Find Academic Supervisors

Max StallerView profile

Assistant Professor

• Transcriptional Regulation
• Gene Expression
• Intrinsically Disordered Proteins
• +3 more

Max Staller is an Assistant Professor in the Department of Molecular and Cell Biology at the University of California, Berkeley, affiliated with the College of Letters & Science and the Center for Computational Biology. His lab focuses on understanding how transcriptional activation domains regulate gene expression through interdisciplinary approaches combining experimental, computational, and theoretical methods. Research Interests: Transcriptional regulation mechanisms in development and stress responses Functional analysis of intrinsically disordered protein domains Machine learning applications in protein sequence-function prediction Evolutionary dynamics of transcription factors Grants & Collaborations: Lead investigator on the NSF-funded PlantSynBio project (2021) for identifying transcriptional activation domains across plant species. Collaborates with the Cohen Lab (Washington University) on mutational scanning studies. Labs & Affiliations: Director of the Staller Lab, which integrates high-throughput experiments with computational modeling. Active in the Berkeley Bioscience community and the Center for Computational Biology.

Vicki FriesenView profile

Professor

• Evolutionary Genetics
• Conservation Genetics
• Conservation Biology
• +3 more

Professor Vicki Friesen is a faculty member in the Department of Biology at Queen's University, part of the Faculty of Arts and Science. Her research focuses on evolutionary and conservation genetics, particularly in seabirds, aiming to understand mechanisms of biodiversity generation and conservation applications. She holds a cross-appointment in the School of Environmental Studies. Her research interests include evolutionary genetics, conservation genetics, biodiversity origins, and the impacts of climate change on seabird populations. She uses next-generation sequencing to study local adaptation and genetic diversity in species such as seabirds, passerines, and fish. Education: Though not explicitly detailed here, her academic roles suggest advanced training in biology or genetics. Labs/Teams: Leads the Friesen Lab, focusing on Arctic ecology and conservation genomics. Advising: Supervises numerous graduate and undergraduate students in topics like migratory mechanisms, conservation genomics, and immunology. Her work emphasizes the application of genetic tools to conservation challenges, such as delineating conservation units and understanding hybridization dynamics in threatened species.

Gemma AtkinsonView profile

Associate Professor

• Antibiotic resistance
• Bacteriophages
• Molecular evolution
• +7 more

Gemma Atkinson is an Associate Professor at Lund University's Faculty of Medicine, leading the Atkinson Lab. She specializes in protein evolution and bioinformatics, focusing on antibiotic resistance, bacteriophages, and toxin-antitoxin systems. She manages LU-Fold, an infrastructure for high-throughput protein structure prediction using AlphaFold. Her research integrates computational tools with experimental methods to uncover microbial defense mechanisms. Key projects include developing bioinformatics tools (e.g., LoVis4u, webFlaGs) and investigating phage defense systems. She leads grants from the Swedish Research Council and the Knut and Alice Wallenberg Foundation. Her lab collaborates internationally, contributing to UN Sustainable Development Goals on health and innovation. Publications highlight advancements in toxin-antitoxin systems, antimicrobial peptides, and ribosome function. The lab's tools are widely used globally, aiding researchers in structural and functional genomics analysis.

Marina KnittelView profile

Assistant Professor

• Graph Algorithms
• Fair Machine Learning
• Mechanism Design
• +3 more

Marina Knittel is an Assistant Professor of Computer Science at Reed College in the Division of Mathematical and Natural Sciences, starting Spring 2025. She currently serves as a postdoctoral researcher at UC San Diego working with Profs. Barna Saha and Sanjoy Dasgupta on graph algorithms. She holds a PhD from the University of Maryland (2023) and a BS in Mathematics and Computer Science from Harvey Mudd College. Her research focuses on graph algorithm theory with applications spanning fairness in machine learning, mechanism design, and massively parallel computation. She explores interdisciplinary connections between computer science, economics, and biology, particularly how computational problems increasingly require cross-domain expertise. Knittel's publication portfolio demonstrates consistent focus on scalable algorithms for massive datasets, with recent work emphasizing fairness in clustering and hierarchical methods. Her articles frequently appear in top theoretical computer science venues and show sophisticated mathematical approaches to practical computational problems.

Miten JainView profile

Assistant Professor

• Nanopore Technology
• Single-Cell Techniques
• Computational Biology
• +3 more

Miten Jain is an Assistant Professor in the Department of Bioengineering at Northeastern University, with a joint appointment in the Department of Physics. His research focuses on nanopore technology, single-cell analysis, and computational biology, aiming to advance genomic and transcriptomic sequencing methodologies. He holds a PhD in Bioinformatics and Biomolecular Engineering from the University of California-Santa Cruz (2017). Dr. Jain leads research projects including 'Characterization of paired tumor and normal cell lines using long read sequencing' (NIST, 2021) and 'Multi-platform, high-coverage, long read sequencing of reference human genomes' (NIST, 2020). His work bridges engineering, physics, and biology, with applications in clinical diagnostics and space microbiology. He was recognized as a top 2% most-cited scientist globally in 2024 by Stanford University. His research outputs span epigenetic profiling, nanopore sequencing innovations, and space-based microbiome analysis. Recent studies include CRISPR-based therapeutic screening for glioma and real-time microbial profiling aboard the International Space Station. Collaborations with institutions like NIST and NASA highlight his interdisciplinary impact. Grants and awards include funding from NIST and recognition for ultra-rapid genome sequencing in critical care settings. His lab focuses on developing scalable, high-resolution genomic tools with applications in precision medicine and fundamental biology.

Ivana MalenicaView profile

Assistant Professor

• Causal Inference
• Machine Learning
• Nonparametric Statistics
• +3 more

Ivana Malenica is an Assistant Professor of Biostatistics at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. Previously, she was a HDSI Fellow at Harvard Data Science Initiative and Postdoctoral Fellow in Statistics at Harvard University. She holds a Ph.D. in Biostatistics from UC Berkeley and a B.S. in Mathematics from Arizona State University. Her research focuses on causal inference, machine learning, nonparametric statistics, efficiency theory, and precision health. She specializes in longitudinal and structured dependent settings including adaptive sequential experiments, online learning, and reinforcement learning applications in personalized health. Her recent publications demonstrate strong methodological contributions to causal inference and machine learning, with applications spanning clinical trials, public health, genomics, and reinforcement learning. Her work consistently develops novel statistical approaches for complex data structures. Awards include: Harvard Data Science Initiative Fellowship (2022) Berkeley Wellness Letter Fellowship (2020) Wellness Scholarship in Honor of Chin Long Chiang (2019) Berkeley Institute for Data Science Moore-Sloan Fellowship (2018) She teaches graduate courses including Advanced Probability and Statistical Inference I (BIOS 760). Her computational work includes contributions to the tlverse ecosystem for causal inference in R.

Stephen AttwoodView profile

Researcher

• Epidemiology
• Infectious Diseases
• Genetic Variation Analysis
• +4 more

Dr. Stephen Attwood is a Post Doc Researcher in the Pybus group at the Natural History Museum, London, and a Scientific Associate at the same institution. He was formerly a Professor of Epidemiology at West China Medical School. His research focuses on evolutionary approaches to infectious disease epidemiology, including SARS-CoV-2 lineage tracking via the Pango nomenclature system and evaluating public health interventions. He has also contributed to understanding schistosomiasis evolution, describing three new species and identifying Mekong schistosomiasis in new regions. His work integrates genetic variation analysis, phylogenetics, and ecological studies to address disease emergence and biodiversity impacts of infrastructure like dams. Research interests span epidemiological modeling, pathogen evolution, and parasitology, with a focus on applying genetic data to real-world public health challenges. Key areas include SARS-CoV-2 transmission dynamics, schistosome taxonomy, and the ecological consequences of human activities on freshwater ecosystems. His articles explore topics ranging from viral mutation impacts on vaccine efficacy to schistosome mitochondrial genome divergence, reflecting a dual focus on contemporary pandemics and long-term parasitic disease evolution. Grants and advising details are not explicitly listed, but his lab affiliations include the Evolve.Zoo group and collaborations with global health institutions. Labs/Teams: Active in the Evolve.Zoo research lab, focusing on evolutionary biology and disease systems.

Ana FiszbeinView profile

Assistant Professor

• Gene Regulation
• RNA Processing
• Systems Biology
• +3 more

Ana Fiszbein is an Assistant Professor of Biology at Boston University, specializing in gene regulation and RNA processing. Her lab employs high-throughput genomics, bioinformatics, and molecular approaches to study co-transcriptional gene regulation in mammalian systems, with a focus on cancer genomics and therapeutic strategies. She earned her PhD from the University of Buenos Aires and leads research on how gene architecture influences transcriptional programs. Her research interests include understanding the interplay between transcription and splicing, particularly through phenomena like Exon-Mediated Activation of Transcription Starts (EMATS). The lab develops computational tools like evopython to predict gene regulatory networks and designs strategies to manipulate gene expression for therapeutic applications. Current projects explore promoter-driven RNA processing decisions, transcriptional interference between promoters, and the connection between transcript initiation and termination. The lab actively recruits undergraduate, graduate, and postdoctoral researchers. Key findings include the discovery of hybrid exons and splicing-dependent transcriptional control mechanisms.

Sang-Woo JunView profile

Professor

• Big Data Analytics
• Hardware Acceleration
• Non-Volatile Memory
• +3 more

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.

Min ChenView profile

Professor

• Statistical Genomics
• Bioinformatics
• Bayesian Methods
• +5 more

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.

• Bioinformatics
• Algorithms
• Computational Biology
• +5 more

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 ObradovicView profile

Professor

• Bioinformatics
• Healthcare informatics
• Data mining
• +12 more

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.

• Biomedical Informatics
• Health Information Systems
• Artificial Intelligence in Healthcare
• +3 more

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 ChenView profile

Professor

• Statistical Methods for Microbiome Data
• Single-Cell Sequencing Data Analysis
• High-Dimensional Data Analysis
• +3 more

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)

Thomas CokelaerView profile

Researcher

• Bioinformatics
• Data Analysis
• Machine Learning
• +12 more

Thomas Cokelaer is a researcher at the Institut Pasteur, affiliated with the Bioinformatics and Biostatistics HUB and based at the Platform Biomics. Since October 2018, he has led the SALSA (dry-lab) group, focusing on bioinformatics software development and data analysis. His interdisciplinary background spans astronomy, plant modeling, systems biology, and drug discovery. Institution: Institut Pasteur Unit: Bioinformatics and Biostatistics HUB Platform: Platform Biomics Group: SALSA (dry-lab group) leader His research interests center on bioinformatics, next-generation sequencing (NGS) analysis, machine learning, data visualization, and reproducible research. He develops and maintains several widely used open-source tools including Sequana (NGS pipelines), BioServices (biological web services), Bioconvert (format conversion), Sequanix (GUI for Snakemake), and GDSCTools (pharmacogenomics). He is deeply committed to promoting reproducibility in computational research through initiatives like reprohackathons. The recent publications reflect a strong trend in computational biology, with a focus on developing bioinformatics methodologies, improving data analysis workflows, and applying these tools to diverse biological problems such as fungal pathogenesis, cancer pharmacogenomics, mitochondrial metabolism, and viral evolution. The work spans genomics, transcriptomics, systems biology, and software engineering. He has contributed to numerous scientific software projects as Principal Investigator, including Sequana, Bioconvert, GDSCTools, DREAMTools, and Sequanix. He has also taught courses in scientific programming with Python and reproducible data analysis at the Institut Pasteur. His collaborative network includes researchers from EMBL-EBI, Sanger Institute, INRIA, and international partners within the Pasteur Network. The SALSA group hosts master’s students and visiting researchers annually.