جست‌وجوی استاد و پژوهشگر

Maizie Zhouمشاهده پروفایل

استادیار

• Computational Genomics
• Bioinformatics
• Computational Neuroscience
• +۶ مورد دیگر

Maizie Zhou is an Assistant Professor in Biomedical Engineering and Computer Science at Vanderbilt University’s School of Engineering. She holds dual PhDs in Computer Science (Stanford University) and Neuroscience (Wake Forest School of Medicine), with additional degrees from Wake Forest University and Huazhong University of Science and Technology. Her research focuses on computational genomics, bioinformatics, and machine learning applied to problems in cancer genomics, single-cell and spatial transcriptomics, and computational neuroscience. She leads the Zhou Lab, which develops algorithms for structural variant detection, neural circuit analysis, and integrative omics approaches. Recent work includes tools like VolcanoSV and stDyer, and she has received grants from NIH, Vanderbilt Brain Institute, and industry partnerships. Key achievements include VUSE Best Paper Awards, Global Engagement Travel Grants, and mentoring students in prestigious programs like the Provost’s Pathbreaking Discovery Award. Her lab also explores the neural underpinnings of cognitive maturation in primates, combining computational and experimental neuroscience. Education: PhDs in Computer Science (Stanford) and Neuroscience (Wake Forest), MS (Computer Science, Wake Forest), BS (Biotechnology, Huazhong). Research interests span computational genomics (e.g., structural variant detection, haplotype phasing), spatial transcriptomics (clustering, integration), and computational neuroscience (neural circuit dynamics, prefrontal cortex plasticity). Her lab’s tools address challenges in precision medicine, cancer genomics, and understanding adolescent brain development. Recent projects include NIH-funded work on spatial transcriptomics and collaborations with Dr. Meltzer’s lab on cancer genomics. Publications highlight advancements in bioinformatics tools and neural mechanisms, with trends toward multi-omics integration and algorithmic innovation in genomics. Awards include the Global Engagement Travel Grant and CCSB Accelerator Fund. Students under her mentorship have excelled in qualifying exams and travel grants, reflecting her impactful training program.

Mirko Trajkovskiمشاهده پروفایل

استاد

• Metabolic Diseases
• Obesity
• Insulin Resistance
• +۵ مورد دیگر

Professor Mirko Trajkovski leads the Laboratory of Metabolic Diseases at the Faculty of Medicine, University of Geneva. He completed his PhD at the International Max Planck School in Dresden (2005), followed by postdoctoral research at ETH Zurich, before establishing his lab at University College London (2012) and moving to Geneva (2013). His work focuses on adipose tissue plasticity , gut microbiota , and their roles in obesity , diabetes , and insulin resistance . Swiss National Science Foundation Professor (2014) ERC Starting Grant (2014) & Consolidator Grant (2019) Dr Walter Seipp Prize & Carl Gustav Carus Prize (2005) His lab investigates fat browning mechanisms , microbiota-host communication , and multi-tissue metabolic regulation using in vivo , in vitro , and human cohort approaches. Recent publications emphasize microbiome-based therapies , temperature effects on metabolism , and gut-bone-adipose crosstalk . Current advisees include PhD student Silas Kieser, with past members like Jing Xue, Salvatore Fabbiano, and Claire Chevalier contributing to immuno-metabolism and microbial engineering projects.

Maria Chikinaمشاهده پروفایل

استادیار

• Computational Biology
• Genomics
• Machine Learning
• +۷ مورد دیگر

Maria Chikina is an Assistant Professor at the University of Pittsburgh School of Medicine's Department of Computational and Systems Biology. She holds a PhD in Molecular Biology from Princeton University. Her research focuses on developing computational methods to analyze large-scale genomic datasets, bridging statistical rigor with biological insights to overcome experimental biases. Key research areas include latent variable modeling (e.g., PLIER, CellCODE), interpretable neural networks for sequence-to-function modeling, evolutionary rate analysis (RERconverge), and applications in tumor immunology, exercise genomics, and infectious disease (e.g., SARS-CoV-2). Her lab has developed tools like InstaPrism, NIFA, and L0 segmentation for data-driven biological discovery. Her work spans collaborations with institutions like UPMC (on tumor microenvironment) and the Molecular Transducers of Physical Activity Consortium (MoTraPAC). Notable projects include analyzing convergent evolution in marine mammals and subterranean species, and developing epigenetic biomarkers for disease states through the ECHO program. Lab members include PhD students (Rezwan Hosseini, Tugrul Balci) and postdocs (Tina Subic, Anish Sevekari). Past students Wynn Meyer now leads a group at Lehigh University. Her group emphasizes open-source tools (GitHub repository ChikinaLab) and interdisciplinary approaches to systems biology challenges.

Kelly V. Rugglesمشاهده پروفایل

دانشیار

• Computational Biology
• Bioinformatics
• Proteomics
• +۱۰ مورد دیگر

Kelly V. Ruggles, PhD serves as Associate Professor in the Department of Medicine at NYU Langone Health's School of Medicine. Her academic office is located at 227 East 30th Street, Room 823, New York, NY 10016, with direct contact via phone (212-263-3642) and email. Her research focuses on computational multi-omics integration for understanding human health through high-dimensional data analysis. The Ruggles Lab develops statistical methods and visualization tools to analyze genomic, transcriptomic, translatomic, proteomic, metabolomic, and microbiome datasets, with primary applications in cancer, inflammation, and cardiovascular disease. The lab also creates novel approaches for interpreting clinical trial data, decision science modeling, and behavioral surveys. While publication details aren't listed in the provided text, her work demonstrates consistent emphasis on translating complex biological data into clinically actionable insights through computational frameworks. The lab actively develops open-source tools for multiomics data analysis as indicated by their dedicated projects section. Dr. Ruggles leads the Laboratory of Computational Multiomics, mentoring a research team focused on predictive modeling and data integration. The lab maintains an internal portal for members and alums, with documented research projects and computational tools development.

Joshua J. Coonمشاهده پروفایل

استاد

• Bioanalytical Chemistry
• Mass Spectrometry
• Proteomics
• +۴ مورد دیگر

Joshua J. Coon is a Professor at the University of Wisconsin-Madison with appointments in the Department of Biomolecular Chemistry and the Department of Chemistry. He leads the Coon Group, focusing on advancing mass spectrometry technologies for proteomics, metabolomics, and lipidomics. His research addresses fundamental questions in cell biology, including stem cell differentiation, epigenetic regulation, and cancer biomarker discovery. Affiliations : Director of the NIGMS National Center for Quantitative Biology of Complex Systems. Research Emphasis : Instrumentation development, data analysis software, ion chemistry, and biological applications of proteomics. Laboratory : Located in the Genome Center of Wisconsin with a dozen hybrid mass spectrometers, including Orbitrap systems. Collaborations : Long-term partnership with Thermo Fisher Scientific and the Wisconsin Alumni Research Foundation (WARF) for technology commercialization. Training : Mentored 27 Ph.D. students since 2009, emphasizing interdisciplinary research and professional development.

Jennifer Dyمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Explainable AI
• +۲ مورد دیگر

Jennifer Dy is a Distinguished Professor at Northeastern University with joint appointments in Electrical and Computer Engineering and Khoury College of Computer Sciences. As Director of AI Faculty at the Institute for Experiential AI, she leads research in machine learning, computer vision, and explainable AI. Her work spans biomedical applications (COPD phenotyping, neuroimaging) and fundamental algorithms (active learning, continual learning). She holds a PhD from Purdue University and is an AAAI Fellow. Research Focus: Dy develops methodologies for robust and interpretable machine learning, including techniques for model stability in continual learning, dependency-aware active learning, and axiomatic explanation frameworks. Her applied research advances diagnostic tools using Raman spectroscopy, CT imaging, and multi-omics biomarker discovery. Awards: Recognized with the NSF CAREER Award, Faculty Research Team Award, and AAAI Fellowship for contributions to unsupervised learning and medical AI. Publication Trends: Recent articles demonstrate strong cross-disciplinary integration, combining theoretical advances in explainability/robustness with applications in healthcare, wireless systems, and particle physics. Methodological themes include optimal transport theory, probabilistic modeling, and transformer architectures.

Prashant Maliمشاهده پروفایل

استاد

• Genome Engineering
• RNA Editing
• Stem Cell Research
• +۴ مورد دیگر

Prashant Mali is a Professor in the Department of Bioengineering at the University of California, San Diego . His research bridges genome engineering, RNA biology, and biomedical applications, with a focus on CRISPR-Cas systems and ADAR-mediated RNA editing. Education : Ph.D. in Bioengineering Key Affiliations : UC San Diego, Altman Clinical and Translational Research Institute Dr. Mali's work centers on CRISPR-Cas9 technology , RNA editing , and human pluripotent stem cells . His lab develops tools for programmable gene regulation, synthetic lethal screens, and metabolic pathway analysis in disease contexts. Recent publications highlight innovations in circular RNA engineering , ADAR activity mapping , and metabolic reprogramming in cancer . His team employs multi-omics approaches and in vivo models to translate genome editing into clinical applications. Students and Collaborators Current Lab Members : Sami Nourreddine (Postdoc), Amir Dailamy (Graduate), Andrew Portell (Graduate), Michael Tong (Graduate) Alumni : Kyle Ford (PhD 2022), Nathan Palmer (PhD 2022), Udit Parekh (PhD 2021) Research Themes CRISPR Screens : Synthetic lethal interactions, oncogenic pathways, metabolic vulnerabilities RNA Editing : ADAR engineering, circular guide RNAs, clinical translation Tissue Engineering : Vascularized organoids, cardiac maturation, ex vivo models

Forest Agostinelliمشاهده پروفایل

استادیار

• Deep Learning
• Reinforcement Learning
• Search Algorithms
• +۷ مورد دیگر

Forest Agostinelli is an Assistant Professor in the Department of Computer Science and Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina, where he is also affiliated with the AI Institute. His research focuses on designing AI algorithms for pathfinding problems, integrating deep learning, reinforcement learning, heuristic search, and formal logic. He holds a Ph.D. in Computer Science from the University of California, Irvine, an M.S. from the University of Michigan, and a B.S. in Electrical and Computer Engineering from The Ohio State University. Research Overview : Agostinelli’s work emphasizes solving pathfinding problems in domains like robotics, theorem proving, and molecular optimization. His group develops explainable AI methods to enable collaboration between humans and machines. Key projects include DeepCubeA (solving the Rubik’s Cube via deep reinforcement learning) and neural activation function research. Funding & Awards : He has secured grants from NSF, NASA EPSCoR, and South Carolina’s ASPIRE and MADE programs. Notable awards include the NSF Graduate Research Fellowship and the Graduate Education for Minority Students Fellowship. Teaching : He teaches courses in Artificial Intelligence (CSCE 580) and Deep Reinforcement Learning and Search (CSCE 790), mentoring over 15 students at undergraduate and graduate levels. Labs & Collaborations : Active in AI-driven education and interdisciplinary projects, his lab contributes to tools like ALLURE for children’s learning and Bioinformatics platforms like CircadiOmics.

Xihong Linمشاهده پروفایل

استاد

• Statistical Machine Learning and AI
• Statistical Genetics and Genomics
• Causal Inference and Mediation Analysis
• +۴ مورد دیگر

Xihong Lin is a Professor of Statistics at Harvard University and a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. She is a distinguished academic, holding membership in both the National Academy of Sciences and the National Academy of Medicine. Her research focuses on scalable statistical inference for big data, statistical machine learning, causal inference, and integrative data analysis, with applications in genomics, public health, and precision medicine. Lin’s work addresses challenges in analyzing large-scale genomic and multi-ancestry data, including methods for rare variant association testing, ancestry-adjusted sample analysis, and scalable computing frameworks. Her contributions span biobank studies (e.g., UK Biobank, TOPMed) and clinical applications in lung cancer, cardiovascular health, and smoking cessation. Her scientific awards reflect her leadership in statistical genetics and public health. Key research trends include leveraging single-cell sequencing for functional genomics, developing ensemble machine learning methods for health subtyping, and enhancing polygenic risk prediction across diverse populations. Lin’s methodologies prioritize interpretability and scalability, enabling impactful analyses of complex observational and genomic datasets. Awards: Member, National Academy of Sciences; Member, National Academy of Medicine Her grants and advising efforts focus on interdisciplinary collaborations, bridging statistics, AI, and domain sciences. Lin leads initiatives to improve genomic data management and ethical use of federated data (e.g., FADI framework). She is affiliated with labs advancing statistical genetics and cloud-based workflows (e.g., STAAR workflow).

Thomas Walterمشاهده پروفایل

استاد

• Biomedical Image Analysis
• Machine Learning
• Computational Pathology
• +۴ مورد دیگر

Thomas Walter is a Professor at Mines ParisTech and Director of the Centre for Computational Biology (CBIO) , a research group affiliated with the Institut Curie and INSERM . His work focuses on applying Machine Learning and Computer Vision to biomedical image analysis, particularly in high-content screening and computational pathology . He also serves as Deputy Director of the Computational Oncology (U1331) unit and leads the Statistical Learning and Modeling of Biological Systems team. PhD in Medical Image Analysis (2003, Mines ParisTech) Postdoctoral work at EMBL (European Molecular Biology Laboratory) Director of CBIO since 2018 Holder of a PRAIRIE Chair (Paris Artificial Intelligence Research Institute) since 2019 Dr. Walter's research bridges biomedical imaging , machine learning , and cancer genomics . Key areas include: Statistical reconstruction of biological networks Prediction of tumor progression at genomic/transcriptomic levels Development of deep learning methods for cell cycle analysis Integration of multi-omics data for precision oncology Tools for spatial transcriptomics (e.g., autoFISH, RNA2seg) Recent publications highlight his work in spatial transcriptomics , immunotherapy outcome prediction , and deep learning for digital pathology . His team has developed open-source tools like FISH-quant and pyHiM for single-molecule RNA imaging analysis. Scientific Honors: PRAIRIE Chair (2019) for AI research in life sciences Dr. Walter actively contributes to teaching deep learning for image analysis in multiple graduate programs across France, including courses at Mines ParisTech , Université Paris-Saclay , and Institut Curie . His software tools (FISH-quant, pyHiM) and methodological frameworks (e.g., Cut-Detector, PointFISH) have become standard resources in bioimage informatics.

Manuel Kleinerمشاهده پروفایل

دانشیار

• Host-Microbe Interactions
• Microbial Ecology
• Metaproteomics
• +۷ مورد دیگر

Manuel Kleiner is an Associate Professor in the Department of Plant and Microbial Biology at North Carolina State University. His research focuses on metabolic and physiological interactions in host-microbe systems, microbial ecology, and the application of metagenomics and high-resolution mass spectrometry to study complex microbiota-host relationships. Research Highlights: Development of metaproteomic techniques to quantify protein expression, analyze community structure via biomass contributions, and track isotope ratios to understand nutrient flow between hosts and microbiota. Creator of the "transductomics" approach to detect horizontal gene transfer via viral transduction in intestinal systems. Collaborates with researchers such as Theriot, Sartor, Sheikh, Ziegler, and Gonzalez. Recent Trends: His 2025 publications emphasize gut microbiome dynamics, maize root-microbe interactions, transplantation biology, and advancements in metaproteomic methodologies. Key themes include dietary impacts on microbiota, stable isotope probing, and synthetic microbial communities for plant and human health. Laboratory Tools: The Kleiner Lab utilizes quantitative metagenomics, high-resolution mass spectrometry, and computational modeling to dissect functional interactions in symbiotic systems across diverse environments, from marine organisms to agricultural crops.

Mohsen Yoosefzadeh Najafabadiمشاهده پروفایل

استادیار

• Dry Bean Breeding
• Computational Biology
• Omics-Based Selection
• +۵ مورد دیگر

Dr. Mohsen Yoosefzadeh Najafabadi is an Assistant Professor in the Department of Plant Agriculture at the University of Guelph, Ontario Agricultural College. He holds a PhD in Plant Breeding from the University of Guelph (2022), following M.Sc. and B.Sc. degrees from the University of Tehran. His research focuses on dry bean breeding, computational biology, and integrating omics technologies to enhance crop resilience and productivity. Key areas include developing stress-tolerant dry bean varieties, leveraging remote sensing for trait prediction, and optimizing genomic selection methods. He leads the Dry Bean Breeding & Computational Biology Program and has contributed to over 30 peer-reviewed publications since 2017. Education : PhD, Plant Breeding, University of Guelph (2022) M.Sc., University of Tehran B.Sc., University of Tehran Research interests emphasize computational tools development (e.g., AllInOne preprocessing framework), omics-based selection strategies, and non-Mendelian heredity mechanisms. His lab combines machine learning with field phenotyping to address agricultural challenges such as disease resistance and climate adaptation. Collaborative projects include soybean cold stress analysis and cannabinoid profile prediction in cannabis. Publications span genomic approaches to crop improvement, remote sensing applications, and transcriptomic studies. He teaches courses in plant breeding methodologies and actively engages in technology transfer initiatives. Lab activities include developing high-yielding dry bean cultivars resistant to biotic/abiotic stresses and advancing data-driven pipelines for crop breeding. Future work aims to synergize AI with multi-omics data to enhance crop resilience in diverse environments.

• Artificial Intelligence
• Computational Biology
• Graph Neural Networks
• +۵ مورد دیگر

Pietro Liò is Full Professor in the Department of Computer Science and Technology at the University of Cambridge, where he leads research in Artificial Intelligence and Computational Biology as part of the AI group and the Cambridge Centre for AI in Medicine. He holds additional affiliations as Fellow and Council member of Clare Hall College, member of Ellis (European Lab for Learning & Intelligent Systems), and member of Academia Europaea. Professor Liò earned dual PhDs in Complex Systems and Non Linear Dynamics from the University of Florence and in Theoretical Genetics from the University of Pavia, Italy. His educational background bridges theoretical computer science with biological sciences, forming the foundation for his interdisciplinary research approach. His research focuses on developing Artificial Intelligence and Computational Biology models to understand disease complexity and advance personalized medicine. Current work emphasizes Graph Neural Network modeling for integrating multi-scale, multi-omics, and multi-physics data; combining deep learning with mechanistic approaches; explainability in medical AI; and developing AI-based medical digital twins and personal decision support systems. His work spans from fundamental algorithm development to clinical applications, with particular emphasis on translating computational advances into medical solutions. Analysis of his recent publications reveals strong activity in geometric deep learning , explainable AI for healthcare , and multi-omics integration , with increasing focus on clinically applicable tools that maintain both predictive power and interpretability. Member of Academia Europaea Listed among Top Italian Scientists by VIA-Academy Professor Liò has mentored over 40 PhD students and postdoctoral researchers, including notable names such as Petar Velickovic, David Buterez, and Chaitanya Joshi. His research is supported through collaborations with the Cambridge Centre for AI in Medicine and various international partnerships. He serves on departmental committees including Student Complaints and Postdoc Mentoring, and has completed equality and diversity training essentials. He leads research within the Artificial Intelligence group at Cambridge, focusing on creating computational frameworks that bridge biological complexity with clinical applications through advanced machine learning techniques.

Tim Conradمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Computational Biology
• Machine Learning
• +۵ مورد دیگر

Dr. Tim Conrad is a researcher at the Zuse Institute Berlin in the Visual and data-centric computing department under the Mathematics of Complex Systems division. He leads projects at the intersection of computational biology, AI, and medical data analysis. Projects: Geometric Learning for Single-Cell RNA Velocity Modeling, MODAL MedLab, Sparse Compressed Sensing in -Omics Data, BIFOLD (Big Data and Machine Learning) Research Networks: Affiliated with MATH+ and MODAL Research Campus His research focuses on applying machine learning , network optimization , and sparse data analysis to biological and medical challenges including disease modeling, microbiome dynamics, and ECG classification. Recent work explores hybrid PDE-ODE epidemic models and federated learning in healthcare. 2023-2025 publications highlight trends in AI for biological networks , temporal community detection , and medical signal processing . He co-authored studies on SARS-CoV-2 simulations, proteomics feature selection, and multi-label ECG analysis. His 2004 doctoral thesis at Monash University laid foundations for later work in metabolic pathway analysis. Awarded as a Zuse Fellow , he contributes to open science initiatives like FAIR data sharing . Collaborations span institutions including Freie Universität Berlin and Charité in medical informatics and clinical applications.

Qi Longمشاهده پروفایل

استاد

• Genomics
• Computational Biology
• Biostatistics
• +۱۰ مورد دیگر

Qi Long is a Professor at the University of Pennsylvania, holding joint appointments in the Department of Biostatistics, Epidemiology and Informatics (Perelman School of Medicine), Department of Computer and Information Science (School of Engineering and Applied Science), and Department of Statistics and Data Science (The Wharton School). He serves as Founding Director of the Center for Cancer Data Science, Associate Director of the Penn Institute for Biomedical Informatics, and Associate Director for Quantitative Data Science at the Abramson Cancer Center. His research bridges statistical and machine learning (ML/AI) method development with biomedical applications, focusing on precision medicine and population health. Education : Ph.D. (2005) and M.S. (2003) in Biostatistics from University of Michigan; B.S. (1998) in Computer Science from University of Science and Technology of China. Research interests include: Robust statistical and ML/AI methods for big health data (-omics, EHRs, imaging, mHealth) Multimodal data integration and subgroup heterogeneity analysis Missing data, causal inference, Bayesian methods, and clinical trials Data privacy, algorithmic fairness, and responsible AI in healthcare Foundation models and agentic AI for biomedicine His publications focus on privacy-preserving AI, fairness-aware ML, and integrative models for multi-omics and EHRs. Recent work explores LLMs and watermark detection in hybrid human-AI settings. Scientific Awards : Elected fellow: AAAS, ASA, IMS, ISI, AMIA He leads large NIH- and ARPA-H-funded initiatives, directing statistical coordinating centers for national clinical trials. His lab trains numerous PhD/Master’s students and postdocs, many of whom hold prestigious academic or industry positions.