Associate Professor Jean (Jiayu) Wen holds positions at The Australian National University (ANU), including Group Leader of The Wen Group, ARC Future Fellow, and Deputy Director of The Shine-Dalgarno Centre for RNA Innovation. She specializes in computational and molecular biology, focusing on RNA regulation, gene expression, and cancer genomics. Her affiliations include ANU’s Division of Genome Sciences and Cancer, and the Centre for Computational Biomedical Sciences. Education: BEng in Electronic Engineering (Beijing), MSc in Computer Science (Lakehead University), PhD in Computational Biology (ANU). Postdoctoral training at Copenhagen University and Memorial Sloan-Kettering Cancer Center. Research interests span RNA structures, microRNA biogenesis, transcriptome dynamics, and epigenetic regulation. Her work addresses intragenomic conflicts, cancer mechanisms, and neural development. Notable projects include RNA-based machine learning models for RNA-RNA interactions and immune cell differentiation studies. Publications highlight contributions to RNA interference pathways, tumor development, and Drosophila genetics. Awards include the ARC Future Fellowship. She leads interdisciplinary teams advancing computational and experimental approaches in genomics and systems biology.
Prof Alison Rodger is a Professor in the Research School of Chemistry at The Australian National University, where she leads research in biophysical spectroscopy. Formerly at Macquarie University (2017–2024) and the University of Warwick (1990s–2017), she specializes in developing advanced spectroscopic techniques for biomacromolecule analysis. Her work integrates circular dichroism, linear dichroism, and Raman methods to study nucleic acids, proteins, and membrane systems. She co-directs the ARC-funded Industrial Transformation Training Centre in Facilitated Advancement of Australia’s Bioactives (FAAB) and runs an open-access biophysical spectroscopy lab. Key awards include Fellowships from the Australian Academy of Science (2021) and Royal Society of Chemistry (2000), and recognition in the Analytical Science Power List (2015). Education: BSc, PhD, DSc (Sydney University) MA (Oxford) DSc (Warwick) BA (Chester) Research Interests: Development of polarized-light spectroscopies for biomacromolecule analysis, including electronic/circular dichroism, Raman spectroscopy, and hybrid techniques. Applications span protein-DNA interactions, membrane biophysics, and biopharmaceutical characterization. She invented five spectroscopic techniques, including micro-volume Couette flow linear dichroism and fluorescence-detected linear dichroism. Awards & Roles: Fellow of the Australian Academy of Science Fellow of the Royal Society of Chemistry Emeritus Professor (University of Warwick) Recipient of Science Teachers of NSW Dedicated Service Award Consultant to European Science Foundation CASPER project Advising & Grants: Supervises PhD students in interdisciplinary biophysical chemistry. Led the EPSRC-funded Molecular Organisation and Assembly in Cells DTC at Warwick. Currently co-directs the ARC FAAB Centre, focusing on bioactive product characterization. Labs & Collaborations: Operates an open-access biophysical spectroscopy lab supporting academic and commercial users. Collaborations span mathematics, medicine, and engineering, with projects on DNA knotting, antimicrobial peptides, and nanomaterials for biosensing.
Dr. Deyou Zheng is Professor in the Saul R. Korey Department of Neurology, Department of Genetics, and Dominick P. Purpura Department of Neuroscience at Albert Einstein College of Medicine. His research specializes in computational genomics and bioinformatics, analyzing high-throughput genomic data to understand genome structure, transcription regulation, and evolutionary dynamics during development and disease. Core research areas include: (1) Neural system development and neuropsychiatric disorders using iPSC-derived neurons and single-cell transcriptomics; (2) Genetic bases of congenital heart diseases; (3) Cancer genomics focusing on tumor microenvironment and immunotherapy resistance. Collaborative projects employ iPSC technology, RNA-seq, and CRISPR-Cas9 to model disorders like autism and schizophrenia. Recent publications emphasize: Single-cell integration algorithms for transcriptomic data Cancer-associated fibroblasts in glioblastoma/osteosarcoma CHD8 mutations in neurodevelopmental disorders Allele-specific expression in human brain cells
Zhengdong Zhang is a Professor in the Department of Genetics at Albert Einstein College of Medicine. His research focuses on the genetics of aging, Alzheimer's disease, and complex human diseases, combining computational methods with clinical data to uncover genetic mechanisms. His lab develops novel algorithms for genetic analysis, including post-GWAS gene prioritization, Bayesian statistical methods for genomic data, and systems biology approaches. Education details are not explicitly listed, but his professional background includes significant contributions to computational genetics and aging research. Key research areas include method development for genetic analysis, aging-related genetic networks, and disease genetics of Alzheimer's and schizophrenia. His lab has pioneered studies on long-lived species' genomic features, such as naked mole rats and beavers, to understand longevity and disease resistance. Research highlights include identifying rare coding variants linked to schizophrenia risk in 22q11.2 deletion syndrome patients, developing frameworks for Alzheimer's immune pathway analysis, and exploring DNA repair mechanisms in long-lived species. His work emphasizes interdisciplinary approaches integrating data science with clinical insights. Zhengdong Zhang's lab is located in the Michael F. Price Center at Einstein College of Medicine, focusing on translational research to bridge genetic discoveries with clinical applications.
Sandra Zilles is a Professor and Canada Research Chair (Tier 1) in Computational Learning Theory at the University of Regina's Department of Computer Science. She holds adjunct appointments at the University of Waterloo and collaborates with the Alberta Machine Intelligence Institute (Amii). Her research focuses on theoretical computer science and artificial intelligence, particularly interactive learning models, formal language theory, and heuristic search algorithms. Her research integrates computational learning theory, formal language theory, and discrete artificial intelligence structures. Key interests include: Machine teaching with limited data Learnability of pattern languages and automata Graph-theoretic approaches in AI Her work bridges theoretical frameworks with applications in medical imaging, bioinformatics, and game theory. Zilles has received numerous honors including: NSERC Canada Research Chair (Tier 1, 2022-2029) Royal Society of Canada College membership Best Paper Awards (KI 2012, ALT 2003, COLT 2002) She mentors over 50 students and postdocs through her research group. Current projects explore symbolic automata, collaborative learning, and geometric teaching models. Her lab maintains international collaborations with institutions in Germany, Canada, and the US.
Tiago Mendes Ferreira is a Ramón y Cajal Researcher at the University of Santiago de Compostela (USC), affiliated with the Center for Research in Biological Chemistry and Molecular Materials (CIQUS) and the Department of Physical Chemistry. His research focuses on computational chemistry, molecular dynamics simulations, and solid-state NMR spectroscopy to study lipid membranes and their interactions with guest molecules. He previously held a Junior Research Group Leader position at Martin Luther University Halle-Wittenberg (MLU), Germany, and received a DFG grant for his work on combining MD simulations with NMR experiments. Education : Bachelor's in Theoretical Chemistry from the University of Coimbra, Portugal. PhD in Computational Chemistry (Portugal), funded by the Portuguese Foundation for Science and Technology. Postdoctoral research at the University of Paderborn and MLU, Germany. Research Interests : His work bridges computational and experimental methods to elucidate molecular mechanisms in biological membranes. Key themes include lipid membrane structure, cholesterol effects, and the integration of solid-state NMR data with molecular simulations. He develops novel methodologies for characterizing membrane systems, such as long n-alkanes and bacterial membranes. Key Contributions : Advances in ssNMR methodology for lipid systems. Development of the NMRlipids Databank for biomembrane analysis. Studies on lipid phase behavior and protein-membrane interactions. Awards & Grants : Ramón y Cajal Fellowship (2024–present). DFG Temporary Principal Investigator Grant (Germany). Labs & Teams : He leads the QTEC Group (Theoretical and Computational Chemistry) , which focuses on interdisciplinary approaches to membrane science. His group collaborates internationally on NMR-based biomembrane studies.
Dr. Viji Ahanathapillai is an Assistant Professor in Biomedical Engineering at the University of Warwick's School of Engineering, specializing in the Systems and Information Engineering stream. She holds a PhD in biomedical signal and image processing from the University of Strathclyde and has extensive industry experience in roles such as Image Processing Algorithms Engineer at Lucid Software Ltd and Development Scientist at Malvern Panalytical Ltd. Her academic career includes post-doctoral research at Warwick's Institute of Digital Healthcare and a Senior Lecturer position at Birmingham City University. Education: B.Eng (India) → PhD (University of Strathclyde). Research focuses on Biomedical Signal/Image Processing , Wearable Health Monitoring , Women's Health , and AI in Healthcare . Recent work includes developing algorithms for venous dislodgement detection, AI-driven healthcare data analysis, and wearable devices for real-time health monitoring. Teaching: Course leader for MSc Diagnostics Data and Digital Health, and module leader for Research Project modules. Provides weekly math support to first-year students. Labs/Teams: Active contributor to the Systems & Information Engineering team and the Institute of Digital Healthcare at Warwick. Editor of IET Healthcare Technology Letters.
Professor Ralph Fyfe, currently at the University of Plymouth , serves as Professor in Geospatial Information and Associate Dean of Research for Science and Engineering. His research focuses on reconstructing past environmental change, with applications to conservation and climate change mitigation. Interdisciplinary expertise in archaeology, ecology, and climate science Lead Leverhulme Trust projects: "Deforesting Europe," "Transforming the Face of the Mediterranean," and "Reclaiming Exmoor" Contributed to over 115 academic papers and book chapters Ralph’s work combines pollen-landscape calibration datasets to quantify historical land cover changes, revealing human-driven transformations dating back to the Neolithic and Bronze Age periods. His research informs modern conservation strategies and has influenced UK National Park Authorities and COP26 climate policies. Recent publications highlight collaborations with international teams on Holocene vegetation dynamics, megafauna extinction impacts, and climate modeling. Key outlets include Global Change Biology and Scientific Reports . Fellow of the Royal Geographical Society Fellow of the Higher Education Academy Fellow of the Royal Society of Arts He teaches GIS skills , palaeoecology , and employability skills , integrating fieldwork and problem-based learning in locations like Iceland and Dartmoor.
Narmina Baghirova is a Researcher at the Department of Informatics within the Interfaculty Informatics Department at the University of Fribourg. She holds a bachelor’s degree in Mathematics from the University of Primorska (Slovenia), a master’s in Mathematical Sciences from the same institution, and has completed exchange semesters at Universidad del País Vasco (Spain) and Eötvös Loránd University (Hungary). She is currently pursuing a PhD under Prof. Bernard Ries and Dr. David Schindl. Her research focuses on Algorithmic Graph Theory, Computational Complexity, and Combinatorial Optimization. Key areas include k-community structure detection in graphs, proportionally dense subgraphs (PDS), and parameterized complexity analysis. Recent work explores efficiently solvable cases for these problems and their applications in social network modeling. Her publications (2022–2025) span theoretical computer science, discrete mathematics, and bioinformatics, addressing graph partitioning, phylogenetic algorithms, and structural graph properties. She has contributed to both journal articles and conference proceedings. Baghirova collaborates with international researchers and is affiliated with the Department of Informatics at the University of Fribourg, where she contributes to research on computational complexity and graph-theoretic problems.
Ilkka Kronholm is an Associate Professor in evolutionary biology at the University of Jyväskylä, affiliated with the Department of Biological and Environmental Science under the Faculty of Mathematics and Science. His research focuses on understanding how genetic and epigenetic variation drives evolutionary adaptation in response to environmental changes. Key areas include epigenetic modifications, chromatin structure impacts on mutation rates, and experimental evolution using microbial systems. Recent work emphasizes the role of epigenetics in microbial adaptation to fluctuating temperatures and stress conditions. Collaborative projects include analyzing fungal growth dynamics via microfluidics and exploring the evolutionary consequences of prophage activation in bacterial pathogens. Kronholm's lab utilizes genomic, bioinformatic, and quantitative genetics approaches to bridge mechanistic and evolutionary questions. Publications highlight themes like epigenetic regulation of antimicrobial resistance, transgenerational effects in fungi, and the interplay between mutation spectra and chromatin architecture. His work contributes to theoretical frameworks linking short-term phenotypic plasticity with long-term evolutionary trajectories.
Pengyu Hong is a Professor of Computer Science at Brandeis University's Michtom School of Computer Science and an affiliated faculty member at the Benjamin and Mae Volen National Center for Complex Systems. His expertise spans Machine Learning, Bioinformatics, Materials Science, and FinTech, with a focus on interdisciplinary applications in healthcare, molecular biology, and complex systems analysis. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign M.E. in Computer Science, Tsinghua University B.Eng. in Computer Science, Tsinghua University Research Interests: Hong's lab develops advanced machine learning techniques for analyzing heterogeneous data (images, text, financial data), with notable contributions in glycomaterials analysis, clinical outcome prediction, and active nematics modeling. His work bridges computational methods with biomedical and material science challenges, including NMR spectroscopy analysis and molecular property prediction. Publications: Recent work focuses on machine learning applications in glycan sequencing, fairness analysis in medical algorithms, and optical flow techniques for fluid dynamics. The lab also maintains benchmark datasets like GlycoNMR for carbohydrate analysis. Labs & Teams: Hong leads research at the Volen National Center for Complex Systems, integrating computational approaches with experimental systems biology and materials science.
Julia Alterman, PhD, is an Assistant Professor at the RNA Therapeutics Institute within UMass Chan Medical School. Her research focuses on developing novel therapeutic oligonucleotides for genetically defined diseases, with particular expertise in siRNA technology and chemical optimization for extrahepatic tissue delivery. Dr. Alterman's lab works on expanding siRNA applicability to diverse tissues including skin, heart, muscle, bone, joint, eye, and inflammation targets. Her research integrates oligonucleotide chemistry and synthesis, chemical biology, and in vitro/ex vivo/in vivo pharmacology to understand structure-activity relationships of therapeutic oligonucleotides. Current projects focus on creating novel chemical architectures enabling whole-body siRNA delivery, with applications ranging from neurodegenerative disorders to inflammatory conditions. Recent publications demonstrate strong focus on CNS delivery optimization, allele-specific silencing strategies, novel scaffold development for tissue-specific delivery, and toxicity mitigation approaches. Therapeutically, her work spans neurodegenerative diseases (Huntington's, prion diseases), muscular dystrophy, ocular pathologies, and inflammatory conditions.
Mark T. W. Ebbert, PhD, is an Associate Professor at the University of Kentucky's Sanders-Brown Center on Aging, where he leads the Ebbert Lab. His work focuses on developing biomarkers for neurodegenerative diseases, particularly Alzheimer's disease (AD), leveraging cutting-edge genomic and transcriptomic techniques. He co-leads efforts in the Alzheimer's Disease Research Center's Biomarker Core, aiming to integrate neuroimaging, fluid biomarkers, and genomic data for precision medicine applications. Key research interests include analyzing genetic variants, long-read sequencing for disease resilience, and understanding inflammation's role in neurodegeneration. He has presented at high-profile venues like the Markesbery Symposium, discussing topics such as using long-read sequencing to identify AD biomarkers. His lab has produced over 55 publications, with a focus on resolving genomic 'dark regions' and improving biomarker accuracy through innovative methods like RNApysoforms visualization tools. Collaborating with interdisciplinary teams, Dr. Ebbert explores the interplay between genetics, epigenetics, and environmental factors in neurodegenerative diseases. His work bridges basic science and clinical translation, emphasizing the importance of genomic data integration for advancing AD diagnosis and treatment strategies.
Dr. Ngoc Nha Vi Tran is an Associate Professor of Computer Science at UiT The Arctic University of Norway. She holds a PhD from UiT and was a visiting scholar at Rutgers University, USA. Her research focuses on high-performance and energy-efficient computing, machine learning, and bioinformatics. She is a member of the NORA.startup Steering Group and leads the Arctic Green Computing Group. Education: PhD in Computer Science (UiT), M.Sc. in Software Engineering via Erasmus Mundus (Blekinge Institute of Technology, Sweden & Technical University of Kaiserslautern, Germany). Research interests include energy-efficient algorithms, bioinformatics tools (e.g., vCOMBAT), and applications of machine learning in healthcare and robotics. She teaches courses such as INF-2200 Computer Architecture, INF-2900 Software Engineering, and INF-2202 Concurrent Programming. Her work spans computational models for antibiotic target-binding, runtime energy optimization (REOH framework), and power models for embedded systems (RTHpower/ICE). She contributed to the EXCESS project on energy-efficient computing systems. Labs/Teams: Arctic Green Computing Group, EXCESS consortium.
Robert J. Doerksen is Professor of Medicinal Chemistry in the Department of BioMolecular Sciences at the University of Mississippi School of Pharmacy , Associate Dean of the Graduate School , and Research Professor in the Research Institute of Pharmaceutical Sciences . Since 2004 he has combined computational chemistry with experimental collaborations to advance drug discovery, particularly in glycoscience and cannabinoid research. Education: B.S. (Double First Class Honours) in Mathematics & Physics, University of New Brunswick, 1986 Graduate Diploma in Christian Studies, Regent College, Vancouver, 1996 Ph.D. in Chemistry, University of New Brunswick, 1998 (Advisor: Prof. Ajit Thakkar) Postdoctoral Fellow, UC Berkeley (with Prof. Martin Head-Gordon) Postdoctoral Fellow, University of Pennsylvania (with Prof. Michael Klein) Research Interests: Dr. Doerksen’s laboratory develops and applies computational medicinal chemistry approaches spanning chemoinformatics , molecular dynamics , virtual screening , and machine learning to understand how small molecules interact with proteins. Central themes include: Glycoscience : lectin–glycan interactions, glycosyltransferase regulation, glycomimetic design. Cannabinoids : CB1/CB2 receptor allosteric modulation, cannabidiol pharmacology, synthetic cannabinoid SAR. Neglected & Infectious Diseases : malaria, hepatitis B, tuberculosis, SARS-CoV-2, urinary-tract infections. Drug Delivery & Formulation : nanoparticle coatings, pharmacokinetic optimization, bioavailability enhancement. Publications Trend: Over 2023–2025 his 15 most recent papers reveal intense activity at the intersection of AI-driven discovery , glycobiology , and cannabinoid pharmacology , with emphasis on anti-infective, anticancer, and CNS-active agents. Key contributions include first-in-class MraY inhibitors for TB, cannabinoid-inspired antivirals against SARS-CoV-2, and glycomimetic antagonists of bacterial adhesins for UTI prevention. Scientific Awards & Honors: UM School of Pharmacy Faculty Service Award (2015–2016) UM School of Pharmacy Faculty Service Award (2010–2011) Editorial Boards: Molecules , AIMS Biophysics , Pharmaceutical Sciences , Perspectives in Medicinal Chemistry Repeated NIH, DoD, NSF, Wellcome Trust, and international grant-review panels (2010–present) Guest Editor for multiple special issues in Molecules and Frontiers journals Advising & Mentoring: As Associate Dean, Dr. Doerksen oversees University-wide graduate programs, chairs the Graduate Recruiting Fellowship and Scholarship Committee, and mentors students across disciplines. Faculty advisor for the UM chapters of the Christian Pharmacists Fellowship International (since 2005) and Taiwanese Student Association (2022–2025). He actively participates in PhD and MS thesis committees worldwide and has delivered NSF GRFP information sessions to support trainee funding. Laboratories & Teams: He directs research within the Computational Chemistry and Bioinformatics Research CORE (CCBRC) , fostering collaborative projects involving medicinal chemists, structural biologists, pharmacologists, and data scientists. The group leverages high-performance computing resources at the University of Mississippi to perform large-scale virtual screening, AI/ML model development, and integrative structural biology studies.
