جست‌وجوی استاد و پژوهشگر

Michele Klingbeilمشاهده پروفایل

استاد

• Parasitology
• Molecular Biology
• Microbiology
• +۴ مورد دیگر

Michele Klingbeil is a Professor in the Department of Microbiology at the University of Massachusetts Amherst, where she leads the Klingbeil DNA Replication Laboratory. She received her PhD in Cell and Molecular Biology from the University of Toledo in 1996 and previously worked at Johns Hopkins School of Medicine before moving to UMass in July 2007. Her educational background includes: PhD in Cell and Molecular Biology, University of Toledo, 1996 Dr. Klingbeil's research focuses on the unique biology of trypanosomatid parasites, particularly Trypanosoma brucei , the causative agent of African sleeping sickness. Her laboratory investigates two main areas: (1) replication of the unusual mitochondrial DNA network called kinetoplast DNA (kDNA), and (2) nuclear DNA replication initiation. Her work on kDNA is particularly significant as this structure is essential for parasite survival but has no counterpart in mammalian hosts, making it an attractive drug target. She employs a combination of reverse genetics (RNAi), cell biology, and biochemistry to understand the replication and repair mechanisms of kDNA, with a special focus on a family of four DNA polymerases related to bacterial Pol I. Dr. Klingbeil's recent publications reveal her laboratory's deep investigation into mitochondrial DNA polymerases in trypanosomatids, with discoveries showing multiple polymerases having specialized functions in kDNA replication and repair. Her research has established that several of these polymerases are essential for parasite viability, opening new avenues for drug development. She has also made significant contributions to understanding the simplified Origin Recognition Complex in trypanosomatids compared to other eukaryotes. Dr. Klingbeil has received the Thomas G. Lessie Distinguished Lectureship Award for her impact on teaching at the graduate level. Her research is funded by the National Institutes of Health, U.S. Department of Agriculture, the Joeph P. Healey Endowment, and the University of Massachusetts Amherst. She has mentored numerous graduate and undergraduate students, including current PhD candidates Dave Bruhn, Jeniffer Concepción, and Juemin Luo, as well as visiting scholar Eva Vidal Rico. Her former students have gone on to positions at institutions including Dana Farber/Broad Institute, Regis College, and Flagship Ventures. The laboratory regularly participates in scientific conferences including the Molecular Parasitology Meeting at Woods Hole and the Kinetoplastid Molecular Cell Biology conference. Dr. Klingbeil teaches several courses including Parasitology (MICRO 590S), Parasitology Lab (MICRO 590L), Molecular Mechanisms of Pathogenesis (MICRO 797P), Advanced Cell Biology (MCB 641), and Writing in Microbiology (MICRO 360). Her laboratory organizes regular social events including pumpkin carving parties and outings to Six Flags New England and Mt. Sugarloaf.

Lourdes Pena-Castilloمشاهده پروفایل

استاد

• Bioinformatics
• Genomics
• Machine Learning
• +۵ مورد دیگر

Dr. Lourdes Pena-Castillo is a Professor jointly appointed in the Departments of Computer Science and Biology at Memorial University of Newfoundland's Faculty of Science. Her research focuses on applying machine learning and bioinformatics to study bacterial gene regulation, with emphasis on transcriptomics, gene expression pathways, and microbiology. She leads the Bioinformatics Lab at MUN, developing computational tools like Promotech for promoter prediction and sRNARFTarget for sRNA target identification. Education: BSc in Information Systems Engineering, ITESM-Mexico MSc in Computer Science, University of Alberta PhD in Computer Science (Doktoringenieurin), Otto-von-Guericke Universität Magdeburg Postdoc in Bioinformatics, University of Toronto Research Interests: Bioinformatics, Genomics, Machine Learning, Artificial Intelligence, Transcriptomics, Gene Regulation, Microbiology Her work integrates computational methods with biological data to address challenges in molecular biology, including analyzing bacterial sRNA functions, promoter recognition, and disease diagnostics using machine learning. She has advised numerous graduate students, including PhD candidates Purvikalyan Pallegar and Bonita McCuaig, and MSc students like Ruben Chevez-Guardado and Kratika Naskulwar. Her lab focuses on translational research with applications in both basic science and clinical contexts. Publications span computational methods for bacterial gene regulation, bioinformatics tool development, and interdisciplinary projects in VR and healthcare informatics. Her research has contributed to understanding symbiotic relationships in marine organisms, inflammatory bowel disease diagnostics, and clavulanic acid production in Streptomyces. Grants & Collaborations: Works with interdisciplinary teams across computer science and biology, supported by grants enabling projects in bacterial genomics and computational tool development. Labs & Teams: Leads the Bioinformatics Lab at MUN, fostering collaborations with researchers in microbiology, computer science, and healthcare.

Ann B Leeمشاهده پروفایل

استاد

• Scientific Machine Learning
• Trust-Worthy UQ
• Likelihood-Free Inference
• +۱ مورد دیگر

Ann B. Lee is a Professor and Co-Director of the PhD Program in Statistics at Carnegie Mellon University , with a joint appointment in the Department of Statistics & Data Science and the Machine Learning Department. Prior to joining CMU, she held positions as a J.W. Gibbs Assistant Professor at Yale University and a visiting research associate at Brown University. PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on statistical methodology for complex data in the physical sciences , emphasizing trustworthy inference, uncertainty quantification, and integration of classical statistics with machine learning. Recent work includes likelihood-free inference, calibrated forecasting, and diagnostics for generative models. The STAMPS research group , which she co-founded in 2018, hosts weekly meetings and public webinars. In Fall 2024, STAMPS will transition into a CMU Research Center. Recent publications span likelihood-free inference , climate modeling , and astronomy . Notable collaborations include applications to hurricane intensity guidance , galaxy redshift estimation , and cosmological parameter biases . She mentors PhD students and has advised multiple award-winning researchers, including ASA Best Student Paper Award winners. Her teaching includes advanced courses on probability, regression, and AI for climate sciences.

Johannes Larschمشاهده پروفایل

استادیار

• Neuroscience
• Social Behavior
• Neuronal Circuits
• +۳ مورد دیگر

Johannes Larsch is a tenure-track Assistant Professor at the Center for Integrative Genomics within the Faculty of Biology and Medicine at Université de Lausanne (UNIL). His research focuses on understanding the neuronal mechanisms underlying social interactions, using larval zebrafish as a model organism. He employs advanced techniques like virtual reality and optical imaging to study how social signals are processed in the brain. Education: Bachelor of Biology, University of Konstanz, Germany PhD in Neurobiology, Rockefeller University, USA (lab of Cori Bargmann) Postdoc at Max Planck Institute of Neurobiology, Germany (with Herwig Baier) Research interests center on neuronal circuits driving group behavior, social recognition, and the interplay between genetics and environment in shaping behavior. His lab investigates how individual brains coordinate collective social behaviors through studies of neuronal activity and circuit dynamics. Current lab openings include fully funded PhD and PostDoc positions focused on social neuroscience and neurogenomics. Contact: johannes.larsch@unil.ch .

Tamara Broderickمشاهده پروفایل

دانشیار

• Machine Learning
• Statistics
• Bayesian Inference
• +۱ مورد دیگر

Tamara Broderick is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT, specializing in machine learning and statistics. Her research focuses on developing methods for uncertainty quantification in data analysis, Bayesian nonparametrics, and scalable inference algorithms. She leads a research group advising PhD students and postdocs in statistical machine learning. Her work spans Bayesian modeling, variational inference, spatial statistics, and applications in epidemiology and environmental science. Recent projects involve uncertainty-aware forecasting, robustness analysis of statistical methods, and efficient algorithms for high-dimensional inference. Broderick teaches Bayesian Modeling and Inference and contributes to MIT's statistics and data science initiatives.

• Virology
• Molecular Biology
• Genomics
• +۶ مورد دیگر

James Van Etten is the William Allington Distinguished Professor of Plant Pathology at the University of Nebraska-Lincoln, affiliated with the School of Biological Sciences and Nebraska Center for Virology. His research focuses on chloroviruses—large dsDNA viruses infecting Chlorella-like algae—with emphasis on DNA replication, restriction systems, and membrane transport proteins. Key research themes include: Viral DNA modification systems Host-virus interactions Structural virology Evolution of organellar genomes Recent work analyzed: SMRT sequencing of viral methylation patterns Chlorovirus cryopreservation methods Potassium channel biophysics Host chemical signaling mechanisms Lab webpage: vanettenlab.unl.edu

• Human Evolutionary Genetics
• Population Genetics
• Ancestry Analysis
• +۳ مورد دیگر

Aylwyn Scally is a researcher at the Department of Genetics, University of Cambridge, specializing in human evolutionary genetics and ancestry. His work focuses on computational and mathematical models of genome evolution, leveraging population-scale datasets and archeogenetic evidence to explore ancient human populations and their spatial dynamics. University: University of Cambridge Department: Department of Genetics Fields of Interest: Human Evolutionary Genetics, Population Genetics, Ancestry Analysis, Paleobiology, Environmental Genomics, Genome Evolution Scally's research integrates spatial dynamics into genetic models, offering insights into demographic, social, and cultural factors in ancient populations. His methodologies have broader applications for studying other species as genomic data becomes available. Recent publications highlight his work on mutation rate analysis, historical migration patterns, and ancestral population structures. These studies often employ computational simulations and large-scale genomic datasets to address evolutionary questions. He is associated with the Cambridge NERC Doctoral Landscape Awards (CREATES) and C-CLEAR DTP, contributing to training and collaborative research in genetics and environmental genomics.

Harri Lähdesmäkiمشاهده پروفایل

دانشیار

• Probabilistic Machine Learning
• Deep Generative Models
• Artificial Intelligence
• +۴ مورد دیگر

Harri Lähdesmäki is an Associate Professor (tenured) at the Department of Computer Science, Aalto University, where he leads the Computational Systems Biology research group. His work focuses on probabilistic machine learning and deep generative models with applications in biomedicine and molecular biology. Key Research Interests: Probabilistic machine learning, deep generative models, computational biology, bioinformatics, longitudinal data modeling Contact: harri.lahdesmaki@aalto.fi | Konemiehentie 2, 02150 Espoo, Finland His recent publications highlight advancements in: Gaussian process priors for scalable deep generative models Single-cell analysis of immune repertoires in leukemia and diabetes Probabilistic deconvolution methods for RNA-seq data Epigenetic analysis using hidden Markov and mixed models Transformer-based survival prediction and missing data handling Harri’s work integrates mechanistic modeling with Bayesian inference, particularly applied to immunology, cancer biology, and early disease prediction.

Jennifer A. Smithمشاهده پروفایل

دانشیار

• Genetic Epidemiology
• Epigenomics
• Aging
• +۴ مورد دیگر

Jennifer A. Smith, PhD, MPH is an Associate Professor of Epidemiology and Research Associate Professor of Survey Research at the University of Michigan School of Public Health . She also serves as Director of the Certificate in Public Health Genetics and is Assistant Director of the Cohort Development working group for U-M Precision Health and the Michigan Genomics Initiative. Education: PhD, Epidemiology, University of Michigan (2011) MA, Statistics, University of Michigan (2009) MPH, Health Management and Policy, University of Michigan (2005) BS, Biological Sciences, Cornell University (2001) Research Interests: Dr. Smith is a genetic epidemiologist whose work lies at the intersection of genomics, epigenomics, and social epidemiology . She investigates how genetic, epigenetic, and transcriptomic variation influence age-related chronic diseases such as cardiovascular disease, hypertension, dementia, and cognitive decline. A major focus is understanding how social, psychosocial, and neighborhood determinants interact with genetic risk to shape socioeconomic and racial/ethnic health disparities . Research Projects: Her work leverages large, multi-ethnic cohorts including the Health and Retirement Study (HRS), GENOA, SWAN, MESA, and LASI. She is a core faculty member of the Center for Social Epidemiology and Population Health (CSEPH) , and affiliated with MiCDA, the Center for Midlife Science, and the PNG Program . She also collaborates with leading consortia such as CHARGE and TOPMed. Selected Trends in Publications: Her recent work (2022–2025) emphasizes epigenetic mediation of social determinants on cardiovascular and cognitive health, polygenic risk scores across diverse ancestries , and multi-omics integration in aging and disease. These studies consistently highlight how social environments and genetic architecture jointly influence health outcomes across populations. Contact: Email: smjenn@umich.edu Office: 734-615-9455 Address: 2631 SPH I, 1415 Washington Heights, Ann Arbor, MI 48109

Volodymyr Kuleshovمشاهده پروفایل

استادیار

• Machine Learning
• Generative Models
• Probabilistic Methods
• +۴ مورد دیگر

Volodymyr Kuleshov is an Assistant Professor at Cornell Tech and Cornell University's Department of Computer Science. His research focuses on machine learning, particularly generative models, probabilistic methods, and applications in health and sustainability. He co-founded Afresh, an AI startup reducing food waste, and has commercialized genome sequencing work via Moleculo (now part of Illumina). Kuleshov earned his PhD from Stanford University, advised by prominent figures like Stefano Ermon and Serafim Batzoglou. He teaches courses like CS 5785 (Applied Machine Learning) and CS 6785 (Advanced Topics in Machine Learning). His awards include the NSF CAREER Award and Arthur Samuel Best Thesis Award. Education: PhD in Computer Science from Stanford University (2018), advised by Stefano Ermon, Serafim Batzoglou, Michael Snyder, Christopher Re, and Percy Liang. Research Interests: Core ML (generative models, approximate inference), health tech (genome sequencing, clinical decision support), sustainability (AI-driven food waste reduction). Notable projects include Caduceus for DNA sequence modeling and Diffusion Duality theory. Awards: Google Research Scholar Award (2025), Outstanding Paper Award (EMNLP 2023), NIH MIRA Award (2023). Students/Advising: Over 20 advisees across PhD, Master’s, and undergraduate programs, including Edgar Marroquin (PhD) and Charlie Marx (Stanford). Alumni include Allan Bishop (Bloomberg) and Yong Huang (UCI PhD). Labs/Teams: Leads research groups at Cornell Tech focusing on generative AI and its real-world applications. Collaborates with institutions like MILA (Montreal) and DeepMind.

Karoline Faustمشاهده پروفایل

دانشیار

• Microbial community dynamics
• Microbial systems biology
• Bioinformatics
• +۶ مورد دیگر

Karoline Faust is an Associate Professor at KU Leuven, affiliated with the Laboratory of Molecular Bacteriology (Rega Institute) and the Faculty of Medicine . She contributes to the iSi Health and Leuven One Health institutes, and serves on senior academic councils. Her research spans microbial systems biology, focusing on community dynamics and network analysis. Education: PhD in bioinformatics (2010, KU Leuven) Affiliations: KU Leuven, ISME Journal editorial board, Belgian Society for Microbiology Her research investigates microbial community dynamics , systems biology approaches to microbiomes, and bioinformatics tool development . She specializes in modeling human gut microbiota , synthetic microbial communities , and environmental microbiomes (e.g., microplastic impacts on Daphnia microbiomes). Her work integrates metabolic modeling , network analysis , and experimental systems to understand microbial interactions. Recent publications highlight her contributions to microbial network inference , 16S rRNA sequencing protocols , microfluidics , and ecological modeling of microbiomes. She develops tools like manta , miaSim , and CoNet to analyze community structures. Teaching: Karoline co-teaches courses in microbiology, bioinformatics, and network analysis at KU Leuven, and has contributed to international workshops on microbial network inference. Scientific Engagement: She serves as Senior Editor at ISME Journal and Secretary of the Belgian Society for Microbiology .

Deniz Tasdemirمشاهده پروفایل

استاد

• Marine Natural Products Chemistry
• Marine Ecology
• Biodiscovery
• +۲ مورد دیگر

Prof. Dr. Deniz Tasdemir is a Full Professor (W3) of Marine Natural Products Chemistry at GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel and serves as Director of the GEOMAR-Biotech center and Head of the Marine Natural Product Chemistry Research Unit. Her career spans institutions including the National University of Ireland Galway and UCL School of Pharmacy. PhD in Pharmacy, ETH Zurich (1997) Post-doctoral work, University of Utah (2001) Dr. Helmut Legerlotz Fellowship, University of Zurich (2002-2025) Her research focuses on marine chemical ecology , metabolomics , and bioprospecting for bioactive compounds from sponges, algae, and marine microbiomes. Recent work explores seagrass pathogen reduction, microbiome interactions, and aquafeed applications. Scientific awards include: Waters Award for Natural Products Innovation (2016) Egon Stahl Silver Medal (2005) Pierre Fabre Prize (2004) ETH Zurich Medal (1997) She leads collaborative projects on ocean sustainability and marine drug discovery, with editorial roles in Marine Drugs , Planta Medica , and Phytochemistry Letters .

Wolfgang Wüsterمشاهده پروفایل

استاد

• Evolutionary Biology
• Snake Systematics
• Venom Toxicology
• +۵ مورد دیگر

Professor Wolfgang Wüster is a Professor in Zoology (Molecular Ecology) at Bangor University's School of Environmental and Natural Sciences. His research focuses on evolutionary biology, snake systematics, and venom toxicology, using snakes as model systems. He teaches courses in zoology and herpetology, including Arizona fieldwork and advanced herpetology modules. Education: PhD in Systematics of Asian cobras from the University of Aberdeen (1990). His research interests include the evolution of venom composition, species delimitation in snake complexes, warning signals, biogeography, and conservation genomics. He supervises postgraduate students in topics like European viper conservation and snake systematics. Recent publications highlight his work on snake venoms, taxonomy, and the impacts of climate change on reptile species. He collaborates globally, contributing to antivenom efficacy studies and venom evolution research. Professor Wüster has been involved in projects such as genomic studies of adders and the phylogeography of vipers. His work bridges molecular ecology with conservation, emphasizing practical applications in biodiversity preservation.

• Hematology
• Multiple Myeloma
• Cancer Genetics
• +۴ مورد دیگر

Annette Juul Vangsted serves as a Clinical Associate Professor in the Department of Clinical Medicine at the University of Copenhagen's Faculty of Health and Medical Sciences. Her clinical specialization is in Internal Medicine with a focus on Hematology, specifically multiple myeloma. Based at Blegdamsvej 9 in Copenhagen, she maintains an active research profile with 67 documented research outputs and extensive collaborations across Nordic countries and internationally. Dr. Vangsted's research primarily centers on multiple myeloma, with particular emphasis on genetic predisposition, treatment outcomes, epidemiological patterns, and bone disease management. Her work bridges clinical practice with genetic research, examining how genetic variants influence disease risk and progression. She investigates geographical variations in myeloma incidence and outcomes, as well as novel treatment approaches for elderly patients and those with specific genetic profiles. Analysis of her recent publications reveals a clear focus on multiple myeloma across several dimensions: genetic susceptibility (including large genome-wide association studies), treatment efficacy (particularly regarding bone disease management), epidemiological patterns across Nordic countries, and specialized care for elderly populations. Her collaborative approach is evident through numerous multi-center studies involving Nordic Myeloma Study Group and international research teams. Dr. Vangsted actively participates in clinical research through the Nordic Myeloma Study Group, contributing to registry studies and clinical trials that inform treatment guidelines for multiple myeloma. Her work has significant implications for understanding disease mechanisms and improving patient outcomes, particularly in the areas of genetic risk assessment and personalized treatment approaches. She maintains strong research collaborations across Denmark, Sweden, and international institutions, with particular focus on hematological malignancies. Her laboratory and clinical research team works within the Department of Clinical Medicine's hematology division, focusing on translational research that directly impacts clinical practice for myeloma patients.

Xiang Jiمشاهده پروفایل

استادیار

• Statistical Phylogenetics
• Bioinformatics
• Viral Evolution
• +۳ مورد دیگر

Xiang Ji is an Assistant Professor in the Department of Mathematics at Tulane University, affiliated with the School of Science & Engineering. His research focuses on statistical phylogenetics, computational biology, and bioinformatics, particularly in viral evolution and genomic epidemiology. He collaborates with Dr. Wu-Min Deng on cancer biology research from a bioinformatics perspective. Education: Ph.D., 2017: Bioinformatics and Statistics (Co-Major), North Carolina State University M.S., 2013: Material Science and Engineering, North Carolina State University B.S., 2011: Economics (Double Major) and Physics, Peking University Research Interests: Dr. Ji develops statistical models and computational tools for phylogenetic analysis, including scalable algorithms for large-scale genomic data. His work spans viral evolution, zoonotic disease surveillance, and parallel computing libraries for Bayesian inference. He emphasizes practical implementations such as Torchtree and TreeFlow . Articles Trends: Recent publications emphasize viral evolution dynamics (e.g., SARS-CoV-2, avian influenza), genomic surveillance strategies, and computational methods for phylogenetic inference. His work often bridges statistical theory with real-world applications in public health and epidemiology. Advising & Grants: While specific grant details are not listed, his active research program indicates involvement in funding initiatives related to computational biology and viral evolution. He teaches advanced courses in data analysis, linear models, and probability theory. Labs & Teams: Collaborates with Tulane’s Cancer Biology group and maintains partnerships with institutions globally, focusing on genomic epidemiology and phylogenetic software development.