جست‌وجوی استاد و پژوهشگر

April Weiمشاهده پروفایل

استادیار

• Computational Genetics
• Population Genetics
• Statistical Genetics
• +۱۰ مورد دیگر

April Wei is an Assistant Professor in the Department of Computational Biology at Cornell University, where she leads the Wei Lab in Computational Genetics. Her research focuses on developing scalable computational methods to analyze massive genomic datasets, addressing fundamental questions in evolutionary processes and human genetics. B.Sc. in Biology, Fudan University (2013) Ph.D. in Computational Biology, University of Michigan (2018) Postdoctoral Research at UC Berkeley and UCLA Joined Cornell in January 2022 The Wei Lab explores diverse areas such as population genetics , computational genetics , and natural selection , with applications in human evolution , gene conversion , and complex traits . Their work leverages graph-based data structures and deep learning to improve genomic analysis scalability. April's research trajectory, as reflected in her publications, emphasizes genomic data compression , selective sweep detection , and ancestral recombination graph (ARG) analysis . Her lab's outputs span algorithm development for biobank-scale data, evolutionary modeling of overlapping genes, and statistical frameworks for gene conversion landscapes. April mentors a team of Ph.D. students and postdoctoral researchers, including current advisees Ziqing Pan, Meera Chotai, Jinmin Li, Aditya Girish, and Lin Yuan, with notable alumni such as Siddharth Avadhanam (2023). The lab is supported by NIH and NSF grants. Lab culture prioritizes collaboration , inclusivity , and scientific curiosity , with social activities like lab barbecues, game nights, and group hikes. April is also a passionate fan of Star Wars and water sports.

Jinchuan Xingمشاهده پروفایل

استاد

• Mobile Elements
• Human Genomic Variation
• Evolutionary Genetics
• +۱۴ مورد دیگر

Jinchuan Xing is a Professor at Rutgers, The State University of New Jersey, where he leads the Xing Lab of Genomics. His research focuses on human genomic variation, mobile DNA elements, evolutionary and population genetics, and their implications for human disease. He integrates computational and experimental methods to study genome-wide variation, with applications in disease gene identification and genomic technology development. His research interests include: Mobile DNA element biology Human demographic history and population diversity Disease gene identification using whole-exome and whole-genome sequencing High-altitude adaptation genetics Transposable element regulation and expression piRNA and small RNA pathways His recent publications highlight a strong trend in reproductive genomics, particularly in identifying genetic risk factors for embryo aneuploidy in IVF patients, using advanced sequencing technologies. Other work spans evolutionary genomics in diverse species (bats, moths, Drosophila), structural variation in neurodevelopmental disorders, and proteogenomic discovery. His lab actively develops and applies bioinformatic tools for variant analysis and gene prioritization. Scientific awards and honors are not explicitly mentioned in the provided text. Jinchuan Xing advises several graduate students, including Nan Wang, Siqi, Ellie Lu, and Tongji Xing. His lab has received significant funding, including an R01 grant from NICHD on aneuploidy risk, a grant from the Center for Human Evolutionary Studies, and a Life Sciences Alliance Pilot Seed Funding grant in collaboration with the Department of Statistics. These grants support research in fertility genomics, disease gene discovery, and genomic tool development. The Xing Lab of Genomics is an active research group conducting interdisciplinary studies combining genomics, bioinformatics, and molecular biology. The lab welcomes new members regularly, including master’s and PhD students, and collaborates across departments. Current projects include understanding the genetic basis of meiosis, fertility, and high-altitude adaptation, as well as developing Markovian gene networks for disease gene discovery.

Sriram Sankararamanمشاهده پروفایل

استاد

• Machine Learning
• Computational Genetics
• Genomics
• +۷ مورد دیگر

Professor Sriram Sankararaman is a computational scientist with triple appointments at the University of California, Los Angeles in the Computer Science Department , Department of Human Genetics , and Department of Computational Medicine . His interdisciplinary research bridges computer science, statistics, and biomedicine. PhD in Computer Science from UC Berkeley Postdoctoral training at Harvard Medical School Research focuses on statistical and computational methods for understanding genomic datasets, including: Population genetic inference and archaic admixture analysis Genetic architecture of complex traits Machine learning for clinical data integration Privacy-preserving genomic analysis Recent publications address scalable algorithms for biobank-scale data, evolutionary insights from archaic DNA, and interpretable machine learning models. His lab develops tools like SCOPE and RHE-mc for population structure and variance components analysis. Scientific Honors NSF Career Award (2020) Microsoft Investigator Fellow (2019) Northrop Grumman Excellence in Teaching (2019) Okawa Foundation grant (2017) Alfred P. Sloan Fellow (2017) NIH Pathway to Independence Award (2014) Visvesvaraya Medal (2004) As director of the Machine Learning and Genomics Lab , he leads research on evolutionary genomics, medical genetics, and scalable algorithms for integrating multi-modal biomedical datasets.

Rajiv McCoyمشاهده پروفایل

استادیار

• Computational Genomics
• Human Evolution
• Reproductive Genetics
• +۳ مورد دیگر

Rajiv McCoy is an Assistant Professor in the Department of Biology at Johns Hopkins University (JHU). He holds a PhD in Biology from Stanford University and completed postdoctoral training at Princeton University and the University of Washington. His research integrates computational and statistical approaches to study human evolution, genetics, and reproduction, with a focus on genomic mechanisms underlying aneuploidy, embryo development, and somatic evolution. The McCoy Lab is affiliated with JHU’s Cell, Molecular, Developmental Biology, and Biophysics (CMDB) doctoral program. Key research interests include understanding how germline and somatic evolution shape genome function and reproductive outcomes. The lab employs advanced genomic methods to analyze chromosomal instability, recombination patterns, and the genetic basis of aneuploidy in human embryos. Recent work has explored maternal meiotic recombination, genomic mosaicism, and the evolutionary implications of Neanderthal introgression. Lab members have presented at conferences such as Cold Spring Harbor’s Biology of Genomes and the Broad Institute’s Mutations in Time and Space. Notable collaborations include studies with the Preimplantation Genetic Diagnosis International Society (PGDIS) and the Origins of Aneuploidy Research Consortium (OARC). The lab actively engages in genomic benchmarking, pangenome research, and the development of improved reference genome tools.

Joshua Akeyمشاهده پروفایل

استاد

• Genomics
• Population Genetics
• Evolutionary Biology
• +۴ مورد دیگر

Joshua Akey is a Professor at Princeton University's Lewis-Sigler Institute for Integrative Genomics, specializing in the biology and evolution of genomes. His interdisciplinary research spans yeast, dogs, and humans, integrating experimental, computational, and theoretical methods to address complex genetic questions. Research Focus: Human population genomics, yeast functional genomics, and canine adverse drug response genetics. Collaborations: Katrina Mealey (Washington State University), Evan Eichler (University of Washington). Key Themes: Archaic introgression, adaptive evolution, and the genetic architecture of phenotypic variation. The Akey Lab leverages large-scale datasets and statistical approaches to explore genome-wide selection patterns, copy number variation, and evolutionary mechanisms. Recent publications emphasize comparative genomics, longevity in companion dogs, and Neanderthal ancestry in African populations. Scientific Recognition: Recognized as a Best Scientific Figure of 2012 by Wired. Students: Ruby Redlich, Susie Song, Caroline (Cara) Weisman, Winnie Xu, Kaiqian Zhang.

Priya Moorjaniمشاهده پروفایل

استادیار

• Human Genetics
• Evolutionary Biology
• Population Genetics
• +۶ مورد دیگر

Priya Moorjani is an Assistant Professor in the Department of Molecular and Cell Biology at the University of California, Berkeley. Her research integrates computational and statistical methods to study human genetics and evolutionary biology, focusing on genetic variation's evolutionary origins and applications to human history and disease. She leads the Moorjani Lab, which investigates mutation rate evolution across primates, admixture events in human populations, and leveraging evolutionary insights for disease gene discovery. Her work combines method development with large-scale genomic data analysis. Key research themes include reconstructing human evolutionary history using ancient and modern genetic data, understanding the impact of founder events and admixture on disease risk, and measuring mutation rates in primates. The lab has made significant contributions to studies of South Asian population history, Neanderthal/Denisovan introgression, and the genomic basis of human adaptation. Notable awards include the NSF CAREER Award (2023) and Novo Nordisk Foundation grants (2022–2024). She advises students such as Jesseon Chang (Master’s recipient) and collaborates on projects funded by major grants. The lab is committed to fostering diversity and inclusion in science, aligning with initiatives like Black Lives Matter.

Jaume Bertranpetitمشاهده پروفایل

استاد

• Human Evolution
• Anthropology
• Genomics
• +۵ مورد دیگر

Jaume Bertranpetit is a Full Professor at the Department of Experimental and Health Sciences, School of Health and Life Sciences, Universitat Pompeu Fabra (Barcelona). He leads the Evolutionary Biology and Complex Systems Program and is a promoter of the Institute for Evolutionary Biology (IBE UPF-CSIC). His research focuses on human genome variation and diversity through Population genetics Molecular evolution Comparative genomics Evolutionary systems biology Key publication themes include Natural selection footprints in human genomes Archaic introgression in Asian/Oceanian populations Chimpanzee genome selection Admixture analysis in Ethiopian populations Scientific honors include Narcís Monturiol Medal (2015) Doctor Honoris Causa (2018) ERASMUS Prize (1990) JSPS Fellowship (2002) King's College Fellowship (2015) He has held leadership roles including Director of ICREA (2007-2015) and Director of the Spanish National Genotyping Center (2004-2011).

Serena Tucciمشاهده پروفایل

استادیار

• Human Evolution
• Population Genetics
• Computational Biology
• +۳ مورد دیگر

Serena Tucci is an Assistant Professor of Anthropology and Principal Investigator of the Human Evolutionary Genomics Laboratory at Yale University. Her research focuses on human evolution and population history using ancient and modern human DNA, integrating anthropology, population genetics, and computational biology to study demographic events and genetic adaptation. She holds a Ph.D. in Evolutionary and Environmental Biology from the University of Ferrara, Italy, and has conducted postdoctoral research at the University of Washington and Princeton University. Her work explores mechanisms of evolutionary change, particularly the genetic legacy of archaic humans like Neanderthals and Denisovans in modern populations of Island Southeast Asia and Oceania. She has been supported by the Lewis and Clark Fund and awarded the Young Investigator Fellowship (2013 & 2015). Her research spans computational methods, fieldwork, and lab-based genomic analysis to uncover evolutionary processes shaping human diversity. Awards include the Lewis and Clark Fund for Exploration and the Young Investigator Fellowship. Her lab at Yale University emphasizes interdisciplinary approaches to unraveling genetic and demographic histories, with a focus on Pacific populations and ancient human interactions. She is also committed to advancing scientific communication through digital tools, as seen in her guide to live-streaming conferences.

Martin Kircherمشاهده پروفایل

پژوهشگر

• Genomics
• Computational Biology
• Bioinformatics
• +۷ مورد دیگر

Martin Kircher is a computational molecular biologist currently leading a research group at the Berlin Institute of Health (BIH), Germany. He previously held research positions at the University of Washington and the Max Planck Institute for Evolutionary Anthropology. His work spans genomics, bioinformatics, and functional genomics, with a focus on understanding the impact of genetic variation. Research Interests: His primary research areas include genomics, computational biology, and evolutionary genetics. He has made significant contributions to ancient DNA analysis, functional genomics, and the development of tools for interpreting non-coding variants. His work integrates high-throughput sequencing, machine learning, and molecular assays to decode regulatory elements and variant pathogenicity. Publication Trends: His recent publications reflect a strong focus on functional genomics, variant interpretation (e.g., CADD), and high-throughput methods like MPRA. He frequently publishes in top-tier journals and contributes to large consortia, indicating collaborative and impactful research in genomic medicine and regulatory biology. Scientific Contributions: He has developed widely used tools such as CADD and IBIS and contributed to landmark studies on Neandertal and Denisovan genomes. His protocols for sequencing library preparation are foundational in the field. Advising and Grants: While no formal list of advisees is provided, he leads a research group and has collaborated extensively. He has been involved in major projects such as the University of Washington's Center for Mendelian Genomics, suggesting substantial grant funding and collaborative leadership. Labs and Teams: He established and leads a computational research group at the Berlin Institute of Health. Previously, he was a key member of Jay Shendure’s lab at the University of Washington and part of Svante Pääbo’s team at the Max Planck Institute, contributing to large-scale genomics initiatives.

• Probability Theory
• Stochastic Processes
• Theoretical Population Genetics
• +۷ مورد دیگر

Joseph C. Watkins is a Professor in the Department of Mathematics at the University of Arizona, where he also serves as Director of the Data Sciences Academy. He holds affiliations with the Interdisciplinary Programs in Statistics and Data Science, Applied Mathematics, and Genetics, as well as the BIO5 Institute. His academic training includes a Ph.D. in Mathematics from the University of Wisconsin (1982), and degrees from the University of Tennessee. Department of Mathematics Interdisciplinary Program in Statistics and Data Science Interdisciplinary Program in Applied Mathematics Interdisciplinary Program in Genetics BIO5 Institute His research lies at the intersection of probability theory, stochastic processes, and biological applications. Key areas include theoretical population genetics, statistical genetics, human evolution, and biophysics. He has led collaborative projects on archaic admixture in human populations, coevolution of language and genes in Austronesian societies, and mathematical modeling of biological systems such as bacterial colonies and honey bee swarms. His work integrates deep mathematical theory with empirical genomic data. The 15 most recent publications highlight a sustained focus on human evolutionary genetics, particularly Neanderthal and Denisovan introgression, X chromosome evolution, and population structure in Africa and Oceania. His earlier work includes foundational contributions to stochastic modeling in genetics and biophysics. The research spans theoretical developments in probability and their application to real-world biological questions, demonstrating a consistent trajectory of interdisciplinary innovation. He has advised doctoral students such as Kevin R. Anderson, whose dissertation focused on bacterial colony dynamics. He teaches core graduate courses including Theory of Probability (Math 564) and Theory of Statistics (Math 566), and is actively involved in interdisciplinary training programs supported by the NSF and Flinn Foundation. His service includes roles as Vice Chair of the Graduate Interdisciplinary Program in Statistics and membership on the Executive Committee for the Computational and Mathematical Modeling of Biomedical Systems NIH training grant. He maintains active research collaborations across departments, including Anthropology and Biosciences, and with external institutions like the Carl Hayden Bee Research Center.

Guy Jacobsمشاهده پروفایل

پژوهشگر ارشد

• Evolutionary genetics
• Population genetics
• Human evolutionary biology
• +۱۲ مورد دیگر

Guy Jacobs is a Research Fellow at the University of Cambridge, Department of Archaeology, specializing in evolutionary genetics and microbiome ecology. His work combines large-scale genomic datasets with computational modeling to investigate human evolutionary history, archaic hominin interactions, and microbiome dynamics during subsistence transitions in traditionally hunter-gatherer communities across India and Indonesia. Primary focus on population genetics, human adaptation, and social network impacts Expertise in computational modeling of evolutionary processes Collaborations with C-CLEAR DTP and CREATES partnerships Research spans ancient DNA analysis, archaic introgression patterns, and the interplay between genetic ancestry, environment, and health outcomes. Key projects include studies of Denisovan ancestry in Island Southeast Asia, microbiome differentiation in lifestyle transitions, and sociocultural influences on genetic diversity. Recent publications highlight trends in human adaptation to malaria and high-altitude environments, microbiome evolution during urbanization, and computational methods for detecting selective sweeps. His work integrates genomic data with social and environmental variables to address questions in biological diversity and human phenotypic variation. As part of the Cambridge NERC Doctoral Landscape Awards, he supervises students on topics ranging from archaic introgression analysis to genetic disease mapping. He utilizes datasets from Indonesia, India, and the Indonesian archipelago, combining genetic, transcriptomic, and mobility data in his research.

Ben Evansمشاهده پروفایل

استاد

• Genome Evolution
• Speciation
• Biodiversity
• +۶ مورد دیگر

Ben Evans is a Professor in the Department of Biology at McMaster University, where he leads the Evans Lab focused on genome evolution, speciation, and biodiversity. His research employs a multidisciplinary approach combining field collection, gene editing, and bioinformatics to investigate evolutionary processes in natural habitats. Dr. Evans received his PhD in Biological Sciences from Columbia University (1993-1998) and his BS (Magna cum Laude) in Biology, Environmental Sciences from Tufts University (1988-1992). His educational background provided a strong foundation for his subsequent research career in evolutionary biology and genomics. Dr. Evans' research primarily focuses on the evolution of sex chromosomes and sex determination mechanisms, the role of social systems in genome evolution, and genome duplication phenomena. His lab has made significant contributions to understanding polyploidy in amphibians, particularly using African clawed frogs (Xenopus) as a model system. More recently, his work has expanded to include studies of macaque monkeys (Macaca), examining how behavior influences genome evolution. The Evans Lab employs cutting-edge techniques including CRISPR/Cas9 gene editing to investigate the genetic basis of evolutionary adaptations. Analysis of Dr. Evans' recent publications reveals a strong focus on sex chromosome evolution across multiple species, with particular attention to the dynamic nature of these genomic regions in amphibians. His work bridges molecular genetics, evolutionary biology, and comparative genomics, often revealing unexpected complexity in systems previously thought to be well-understood. A notable trend in his recent work is the application of advanced genomic techniques to address fundamental questions in evolutionary biology. Dr. Evans has been actively involved in teaching at McMaster University, instructing courses such as Genetics (BIOLOGY 2C03/MOLBIOL 2C03), Biodiversity, Evolution and Humanity (BIOLOGY 1M03), Molecular Evolution (MOLBIOL 6DD3/4DD3), and Introduction to Bioinformatic Methods (BIOLOGY 722). His teaching reflects his research expertise, providing students with both theoretical knowledge and practical skills in modern genomic analysis. The Evans Lab maintains an active research program with numerous collaborations, as evidenced by the extensive co-author network and significant media coverage of their findings. Their research has been featured in multiple news outlets and has received attention from scientific communities worldwide, as indicated by the high number of readers on platforms like Mendeley.

Diyendo Massilaniمشاهده پروفایل

استادیار

• Evolutionary Genetics
• Ancient DNA Analysis
• Human Population History
• +۳ مورد دیگر

Diyendo Massilani is an Assistant Professor of Genetics at Yale School of Medicine, leading the Massilani Lab. His research focuses on using ancient DNA to study human evolutionary history, adaptation, and population dynamics. He holds affiliations with the Yale Center for Genomic Health and the Janeway Society. Education: PhD in Genomics (Paris Cité University, 2016), postdoctoral training at the Max Planck Institute for Evolutionary Anthropology (2017–2022). Specialized in ancient DNA recovery from bones and sediments, particularly in under-sampled regions like East Eurasia and Africa. Research Interests: Peopling of the world, human adaptation to environments, genetic basis of traits (e.g., altitude adaptation, dietary changes), and methods for ancient DNA extraction from sediments. His lab develops genomic approaches to analyze DNA preserved in archaeological materials, even in the absence of skeletal remains. Publications: Over 20 peer-reviewed articles, including landmark studies on Denisovan ancestry in East Asians, the earliest human burial in Africa, and sediment DNA methodologies. Recent work explores genetic adaptations linked to amylase locus duplications. Awards: NIH Director’s New Innovator Award (2024–2029), Kingsley Fellow (2024). Labs/Teams: Leads the Massilani Lab at Yale, collaborating with institutions globally. Current team includes postdocs, graduate students, and interns focusing on ancient DNA analysis and population genetics.

• Population Genetics
• Evolutionary Genetics
• Human Evolution
• +۳ مورد دیگر

Leonardo Iasi is a researcher in the Department of Evolutionary Genetics at the Max Planck Institute for Evolutionary Anthropology, affiliated with the University of Leipzig. His work focuses on developing computational methods to study human evolution, particularly detecting and dating admixture events between modern humans and archaic hominins. He completed a PhD in Evolutionary Genetics under Svante Pääbo and Benjamin Peter, investigating admixture signatures in ancient and modern genomes. His research integrates population genetics, evolutionary theory, and bioinformatics to address questions about genetic diversity and adaptive traits. Education: PhD in Evolutionary Genetics (2019–2025), University of Leipzig MSc in Biological Sciences (2016–2018), University of Cologne MSc in Toxicology (2015–2016), Heinrich-Heine-University, Düsseldorf BSc in Biology (2012–2015), University of Cologne Research Interests: Combining computational methods with genetic data to trace admixture events, analyze archaic hominin ancestry (Neandertals/Denisovans), and model demographic histories. His work emphasizes improving methodologies for dating admixture and interpreting genetic signals in modern and ancient populations. Publications: His work spans high-impact journals like Nature and Science , focusing on ancient genomic analysis, admixture dynamics, and computational tools for evolutionary genetics. Recent contributions include studies on the earliest modern human genomes and Neanderthal social organization inferred from genetic data. Teaching & Research Experience: Taught undergraduate courses in Genetics and Cellular Biology at the University of Cologne. Conducted research projects on aging biology (Max Planck Institute for Biology of Aging) and population genetics (Cologne Center for Genomics). Labs/Teams: Collaborates within the Department of Evolutionary Genetics and with international teams led by Svante Pääbo, Benjamin Peter, and Dario Valenzano.

• Population genomics
• Human genome
• Coalescence theory
• +۵ مورد دیگر

Vladimir Lvovich Shchur is a Professor at the Department of Big Data and Information Retrieval within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE). He serves as Head of the International Laboratory of Statistical and Computational Genomics at the Institute of Artificial Intelligence and Digital Sciences and is a Member of HSE's Academic Council. Dr. Shchur began working at HSE in 2018 and has accumulated 12 years of scientific and teaching experience across multiple prestigious institutions. Dr. Shchur's educational background includes: Doctor of Physical and Mathematical Sciences from HSE (2023) PhD from University of Paris XI (2013) Specialist degree in Mathematics from Lomonosov Moscow State University (2009) His research focuses on population genomics, human genome analysis, coalescence theory, hidden Markov models, and mathematical approaches to genomic data. Dr. Shchur has developed sophisticated computational methods that bridge theoretical mathematics with practical applications in genomics and population genetics. His work demonstrates a consistent progression from pure mathematical theory to applied computational biology, with recent emphasis on machine learning integration. Analysis of his recent publications reveals a strong focus on developing advanced computational tools for genomic analysis, with applications ranging from human population history reconstruction to viral epidemiology. His work shows increasing sophistication in integrating machine learning techniques with traditional population genetic methods, particularly evident in his deep learning approaches for inferring coalescence times and ancestry. His laboratory has made significant contributions to understanding SARS-CoV-2 transmission dynamics in Russia through genomic epidemiology. Dr. Shchur has received numerous scientific recognitions: Letter of thanks from HSE Vice-Rector (December 2024) Allowance for doctoral dissertation defense (2023-2026) Multiple publication bonuses for high-impact journals (2019-2026) Membership in HSE Personnel Reserve (High Professional Potential Group) Dr. Shchur actively supervises student research, with Khomutov E. V. currently working on machine learning applications in population genomics. He has secured multiple competitive research grants, including RSF projects on genetic mixing modeling and deep learning applications for population structure analysis. As laboratory head, he leads a multidisciplinary team developing tools like VGsim (viral genealogy simulator) and AncestryGNN (neural network for population prediction), with growing industry partnerships in agriculture and healthcare.