Professor Sang-Woo Jun is a leading researcher in systems and software for big data analytics, focusing on FPGA-based hardware acceleration and non-volatile memory (NVM) storage. His work spans applications such as graph analytics and bioinformatics, with a strong emphasis on cost-effective, high-performance computing architectures. He advises PhD students like Shengquan Ni and Yicong Huang, both of whom have achieved notable milestones (e.g., thesis defense, fellowship awards). Research Interests: Hardware Acceleration for Big Data FPGA-Based System Architectures Non-Volatile Memory Systems Graph Analytics and Bioinformatics Edge Computing and Low-Power Systems Recent Contributions: His articles highlight innovations in edge accelerators (e.g., IceSpy, Eciton), genomics acceleration (Bancroft), and scalable graph processing (Durin, Sting). These works emphasize reconfigurable systems, privacy-preserving techniques, and energy-efficient designs. Lab & Team: As part of the Intelligent Systems Group (ISG), he collaborates on events like the Southern California Database Day. His research bridges hardware-software co-design with real-world applications in IoT, environmental monitoring, and genomics.
Dr. Min Chen is a Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UTD), affiliated with the School of Natural Sciences and Mathematics. He holds an adjunct professorship at the University of Texas Southwestern Medical Center. His expertise spans statistical genomics, bioinformatics, Bayesian methods, and sampling techniques. He completed his Ph.D. in Statistics and Decision Science at the University of Texas at Austin and a postdoctoral fellowship in statistical genomics at Yale University. Education: B.S. in Computer Science (University of Science & Technology of China, 1994); M.A. in Statistics (University of Pittsburgh, 1999); Ph.D. in Statistics (UT Austin, 2006); Postdoc in Statistical Genomics (Yale University, 2008–2010). Research focuses on statistical methodologies in genomics, including genome-wide association studies, network-based modeling, and Bayesian integrative analysis. He also explores spatial modeling and ranked set sampling. His work addresses challenges in cancer genetics, epigenetics, and single-cell gene regulation. Notable awards include the NIH Career Development Award (2013), David Bruton Fellowship (2006), and R.L. Anderson Student Paper Award (2006). He is a member of the American Statistical Association and International Chinese Statistical Association. He advises graduate students in Data Science, Statistics, and Bioinformatics & Computational Biology (BCBM) programs. His teaching includes courses on advanced statistical methods and data science. Research contributions span over 40 peer-reviewed articles, with recent work in tumor pathology imaging, antibiotic resistance, and Alzheimer’s disease mechanisms.
Gabriel Alejandro Valiente Feruglio is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB) and the Department of Computer Science. He is a member of the ALBCOM research group, focusing on Algorithms, Bioinformatics, Complexity, and Formal Methods. His work integrates theoretical computer science with applications in computational biology, including phylogenetic analysis, graph algorithms, and metagenomics. Valiente’s research emphasizes the development of algorithms for biological networks, phylogenetic tree and network comparison, and efficient graph representation. He has contributed to tools like TANGO for taxonomic assignment in metagenomics and AligNet for protein-protein interaction network alignment. His publications span over 140 works in journals like BMC Bioinformatics, IEEE-ACM Transactions on Computational Biology, and Bioinformatics. He leads and collaborates in competitive research projects funded by institutions like the Catalan government, focusing on bioinformatics, computational biology, and algorithmic methods. His research also extends to LaTeX typesetting for scientific documents and the structural analysis of scientific collaborations in graph transformations.
Zoran Obradovic holds the prestigious L.H. Carnell Professorship of Data Analytics at Temple University where he serves as Professor in the Computer and Information Sciences Department and the Statistics Department. He also directs the Center for Data Analytics and Biomedical Informatics and maintains secondary appointments at Temple's Fox School of Business. Internationally, he serves as Research Professor at The Mathematical Institute of the Serbian Academy of Sciences and Arts, and as Visiting Professor at both the School of Medicine and School of Management at the University of Belgrade. His research spans multiple cutting-edge domains of data science: Bioinformatics and protein disorder prediction Healthcare informatics and clinical decision support systems Machine learning for complex networks and big data Spatial and temporal data analytics Applications in healthcare management, social networks, and earth science Dr. Obradovic pioneered research on intrinsically disordered proteins, earning multiple CASP awards. His work addresses challenges related to heterogeneous, spatial, and temporal data analytics with applications across healthcare, power systems, earth science, and social sciences. His research has been funded by prestigious organizations including NIH, NSF, DARPA, DOE, and industry partners. With approximately 450 publications and an H-index of 68 (over 33,000 citations), his scholarly impact is substantial. He serves as editor-in-chief of the Big Data journal and chairs the SIAM Data Mining conference steering committee, reflecting his leadership in the field. Scientific Recognition Elected Member of Academia Europaea (2015) Foreign Member of Serbian Academy of Sciences and Arts (2015) Asia-Pacific Artificial Intelligence Association Fellow (2021) Multiple best paper awards at international conferences Temple University's President's Outstanding Faculty Research Award (2009) Three consecutive CASP awards for protein structure prediction (2002-2006) Dr. Obradovic has mentored approximately 50 postdoctoral fellows and Ph.D. students, many of whom now hold positions at leading academic institutions and tech companies including Amazon, Facebook, IBM, Microsoft, and Uber. He has served on editorial boards for 13 journals and organized numerous international conferences in data mining and bioinformatics. His Center for Data Analytics and Biomedical Informatics develops advanced data science methods addressing real-world challenges in healthcare, science, engineering, and business applications, with active recruitment for Ph.D. students and postdoctoral researchers.
Ankur Agrawal is the Chair and Professor of Computer Science at St. Edward's University, School of Natural Sciences. His research focuses on Biomedical Informatics with an emphasis on ontology engineering and quality assurance of healthcare terminologies like SNOMED CT. Dr. Agrawal also serves as CAC Commissioner and Team Chair at ABET, demonstrating leadership in accreditation and educational standards. His work centers on improving healthcare data systems through machine learning applications, lexical analysis, and structural consistency checks in biomedical ontologies. Key contributions include developing tools for crowd-sourced ontology curation and visualizing SNOMED CT hierarchies. Publications from 2013-2024 consistently address quality assurance challenges in healthcare terminologies, leveraging both algorithmic and contextual methods. No scholarly awards are explicitly mentioned in the provided texts. Advising and grants: No specific student advisees or grant details provided in the text. His professional roles extend beyond academia to ABET, where he contributes to accreditation processes for engineering and technology programs. Labs/teams: Involved with ABET's accreditation committees and maintains a research focus through BioPortal and SNOMED CT initiatives.
Jun Chen, Ph.D., is a Professor of Biostatistics in the Department of Quantitative Health Sciences at Mayo Clinic, Rochester, Minnesota. His research focuses on developing robust statistical methods for high-dimensional omics data, particularly microbiome and single-cell sequencing data, with applications in disease subtyping, biomarker discovery, and integrative genomics. Education: Postdoctoral Fellowship in Biostatistics, Harvard School of Public Health Ph.D. in Genomics and Computational Biology (Biostatistics emphasis), University of Pennsylvania M.A. in Statistics, University of Pennsylvania M.E. in Computer Science (Pattern Recognition and Machine Intelligence), Shanghai Jiao Tong University M.D., Fudan University Dr. Chen’s work addresses challenges in microbiome data (phylogeny-constrained, zero-inflated counts) through methods for association testing, subtype discovery, and predictive modeling. He also develops techniques for single-cell sequencing data, including clustering, differential abundance analysis, and multimodal integration, while incorporating biological priors and structural constraints. His collaborative research spans diverse diseases, aiming to quantify microbiome contributions to pathogenicity and inform clinical decision-making via personalized microbiome profiling. Professional Highlights: Associate Editor, Frontiers in Genetics (2021–present) Associate Editor, Statistics in Biosciences (2021–present) Bioinformatics Section Editor, PeerJ (2019–present)
Michele Boniotto is a researcher at the University of Verona's Department of Molecular and Translational Medicine, focusing on immunology, genetics, and dermatology. His work spans molecular mechanisms in skin diseases and evolutionary aspects of host defense peptides. Research Focus Dr. Boniotto's research examines: Genetic factors in hidradenitis suppurativa Beta-defensin evolution and function Aquaporin-3 role in skin homeostasis Photobiomodulation therapies HLA-DR expression in septic shock His publications show interdisciplinary approaches combining molecular biology, clinical dermatology, and bioinformatics to understand complex disease mechanisms. Scientific Contributions 2025: Keratin filament-melanin interactions 2025: Polygenic risk scoring for HS 2024: Aquaporin-3 dysregulation in HS 2023: NCSTN mutations in familial HS 2022: Holistic HS health records 2020: Photobiomodulation for HS
Prof Benjamin Schwessinger is a Professor at the Australian National University (ANU), affiliated with the Division of Plant Sciences. His research focuses on plant pathogens, fungal genomics, and the evolutionary dynamics of pathogen adaptation. Key areas include understanding rust fungi biology, host-pathogen interactions, and structural genomic variations in plants like Eucalyptus. He has pioneered methodologies for high-throughput protein secretion optimization in yeast and developed diagnostic tools for invasive pathogens such as Austropuccinia psidii (myrtle rust). Research Interests: Plant-microbe interactions and immunity Fungal genomics and sexual recombination mechanisms Evolving pathogen populations in agricultural systems Genomic tools for disease surveillance and biosecurity His recent work explores the genomic basis of pathogen adaptation, including studies on wheat stripe rust and myrtle rust. Collaborations span fungal pathogenomics, eucalyptus structural genomics, and yeast biotechnology. He leads multiple projects funded by ANU and ARC, including the Plant Biosecurity Training Centre. His lab emphasizes reproducible research practices and early career researcher development. Key projects include: Digital yeast bioprospecting for non-alcoholic beer production Surveillance of airborne pathogens in Australian Botanic Gardens Genome evolution of cereal rust fungi Labs/Teams: Core member of ANU's Plant Biosecurity Group and collaborator in the ARC Training Centre in Plant Biosecurity.
Achim Kramer serves as Professor of Chronobiology (W2, tenured) at Charité - Universitätsmedizin Berlin, where he chairs the independent Research Unit for Chronobiology within the Institute of Medical Immunology. His academic career spans molecular chronobiology research and teaching since 2002, with significant contributions to understanding circadian clock mechanisms in mammals. His educational background includes a Biochemistry degree from Freie Universität Berlin (1988-1993), a Ph.D. in Biochemistry (summa cum laude, 1996) from Humboldt Universität zu Berlin, and piano training at Berlin's Hochschule der Künste (1990-1994). Postdoctoral work included research at Harvard Medical School (1999-2001) under Charles Weitz and positions at IRBM Rome and Charité. Dr. Kramer's research centers on molecular mechanisms of circadian clocks, with expertise in protein interactions (CRY1-PER2), post-translational modifications, peripheral tissue clocks (macrophages, skin), and translational applications like circadian blood biomarkers. His work bridges structural biology, immunology, and sleep medicine to address how biological timing affects health and disease. Analysis of his publications reveals consistent focus on circadian molecular machinery across diverse biological contexts, with increasing translational emphasis in recent years on diagnostic applications and tissue-specific clock functions. His structural work on cryptochromes and phosphorylation mechanisms underpins fundamental understanding of clock regulation. Key honors include the Heinz-Maier-Leibnitz Award (DFG, 2002), Brooks Fellowship (Harvard, 2001), multiple Teaching Awards for Medical Neurosciences (2010-2015), and a Young Researcher's Award from Charité (1998). He leads the Chronobiology Research Unit within Charité's Institute of Medical Immunology, participating in the SFB/TRR186 consortium on molecular switches. His service includes chairing the 2015 Gordon Research Conference on Chronobiology, editorial roles at PLoS Genetics and Journal of Biological Rhythms, and leadership positions in the Society for Research on Biological Rhythms and European Biological Rhythms Society.
Aviv Bergman is a Professor at Albert Einstein College of Medicine, holding appointments in the Department of Systems & Computational Biology, Department of Pathology, and Dominick P. Purpura Department of Neuroscience. He is also Founding Chairman of the Department of Systems & Computational Biology and Director of the Albert Einstein Institute for Advanced Study in the Life Sciences. His research integrates computational, mathematical, and experimental approaches with philosophical frameworks (phenomenology, hermeneutics, pragmatism) to study evolutionary and developmental systems biology, focusing on complex traits, biological networks, and the interplay between function, organization, and agency in living systems. Prior publications highlight his work on evolutionary capacitance, gene network topology, developmental canalization, and genome-wide amino acid patterns. He emphasizes iterative refinement of theoretical models through empirical data validation.
Gilles Peslherbe is a Professor at Concordia University, cross-appointed in the departments of Chemistry and Biochemistry , Physics , and Chemical and Materials Engineering . He leads the Centre for Research in Molecular Modeling (CERMM) and supervises graduate students in programs spanning Chemical Engineering, Chemistry, Physics, and Nanoscience. PhD in Physical Chemistry with Minor in Computer Engineering (Wayne State University, USA) Diplôme d'Ingénieur Chimiste (Ecole Supérieure de Chimie Industrielle de Lyon, France) His research integrates computational chemistry , quantum mechanics , and machine learning to model chemical processes in extreme conditions, drug delivery systems, and environmental impact assessments. Collaborative projects include studies on spin catalysis , nanostructured materials , and RNA structure fundamentals . Recent publications highlight applications of artificial intelligence in therapeutics development , density-functional theory in nanomaterials , and ultrasfast spectroscopy in electron solvation . His work bridges quantum computing and biological simulations . Teaching includes courses on quantum theory , computational chemistry , and statistical mechanics . He actively seeks students for ongoing projects in chemical interactions in extreme conditions and environmental remediation .
Aurora Martinez is a Professor in the Department of Biomedicine at the University of Bergen, Faculty of Medicine. She leads the Biorecognition research group, focusing on the structural and functional aspects of biomolecules in neurometabolic disorders such as phenylketonuria (PKU) and dopamine synthesis defects. She is also a partner in the KG Jebsen Centre for Neuropsychiatric Disorders and a Toppforsk-funded project on the Arc protein, a master regulator of synaptic plasticity. Position: Professor Institution: University of Bergen Department: Department of Biomedicine Research Group: Biorecognition Email: aurora.martinez@uib.no Her research integrates structural biology, molecular recognition, and drug discovery to develop therapeutic strategies for genetic and neurodegenerative diseases. Key areas include tyrosine hydroxylase regulation, mitochondrial dysfunction in dopaminergic cells, and neurotransmitter transport mechanisms. She employs biochemical, cellular, and computational approaches to understand disease mechanisms and identify novel therapeutics. The recent publications (2023–2025) reflect a strong trend toward understanding dopamine-related pathways, protein stabilization, and therapeutic interventions in Parkinsonism and related disorders. Themes include chaperone-mediated protein stabilization, high-throughput screening for VMAT2 modulators, and computational tools for drug discovery. The work combines experimental validation with translational applications in neurodegenerative models. She has supervised multiple Master’s students, including Md. Ekhtear Mahmud, Sofie Breisnes Wormdahl, and Kristine Kippersund Brokstad, indicating active mentorship and training roles. Her involvement in large-scale collaborative projects highlights leadership and interdisciplinary engagement. While no specific awards are listed, her participation in prestigious programs like Toppforsk and the KG Jebsen Centre underscores recognition and funding success. She has no listed grants explicitly, but project affiliations suggest competitive funding support. Her research group maintains strong technical capabilities in protein analysis, cellular screening, and structural modeling. The Martinez Lab is actively involved in both fundamental and applied research, with future directions likely to expand into gene therapy, precision medicine for metabolic disorders, and neuroprotective strategies.
John H. Reif is a Professor of Computer Science at Duke University, with secondary appointments in the Thomas Lord Department of Mechanical Engineering and Materials Science (since 2024) and the Department of Electrical and Computer Engineering (since 2016). His research spans DNA computing , molecular assembly , robot motion planning , and quantum computation , focusing on programmable biomolecular systems and parallel algorithms. Education: Ph.D. in Applied Mathematics, Harvard University (1977) M.S. in Applied Mathematics, Harvard University (1975) B.S. in Applied Mathematics and Computer Science, Tufts University (1973) His recent publications emphasize DNA strand displacement , molecular-scale learning systems , and 3D DNA nanostructures , with applications in diagnostics, data storage, and nanofabrication. Articles highlight innovations in error-resilient DNA circuits, programmable self-assembly, and algorithmic modeling of molecular processes. Scientific Awards: Fellow of the American Association for the Advancement of Science (AAAS) Fellow of the Association for Computing Machinery (ACM) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) He has supervised numerous Ph.D. students and postdoctoral researchers , including Rajiv Nagipogu (adaptive molecular systems) and Xin Song ( Daniel Fu , DNA computation on cell membranes). His work is funded by grants from the Defense Advanced Research Projects Agency (DARPA) and the National Science Foundation (NSF) , with recent focus on molecular-scale AI and DNA polymerase reaction networks. Reif co-founded Domus Diagnostics , developing affordable infectious disease testing solutions. He served as General CoChairman of FNANO24 and contributes to teaching courses like Computational Complexity and Molecular Assembly and Computation .
Prof. Mile Šikić is a Full Professor at the Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing (University of Zagreb). His research spans computational biology, genomics, and machine learning applications in sequencing technologies. Focus on nanopore sequencing analysis, genome assembly, and protein interaction prediction Developed tools like GraphMap , RiNALMo , and Orthobalancer Active in metagenomics, RNA structure prediction, and CUDA-based algorithm acceleration Scientific contributions include: Advances in de novo genome assembly for error-prone long reads Deep learning models for base modification detection Efficient algorithms for sequence alignment and similarity searches Technical implementations cover: GPU-accelerated sequence alignment libraries (e.g., SW# ) Web platforms for comparative protein analysis Simulation tools for epidemic spread on complex networks
Raşit Bilgin is an Associate Professor at the Institute of Environmental Sciences, Boğaziçi University, Istanbul, where he has been serving since 2012. He previously held positions as Assistant Professor (2008–2012) and Instructor (2007–2008) at the same institute. His academic leadership includes serving as the Chair of the Department of Environmental Sciences. Education: Ph.D., Department of Ecology, Evolution and Environmental Biology, Columbia University, USA (2006) M.Phil., Department of Ecology, Evolution and Environmental Biology, Columbia University, USA (2003) M.A., Department of Ecology, Evolution and Environmental Biology, Columbia University, USA (2002) M.S., Institute of Environmental Sciences, Boğaziçi University, Turkey (2000) B.S., Civil Engineering Department, Boğaziçi University, Turkey (1998) His research lies at the intersection of molecular ecology, evolutionary biology, and conservation genetics, with a strong focus on environmental DNA and phylogeography. He investigates genetic patterns in marine and terrestrial species, particularly in Anatolia and the Mediterranean, aiming to understand biodiversity dynamics and inform conservation strategies. His work often integrates genomic tools with ecological modeling to explore population history and species resilience. The recent publications highlight a consistent trend in using molecular markers—especially mitochondrial and nuclear DNA—to study population structure, historical demography, and species distribution. His research spans marine crustaceans, bats, and broader biodiversity assessments, reflecting a multidisciplinary approach combining field ecology, genomics, and computational tools. Scientific Awards: No specific awards mentioned in the provided text. Raşit Bilgin has been actively involved in research funding and project leadership, including TÜBİTAK 1001 and 2501 projects, as well as internal Boğaziçi University grants. These projects focus on conservation genomics of seabirds, coral disease resistance, and Antarctic amphipods. While direct student advising is not explicitly listed, his leadership in research projects suggests mentorship of graduate students. He has developed analytical tools like Kgtests, indicating a contribution to methodological advancements in population genetics. He leads research initiatives involving multi-omics, acoustic monitoring, and GPS-GSM tracking, suggesting an interdisciplinary lab environment focused on innovative conservation technologies. His team likely includes researchers working on genomics, bioinformatics, and field ecology, particularly in extreme environments such as the Antarctic and Mediterranean ecosystems.
