplinary approach, such as: Identifying genetic contributions to congenital heart diseases Elucidating mTOR dysregulation in lissencephaly Exploring immune checkpoint inhibitors’ resistance mechanisms in lung cancer Awards & Recognition Recipient of the National Cancer Institute Research Specialist Award (R50) in 2023, recognizing his innovative contributions to biomedical data science and translational research. Collaborations Lopez-Giraldez collaborates with leading researchers, including Jeffrey Townsend (Yale), Murat Gunel (Neurology), and Kasia Politi (Oncology), to advance team science in precision medicine and genomics. Labs & Teams He contributes to the Yale Center for Genome Analysis (YCGA) and the Yale Cancer Center, focusing on genomic data integration and bioinformatics pipelines for clinical research.

Marco Coolenمشاهده پروفایل

دانشیار

• Molecular Paleobiology
• Geomicrobiology
• Paleoecology
• +۳ مورد دیگر

Associate Professor Marco Coolen is an academic at Curtin University, serving as Deputy Director of the WA Organic and Isotope Geochemistry Centre. He holds a joint affiliation with the Faculty of Science and Engineering and the School of Earth and Planetary Sciences. His research focuses on molecular palaeoecology and geomicrobiology, particularly using ancient DNA (sedaDNA) to study past ecosystem-climate interactions and microbial responses to environmental changes. Coolen has led projects funded by institutions like the US National Science Foundation (NSF), investigating topics such as monsoon-driven OMZ expansions in the Indian Ocean and microbial activities in subsurface hydrocarbon reservoirs. Education and Professional Background: Coolen completed his PhD in Oldenburg and held postdoctoral roles at the Royal Netherlands Institute for Sea Research (NIOZ) and the University of Groningen. He later served as an Associate Scientist at the Woods Hole Oceanographic Institution (WHOI) before joining Curtin in 2015. His expertise spans microbial ecology, lipid biomarkers, and bioinformatics analysis of metagenomic datasets. Research Interests: Coolen’s work integrates cutting-edge molecular tools (e.g., metatranscriptomics) with traditional geochemical proxies to reconstruct ancient ecosystems. Key areas include microbial life in impact craters (e.g., Chicxulub), permafrost thaw dynamics, and the role of organic matter in the carbon cycle. His recent studies explore microbial communities in extreme environments like hydrothermal systems and granitoid rocks. Awards and Grants: He has received the WHOI Ocean Life Institute Fellowship (2008–2011) and the NWO-ALW VENI Award (2004). His collaborations include high-impact groups such as Prof. Kliti Grice’s team at Curtin and international projects like IODP-ICDP drilling of the Chicxulub crater. Lab and Teams: Coolen leads the WA Organic and Isotope Geochemistry Centre, focusing on interdisciplinary research in geobiology and organic geochemistry. He co-advises PhD students and supervises postdoctoral researchers in projects involving ancient DNA analysis and microbial genomics.