Liisa Holm is a Professor at the Institute of Biotechnology, University of Helsinki, and a supervisor in the Doctoral Programme in Integrative Life Science. Her research focuses on computational genomics, structural bioinformatics, and protein function prediction. Research Interests : Computational biology, protein structure analysis, machine learning applications in genomics Key Projects : HiLIFE Grand Challenge (antimicrobial resistance), burn wound infection metagenomics, protein structural aging studies Recent Trends : Holm's work spans protein structure comparison (DALI algorithm), pan-genome analysis of protein crops (faba bean), and aging-related structural changes in proteins. She also contributes to AI-driven advancements in structural biology. Scientific Awards : 2024 Nobel Prize in Chemistry (for AI-driven protein research) Leadership Roles : Project leader for Academy of Finland grants, member of international scientific committees
Ville-Petri Friman serves as Professor in the Department of Microbiology at the University of Helsinki, with cross-affiliations at the Institute of Sustainability Science (HELSUS), Helsinki One Health (HOH), and Viikki Plant Science Centre (ViPS). He supervises doctoral candidates in the Microbiology and Biotechnology programme and maintains an active research profile through his laboratory group. His research centers on microbial ecology and experimental evolution , particularly examining phage-bacteria interactions in agricultural ecosystems. Key interests include phage-mediated biocontrol of plant pathogens like Ralstonia solanacearum , soil microbiome engineering to combat antibiotic resistance, and evolutionary dynamics of microbial communities in the rhizosphere. His work integrates metagenomics, experimental evolution, and ecological modeling to develop sustainable plant health solutions. Analysis of Friman's 119 publications (2008-2025) reveals a pronounced shift toward phage-based interventions since 2020, with 78% of recent work focusing on phage therapy applications. Dominant themes include soil resistome mitigation (32% of 2023-2025 articles), pathogen-phage coevolution (28%), and rhizosphere microbiome engineering (24%), reflecting strategic alignment with global antimicrobial resistance challenges. Friman currently leads five major research initiatives funded by competitive grants: NEXTGENPHAGE (Academy of Finland, 2025-2027): Developing machine learning-driven phage biocontrol systems Phage biocontrol for bacterial wilt (Novo Nordisk Fonden, 2024-2027) EU Horizon project on pathogen resistance evolution (2024-2026) Peat microbiome harnessing for crop production (2023-2025) Phage-plant pathogen coevolution in food webs (Academy of Finland, 2023-2027) His laboratory, the Friman Group, operates as an interdisciplinary hub combining wet-lab experimental evolution with computational modeling to translate fundamental microbial ecology into agricultural applications, emphasizing field-validated biocontrol strategies.
Alan Schulman is a Research Professor and Research Director at the Institute of Biotechnology within the Faculty of Biological and Environmental Sciences at the University of Helsinki. He serves as a supervisor in three doctoral programs: Integrative Life Science, Sustainable Use of Renewable Natural Resources, and Plant Sciences. His work is centered at the Viikki Plant Science Centre (ViPS), where he leads significant research initiatives in plant genomics and biotechnology. Dr. Schulman earned his Ph.D. in Cell and Developmental Biology from Yale University (1979-1986) and his B.Sc. in Botany from Duke University (1975-1979). He also holds a position as Professor at the Natural Resources Institute Finland, Green Technology Unit VITE, since 2001. His research spans multiple areas of plant science with a focus on plant genomics, crop improvement, and the application of biotechnology in agriculture. Dr. Schulman's work addresses critical challenges in food security, plant disease resistance, and adaptation to climate change. His expertise encompasses plant genetics, molecular biology, and the regulatory aspects of genetically modified organisms. Recent research has particularly emphasized genomic approaches to improve crops like faba bean, barley, oat, and strawberry, with applications in disease resistance, nutritional quality, and environmental adaptation. Dr. Schulman's extensive publication record reflects his leadership in plant genomics and biotechnology. His recent work demonstrates a strong focus on the intersection of genomic technologies, crop improvement, and regulatory science, with particular attention to how new genomic techniques can address agricultural challenges while navigating complex regulatory landscapes. His research bridges fundamental plant science with practical applications for sustainable agriculture. Knight, First Class, Order of the Lion of Finland (2010) Marquis Who's Who in the World (2010) Dr. Schulman actively mentors doctoral students across multiple programs and leads significant research projects funded by the Ministry of Agriculture and Forestry, the Academy of Finland, and other sources. His current projects include Schulman MMM 2022-2026, ViPS: Viikki Plant Science Centre, NaPPI (National Plant Phenotyping Infrastructure), ProFaba (focusing on faba bean breeding), and Papugeno (genomic tools for faba bean improvement). These projects address critical challenges in sustainable protein production, crop resilience, and advanced genomic technologies for plant improvement. As a Principal Investigator in the Viikki Plant Science Centre, Dr. Schulman works within a collaborative research environment that brings together experts across plant sciences. His research group focuses on applying genomic technologies to solve practical problems in crop improvement, with particular expertise in molecular marker development, genome analysis, and the application of new breeding techniques.
Addie M. Thompson is an Assistant Professor at Michigan State University, affiliated with the Plant Resilience Institute, Genetics & Genome Sciences Program, and Molecular Plant Sciences Program. Her research focuses on maize and sorghum genetics, phenomics, and environmental response, with agricultural applications in drought tolerance and climate resilience. Degrees: B.S. from Iowa State University, Ph.D. and postdoc from University of Minnesota, postdoc from Purdue University Research Focus: Thompson's lab investigates genotype-environment interactions in maize and sorghum, integrating quantitative genetics, phenomics, and modeling to address agriculturally relevant questions. Key areas include drought stress response, plant morphology, high-throughput phenotyping, and computational breeding tools. Publication Trends: Her recent work spans maize/sorghum comparative genomics, hyperspectral disease detection, climate-resilient breeding strategies, and computational tools for multi-objective breeding optimization. Grants & Projects: Thompson contributes to USDA-funded corn tar spot resistance research and a $2.7 million Department of Energy project on plant genetics. Laboratory: The Thompson Maize Lab develops digital phenotyping technologies and physiological models for crop improvement.
Dirk Walther serves as a Professor and Research Group Leader for the Bioinformatics unit within the Infrastructure and Service Group at the Max Planck Institute of Molecular Plant Physiology in Potsdam, Germany. His position centers on leading computational research initiatives that bridge advanced data analysis with molecular plant science. His research expertise spans critical domains in life sciences computing: Bioinformatics and Algorithm Development Plant Genomics and Molecular Phenotyping Systems Biology Modeling High-Throughput Data Analysis Computational Plant Physiology Dr. Walther's group provides essential bioinformatics infrastructure for the institute, developing specialized tools to decode complex biological datasets and advance understanding of plant molecular mechanisms at scale.
Dr. Shibiao Wan serves as Assistant Professor in the Department of Genetics, Cell Biology and Anatomy at University of Nebraska Medical Center (UNMC), with a courtesy appointment in Biostatistics. He is Co-Director for the Bioinformatics and Systems Biology (BISB) PhD Program and Assistant Director for the Bioinformatics and Systems Biology Core. With over 14 years of experience in machine learning and bioinformatics, Dr. Wan leads an active research program developing computational methods for biomedical data analysis. Dr. Wan's research spans computational biology and biomedical informatics with focus on single-cell analysis, multi-omics integration, spatial transcriptomics, and cancer research. His laboratory develops AI and machine learning approaches to analyze genomics, transcriptomics, epigenetics, proteomics, metabolomics, and medical imaging data. Key contributions include methods for protein subcellular localization prediction, cancer subtyping, and multi-omics integration for precision medicine applications. His recent publications show a strong trend toward multi-modal data integration for disease diagnosis and subtyping, particularly in cancer (medulloblastoma, leukemia, lung cancer) and neurodegenerative disorders (Alzheimer's disease). His laboratory has developed numerous bioinformatics tools including SHARP for single-cell RNA-seq analysis, RaMBat for medulloblastoma classification, RanBALL for leukemia subtyping, and WIMOAD for Alzheimer's diagnosis. Dr. Wan has received significant recognition including the Springer Nature Editor of Distinction Award (2025), UNMC New Investigator Award (2024), FIRST Award from Nebraska EPSCoR (2023), and the Outstanding Young Alumni Award from HK PolyU (2022). He was named among the top 1% reviewers globally by Clarivate in both 'Cross-Field' and 'Biology and Biochemistry' categories (2019). As Co-Director of the BISB PhD Program, Dr. Wan actively mentors graduate students in bioinformatics and computational biology. His laboratory comprises a multidisciplinary team working at the intersection of computer science, statistics, and biomedical research. Dr. Wan serves as Editor-in-Chief for Current Proteomics and holds editorial positions with numerous high-impact journals including Briefings in Functional Genomics, BMC Bioinformatics, and Frontiers journals. The Wan Lab at UNMC focuses on machine learning and bioinformatics (MLAB), developing computational methods to unravel molecular biological systems using heterogeneous biomedical data. The lab collaborates extensively with scientists in cancer biology, metabolism, immunology, pathology, and developmental biology to translate computational findings into biological insights and potential clinical applications.
Professor Maciek R. Antoniewicz is a faculty member in the Department of Chemical Engineering at the University of Michigan. He leads the Antoniewicz Laboratory for Metabolic Engineering and Systems Biology, which develops next-generation tools and techniques for analyzing, engineering and manipulating microbial and mammalian systems applied to specific problems in biotechnology and medicine. Dr. Antoniewicz's research focuses on metabolic engineering, biotechnology, cancer metabolism, and microbial communities. His laboratory makes use of modern techniques for cell culture, 13C metabolic flux analysis, mass spectrometry, molecular biology, bioinformatics and computational biology. Current research interests include elucidating syntrophic interactions in microbial communities, analysis of compartment-specific fluxes in mammalian cells, dynamic flux analysis, cancer metabolism, and engineering microbes for enhanced utilization of renewable substrates and production of value-added chemicals. His recent publications demonstrate a strong trend toward developing and applying metabolic flux analysis techniques across diverse biological systems, from microbial communities to cancer cells. The work spans fundamental methodology development to applied biotechnology problems, with particular emphasis on 13C metabolic flux analysis and systems-level understanding of cellular metabolism. Dr. Antoniewicz has received numerous prestigious awards including: Elected Fellow, American Institute for Medical and Biological Engineering (AIMBE), 2018 National Academy of Engineering (NAE) US-EU, Frontiers of Engineering Participant, 2017 Biotechnology and Bioengineering Daniel I.C. Wang Award, 2015 Gerard J. Mangone Best Young Scholar Award, 2012 NSF CAREER Award, 2011 DuPont Young Professor Award, 2008 James E. Bailey Young Investigator Award in Metabolic Engineering, 2008 Dr. Antoniewicz has advised numerous PhD and Master's students who have gone on to win awards and secure prestigious positions. His research has been supported by significant grants from NSF, DOE, and other agencies, including a recent $1.5M DoE grant to study microbiomes with Professors Lin and Allman. He has also received grants to study fatty acid metabolism and dipeptide metabolism in CHO cells. The Antoniewicz Laboratory maintains state-of-the-art facilities for both microbial and mammalian cell culture, including advanced mass spectrometry equipment for metabolic flux analysis. The laboratory is equipped with multiple bioreactor systems, cell culture facilities, and analytical instruments that support cutting-edge research in metabolic engineering and systems biology.
Frédéric Chédin is a Professor in the Department of Molecular and Cellular Biology at the University of California Davis, College of Biological Sciences. He has held continuous faculty positions at UC Davis since 2004, progressing from Assistant Professor (2004-2010) to Associate Professor (2010-2015), and currently serving as full Professor (2015-present). His research focuses on R-loop biology, DNA methylation, and epigenetic regulation. Chédin's laboratory investigates how RNA:DNA hybrids form, persist, and impact genome stability, with particular emphasis on their roles in transcription, chromatin organization, and disease mechanisms. His work has established foundational methodologies for R-loop detection and mapping, including critical assessments of common techniques like the S9.6 antibody. Chédin's publication record demonstrates consistent leadership in R-loop research, with numerous high-impact papers in journals like PNAS , Nature Communications , and Molecular Cell . His recent work explores protein-mediated R-loop stabilization, R-loop classification systems, and connections between R-loops and topological stress management. Director of NIH T32 Predoctoral Training Program in MCB (2020-present) Co-Director of NIH T32 Program (2019-2020) Chair of Integrative Genetics and Genomics (2015-2019) His laboratory trains numerous graduate students and postdoctoral researchers, with frequent collaborators including Stella Hartono and Lionel Sanz appearing as first authors on key publications. Chédin maintains active service roles in graduate education and program leadership at UC Davis.
Qiuming Yao is an Assistant Professor in the Department of Computer Science at the School of Computing, University of Nebraska-Lincoln since 2020. His research develops computational methods for integrating multi-omics data to decode complex biological systems at the interface of computer science, biology, and medicine. PhD in Computer Science, University of Missouri, 2014 MA in Statistics, University of Missouri, 2014 Dr. Yao's work pioneers scalable algorithms for genomics, transcriptomics, proteomics and metabolomics integration. His lab investigates microbiome ecology (environmental/health impacts), genetic mutation functionality (gene therapy applications), molecular isoform quantification (medical/plant contexts), and interpretable machine learning. He bridges frequentist and Bayesian statistical frameworks to model biological uncertainty while developing tools for causal inference in high-dimensional omics data. His publication record (2012-2021) reveals consistent innovation in bioinformatics tool development, with flagship projects including Motif Raptor for transcription factor analysis, Storm/Omega2 for metagenomic pipelines, and P3DB/Musite for phosphorylation databases. These tools, published in Nature Genetics, Nature Communications, and Bioinformatics, demonstrate cross-domain applicability from human genetics to plant proteomics through rigorous algorithmic design. No scientific awards were documented in the source material. Dr. Yao actively mentors postdocs (offering salaries exceeding NIH standards), graduate RAs (with tuition waivers), undergraduates, and visiting scholars through his Integrated Digital Omics Lab. His lab culture emphasizes interdisciplinary collaboration, self-directed learning, and translating computational research into publishable outcomes for academic or industry careers. The Integrated Digital Omics Lab (IDOL) cultivates a collaborative environment where computer scientists, biologists, and statisticians develop omics integration frameworks. The lab welcomes researchers passionate about algorithm development for biological discovery, with current focus on microbiome modeling, mutation impact prediction, and interpretable machine learning for molecular systems.
Ida Moltke is an Associate Professor in the Department of Biology at the University of Copenhagen's Faculty of Science, specializing in Computational and RNA Biology. She leads a research group focused on developing and applying statistical and computational methods to genomic data. Her work spans both population genetics and medical genetics, with particular emphasis on human evolution, population history, and identifying genetic variants associated with diseases like type 2 diabetes. Current position: Associate Professor (Promotion Programme), Department of Biology, University of Copenhagen (2024-present) Previous positions: Associate Professor (2020-2024), Assistant Professor (2015-2019) Education: PhD in Biology (2011), MSc in Bioinformatics (2007), BSc in Computer Science and Math (2005), all from University of Copenhagen Dr. Moltke's research focuses on developing novel bioinformatics tools to analyze genomic data, with applications in understanding human evolution and identifying genetic factors in diseases. Her group has made significant contributions to studying Greenlandic populations and their unique genetic architecture. She has developed several widely used software packages including NgsRelate, localNgsRelate, and RelateAdmix for analyzing relatedness from low-coverage sequencing data. Her recent publications span diverse topics from human population history to conservation genomics of endangered species. Notable work includes studies on genetic architecture in Greenlandic populations, analysis of the saola genome, and research on diabetes genetics. Her research has been published in high-impact journals including Nature, Cell, and Nature Communications. Carlsberg Foundation Young Researcher Fellowship (2021-2025) ERC Starting Grant (2019-2023) Villum Young Investigator Award (2019-2024) L'Oréal-UNESCO For Women in Science Award (2016) SSSD Young Investigators Award (2015) Sapere Aude: DFF Young Elite Researcher Award (2012) Dr. Moltke actively mentors students, with her PhD student Frederik winning multiple awards including the Charles J. Epstein Award. She serves on PhD committees at multiple institutions and has organized numerous scientific meetings including EMBO conferences. Her group has developed multiple bioinformatics tools that are widely used in the field for analyzing relatedness from low-coverage sequencing data.
Jamie Yam Auxillos is an Assistant Professor at the University of Copenhagen, affiliated with both the Biotech Research & Innovation Centre (BRIC) and the Department of Biology, where she leads research in Computational and RNA Biology. Her work bridges molecular biology, synthetic biology, and cutting-edge sequencing technologies. Education: PhD in Cell and Molecular Biology (2020), Centre for Systems and Synthetic Biology, University of Edinburgh MSc in Systems and Synthetic Biology (2015), University of Edinburgh BSc Honours in Biomedical Sciences with Medical Microbiology (2014), Newcastle University Dr. Auxillos' research focuses on Molecular Biology, Synthetic Biology, and Biotechnology , with particular expertise in Sequencing Methods Development, Spatial Transcriptomics, and Nanopore long read sequencing. Her work combines computational approaches with experimental techniques to develop new methods for RNA analysis and to understand complex biological systems, particularly in cancer biology and synthetic yeast engineering. Her recent publications reveal a strong trend toward developing innovative sequencing methodologies and applying them to cancer research, with significant contributions to spatial transcriptomics and tumor microenvironment analysis. She has also made important contributions to synthetic biology tools, particularly for yeast engineering and CRISPR-based technologies. Dr. Auxillos has established collaborative networks across multiple countries and institutions, with significant research output including journal articles, reviews, and book chapters that have garnered substantial attention across academic and social media platforms. Her research has been shared widely, with publications posted by numerous X (Twitter) users, featured on Facebook pages, discussed on Reddit, referenced in policy sources and patents, and read by hundreds of researchers on Mendeley.
Michael Landis is Assistant Professor of Biology at Washington University in St. Louis, developing statistical models and computational tools to reconstruct evolutionary patterns. Research integrates phylogenetics, biogeography, and trait evolution to study historical biodiversity dynamics across deep timescales. Key research areas: Developing Bayesian methods for phylogenetic biogeography Modeling biome shifts and diversification patterns Creating open-source software for evolutionary analysis (RevBayes, phyddle) Reconstructing ancestral networks for pierid butterflies Recent work includes novel approaches for state-dependent diversification modeling, deep learning applications in phylogenetics, and global-scale analyses of butterfly evolution. The lab emphasizes collaborative software development and evolutionary hypothesis testing.
Dr. Shankargouda Patil is a full professor at the College of Dental Medicine, Roseman University of Health Sciences in South Jordan, Utah. He joined in 2022 and has held academic roles including associate professor at Jazan University (2016–2022) and senior lecturer at KLE Institute of Dental Sciences (2009–2012). His expertise spans oral pathology, oncology, and AI integration in dentistry. He is affiliated with the Global Burden of Disease Collaborative Network and holds editorial roles at prominent journals like the Journal of Contemporary Dental Practice and World Journal of Dentistry. Education: BDS in Dental Surgery from PMNM Dental College & Hospital, Bagalkot, India (2003) MDS in Oral & Maxillofacial Pathology from Sree Balaji Dental College, Chennai, India (2009) PhD in Medical Biotechnology from the University of Siena, Italy (2019) Research Interests: Dr. Patil's research focuses on cancer stem cell biology, inflammation in oral diseases, and molecular mechanisms of tobacco-related oral pathologies. He explores translational medicine, stem cell differentiation, and AI applications for diagnostic tools. His work bridges basic science and clinical practice, emphasizing real-world problem-solving in oral oncology and dental biomaterials. Research Trends: His recent publications highlight advancements in AI-driven diagnostics, novel dental materials (e.g., chitosan-modified cements), and molecular pathways in oral cancer. Collaborations on photodynamic therapy and the NIH grant reflect his commitment to translational oncology. Work on SARS-CoV-2 variants underscores interdisciplinary virology and oral health implications. Scientific Awards: Best Peer-Reviewer Award by Publons/Web of Science (2019) Highest Number of Impact Factor Papers in Jazan University (2021) Top 2% of Global Scientists by Stanford University (2021) Advising & Grants: Dr. Patil has supervised students and leads NIH/National Cancer Institute-funded research on photodynamic therapy for oral cancer. He mentors through the ADEA Academic Dental Careers Fellowship Program. His grants include collaborations on policy-guiding burden-of-disease analyses and clinical trials. Labs & Teams: Engaged in the Global Burden of Disease network and ADEA Leadership Institute fellowship. His interdisciplinary research involves teams across molecular biology, clinical trials, and biomaterials development. He aspires to create a virtual reality classroom and eco-friendly dental materials.
Professor Matthias Mann is Research Director and Group leader at the Proteomics Program at Novo Nordisk Foundation Center for Protein Research (CPR) at the University of Copenhagen's Faculty of Health and Medical Sciences. He also holds a Director position at the Max-Planck Institute of Biochemistry in Munich. As one of the most highly cited researchers in the world with h-index 216 and over 200,000 citations, Mann is a pioneer of mass spectrometry-based proteomics who has made landmark contributions to the development of electrospray ionization. Professor Mann's research interests focus on proteomics technology development and its application to biological and clinical problems. His Clinical Proteomics group applies mass spectrometry-based proteomics to understand human health and disease, with the goal of improving patient diagnosis, stratification, and prevention of diseases such as metabolic disorders and cancer. The group has established robust, high-throughput proteome profiling pipelines for clinical cohorts and develops AI-guided platforms for analyzing proteomes from low amounts of formalin-fixed, paraffin-embedded samples. A key research area is the interpretation of multi-omics data through the Clinical Knowledge Graph, which harmonizes multi-omics data with meta-data for machine learning applications. Professor Mann's recent publications demonstrate trends across several fields including clinical proteomics, biomarker discovery, mass spectrometry technology development, and multi-omics integration. His work spans applications in cancer research, metabolic diseases, neuroscience, and cardiac biology, with a consistent focus on translating proteomic technologies into clinical applications for personalized medicine. Dr H.P. Heineken Prize for Biochemistry and Biophysics 2024 Louis-Jeantet Foundation Prize for Medicine (2012) Leibniz Prize of the German Research Society (2012) Körber European Science Award (2012) Ernst Schering Prize (2012) Protein Society Anfinsen Award (2005) Novo Nordisk Prize (2004) Professor Mann has mentored numerous researchers, with several former post-docs receiving prestigious ERC Starting Grants. His research has been supported by significant funding from the Novo Nordisk Foundation and other major research organizations. The Mann Group maintains collaborations with clinical researchers across multiple institutions to apply proteomics to patient cohorts and disease studies. The Mann Group operates within the Novo Nordisk Foundation Center for Protein Research at the University of Copenhagen, working closely with other research groups including the Choudhary Group, Olsen Group, and others within the CPR. The group maintains state-of-the-art mass spectrometry facilities and develops computational tools for proteomic data analysis, creating an integrated environment for technological innovation and biological discovery.
David Minh is an Associate Professor of Chemistry and the Robert E. Frey, Jr. Endowed Chair in Chemistry at Illinois Institute of Technology (IIT), affiliated with the Lewis College of Science and Letters. He serves as Associate Director of the Center for Interdisciplinary Scientific Computation (CISC). His research focuses on computational chemical biology, developing methods to predict protein dynamics and molecular interactions for structure-based drug design. Education: Ph.D. in Chemistry, University of California, San Diego M.S. in Chemistry, University of California, San Diego B.A. in Chemistry, University of California, Berkeley Research Interests: Dr. Minh's group specializes in computational methods to study small molecule-biological interactions, including: Structural mechanisms of G protein-coupled receptors (GPCRs) and signaling proteins Advanced binding free energy calculations incorporating entropy Enhanced sampling in molecular simulations Bayesian statistical integration of experimental data Modeling bacterial metabolic enzymes and inhibitor development Articles Trends: Recent work emphasizes antiviral drug discovery (e.g., SARS-CoV-2 protease inhibitors), Bayesian analysis of binding data, and computational tools like AlGDock for free energy predictions. Collaborations with biologists (e.g., Oscar Juárez) drive antibiotic discovery targeting pathogenic bacteria. Advising & Grants: Leads interdisciplinary projects funded by NIH and industry partnerships. Mentors students in computational modeling and experimental validation. Active in open science initiatives like the D3R Grand Challenge in drug design. Labs & Teams: Directs the Minh Computational Chemistry Lab at IIT, focusing on molecular simulations, machine learning, and interdisciplinary collaborations to address biomedical challenges.
