جست‌وجوی استاد و پژوهشگر

Arti Singhمشاهده پروفایل

استادیار

• Plant breeding
• Soybean diseases
• Genomics
• +۳ مورد دیگر

Arti Singh is an Assistant Professor in the Department of Agronomy at Iowa State University. Her research focuses on plant breeding, soybean diseases, genomics, and phenomics, with a strong emphasis on integrating artificial intelligence and high-throughput technologies into agricultural systems. She leads projects involving AI-driven disease identification, precision agriculture, and crop improvement strategies. Her expertise includes developing machine learning models for real-time weed and insect classification (e.g., WeedNet and InsectNet), deploying drones and ground robots for crop phenotyping, and leveraging genomic data to map traits like flowering time and disease resistance in legumes. Singh collaborates on initiatives like the AIIRA Institute for Resilient Agriculture and the BioTrove biodiversity dataset. Singh’s work spans plant stress phenotyping, digital twin technologies for plant sciences, and multi-sensor phenotyping for early disease detection. Her research bridges computational methods with traditional agronomy, aiming to enhance crop resilience and sustainability in the face of environmental challenges. Her recent projects include optimizing robotic navigation for precision agriculture, improving soybean yield estimation via video analysis, and dissecting genetic architectures of traits in mungbean and soybean using GWAS and genomic tools. She actively contributes to conferences and publishes in high-impact journals, advancing both foundational and applied aspects of agricultural science.

Martin T. Wellsمشاهده پروفایل

استاد

• Applied Statistics
• Theoretical Statistics
• Bayesian Statistics
• +۷ مورد دیگر

Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.

• Evolutionary Biology
• Marine Biology
• Taxonomy
• +۵ مورد دیگر

Mikael Thollesson is a Senior Lecturer at Uppsala University, affiliated with the Department of Organismal Biology; Systematic Biology and Klubban’s Biological Station. His research focuses on evolutionary biology, phylogenetics, taxonomy, and molecular biology, particularly in marine and freshwater sponges (Porifera), bacterial pathogens, and computational methods in evolutionary analysis. Evolutionary Biology Marine Biology Taxonomy His recent publications highlight trends in sponge biodiversity, phylogeography, bacterial horizontal gene transfer, and mitochondrial gene evolution. Key articles include studies on Swedish demosponge faunas, Silene sect. Arenosae systematics, and computational tools like SPRIT for detecting gene transfers. No explicit awards or grants are mentioned.

Jörg Bohlmannمشاهده پروفایل

استاد

• Plant Defense Mechanisms
• Terpenoid Biochemistry
• Forestry Genomics
• +۱۲ مورد دیگر

Jörg Bohlmann is a Professor in the Department of Forest and Conservation Sciences at the University of British Columbia , affiliated with the Michael Smith Laboratories, Botany Department (Faculty of Science), and Wine Research Centre. His research spans genomics, biochemistry, and chemical ecology of plant specialized metabolism, focusing on terpenoids and phenolics for applications in forest health and bioproducts. Research Streams : Plant defense against insects/pathogens, metabolic engineering of bioproducts, forest genomics, and molecular evolution of terpenoid pathways. Collaborations : National and international partnerships with academia, government, and biotech industries. Recent Publications highlight genomic insights into Western Redcedar (2023), including its low genetic diversity and adaptation mechanisms despite self-fertilization. Earlier work (2001–2022) covers conifer defense systems, Arabidopsis terpenoid pathways, cannabis flavor genetics, and mass spectrometry infrastructure. Scientific Awards include: NSERC E.W.R. Steacie Fellow (2015), Fellow of the Royal Society of Canada (2015), Feodor Lynen Postdoctoral Fellowship (1995–1998), and Distinguished University Scholar (UBC). Funding Sources : NSERC (Discovery/Strategic grants), CFI, Genome Canada/BC, provincial/federal agencies. Labs & Facilities : Michael Smith Laboratories (UBC), National Centre of Excellence, Forest Sciences Centre, and a dedicated mass spectrometry lab for metabolite analysis.

Wengong Jinمشاهده پروفایل

استادیار

• AI for drug discovery
• Generative AI models
• Geometric deep learning
• +۵ مورد دیگر

Wengong Jin is an Assistant Professor at the Khoury College of Computer Sciences, Northeastern University, and a visiting research scientist at the Eric and Wendy Schmidt Center at the Broad Institute. He holds a PhD from MIT CSAIL, advised by Prof. Regina Barzilay and Prof. Tommi Jaakkola. Research Interests: His work focuses on geometric and generative AI models for drug discovery, biology, and chemical engineering. Key areas include equivariant neural networks (e.g., FAFormer), diffusion models for binding energy prediction, antibody/enzyme design (RefineGNN, SurfPro), and molecular design through graph neural networks (Junction Tree VAE). He also explores domain generalization and systems for autonomous molecular discovery. Publications: His research has been published in top venues like NeurIPS, ICLR, ICML, Nature, Science, and Cell. Recent breakthroughs include discovering novel antibiotics using explainable AI and designing synergistic drug combinations for cancer treatment. Awards: He has received the BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award for his contributions to computational biology and AI-driven drug discovery. Teaching: Currently teaches a PhD seminar on AI for Science, focusing on integrating machine learning into scientific discovery processes.

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.

Michael Baymمشاهده پروفایل

دانشیار

• Microbial Evolutionary Genomics
• Antibiotic Resistance
• Computational Biology
• +۷ مورد دیگر

Dr. Michael Baym is an Associate Professor of Biomedical Informatics at Harvard Medical School with affiliate appointments in Microbiology and the Laboratory of Systems Pharmacology, and as an Associate Member of the Broad Institute. He leads the Baym Lab, which studies microbial evolutionary genomics and antibiotic resistance through a hybrid of experimental, computational, and theoretical approaches. His research focuses on: Antibiotic Resistance Evolution and practical interventions Mobile Genetic Elements (plasmids, phages, transposons) Computational Genomic Algorithms for big data analysis Synthetic Biology tools and technologies Key recent publications explore phage discovery systems , phylogenetic compression of microbial genomes, and RNA-guided gene drives in plasmids. His work is supported by multiple NIH/NIGMS and NSF grants including a MIRA award. Scientific honors include: Packard Fellowship (2018) Pew Biomedical Scholarship (2020) Sloan Research Fellowship (2020) A. Clifford Barger Excellence in Mentoring Award (2021) SSQBio Mentorship Award (2022) The lab actively trains PhD students and postdoctoral fellows with alumni occupying academic and industry positions globally. Current team members include researchers from interdisciplinary backgrounds working at the intersection of experiment, computation, and theory .

Pekka Marttinenمشاهده پروفایل

دانشیار

• Machine Learning
• Large Language Models
• Reinforcement Learning
• +۸ مورد دیگر

Pekka Marttinen is a tenured Associate Professor of Machine Learning at Aalto University, Department of Computer Science, and leads the Machine Learning for Health (Aalto-ML4H) group within the Helsinki Institute for Information Technology HIIT. Education: M.Sc. in Applied Mathematics, University of Helsinki (2004) Ph.D. in Statistics, University of Helsinki (2008) Title of Docent in Information and Computer Science, Aalto University (2015) Research Focus: His methodological work spans large language models, reinforcement learning, deep learning, probabilistic machine learning, and causal inference . These techniques are applied to critical domains of healthcare, bioinformatics, statistical genetics, epidemiology, and personalized medicine . The group develops novel algorithms, theoretical guarantees, and open-source software that enable data-driven discovery and decision-making in medicine and biology. Publication Trends: Across 2022-2024 the lab has concentrated on (i) rigorous causal reasoning over temporal clinical data, (ii) principled uncertainty quantification in LLMs, (iii) representation learning for neural network comparison, and (iv) translational projects that turn raw EHRs into actionable clinical insights. Earlier work integrated high-dimensional genomics with metabolomics and mapped evolutionary forces in bacterial pathogens. Scientific Awards & Recognition: While no specific awards are listed, his sustained publication record in top-tier venues (NeurIPS, ICML, AISTATS, Nature Genetics, PLOS CB) and his role as responsible professor of the Machine-Learning, Data-Science and AI major signify significant peer recognition. Advising & Grants: Prof. Marttinen currently mentors 8 PhD students as primary supervisor and an additional 7 PhD students as co-supervisor. He has already graduated 8 PhDs since 2014. The group is supported through competitive funding including the Finnish Center for Artificial Intelligence (FCAI) doctoral program. Labs & Teams: He directs the Machine Learning for Health (Aalto-ML4H) research group, comprising postdocs Hans Moen, Ti John, Alexander Nikitin, Negar Safinianaini, Linli Zhang, Zhiyuan Li, and the above-mentioned PhD cohort.

Max Lauمشاهده پروفایل

استادیار

• Infectious Disease Dynamics
• Machine Learning
• Artificial Intelligence

Max Lau is an Assistant Professor in the Department of Biostatistics and Bioinformatics and the Department of Epidemiology at Emory University. His research focuses on integrating machine learning and computational methods with epidemiological and genomic data to study infectious disease dynamics. He teaches courses such as BIOS 790R (Advanced Seminar in Biostatistics) and DATA 534 (Applied Machine Learning). Dr. Lau's work emphasizes scalable Bayesian inference, graph neural networks, and stochastic modeling to address challenges in disease transmission, outbreak control, and pathogen evolution. His recent research includes developing tools like ScITree and Epilearn, and he has contributed to understanding measles dynamics, tuberculosis treatment, and livestock disease management. His academic contributions span over 30 publications since 2010, with a particular focus on phylodynamics, epidemic modeling, and vaccine strategy evaluation. His interdisciplinary approach bridges computational methods with public health applications, aiming to enhance disease prediction and intervention efficacy.

• Quantum Algorithms
• Quantum Computing
• Bioinformatics
• +۴ مورد دیگر

Sergii Strelchuk is an Associate Professor of Computer Science at the University of Oxford, specializing in quantum computing and its applications. His research sits at the intersection of quantum information theory, computer science, and bioinformatics, with a focus on developing quantum algorithms for practical problems in genomics and beyond. Professor Strelchuk's primary research interests include quantum algorithms and their applications (particularly in bioinformatics), classical simulation methods for quantum computation, quantum complexity theory, and quantum learning theory. His work bridges theoretical quantum computing with practical applications, especially in the emerging field of quantum genomics and pangenomics, with significant implications for understanding human and pathogen genomes. His recent publications demonstrate a strong focus on applying quantum computing techniques to genomic data analysis, developing efficient fermion-qubit mappings for quantum simulation, and exploring fundamental aspects of quantum complexity theory. His research shows a clear trajectory toward making quantum computing practically applicable to biological data analysis and advancing our theoretical understanding of quantum computational models. Among his notable scientific achievements are: Royal Society University Research Fellow Leverhulme Early Career Fellow John and Delia Agar Research Fellow Professor Strelchuk leads several significant research projects including the Wellcome Leap "Human and Pathogen Quantum Pangenomics" project (2023-2026), which recently entered Phase 3 in April 2025, the EPSRC "Structure and symmetry in quantum verification" grant (2023-2025), and the "Quantum Algorithms for Quantum Field Theory" project (2022-2025). His research has attracted substantial funding for quantum computing applications in genomics. His work has received significant attention in both academic and popular science media, including coverage in Quanta Magazine and collaborations with institutions like the Sanger Institute to tackle complex genomic challenges using quantum computing approaches, with recent publicity about his leadership in the final phase of the Wellcome Leap-funded quantum pangenomics project.

Louis Du Plessisمشاهده پروفایل

مدرس

• Computational Epidemiology
• Phylodynamics
• Viral Evolution
• +۳ مورد دیگر

Louis Du Plessis is a Lecturer at ETH Zürich's Department of Biosystems Science and Engineering in Basel, Switzerland. His research focuses on computational evolution with particular emphasis on infectious disease dynamics and genomic analysis. He maintains an active research profile with numerous high-impact publications in top-tier journals. Dr. Du Plessis completed his doctoral studies at ETH Zürich in 2016 with a thesis titled 'Understanding the spread and adaptation of infectious diseases using genomic sequencing data,' building upon his 2011 Master's work on evolutionary rate variation. His current research sits at the intersection of computational biology, epidemiology, and evolutionary genetics. His research interests span computational epidemiology, phylodynamics, viral evolution, and infectious disease modeling. He has made significant contributions to understanding pandemic dynamics, particularly regarding influenza and SARS-CoV-2, using genomic and epidemiological data integration. His methodological work includes developing computational approaches for estimating epidemic dynamics and viral transmission patterns. Analysis of his recent publications reveals a strong focus on how pandemics disrupt normal viral circulation patterns, with particular attention to influenza evolution during the 2009 H1N1 and COVID-19 pandemics. His work often combines phylogenetic analysis with epidemiological modeling to extract maximum information from genomic and case count data. Dr. Du Plessis has received research funding from European Commission projects including 'From Foundations of Phylodynamics to new applications in Cell Biology' (grant 101001077) and 'MOnitoring Outbreak events for Disease surveillance in a data science context' (grant 874850). He is actively involved in developing computational tools for analyzing pathogen genomic data and has contributed to several software packages used in the field. His work has significant implications for public health surveillance and pandemic preparedness.

Nathan G. Swensonمشاهده پروفایل

استاد

• Ecology and Environmental Biology
• Evolutionary Biology
• Genetics and Genomics
• +۱۰ مورد دیگر

Nathan G. Swenson is a Professor in the Department of Biological Sciences at the University of Notre Dame and serves as the Gillen Director of the Environmental Research Center (UNDERC). He has held prior academic positions at the University of Maryland and Michigan State University, advancing from Assistant to full Professor. His research integrates genomics, ecology, and evolutionary biology to understand forest biodiversity and dynamics. Research Interests: Ecology and Environmental Biology Evolutionary Biology Genetics and Genomics Global Change Biology Forest Ecology Functional and Phylogenetic Ecology Tree Physiology and Demography His research focuses on leveraging intra- and interspecific variation in tree performance to predict forest biodiversity patterns. He employs integrative approaches from genomes to forest canopies and utilizes large-scale global datasets. Recent publications emphasize intraspecific trait variation, transcriptomic responses to drought, ecological forecasting, and functional group dynamics in tropical and temperate forests. Scientific Awards: Winner of 2017 British Ecological Society John Harper Prize (awarded to J. Zambrano; Swenson was senior author) Advising and Grants: Dr. Swenson has mentored numerous researchers, including M.N. Umana, S.J. Worthy, and J. Yang, who frequently co-author high-impact papers. His lab receives substantial research funding, evident from large collaborative projects and participation in global networks like ForestGEO and the TRY plant trait database. He has secured support for long-term ecological research, genomic studies, and international fieldwork. Labs and Teams: He leads the Swenson Lab, which conducts research on woody plant ecology in dynamic environments. The lab emphasizes community transcriptomics, functional trait analysis, and large-scale ecological modeling. It collaborates widely across institutions and is involved in major initiatives such as ForestGEO and NEON.

Lailiang Chengمشاهده پروفایل

استاد

• Plant Physiology
• Fruit Crop Nutrition
• Sugar Metabolism
• +۵ مورد دیگر

Lailiang Cheng is a Professor in the Horticulture Section of Cornell University's School of Integrative Plant Science, specializing in the physiological processes of deciduous fruit crops (particularly apple). His research focuses on sugar/malate metabolism, mineral nutrition, and environmental stress responses to improve orchard productivity and fruit quality. He integrates physiological, biochemical, and molecular approaches to study sorbitol signaling and nutrient management in high-density orchards. Education: Doctorate, Oregon State University (1999) Master of Science, Shandong Agricultural University (1989) Bachelor of Science, Shandong Agricultural University (1984) Research Focus: Cheng's work investigates carbon partitioning, malate accumulation, and nutrient transport dynamics in apple trees under abiotic stresses. His extension program delivers nutrient management strategies to the apple industry, while his teaching emphasizes analytical techniques (PLSCI 6170) and crop nutrition principles (PLSCI 4551/6551). Publication Analysis: Recent articles (2021-2025) reveal dominant themes in molecular regulation of fruit acidity, transporter biology, stress-responsive transcription factors, and omics-assisted breeding. Over 70% of publications involve collaborative work on apple genomics, with consistent emphasis on translating basic research into orchard management solutions. Awards & Recognition: No scientific awards mentioned in source materials. Academic Contributions: Advises graduate students in Horticulture; research supported by Cornell University Agricultural Experiment Station. Collaborates extensively with pomology breeders, extension specialists, and molecular biologists globally.

Jochen Wolfمشاهده پروفایل

استاد

• Evolutionary Biology
• Speciation Genomics
• Genome Evolution
• +۷ مورد دیگر

Jochen Wolf is Chair of the Evolutionary Biology Division at Ludwig-Maximilians-Universität München (LMU) and a Max Planck Fellow of the Max Planck Institute for Biological Intelligence since 2022. His research integrates evolutionary biology, genomics, and ecology to address fundamental questions about speciation, adaptation, and biodiversity across multiple biological systems. Dr. Wolf's research program applies an integrative approach to understand microevolutionary processes and genetic mechanisms underlying species divergence. His work combines large-scale genomic analyses with laboratory and field experiments to characterize genomic divergence across populations and species. Key empirical systems include natural populations of birds (particularly corvids, swallows, and cuckoos), marine mammals (pinnipeds and killer whales), plant communities, and experimental evolution in fission yeast. His research spans multiple scales from immediate microevolutionary processes to broader evolutionary patterns across time. His recent publications reveal a sophisticated integration of genomic, epigenetic, and ecological perspectives. A notable trend shows increasing focus on structural genomic variation, chromosomal rearrangements, and epigenetic mechanisms as drivers of evolutionary processes. His work demonstrates how these molecular mechanisms interact with ecological factors to shape patterns of biodiversity and adaptation. Dr. Wolf's research has gained significant recognition through publications in top-tier journals including Nature, Science, and Nature Ecology & Evolution. His groundbreaking studies on crow hybrid zones, killer whale ecotypes, and experimental evolution of speciation have been featured in prominent media outlets such as The New Yorker, The Guardian, Scientific American, and Der Spiegel, demonstrating the broad impact of his work. As Principal Investigator, Dr. Wolf actively mentors doctoral students and postdoctoral researchers, fostering the next generation of evolutionary biologists. His lab maintains strong international collaborations, particularly through affiliations with SciLifeLab in Uppsala. Research in his group is supported by multiple funding sources including German Research Foundation grants and European Union programs, enabling both fundamental research and applications to conservation biology. The Wolf lab operates within LMU's Division of Evolutionary Biology, which provides access to state-of-the-art facilities including the Leibniz Supercomputing Centre. The lab maintains strong connections with the Max Planck Institute for Biological Intelligence and SciLifeLab in Uppsala, creating a rich collaborative environment for interdisciplinary research in evolutionary genomics. This network enables comprehensive studies spanning from molecular mechanisms to ecological and evolutionary consequences across diverse biological systems.

Sudhir Kumarمشاهده پروفایل

استاد

• Molecular Evolution
• Phylomedicine
• Functional Genomics
• +۵ مورد دیگر

Sudhir Kumar is a Professor and Principal Investigator at Temple University, leading a research laboratory focused on molecular evolution, phylomedicine, and functional genomics. His lab develops mathematical methods, computational algorithms, and software packages for analyzing genomic variation across populations, pathogens, tumors, and species. Key contributions include the widely used MEGA software (www.megasoftware.net) for molecular evolutionary analysis and the TimeTree knowledge-base (www.timetree.org) that synthesizes evolutionary knowledge on species divergence times. Dr. Kumar's research interests center on integrating mathematical and computational techniques into evolutionary biology and biomedicine. His lab pursues a holistic paradigm where evolutionary and genomic patterns are discovered through comparative analysis of big datasets, then used to reveal underlying biological processes and develop predictive models. His work spans phylomedicine of genetic diseases, molecular phylogenomics, and the timetree of life, with recent innovations including Bayesian methods, machine learning algorithms, and statistical approaches for inferring molecular phylogenies, divergence times, and pathogenic mutations. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence and machine learning to evolutionary genetics, with multiple 2025 papers focused on sparse learning techniques, transformer-based models, and AI-assisted analytical protocols. His work increasingly bridges evolutionary biology with cancer genomics and precision medicine applications. His scientific achievements have been recognized with the prestigious 2025 George W. Beadle Award from the Genetics Society of America, which honors his "efforts to democratize evolutionary genetics." Dr. Kumar has mentored numerous doctoral candidates, postdoctoral researchers, and graduate students, many of whom have gone on to faculty positions at institutions including Oakland University and universities in Brazil. His lab includes current doctoral candidates working in bioinformatics and statistical molecular evolution, supported by technical staff including programmers, genome tech specialists, and informatics specialists. The Kumar Laboratory operates as an interdisciplinary research hub with multiple projects including MEGA (Molecular Evolutionary Genetics Analysis), TimeTree, myPEG (web-based evolutionary tools), and FlyExpress (a knowledge base for Drosophila melanogaster embryo images). The lab emphasizes green computing efforts aimed at democratizing scientific practice and making big data analytics more accessible.