Find Academic Supervisors

Deborah Bell-PedersenView profile

Professor

• Circadian Rhythms
• Molecular Biology
• Genetics
• +7 more

Dr. Deborah Bell-Pedersen is a Thomas University Professor and Associate Head of Biology at Texas A&M University (TAMU), renowned for her pioneering work in circadian clock mechanisms and their implications for human health. Her research focuses on how circadian oscillators regulate rhythmic gene expression in the model organism Neurospora crassa , with particular emphasis on mRNA transcription, translation, and links to cancer and aging. Key affiliations: Texas A&M University (1997–present) Leadership roles: Associate Editor of the Journal of Biological Rhythms , Board of Directors for the Society for Research on Biological Rhythms and Fungal Genetics Society Honors: University Professor title, Fellow of American Academy of Microbiology and AAAS, multiple research excellence awards Her work has revealed that the circadian clock controls translation fidelity via eIF2α activity and ribosome interactions, linking reduced clock function to aging. Articles highlight her expertise in MAPK signaling, glycogen metabolism regulation, and fungal stress response, with implications for human diseases like glioma and metabolic disorders. Scientific recognition includes TAMU Jo Ann Treat Award for Excellence in Research Eminent Scholar and Distinguished Research Awards Fellowships from AAAS and American Academy of Microbiology Dr. Bell-Pedersen mentors students in circadian biology and emphasizes independence in research, inspired by her PhD advisor Marlene Belfort. She also explores interdisciplinary connections between circadian rhythms and vaccination timing, cognitive performance, and physical balance. Outside academia, she is learning violin (in honor of her late father’s musical legacy) and cares for rescue animals including 4 cats, a dog, a horse, and 3 miniature donkeys.

Leluo GuanView profile

Professor

• Animal Microbiome
• Functional Genomics
• Host-Microbe Interactions
• +3 more

Leluo Guan is a Professor in the Department of Applied Biology within the Faculty of Land and Food Systems at the University of British Columbia (UBC), holding the prestigious Canada Research Chair (Tier 1) in Animal Functional Genomics and Microbiome. Based in Vancouver's Food, Nutrition and Health building, he leads cutting-edge research at the intersection of animal science and microbiome biology. His academic foundation includes: 1985-1989: BSc in Biological Sciences, Wuhan University, PR China 1990-1993: MSc in Pharmaceutical Science, Kyoto University, Japan 1993-1996: PhD in Pharmaceutical Science, Kyoto University, Japan Dr. Guan's research program centers on host-microbiome interactions in food-producing animals, primarily cattle. He employs metagenomics, metatranscriptomics, metabolomics, and functional genomics to characterize bovine gut microbiomes, elucidate molecular mechanisms of host-microbe crosstalk, and investigate associations between microbial ecology and feed efficiency, methane emissions, and gut immunity. His work bridges microbial ecology with economically significant livestock traits through advanced omics integration. Analysis of his publication record reveals consistent emphasis on rumen microbiome dynamics across cattle production systems. Key trends include multi-omics approaches to link microbial communities with host physiology, investigation of early-life microbiome development, and exploration of host genetic influences on microbiome composition. His work increasingly focuses on functional characterization of microbial activities rather than mere taxonomic identification. His scientific recognition includes: Canada Research Chair (Tier 1) in Animal Functional Genomics and Microbiome Dr. Guan mentors graduate students in applied animal biology while directing a research program funded by the Canada Research Chairs initiative and competitive grants. His team develops innovative methodologies for microbiome analysis, contributing to sustainable livestock production through improved understanding of host-microbe symbiosis. Current work integrates host genomics with microbiome functional profiling to develop precision nutrition strategies for cattle.

• Nephrology
• Renal Medicine
• Autosomal Dominant Polycystic Kidney Disease (ADPKD)
• +5 more

Professor Albert CM Ong is Professor of Renal Medicine at the University of Sheffield's School of Medicine and Population Health, where he directs specialist services for autosomal dominant polycystic kidney disease (ADPKD) and other genetic kidney diseases at the Sheffield Kidney Institute. A globally recognized expert in ADPKD research, he serves as Co-Director of the MRC-NIHR UK Renal Ciliopathies National Network (CILIAREN) and previously contributed to the European Renal Association Council. Ong studied medicine at the University of Oxford and trained as a clinician-scientist at University College London and Oxford. His research has focused on the molecular basis of cyst formation, the genetic basis for disease variability, and drug discovery in ADPKD. The Ong lab investigates cyst initiation through studies of PKD1, PKD2, and related proteins within the polycystin network, while also examining non-coding RNAs and RNA metabolism in cyst expansion. His publication portfolio demonstrates consistent advancement in understanding ADPKD mechanisms and clinical management. Recent work emphasizes AI applications for kidney volume measurement, novel therapeutic targets including glucosylceramide synthase inhibitors, and international consensus on kidney disease management. His research bridges basic molecular mechanisms with clinical translation, employing human cellular models, model organisms, computational modeling, and analysis of deeply phenotyped clinical cohorts. Recipient of the ISN Lillian Jean Kaplan International Prize for contributions to ADPKD research Held senior research fellowships from Kidney Research UK and the Wellcome Trust Extensive involvement in international clinical guideline development Professor Ong continues to provide strategic leadership in the kidney field through his roles in major research networks and clinical consortia. His work has established the Sheffield Kidney Institute as a leading center for ADPKD research and specialized clinical care, with particular expertise in complex genetic kidney disorders and the development of novel therapeutic approaches.

• Computational Semantics
• Syntax-Semantics Interface
• Natural Language Processing (NLP)
• +2 more

Gábor Kata is a Lecturer at INALCO (National Institute of Oriental Languages and Civilizations) in Paris, France. He works within the Computer Science Multilingualism department, contributing to the ERTIM research team . His expertise spans Computational Semantics , Syntax-Semantics Interface , and Natural Language Processing (NLP) . CNU Discipline: 27 - Computer Science Areas of Expertise: Europe Teaching Themes: Lexical Semantics, Computational Semantics, Linguistics for NLP, NLP His research focuses on Computational Semantics and Syntax-Semantics Interface , with particular attention to NLP applications. His scholarly output reveals trends in semantic relation extraction , anomaly detection in text data , and knowledge base population using both supervised and unsupervised learning approaches. This work often employs graph-based methods and vector space analysis across multilingual contexts.

Boris BerangerView profile

Senior Lecturer

• Extreme Value Theory
• Environmental Extremes
• Spatial Statistics
• +3 more

Boris Beranger is a Senior Lecturer in Statistics and Data Science at the School of Mathematics and Statistics, UNSW Sydney . He is also a member of the UNSW Data Science Hub (uDASH) and previously served as an Associate Investigator at the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) . His research spans theoretical and applied statistics, focusing on Extreme Value Theory (environmental, financial, and insurance applications) and Symbolic Data Analysis (complex/non-standard data structures). Education: PhD in Statistics (Université Pierre and Marie Curie & UNSW, 2016), MSc in Mathematics (Université Pierre and Marie Curie, 2011) Research Trends are evident in: High-dimensional extremal dependence modeling (ExtremalDep package) Spatial extremes and max-stable processes Symbolic/histogram/interval-valued data analysis Composite likelihood and aggregated data methods Tail density estimation via kernel methods Scientific Awards & Grants include: J.B. Douglas Award for Postgraduate Excellence (2014) Multiple ARC ACEMS Research Support Schemes Discovery Project DP220103269 ($405,000) for modeling real-world extremes Supervision covers PhD, Masters, and Honours students in areas like Symbolic Data Analysis, Spatial Extremes, and Statistical Computing. He also co-organized workshops and served as Vice-President (2025-26) of the Statistical Society of Australia's NSW Branch.

Jin ZhangView profile

Associate Professor

• Computational Biology
• Bioinformatics
• Cancer Genomics
• +5 more

Dr. Jin Zhang is an Associate Professor of Radiation Oncology at Washington University School of Medicine, with affiliations to the Siteman Cancer Center, Institute of Clinical and Translational Sciences (ICTS), and multiple divisions within the Roy and Diana Vagelos Division of Biology & Biomedical Sciences (DBBS), including Biomedical Informatics and Data Science, Cancer Biology, and Computational and Systems Biology. PhD in Computer Science (University of Connecticut, 2012) MPHS in Population Health Sciences (Washington University, 2022) Prior postdoctoral work at McDonnell Genome Institute and Department of Medicine, Washington University His research develops deep learning/AI models and multi-omics approaches (genomics, proteomics, metabolomics, imaging) to advance translational cancer research at the intersection of genomics and radiation oncology. Recent work includes Nature studies on dietary fructose-induced tumor growth mechanisms, Patterns publications on GAN-based gene expression analysis, and Clinical Cancer Research papers on HPV+ cancer MRD detection. Scientific awards include NCI K22, ITCR R21, R37, and R01 grants totaling over $9.7 million in funding. His lab trains DBBS and MSTP rotation students, with alumni pursuing academic/research roles at institutions like University of Washington and Memorial Sloan Kettering Cancer Center.

Mohammed El-KebirView profile

Associate Professor

• Cancer Genomics
• Computational Biology
• Combinatorial Optimization
• +4 more

Mohammed El-Kebir is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC) , with affiliate appointments in Electrical & Computer Engineering, the Institute of Genomic Biology, and the National Center for Supercomputing Applications. His research focuses on combinatorial optimization algorithms for fundamental challenges in cancer genomics and computational biology , particularly tumor phylogenetics , single-cell sequencing data analysis , and metastasis migration history modeling . PhD in Computer Science, Centrum Wiskunde & Informatica (Netherlands) and VU University Amsterdam (2015) Postdoctoral training at Brown University and Princeton University (2014-2017) El-Kebir's work addresses intra-tumor heterogeneity by developing mathematical models to reconstruct evolutionary trees of cancer mutations from mixed sequencing data. His lab has pioneered methods like SPhyR and MACHINA for phylogeny inference under data limitations, and PhyDOSE for optimizing single-cell sequencing experiments. His research bridges computational challenges with clinical implications for personalized medicine and therapy resistance prediction . Key contributions include: Theoretical foundations for cancer phylogenetic problems (hardness proofs, algorithm design) Single-cell sequencing algorithms accounting for data sparsity and errors Migration graph frameworks for metastasis analysis Interactive tools like ClonArch and CNAViz for tumor data visualization El-Kebir has received multiple accolades including the NSF CAREER Award (2021) and CRII Award (2019) . His group has produced 15+ peer-reviewed publications since 2020, with student collaborators securing prestigious fellowships like the NSF GRFP . He actively contributes to conference program committees (RECOMB, ISMB, WABI) and teaches graduate-level courses in Bioinformatics and Computational Cancer Genomics .

Sharad RamanathanView profile

Professor

• Developmental Biology
• Stem Cell Research
• Morphogenesis
• +4 more

Sharad Ramanathan is the Llura and Gordon Gund Professor of Neurosciences and of Molecular and Cellular Biology at Harvard University, with a dual appointment as Professor of Applied Physics and of Stem Cell and Regenerative Biology in the John A Paulson School of Engineering and Applied Sciences. He serves as Principal Investigator of the Ramanathan Lab and is Co-Director of the Quantitative Biology Initiative. His research focuses on understanding how cells and organisms process environmental signals to make developmental decisions, particularly in the context of human development and stem cell morphogenesis. Ramanathan employs a multidisciplinary approach combining stem cell and molecular biology, genomics, bioengineering, and computational biology to investigate these fundamental biological processes. His lab recently achieved a groundbreaking discovery in controlling stem cell colony arrangements to reliably produce embryonic body plans, with 99% of colonies developing proper anterior-posterior axes. This work, which uses hexagonal arrangements of stem cell colonies, has been described as revolutionary for mimicking mammalian embryogenesis in vitro. Ramanathan's publications reveal a strong focus on developmental dynamics, with recent work emphasizing human organoid systems, stem cell differentiation, neural circuit mapping, and the computational analysis of cellular decision-making processes. His research bridges physics, engineering, and biology to tackle fundamental questions in developmental biology. As an educator, he teaches MCB198 Mathematical Techniques for Modern Biology and Applied Physics 286 / Physics 286 Inference, Information Theory, Learning and Statistical Mechanics, demonstrating his commitment to quantitative approaches in biological sciences. The Ramanathan Lab has made significant contributions to understanding stem cell differentiation, embryonic development, and neural circuit organization, with work that has important implications for regenerative medicine and understanding developmental disorders.

• Genetic Mutations
• Schizophrenia Research
• Somatic Mosaicism
• +3 more

John Moran is a Professor in the Department of Human Genetics at the University of Michigan , focusing on genetic mutations , somatic mosaicism , and neurodevelopmental disorders . His work bridges molecular biology and neurogenetics , with key contributions to understanding schizophrenia origins in prenatal brain development. Key Collaborations : Icahn School of Medicine at Mount Sinai, Harvard Medical School, Boston Children's Hospital Research Themes : LINE-1 retrotransposons, Alu elements, somatic copy-number variants (sCNVs), and their role in disease etiology Research Focus : Dr. Moran’s research explores how non-inherited genetic mutations during embryonic development contribute to schizophrenia. He has pioneered methods like DeepMosaic for variant detection and analyzed single-cell genomic data to map mutation patterns in neurons. His work highlights NRXN1 and ABCB11 as disrupted genes in schizophrenia cases. Publication Trends : Recent articles emphasize somatic mosaicism in neuropsychiatric diseases , L1 retrotransposition in human cells, and genomic data resources for the Brain Somatic Mosaicism Network. Collaborations with institutions like Mass General Psychiatry and Physician's Weekly reflect interdisciplinary impact. Key Grants & Programs : Involved in the Allen Discovery Center and led projects like Comprehensive Genomic Datasets in BSMN . His team’s pandemic-era research on single-neuron genomes revealed non-random CNV patterns in neurotypical individuals. Labs & Teams : Leads a lab at the University of Michigan, collaborating with researchers such as Chris Walsh , Ryan Mills , and Edward Maury . His work intersects with the Brain Somatic Mosaicism Network , advancing tools for ultra-deep genome sequencing and mosaicism analysis .

• Sleep Neuroscience
• Memory Consolidation
• Stroke Recovery
• +5 more

Anna Guttesen is a Postdoctoral Researcher at the Wellcome Centre for Integrative Neuroimaging within the Nuffield Department of Clinical Neuroscience at the University of Oxford, where she works under Prof Heidi Johansen-Berg and Dr Melanie Fleming in the Plasticity Group and OxCIN Neuroplastics Network. Her academic background includes a PhD in Psychology from the University of York, supervised by Dr Scott Cairney and Prof Gareth Gaskell, with research focused on sleep-dependent declarative memory consolidation. Dr. Guttesen's work centers on sleep neuroscience in clinical contexts, specifically investigating how stroke alters sleep-related memory consolidation processes and whether targeted memory reactivation can enhance recovery. Her research bridges cognitive neuroscience, clinical neurology, and sleep medicine through EEG-based studies of neural markers during sleep. She examines both fundamental mechanisms (e.g., neural signatures of learning) and clinical applications (e.g., sleep regularity as a biomarker for stroke rehabilitation). Her recent publications demonstrate consistent output in high-impact journals, with work spanning memory reactivation paradigms, sleep disturbances in neurological populations, and neural correlates of learning. As a postdoctoral researcher, she contributes to the Plasticity Group's grant-funded projects at the Wellcome Centre, focusing on translating sleep neuroscience discoveries into stroke rehabilitation strategies. Students collaborating with her gain expertise in EEG methodology, sleep monitoring, and clinical neuroscience research within Oxford's world-class neuroimaging infrastructure.

• Metabolomics
• Machine Learning
• Breast Cancer
• +3 more

Julia Debik is an Associate Professor at the Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), with a dual research role at the Musculoskeletal Research Group and CIMORe. She holds a PhD in Medical Technology and a Master of Science in Industrial Mathematics from NTNU, combining computational expertise with biomedical research. Academic Roles: Associate Professor (Onsager Fellow - AI and Health) Departments: Public Health and Nursing, Circulation and Medical Imaging Collaborations: National Network for Breast Cancer Research, Nordic Metabolomics Society, Metabolomics Society Research Focus: Integrating machine learning with metabolomics to uncover breast cancer risk factors, treatment response, and prognostic signatures. Her work emphasizes NMR-based metabolic profiling, biobank utilization (HUNT2, UK Biobank), and sample handling standardization (freeze-thaw cycles, delayed centrifugation). Publication Trends: Over 15 recent articles focus on metabolomic-biomarker discovery for breast cancer, circadian exercise effects, and technical validation of NMR platforms. Key subfields include lipoprotein associations, multi-omics integration, and AI-driven analysis of spatial -omics data. Scientific Contributions: Onsager Fellowship in AI and Health Key collaborator in HUNT biobank studies International presentations at Metabolomics Society conferences Developer of machine learning frameworks for multi-cancer profiling Outreach Activities: Regularly participates in public science events like Researchers' Night, delivers lectures on AI and molecular epidemiology, and contributes to popular science communication through initiatives like 'Tumorteltet'.

• Communication Networks
• Cybersecurity
• Information Security
• +5 more

Josep Rafel Pegueroles Valles is a Professor at the Department of Telematic Engineering , Universitat Politècnica de Catalunya . He leads the Information Security Group - Mathematics Applied to Cryptography (ISG-MAK) and has contributed to 174 academic activities, including research projects, conference papers, and technical documents. Education: Ingeniero de Telecomunicaciones (Telecommunications Engineering), Doctorat en Ingeniería Telemática (Telematics Engineering) Research Focus: Cybersecurity, Digital Forensics, Electronic Payments, Communication Networks, and Legal Tech His 15 most recent articles (2023-2025) emphasize cybersecurity in IoMT , MITRE Framework analysis , and digital forensic methodologies . Topics include intrusion detection systems, threat actor attribution, and XSS vulnerability mitigation. Scientific Awards: Premio Accenture al mejor PFC Grants and Projects: Cátedra CARISMATICA (2022) SSADE - Secure Multimedia Access System (2001) Digital Innovation Hub de Catalunya EU-funded projects (2008-2011)

Rongxin FangView profile

Assistant Professor

• Genomics
• Neuroscience
• Computational Biology
• +6 more

Rongxin Fang is an Assistant Professor of Neurosurgery and Genetics (by courtesy) at Stanford University. He received his Ph.D. in Bioinformatics and Systems Biology from UC San Diego (2015-2019) and completed a postdoctoral fellowship in Spatial Genomics at Harvard University (2019-2024). His work bridges high-throughput genomic technologies, computational tools, and transcriptome imaging to unravel the molecular architecture of the brain across species and developmental stages. Research Highlights: Developed 3D MERFISH imaging for thick tissues (2024), enabling spatial genomics at unprecedented resolution. Led cross-species MERFISH studies (2022) revealing conserved cortical organization with human-specific glia-neuron interactions. Created SnapATAC (2021), a pivotal tool for large-scale single-cell ATAC-seq analysis. Scientific Awards: Damon Runyon-Dale F. Frey Award for Breakthrough Scientists (2024) Next Generation Leader, Allen Institute (2024) Rising Star in Health in Engineering (2022) Advising and Collaborations: Doctoral Dissertation Advisor for Eduardo Chaparro Barriera Postdoctoral Faculty Sponsor for Ya Jiang Collaborated with Xiaowei Zhuang (Harvard), Adam Cohen (Harvard), and Bing Ren (UCSD)

• Music Information Retrieval
• Digital Musicology
• Computational Music Similarity
• +3 more

Daniel Wolff is a researcher affiliated with City University London , specializing in computational music similarity modeling and big data applications in musicology. His work bridges machine learning, audio signal processing, and music theory to analyze large-scale music collections. Doctoral thesis (2014): Spot the Odd Song Out: Similarity Model Adaptation and Analysis using Relative Human Ratings Key research focus: Adaptive similarity metrics, chord progression mining, and dataset automation Active in conferences like ISMIR, ACM Digital Libraries, and Interdisciplinary Musicology workshops Research Trends: His publications emphasize: Big data infrastructure for musicological research Machine learning techniques for audio feature extraction Interactive visualization of harmonic patterns User-driven similarity modeling via comparative ratings Technical Contributions: Developed uncertainty sampling methods for dataset curation, parallel computing approaches for chord analysis, and reproducible frameworks for music similarity evaluation.

• Innovation Management
• Intellectual Property Rights
• Global Careers
• +4 more

Dr. Giovanna Capponi is a faculty member at Utrecht University's Copernicus Institute of Sustainable Development, working in the Innovation Sciences department. Her research focuses on innovation management, intellectual property rights, and global career dynamics. Assistant Professor in Innovation Studies Focus on Dutch scientific history and modern innovation protection Active in telecom policy and mobile communication pricing research Her research interests span: Appropriability strategies in circular economy Impact of global mobility on compensation Collaboration patterns in mHealth Historical analysis of Dutch science development Multiple affiliation networks in academia Customer retention through personalized pricing Recent publications show trends in: Combining patents and secrecy for innovation protection Breakthrough innovation geography Sustainable business model tensions Syndication networks in fintech and green startups Diversity management theory Historical analysis of academic performance Scientific recognition: Referenced in Queen's Award for Innovations research Active conference participation (Academy of Management, DRUID Society) Wharton Global Faculty Development Program participant Key activities: Presentations at Strategic Management Society conferences Participation in TIM Junior Faculty Consortium Contributions to EU-funded sustainability research