Find Academic Supervisors

• Mycovirology
• Fungal Pathogen Biology
• Biocontrol Strategies
• +3 more

María Ángeles Ayllón is an Associate Professor at the Polytechnic University of Madrid (UPM), leading the Virus-Fungus-Plant Interaction group. Her research focuses on understanding mycovirus-fungal host interactions and their application in biocontrol. She collaborates with the International Committee on Taxonomy of Viruses (ICTV) on viral family classifications, such as Botourmiaviridae and Hypoviridae . Her work addresses fungal pathogen control via viral agents to reduce pesticide use, aligning with EU’s Green Deal goals. Ayllón oversees a multidisciplinary team including PhD students and postdocs, such as Javier Pardo Medina and Julio L. Rodríguez-Romero. Her research spans three main areas: mycovirus diversity and evolution, molecular mechanisms of viral-fungal interactions, and development of synthetic mycoviruses as biocontrol tools. Key projects include analyzing the mycovirome of Botrytis cinerea isolates and constructing infectious viral clones for reverse genetics. She has secured grants like Molecular Mechanisms in Botrytis-Plant Interaction (PID2020-120106RB-I00) and the H2020-funded ViroPlant initiative, fostering international collaboration. Ayllón’s team explores novel mycoviruses and their impact on fungal virulence, aiming to develop eco-friendly alternatives to chemical fungicides. Her contributions include discovering new viral families and elucidating RNA silencing pathways in fungi. Ongoing efforts focus on synthetic biology approaches to enhance mycovirus efficacy in agricultural settings.

Jinchuan XingView profile

Professor

• Mobile Elements
• Human Genomic Variation
• Evolutionary Genetics
• +14 more

Jinchuan Xing is a Professor at Rutgers, The State University of New Jersey, where he leads the Xing Lab of Genomics. His research focuses on human genomic variation, mobile DNA elements, evolutionary and population genetics, and their implications for human disease. He integrates computational and experimental methods to study genome-wide variation, with applications in disease gene identification and genomic technology development. His research interests include: Mobile DNA element biology Human demographic history and population diversity Disease gene identification using whole-exome and whole-genome sequencing High-altitude adaptation genetics Transposable element regulation and expression piRNA and small RNA pathways His recent publications highlight a strong trend in reproductive genomics, particularly in identifying genetic risk factors for embryo aneuploidy in IVF patients, using advanced sequencing technologies. Other work spans evolutionary genomics in diverse species (bats, moths, Drosophila), structural variation in neurodevelopmental disorders, and proteogenomic discovery. His lab actively develops and applies bioinformatic tools for variant analysis and gene prioritization. Scientific awards and honors are not explicitly mentioned in the provided text. Jinchuan Xing advises several graduate students, including Nan Wang, Siqi, Ellie Lu, and Tongji Xing. His lab has received significant funding, including an R01 grant from NICHD on aneuploidy risk, a grant from the Center for Human Evolutionary Studies, and a Life Sciences Alliance Pilot Seed Funding grant in collaboration with the Department of Statistics. These grants support research in fertility genomics, disease gene discovery, and genomic tool development. The Xing Lab of Genomics is an active research group conducting interdisciplinary studies combining genomics, bioinformatics, and molecular biology. The lab welcomes new members regularly, including master’s and PhD students, and collaborates across departments. Current projects include understanding the genetic basis of meiosis, fertility, and high-altitude adaptation, as well as developing Markovian gene networks for disease gene discovery.

Siegfried NijssenView profile

Assistant Professor

• Data Mining
• Pattern Mining
• Exploratory Data Analysis
• +5 more

Siegfried Nijssen is an Assistant Professor of Data Mining and Artificial Intelligence at the Catholic University of Louvain (UCLouvain) in Belgium, working within the ICTEAM research institute's Artificial Intelligence and Algorithms group. He has been at UCLouvain since 2016, previously serving as an Assistant Professor at University Leiden (2012-2016) and completing postdoctoral work at KU Leuven (2006-2015). He earned his PhD in Computer Science from University Leiden in 2006. His research focuses on making data analysis simpler through intersections between pattern mining, exploratory data analysis, and programming paradigms in Artificial Intelligence, particularly constraint programming and probabilistic programming. He has developed techniques for analyzing diverse data types including graphs, networks, and multi-relational data. His work bridges theoretical foundations with practical applications in decision tree learning, probabilistic networks, and source code analysis. Nijssen's recent publications demonstrate a strong focus on optimal decision trees, constraint-based pattern mining, and applications in bioinformatics and education. His research shows consistent evolution from foundational graph mining work (including the Gaston algorithm developed in 2004) to current work integrating machine learning with constraint programming for interpretable AI solutions. As an educator, he teaches courses including Mining Patterns in Data, Databases, and Artificial Intelligence and Machine Learning seminars at UCLouvain. He has advised numerous PhD students and postdocs, primarily in collaboration with Pierre Schaus, with former students like Tias Guns now holding professorships.

Jouni JärvinenView profile

Lecturer

• Rough Set Theory
• Lattice Theory
• Non-Classical Logics
• +4 more

Jouni Järvinen is a University Lecturer in Software Engineering at LUT University in Lahti, Finland, and a Docent in the Department of Mathematics and Statistics at the University of Turku. He holds a PhD in Mathematics and serves as a Mathematics Consultant at TXODDS. His work bridges theoretical and applied research across multiple domains. Affiliations : LUT School of Engineering Sciences, University of Turku Education : PhD in Mathematics Research Interests lie at the intersection of Rough Set Theory , Lattice Theory , and Non-Classical Logics . He explores algebraic structures like Nelson algebras, Kleene algebras, and Galois connections, applying them to problems in Artificial Intelligence and Bioinformatics . His work often connects abstract mathematical frameworks to practical applications in Information Systems and Machine Learning . Publication Trends reveal a focus on Rough Sets and Algebraic Structures across 51 works (2025–1998). Recent studies (2025–2020) examine multigranular rough sets, pseudocomplemented Kleene algebras, and relational correspondences for fuzzy rough approximations. Earlier works (2015–2007) address topics like Tolerance-Based Rough Sets , Galois Connections , and Natural Language Disambiguation . Peer Review Activities include contributions to journals such as Fuzzy Sets and Systems , International Journal of Approximate Reasoning , and Studia Logica . His research spans Theoretical Computer Science , Mathematical Logic , and Biomedical Data Analysis .

Zamin IqbalView profile

Professor

• Genomics
• Microbial Genomics
• Plasmid Research
• +5 more

Zamin Iqbal is a Professor of Algorithmic and Microbial Genomics at the Department of Life Sciences, Milner Centre for Evolution, University of Bath. His office is located in MILNER CENTRE 1.25, and he is currently accepting doctoral students for supervision. His research expertise spans multiple areas of microbial genomics, with particular focus on plasmid biology, antibiotic resistance mechanisms, and computational approaches to genomic analysis. His work contributes to understanding bacterial evolution, pathogenesis, and transmission dynamics of clinically important pathogens like Klebsiella pneumoniae and Mycobacterium tuberculosis . Professor Iqbal's recent publications demonstrate a strong trend toward developing computational methods for microbial genome analysis, with emphasis on plasmid epidemiology, phylogenetic compression algorithms, and metagenomic analysis. His work bridges the gap between theoretical computer science and practical microbiological applications, particularly in tracking antibiotic resistance transmission. His research contributes to multiple UN Sustainable Development Goals related to health and well-being. The fingerprint analysis of his work shows strong concentrations in plasmid research (100%), genomics (60%), Mycobacterium tuberculosis (45%), whole genome sequencing (43%), and related fields including metagenomics, serotyping, and antibiotic resistance mechanisms. Professor Iqbal maintains active collaborations across multiple international institutions, as evidenced by his co-authorship on studies involving researchers from numerous countries. His work has garnered attention across academic social media platforms, with multiple posts on X (formerly Twitter) and Bluesky, and has been referenced in Wikipedia pages related to his research areas.

Daniel BarbaráView profile

Professor

• Data Mining
• Machine Learning
• Databases
• +3 more

Daniel Barbará is a Professor in the Department of Information and Software Engineering at George Mason University since 1997. He holds a PhD and MS in Computer Science from Princeton University, and a BS in Electrical Engineering from Universidad Metropolitana. Research Interests: Data mining, machine learning, cybersecurity, mobile computing, and fractal-based data analysis. His work focuses on clustering algorithms, intrusion detection, data compression, and handling high-dimensional datasets. Grants: Multiple NSF and Air Force grants including "Clustering by Impact" (2002-2005) and "Data Mining Support for NIAC" (1999-2000). Scientific Awards: Outstanding Researcher, CS Department, George Mason University Best Paper Finalist at WWW 2002 Projects: Developed ADAM (Intrusion Detection), COOLCAT (Categorical Clustering), Fractal Mining, and Quasi-Cubes (Approximate Query Processing).

Chao SunView profile

Associate Professor

• Neurobiology
• Molecular Cell Biology
• Nanotechnology and Nanoscience
• +5 more

Chao Sun is an Associate Professor affiliated with multiple departments at Aarhus University , including the Department of Molecular Biology and Genetics, DANDRITE, the Interdisciplinary Nanoscience Center (INANO-MBG), and the Department of Biomedicine. His research integrates neurobiology , molecular cell biology , and nanotechnology , focusing on proteostasis, synaptic regulation, and advanced imaging techniques. PhD in Chemistry from Cornell University (2013-2018) Research interests include: Cellular neurobiology Proteostasis and protein turnover Super-resolution microscopy applications Proteomics of neuronal compartments Synaptic biology and plasticity Article trends reveal expertise in: Proteasome dynamics in synapses Multi-omics approaches to subcellular protein synthesis Single-molecule imaging in synaptic contexts Quantitative analysis of neuronal protein turnover Collaborations include leading researchers like Poul Nissen and Erin Schuman, with publications in high-impact journals such as Science and Current Opinion in Neurobiology . His work bridges molecular neuroscience with cutting-edge nanoscale imaging technologies.

• Molecular Biology
• Physiology
• Neurodegenerative Diseases
• +5 more

Prof. Dr. Belkis Atasever Arslan is a Professor in the Department of Molecular Biology and Genetics at Uskudar University. Her research focuses on molecular mechanisms of neurodegenerative diseases, cancer biology, and pharmacological effects of natural compounds. PhD in Physiology (2008, Istanbul University, Istanbul Faculty of Medicine) MSc in Physiology (2004, Istanbul University, Istanbul Faculty of Medicine) Licence in Biology (2001, Istanbul University) Research interests include: Neuroinflammation and blood-brain barrier integrity Therapeutic potential of phytochemicals (sesamol, quercetin, hypericum) Metal-induced toxicity and oxidative stress Computational modeling of disease pathways Molecular signaling in leukemias and neurodegeneration Grant leadership includes projects on: Sesamol's neuroprotective effects Plant extracts targeting Alzheimer's disease Metal toxicity modulation Cancer signaling pathways Laboratory and teams : She serves as Laboratory Manager and leads research groups in neuroimaging and molecular biotechnology, with extensive supervision of student theses.

Gang ChenView profile

Associate Professor

• Pharmacogenetics
• Cancer Biology
• Metabolism
• +3 more

Gang Chen is an Associate Professor in the Department of Pharmaceutical Sciences at Washington State University, College of Pharmacy. He holds a PhD in Cancer Biology from Wayne State University and a graduate degree in Biochemistry from CalPoly State University, with additional postdoctoral training in Epidemiology at Fred Hutchinson Cancer Research Center. His research focuses on pharmacogenetics, nicotine metabolism, and cancer susceptibility, particularly through the lens of enzymatic glucuronidation and genetic polymorphisms. PhD in Cancer Biology, Wayne State University MSc in Biochemistry, CalPoly State University BSc in Chemistry, Jilin University Chen's work involves studying metabolic pathways of tobacco carcinogens, the role of UDP-glucuronosyltransferase (UGT) enzymes in drug metabolism, and implications of genetic variations in UGT genes for cancer risk and treatment. His recent publications highlight collaborations on analytical methodologies, clinical trial impacts, and interdisciplinary approaches to understanding metabolic phenotypes and DNA repair mechanisms. Key trends across his publications include the biochemical analysis of UGT enzyme activity, the genetic basis of metabolic variation in cancer susceptibility, and applications of chromatography-mass spectrometry in metabolomics. Subfields span tobacco carcinogenesis, pharmacokinetics, and computational modeling of genomic instability. Chen has not been explicitly associated with any listed scientific awards in the provided data. However, his extensive research output suggests ongoing contributions to pharmacogenetics and cancer biology through peer-reviewed publications and collaborative studies. His laboratory collaborates on projects involving drug metabolism, environmental carcinogens, and nutritional impacts on DNA damage, often employing biochemical assays, genetic modeling, and clinical trial data analysis. Detailed information about specific grants, teaching roles, or future research directions is not included in the provided text.

Marc HellmuthView profile

Associate Professor

• Discrete Mathematics
• Combinatorics
• Graph Theory
• +10 more

Marc Hellmuth is an Associate Professor of Computational Mathematics at the Department of Mathematics, Stockholm University, Sweden. His academic career includes previous positions as Junior Professor for Biomathematics and Computer Science at University of Greifswald, Germany (2015-2020), Lecturer at School of Computing, University of Leeds, UK (2020), and PostDoc positions at Saarland University and Max-Planck Institutes. Dr. Hellmuth earned his PhD in Computer Science from University of Leipzig, Germany (2007-2010, summa cum laude), supervised by Peter F. Stadler, and completed his Venia Legendi (habilitation) at Saarland University, Germany (2016). His research spans the interface of discrete mathematics, computer science, and life sciences with emphasis on: Discrete Mathematics including Graph Theory, Combinatorics, and Optimization Algorithm Design and Complexity Theory Mathematical and Computational Biology Phylogenomics and Evolutionary Analysis Computational Chemistry and Atom Tracking His publication record demonstrates a consistent focus on developing mathematical frameworks and efficient algorithms for biological problems, particularly in phylogenomics, orthology detection, and evolutionary analysis. His work bridges theoretical computer science with practical applications in biology and chemistry, often resulting in open-source software tools. Dr. Hellmuth has developed numerous software tools including AsymmeTree for phylogenetic simulation, tralda for tree algorithms, and specialized tools for phylogenomics, atom tracking, and graph analysis. His collaborative work extends internationally with research visits to institutions including Yale University, University of Leoben, Vienna University of Economics, University of Southern Denmark, Université de Montréal, and institutions in China.

Serena SannaView profile

Researcher

• Genetics
• Microbiome
• Genome-Wide Association Study
• +5 more

Serena Sanna, PhD , is a researcher at the Faculty of Medical Sciences of the University of Groningen, affiliated with the Groningen Institute for Gastrointestinal Genetics and Immunology (3GI) . She contributes to UN Sustainable Development Goals through work on genetics, microbiome, and complex disease mechanisms. Expert in genetics and microbiome research Specializes in genome-wide association studies and Mendelian randomization Research Focus : Host genetic regulation of gut microbiome composition, causal relationships between microbiome and diseases (e.g., endometriosis, hemorrhoidal disease), and integration of metabolomics with immune phenotypes. Her work involves large-scale biobank studies like the Lifelines Cohort . Scientific Awards : Donna di Scienza 2023 Prize Guido Dorso Prize (2018) Highly Cited Researcher (2018) ESHG Meeting Young Scientist Award (2008) Recent Publications : 2025 studies on MBL pathway/microbiome in endometriosis and age-related microbiome effects; 2024 works on hepatic fat genetics, diverticular disease comorbidities, and structural microbiome variation. Active in multi-omics and data integration methodologies.

Hugh ChipmanView profile

Professor

• Statistical Learning
• Data Mining
• Bayesian Methods
• +5 more

Hugh Chipman is a Professor in the Department of Mathematics and Statistics at Acadia University , Canada. He holds a BScH (Acadia), MMath and PhD (University of Waterloo), and is a P.Stat. member of the Statistical Society of Canada. Education: BScH, Acadia University MMath, University of Waterloo PhD, University of Waterloo P.Stat. (Professional Statistician) Research Interests: Dr. Chipman specializes in statistical learning and data mining with applications to complex data structures. Key areas include Bayesian ensemble methods , tree-based models , design of experiments , network analysis , and high-dimensional data problems. He has contributed to drug discovery and functional data monitoring through computational statistics. Recent Talks: His 2024 presentation at UNC Chapel Hill focused on simulation studies for experimental design. Earlier work (2015) addressed big data education at Dalhousie University, while 2011-2014 talks covered active learning and network data mining at institutions like Harvard and MITACS. Scientific Awards: Co-organizer of NSERC-funded workshops Contributed to major statistical learning summer schools (e.g., AARMS 2014) Teaching: Courses include Statistics I and Design and Analysis of Experiments . He maintains software tools like BART (Bayesian Additive Regression Trees) and hybrid hierarchical clustering algorithms.

• Food Chemistry
• Flavor Science
• Sensory Analysis
• +3 more

Imre Blank serves as a Lecturer in the Department of Chemistry and Chemical Engineering at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences. His research focuses on food chemistry and flavor science, particularly investigating taste-active compounds, aroma perception mechanisms, and sensory evaluation of food products. His recent work explores umami taste perception through EEG-based brain rhythm analysis Computational modeling of taste peptides Multi-omics characterization of odorants in plant-based and traditional foods Mechanistic studies of saltiness enhancement by volatiles His publications emphasize analytical chemistry techniques like LC–MS and GC-IMS for flavor profiling. Current research trends examine the molecular dynamics of odorant release, biosensor development for taste detection, and sustainable flavor production inspired by natural processes. He also investigates the impact of processing conditions on coffee aroma formation and bitterness reduction.

Kristen W. LynchView profile

Professor

• RNA Biology
• Alternative Splicing
• Alternative Polyadenylation
• +7 more

Kristen W. Lynch, PhD, is the Benjamin Rush Professor of Biochemistry and Chair of the Department of Biochemistry and Biophysics at the Perelman School of Medicine, University of Pennsylvania. She leads research at the Lynch Lab and co-directs the Institute for RNA Innovation , focusing on RNA processing mechanisms in human immune responses. Education: Harvard University (B.A. 1990, Ph.D. 1996) Postdoctoral Training: University of California, San Francisco (1997-2001) Her work bridges RNA splicing , alternative polyadenylation , and gene regulation in immune cells and cancer. She discovered kinase pathways controlling RNA-binding protein (RBP) activity and demonstrated how antigen stimulation and viral infections reprogram splicing networks to modulate immunity. Collaborative studies with oncologists revealed splicing defects phenocopying genetic mutations in leukemias and identified therapeutic vulnerabilities in RNA processing . Her recent publications highlight TREX-2 complex in mRNA export, hnRNP L repressing cryptic exons, and DDX39B structural analysis . Collaborations span RNA-Seq , splicing codes , and epithelial cell post-transcriptional programs . She explores spliceosomal assembly mechanisms and signal-responsive RBPs in T cells and influenza pathogenesis.

Robert ReenanView profile

Professor

• Aging
• ALS
• Alzheimer's Disease
• +10 more

Dr. Robert Reenan is a Professor of Biology at Brown University, affiliated with the Department of Molecular Biology, Cell Biology, and Biochemistry. His research spans evolutionary genetics, RNA editing, and neurogenetics, focusing on how genomes regulate rapid signaling in the brain through mechanisms like RNA editing and comparative genomics. He has contributed extensively to understanding RNA editing’s role in diseases such as ALS, Alzheimer’s, and epilepsy. Education: PhD in Genetics (Harvard Medical School, 1991), AB in Biology (University of Missouri, 1984) Research Interests: Dr. Reenan investigates RNA editing, ion channels, neurogenetics, and their implications in aging, neurological disorders, and evolutionary adaptation. His work reveals how RNA editing modulates gene function in Drosophila, impacting diseases like ALS and epilepsy. Grants & Collaborations: He has secured major grants from the ALS Association, NIH (NINDS), Ellison Medical Foundation, and NSF. Collaborations include partnerships with researchers like Stephen Helfand, Gary Wessel, and Louis Lapierre. Teaching: Dr. Reenan teaches courses in genetics, molecular genetics, neurogenetics, and scientific communication.