Find Academic Supervisors

Jennifer BhatnagarView profile

Associate Professor

• Microbial Ecology
• Biogeochemistry
• Ecosystem Science
• +3 more

Jennifer Bhatnagar is an Associate Professor in the Department of Biology at Boston University, affiliated with the College of Arts and Sciences. She holds a PhD from the University of California, Irvine, and leads research focused on microbial ecology, biogeochemistry, and ecosystem science. Her work explores the biochemical mechanisms by which fungi drive large-scale biogeochemical processes, particularly in forest ecosystems. Key research areas include fungal species interactions, mycorrhizal symbiosis, and microbial responses to climate change. Dr. Bhatnagar’s research integrates biochemical analyses, sequencing technologies, and computational modeling to study fungal roles in nutrient cycling, decomposition, and stress adaptation. Her current projects investigate molecular mechanisms of fungal interactions, secretome dynamics in plant-fungal associations, and microbial resilience to climate shifts. She maintains an active lab group and collaborates on interdisciplinary studies linking microbiome data to ecological forecasts. Education: PhD in Biology, University of California, Irvine Lab Website: https://microbesatbu.wordpress.com/ Focus Areas: Fungal Ecology, Biogeochemical Cycles, Climate Change Impacts Publications highlight her contributions to understanding fungal-driven processes in soils, including studies on ectomycorrhizal symbiosis, litter decomposition, and microbiome predictability. Her work bridges microbial biology with ecosystem-level phenomena, informing strategies for ecosystem restoration and climate resilience.

• Bioinformatics
• Immunology
• Computational Biology
• +4 more

Gisela Gabernet is an Associate Research Scientist in the Department of Pathology at Yale School of Medicine. She is affiliated with the Kleinstein Lab, focusing on computational and systems biology research. Her academic training includes a PhD from ETH Zurich in Computational Drug Design (2018), an MSc from TU Dresden in Molecular Bioengineering (2014), and a BSc from Universitat Autònoma de Barcelona in Biotechnology (2012). Her research emphasizes the development of bioinformatics tools and pipelines, particularly for analyzing immune receptor repertoires, genomic data, and multi-omics datasets. She contributes to projects such as nf-core/airrflow, NCBench, and AMULETY, which advance reproducible research and computational workflows in immunology and genomics. Her work addresses clinical challenges like long COVID prediction, multiple sclerosis antibody dynamics, and cancer immunology. She collaborates extensively with interdisciplinary teams to translate computational methods into clinical and experimental applications. Professional Roles: Associate Research Scientist, Yale School of Medicine Pathology Member of Kleinstein Lab Key Research Themes: Immune repertoire analysis and computational immunology Development of bioinformatics pipelines (e.g., nf-core, Nextflow) Machine learning applications in healthcare and genomics Multi-omics approaches to disease mechanisms Recent Projects: Includes studies on SARS-CoV-2 transmission dynamics, predictive modeling for long-term disease outcomes, and the role of immune cells in premature infants. Her work bridges computational innovation with clinical relevance in oncology, neurology, and infectious disease.

• Biomedical Data Science
• Structural Biology
• Genomics
• +3 more

Declan Clarke is an Associate Research Scientist in the Department of Molecular Biophysics and Biochemistry at Yale University, affiliated with the Yale School of Medicine. He holds a PhD from Yale University (2016). His research focuses on integrating structural biology, genomics, and computational methods to advance biomedical data science. Key areas include network analysis, protein dynamics, and computational tools for genomic data interpretation. Clarke’s work spans cancer research, systems biology, and structural bioinformatics. Notable contributions include developing the VAT framework for genomic variant annotation and identifying biomarkers for breast cancer recurrence. His collaborative projects often involve interdisciplinary teams addressing challenges in protein interactions and disease mechanisms. Publications highlight trends in network biology, computational genomics, and structural insights into molecular systems. His research bridges experimental and computational approaches to understand biological processes at the molecular level.

• Bioinformatics
• Health Data Science
• Metabolomics
• +5 more

Alexandre Perera Lluna is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB) and the Department of Systems Engineering, Automation, and Computer Engineering. He holds a PhD in Physics and is a leading researcher in bioinformatics, health data science, and AI-driven healthcare solutions. His work focuses on integrating omics data, metabolomics, and machine learning for disease diagnosis and precision medicine, particularly in oncology and diabetes. Education: Ingeniero Superior en Electrónica (Electronics Engineering), Licenciado en Ciencias Físicas (Physics), and a PhD in Physics from UPC. He coordinates the Doctoral Program in Bioinformatics and leads the B2SLab and TecSalut research groups, collaborating with institutions like the Institut de Recerca i Innovació en Salut. His research spans AI applications in healthcare, including early cancer detection via liquid biopsies, diabetes subtyping, and sensor technologies for environmental and health monitoring. He has published over 250 scientific works and secured funding from national/international grants (e.g., H2020, RIS3CAT). His projects include developing intelligent diagnostics systems and participatory citizen science initiatives in rare diseases. Notable contributions include EpiGe (medulloblastoma classification), Mediterranean diet-atherosclerosis studies, and gas sensor arrays for elderly home monitoring. He advises on AI audits (Vincer.AI) and promotes interdisciplinary collaboration through networks like Xarxa R+D+I en Tecnologies de la Salut.

Jerzy TiurynView profile

Professor

• Bioinformatics
• Computational Biology
• Sequence Analysis
• +9 more

Jerzy Tiuryn is a Full Professor at the Faculty of Mathematics, Informatics and Mechanics, University of Warsaw (since 1992), with a PhD in Mathematics from the same university (1975). He served as Vice Dean for Scientific Matters and International Relations (2005-2012) and is actively engaged in editorial roles for journals like Theoretical Computer Science , Information and Computation , and BioMed Research International . Education: PhD in Mathematics (University of Warsaw, 1975). Research Interests: His work spans past contributions in program verification, semantics, typed lambda calculus, and type theory, with current focus on bioinformatics and computational biology. Specific areas include sequence analysis, gene family evolution, protein-protein interaction networks, gene regulatory regions, gene mutations in bacterial drug resistance, and comparative genomics. Trends in Publications: His recent articles emphasize computational methods in bacterial genomics (CAMBerVis, CAMBer), regulatory element prediction (Bi-Billboard), protein interaction mapping via phylogeny, and Bayesian network modeling for gene expression. Themes center on genomic data analysis, evolutionary conservation, and algorithm development for biological systems. Scientific Contributions: Awards and memberships highlight leadership in European and Polish academic networks: Board Member, European Research Consortium for Informatics and Mathematics (ERCIM) (2011) First President, Polish Bioinformatics Society (2008-2010) Founding Member/President, PLERCIM (2007-2012) Member, Academia Europaea (since 1996)

Zhiyu LiView profile

Professor

• Computational Bioinformatics
• Protein Structure Prediction
• GPCR Research
• +4 more

Professor Zhiyu Li is a Professor and Vice-Chair in the Department of Pharmaceutical Sciences. His research focuses on computational and bioinformatics methods to elucidate protein sequence-structure-function relationships, with emphasis on membrane proteins like GPCRs. Key areas include structural modeling of transmembrane proteins, drug design for cancer and diabetes, and applications of network medicine. Dr. Li holds a PhD from Vanderbilt University and BS/MS degrees from Tsinghua University. His work bridges computational tools (e.g., PyMine) with experimental collaborations, contributing to advancements in antibody engineering, targeted drug delivery, and viral inhibitor design. Education: BS, Tsinghua University (China) MS, Tsinghua University (China) PhD, Vanderbilt University (USA) Research Interests: Computational modeling of GPCRs and membrane proteins Allosteric modulators and drug design for metabolic/cancer therapies Network medicine approaches for kinase inhibitors Protein-engineered antibody delivery systems Publications Overview: Over 40 peer-reviewed articles spanning structural biology, computational chemistry, and medical applications. Recent work emphasizes GLP-1 receptor modulation, HIV/influenza inhibitors, and fusion protein therapies. Grants & Advising: No listed advisees; collaborations include experimentalists in structural biology and medicinal chemistry. Labs/Teams: Leads computational drug design efforts within the Department of Pharmaceutical Sciences, with contributions to platform tools like PyMine.

• Visual Analytics
• Predictive Analytics
• Geographical Visualization
• +7 more

Ross Maciejewski is an Ira A. Fulton Professor of Computer Science at Arizona State University (ASU), leading the School of Computing and Augmented Intelligence. He serves as Director of the Department of Homeland Security-funded Center for Accelerating Operational Efficiency. His research focuses on visual analytics, geographical visualization, and predictive analytics, with applications in homeland security, public health, dietary analysis, and the food-energy-water nexus. He holds affiliations with the Center for Biodiversity Outcomes, Water Institute, and Global Futures Scientists program. Education: PhD (2009), M.S. (2004), and B.S. (2001) in Computer Science/Engineering from Purdue University and the University of Missouri. He advises students through courses like CSE 792 (Research) and CSE 499 (Individualized Instruction), and has led over 20 funded projects including NSF grants and industry collaborations. Research Interests: Visual analytics, predictive modeling, and interdisciplinary problem-solving. His lab, VADER, develops tools for decision support in complex systems. Key innovations include FEWSim (food-energy-water nexus simulations) and LossLens (machine learning diagnostics). Awards: NSF CAREER Award (2014), Best Paper (EuroVis 2017), ACM CHI Honorable Mentions (2018, 2022) Grants: INFEWS/T2 (2016-2021), NSF CAREER (2014-2019) Labs/Teams: Visual Analytics and Data Exploration Research (VADER) Lab

Lude FrankeView profile

Professor

• Functional Genomics
• Computational Biology
• Genetics & Heredity
• +5 more

Prof. Lude Franke is a Full Professor of Functional Genomics at the University of Groningen's Faculty of Medical Sciences. He leads research in computational methods for functional genomics, particularly focusing on eQTL mapping and the molecular effects of disease-associated genetic variants. His work bridges genetics, bioinformatics, and clinical applications, with key contributions to understanding regulatory effects of SNPs on gene expression. Education: PhD (cum laude) in 2008 from Groningen, followed by postdoc positions at the University Medical Center Groningen and London's Institute of Cell and Molecular Science. He co-initiated the eQTLGen consortium, leading large-scale blood eQTL meta-analyses identifying trans-eQTL effects for over 100 disease-associated SNPs. Research Interests: Functional genomics, statistical genetics, pharmacogenetics, and applications of RNA-seq in diagnostics. His lab develops tools like DEPICT for interpreting GWAS results through predicted gene functions. Awards: Highly Cited Researcher (2018). Active in committees (KWF, FNRS, Dutch Society of Human Genetics) and collaborates internationally. Recent work includes precision medicine approaches using pharmacogenetic passports and organ-on-chip models. Labs/Teams: Leads the Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI). Supervises research on genetic causes of cancer and immune-related diseases. Engages in public health initiatives through the Lifelines cohort study.

Patricia ShawView profile

Senior Lecturer

• Robotics
• Developmental Robotics
• Cognitive Developmental Systems
• +12 more

Patricia Shaw is a Senior Lecturer in the Department of Computer Science at Aberystwyth University. Her research lies at the intersection of robotics, cognitive science, and developmental psychology, with a focus on creating intelligent systems inspired by infant learning. She actively contributes to projects in human-robot interaction, assistive technologies, and developmental robotics. Her research interests include: Developmental and cognitive robotics Schema-based learning and hierarchical behavior modeling Human-robot interaction and assistive robotics Object recognition and multi-modal perception Autonomous navigation using environmental data Infant psychology-inspired AI Her recent publications demonstrate a strong trend in using humanoid robots to model infant cognitive development, developing smart assistive devices like wheelchairs and robotic companions, and advancing perception and navigation in unconstrained environments. These works span journals such as IEEE Transactions on Cognitive and Developmental Systems and conference proceedings from TAROS and UKRAS. Her scientific awards include: EPSRC Photo Competition 2014 EPSRC Photo Competition 2015 EPSRC Photo Competition 2016 She has supervised multiple PhD students including Tobi Alagbe, Cloud Coppola, Sam Heron, and Nikola Nikolov, and has been involved in externally funded research grants from the EPSRC and the Royal Society. She has also led projects on robotic assistive living and smart home technologies. Her leadership extends to managing research infrastructure such as the iCub humanoid robot and the Smart Home Facility. She is actively involved in research labs and teams including: iCub Humanoid Robot research group Smart Home Facility team Aberystwyth Robotics Lab Human-Robot Interaction research group Developmental Robotics Lab Assistive Technology Research Group

• Prostate Cancer
• Genomics
• Cancer Genetics
• +4 more

Justin Hwang is an Assistant Professor at the University of Minnesota Medical School within the Department of Medicine - Hematology, Oncology, Transplant . His research focuses on Prostate Cancer , Genomics , and Cellular Plasticity in castration-resistant and neuroendocrine subtypes. He leads projects on B7-H3 (CD276)-targeted therapeutics and circulating tumor cell analysis , while contributing to multi-omics studies of tumor-host interactions in liver metastasis. His work intersects with UN Sustainable Development Goals through cancer research and precision oncology . Collaborations include teams at the Midwest Murine-Tissue Mapping Center and clinical partnerships with NIH and Prostate Cancer Foundation . Recent projects explore alternative polyadenylation , RET regulation , and metabolic plasticity in advanced tumors. Current research trends emphasize therapeutic targeting of DLL3 across neuroendocrine tumors, genomic instability in BRCA-associated cancers , and metastatic niche analysis via multi-omics platforms . Grants include funding from NIH NCI , NIH NIA , and Astrin Biosciences . Key methodologies involve targeted proteomics , single-cell transcriptomics , and exosome secretion studies. Scientific awards and honors are not explicitly mentioned in the provided data. No student advising information is detailed. Laboratory teams include collaborators across Midwest institutions and multi-omics researchers.

• Comparative bioinformatics
• Integrative multi-omics
• Genome editing
• +5 more

Chul Lee is a Research Fellow at Rockefeller University (Laboratory of Neurogenetics of Language, Supervisor: Dr. Erich D. Jarvis). He holds a Ph.D. in Interdisciplinary Bioinformatics from Seoul National University (2022), an M.Sc. in the same field from Seoul National University (2015), and a B.Sc. in Bioscience and Biotechnology from Hankuk University of Foreign Studies (2011). Ph.D. - Interdisciplinary Program in Bioinformatics, Seoul National University (2022) M.Sc. - Interdisciplinary Program in Bioinformatics, Seoul National University (2015) B.Sc. - Bioscience and Biotechnology, Hankuk University of Foreign Studies (2011) Lee's research focuses on comparative genomics , integrative multi-omics , and genome editing to unravel molecular mechanisms behind human-specific traits, brain evolution, vocal learning, and zoonotic pathogen transmission. His work spans viral infectivitiy (SARS-CoV-2, HIV), mental disorders , and evolutionary adaptation . His 15 most recent publications (2018-2025) cover themes in vertebrate genome evolution (Vertebrate Genomes Project), pathogen-host interactions , and computational tools for genome assembly (e.g., Galaxy platform). Notable topics include convergent evolution in vocal learners , viral transmission mechanisms , and genomic adaptations in transitional species (mudskippers, coelacanth). Lee collaborates with leading institutions on integrative data browsers for multi-omics datasets and contributes to functional validation of genetic signatures using genome-editing techniques. His work bridges evolutionary biology , neurogenetics , and computational biomedicine .

Jacob LuberView profile

Assistant Professor

• Bioinformatics
• Precision Medicine
• Biomedical Informatics
• +16 more

Dr. Jacob Luber is an Assistant Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington, with affiliate appointments in Bioengineering and as a Guest Researcher at the National Cancer Institute (NIH). He directs the Health Data Science Lab and is a CPRIT Scholar in Cancer Research. PhD in Biomedical Informatics from Harvard University B.S. in Computer Science from Trinity University Dr. Luber's research focuses on: Building computational tools for petabyte-scale cancer imaging analysis Developing interpretable Bayesian models for oncology diagnostics Investigating microbiome impacts on cancer immunotherapy Integrating multi-omics data (single cell transcriptomics, spatial proteomics) Creating AI-driven compression/search systems for pathology slides His lab has received $2.25M in grants including CPRIT First Time Faculty Award and UT System Rising STARs Award. Recent publications demonstrate expertise in: Adversarial AI attacks on pathology models Variational autoencoder-based cancer slide compression Microbiome-driven lactate metabolism in athletes T-cell receptor-based tumor response tracking Single-cell immunology of diabetic NOD mice Polygenic risk scores for diverse populations Scientific Awards: CPRIT First Time Faculty Award UT System Rising STARs Award NSF GRFP Fellowship Keystone Symposia Travel Fellowship Function SIG/ISMB Best Poster Mach Research Fellowship Trinity CS Research Awards GCURS Research Talk Advising & Grants: Mentoring 12 active PhD/Master's students while leading a lab funded by $2M+ CPRIT grant and $250k UT STARs award. Current projects include cancer imaging search engines and microbiome-immunotherapy interactions.

• Animal Genetics
• Dairy Sheep
• Feed Efficiency
• +6 more

Aroa Suárez Vega is a Professor in the Department of Animal Production at the University of León's Faculty of Veterinary Medicine. Her research focuses on integrating genomic and transcriptomic tools to improve dairy sheep production efficiency, particularly through epigenetic regulation and biomarker discovery. She leads the MEGA (Animal Genetic Improvement) research group and specializes in feed efficiency, milk traits, and bioinformatics applications for livestock improvement. PhD in Animal Production (University of León, 2015) Supervised by Dr. Beatriz Gutiérrez Gil and Dr. Juan José Arranz Santos Her work combines machine learning approaches with omics technologies to understand complex traits in sheep, including milk fat regulation, fertility mechanisms, and host-pathogen interactions during mastitis. She also contributes to educational innovation in animal breeding courses through gamification and interactive learning tools. Recent research trends highlight her expertise in: Milk transcriptome analysis for feed efficiency biomarkers Epigenetic programming from early-life nutritional interventions Circular RNA and lncRNA characterization in adipose tissues Meta-analysis of genomic data across livestock species Educational tool development for quantitative genetics Aroa's publications demonstrate strong interdisciplinary focus bridging genomic science with practical dairy management applications, particularly in Spanish dairy breeds like Assaf and Churra.

• Computer Vision
• Machine Learning
• Image Processing
• +5 more

Dr. Xin Lin is a Professor at the School of Computer Science and Technology, University of Science and Technology of China in Hefei. With an extensive publication record spanning computer vision, machine learning, and artificial intelligence, Dr. Lin leads a research group focused on solving challenging problems in image processing, robotics, and wireless communications. His work bridges theoretical advancements with practical applications across healthcare, autonomous systems, and industrial manufacturing. Dr. Lin's research interests encompass computer vision, machine learning, image processing, and artificial intelligence, with particular expertise in image restoration, 3D object detection, and human pose estimation. His laboratory develops innovative approaches to handle multiple image degradations simultaneously and create lightweight, efficient vision systems suitable for real-world deployment. The research demonstrates strong interdisciplinary connections, applying computer vision techniques to medical imaging, satellite communications, and industrial IoT applications. Analysis of Dr. Lin's recent publications reveals a strong focus on multi-task learning approaches that address multiple image degradation problems simultaneously. His work shows increasing sophistication in handling complex real-world scenarios, from low-light conditions to rain interference, while maintaining computational efficiency. The research trajectory demonstrates a clear path from fundamental image processing techniques to practical applications in autonomous driving, healthcare, and industrial systems. Dr. Lin has received recognition for his contributions to the field through numerous publications in top-tier venues including CVPR, IEEE Transactions, and ACL. His work on image restoration, particularly the Dual Degradation Representation framework, has gained significant attention in the computer vision community. Dr. Lin actively supervises graduate students and collaborates with researchers worldwide. His laboratory works on cutting-edge projects involving digital twins for manufacturing, satellite communications, and medical imaging applications. Current research directions include developing more robust and efficient models for real-world deployment scenarios, with particular attention to resource-constrained environments.

• Bioinformatics
• Computational Biology
• Data Science
• +5 more

Dr. Fan Zhang is a Lecturer at the Microbiome Research Centre, University of New South Wales (UNSW), Sydney. He holds a PhD in Computer Science from Nanyang Technological University (Singapore) and has research expertise in bioinformatics, computational biology, data science, and statistical inference. His current focus includes microbiome research, particularly in inflammatory bowel disease (IBD) and cancer-related microbiome dynamics. Dr. Zhang's educational background includes a B.E. from Harbin Institute of Technology (China), an M.E. from Zhejiang University (China), and a PhD from NTU Singapore. His research spans microbial immunomodulation, virome analysis in hepatocellular carcinoma, and the impact of polypharmacy on gut microbiota. He has secured grants such as the NHMRC Ideas Grant (2024-2027) for IBD-related research. Key research interests include microbiome diversity in disease onset, drug-burden effects on aging microbiomes, and transcriptomic analysis of radioresistance in nasopharyngeal carcinoma. He currently supervises 3 PhD students in microbiome-related fields and has successfully mentored one doctoral student to completion. Publications highlight work on virome characterization, oral microbiome sampling in head and neck cancers, and radiomics tools for cancer phenotyping. His contributions bridge computational methods with clinical applications in gastroenterology and oncology.