جست‌وجوی استاد و پژوهشگر

• 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.

Waldemar Kolanusمشاهده پروفایل

استاد

• Molecular Immunology
• Cell Signaling
• Signal Transduction Pathways
• +۴ مورد دیگر

Prof. Waldemar Kolanus leads the Molecular Immunology and Cell Biology department at the University of Bonn's Life & Medical Sciences Institute (LIMES) . His research bridges immunoregulation , stem cell dynamics , and metabolic stress responses in immune cells. Unit 2 member at LIMES Principal investigator in SFB 704 and ImmunoSensation Cluster Leads a multidisciplinary lab with postdocs, PhD students, and technical staff His work focuses on intracellular signaling pathways connecting immune activation to tissue homeostasis, particularly through: Cytohesin proteins in integrin-mediated adhesion and migration TRIM71 in stem cell regulation and congenital hydrocephalus High-salt environments affecting macrophage function Publication trends show expertise in immune cell migration , genetic models , and chemical inhibition , with frequent use of mice and zebrafish for in vivo studies. Key articles explore: TRIM71's dual role in auditory development and germ cell maintenance Cytohesin family's Golgi regulation and insulin signaling Ruxolitinib's off-target migration inhibition of dendritic cells Contact details: Address: LIMES Institute, Carl-Troll-Straße 31, Bonn Email: kolanus.sekretariat@uni-bonn.de Phone: +49 228 73-62788

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 .

• Secure Decentralized Systems
• Blockchain Technology
• Systems Security
• +۳ مورد دیگر

Prof. Bryan Ford leads the Decentralized/Distributed Systems (DEDIS) lab at EPFL. He focuses on secure decentralized systems, including blockchain technology, privacy, and systems security. He earned his Ph.D. from MIT and held faculty positions at Yale University and EPFL. His work spans distributed consensus protocols, peer-to-peer networking, and privacy-preserving systems. Key projects include QuePaxa (timeout-free consensus), UIA (global connectivity for mobile devices), and MedCo (secure healthcare data sharing). He advises numerous PhD students and contributes to open-source projects like Bitcoin collective signing and privacy networks like Riffle. Education: Ph.D., MIT; Postdoctoral work at Yale Research interests include blockchain scalability, consensus algorithms, and cryptographic privacy. His lab develops systems like TRIP for coercion-resistant voting and F3B to mitigate blockchain front-running. His work on NAT traversal and peer-to-peer protocols (e.g., STUN/ICE) remains foundational in network architecture. He emphasizes practical, auditable security solutions such as CertiKOS and atomic cross-chain transactions (Atom). Notable contributions: CoSi (collective signing), OmniLedger (sharded blockchain), and privacy-preserving protocols like PURBs (Protected Unsealable Recursive Boxes). His lab collaborates with Swiss Post to audit e-voting systems and designs democratic cryptocurrencies like PoPCoin.

Martin Burkeمشاهده پروفایل

استاد

• Organic Chemistry
• Small-Molecule Synthesis
• Molecular Prosthetics
• +۷ مورد دیگر

Martin Burke is the May and Ving Lee Professor for Chemical Innovation and Professor of Chemistry at the University of Illinois Urbana-Champaign , with additional appointments in Biochemistry, Biomedical & Translational Sciences, and multiple campus institutes including the Beckman Institute and the Carl R. Woese Institute for Genomic Biology. Education B.S. Johns Hopkins University , 1998 Ph.D. Harvard University , 2003 M.D. Harvard Medical School , 2003 Research Interests Burke’s program centers on molecular prosthetics : the design, synthesis and application of small molecules that replicate or replace missing or dysfunctional proteins. His group pioneered iterative cross-coupling (ICC) using MIDA-protected haloboronic acids to automate the construction of complex natural products and function-oriented small molecules. Current projects target ion-channel replacement in cystic fibrosis, iron-transport restoration in anemia, and non-toxic antifungals that overcome drug resistance. Scientific Awards & Honors National Academy of Medicine (2021) AAAS Fellow (2021) ASCI Member (2021) iCON Award (2019) Mukaiyama Award, Japan (2019) ACS Nobel Laureate Award for Graduate Education (2017) Thieme-IUPAC Prize in Synthetic Organic Chemistry (2014) Elias J. Corey Award (2013) Arthur C. Cope Scholar Award (2011) Research Output & Impact Burke has authored >120 peer-reviewed articles, >30 patents, and his work has been cited >20,000 times. High-impact publications in Nature , Science , and Angewandte Chemie have advanced automated synthesis, molecular prosthetics, and cystic fibrosis therapeutics. Laboratory & Training The Burke Laboratories house a multidisciplinary team of graduate students, post-doctoral researchers, and physician-scientists developing next-generation molecular prosthetics. The group is supported by NIH, NSF, private foundations, and industry partnerships aimed at democratizing molecular innovation.

Christopher M. Wolvertonمشاهده پروفایل

استاد

• Computational Materials Science
• Energy Storage Materials
• First-Principles Quantum Mechanics
• +۷ مورد دیگر

Dr. Christopher M. Wolverton is a Professor of Materials Science and Engineering at Northwestern University , where he leads the Wolverton Research Group . His work focuses on computational materials science with applications in energy sustainability , particularly in batteries , hydrogen storage , and thermoelectrics . PhD in Physics from University of California, Berkeley BS in Physics (summa cum laude) from University of Texas, Austin His research leverages first-principles quantum mechanical simulations and machine learning to enable virtual materials synthesis before laboratory testing. The group specializes in hybrid computational methods integrating Density Functional Theory (DFT) , Monte Carlo simulations , and phase-field microstructural models . The article portfolio shows leadership in energy storage materials , with recent work on data-driven nanoparticle facet control , mixed-anion semiconductors , and machine learning-accelerated discovery . Publications span top journals including Nature Energy , Nature Materials , and Science . 2006 Ford Motor Company Technical Achievement Award 2005 Ford Patent & Publication Awards 2003 Ford Environmental/Physical Sciences Recognition As advisor to PhD candidates Zhenpeng Yao , Shiqiang Hao , and Shane Patel , he fosters interdisciplinary research connecting materials informatics with experimental validation . The group maintains active collaborations with Argonne National Lab and MIT/Harvard teams.

• Machine Learning
• Artificial Intelligence
• Large Language Models
• +۷ مورد دیگر

Pengtao Xie is an Associate Professor (with tenure as of June 2025) in the Department of Electrical and Computer Engineering at the University of California San Diego. He also serves as Associate Adjunct Professor in the Division of Biomedical Informatics, Department of Medicine, and holds affiliate appointments with the Halıcıoğlu Data Science Institute, School of Biological Sciences, Shu Chien-Gene Lay Department of Bioengineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, and multiple research institutes including the AI Group, Center for Machine-Intelligence, Computing and Security, Institute of Engineering in Medicine, and Institute for Genomic Medicine. Education: PhD in Machine Learning, School of Computer Science, Carnegie Mellon University Research Interests: His research focuses on machine learning inspired by human learning skills, such as self-explanation, small-group learning, and learning by teaching. He applies these techniques to large language models, foundation models, healthcare, and biomedicine. His work spans generative AI, medical imaging, protein modeling, and drug discovery. Recent Research Trends: His 2024–2025 publications emphasize generative AI for ultra-low-data medical image segmentation, multimodal large language models for biomedical applications, protein function prediction, and novel training strategies like task-adaptive pretraining and bi-level optimization for model adaptation. Scientific Awards: NIH MIRA Award (2025) NSF CAREER Award (2024) Best Graduate Teacher Award – UCSD ECE (2023) ICLR Notable-Top-5% Paper (2023) Global Top-100 Chinese Young Scholars in AI (2022) UCSD Faculty Career Development Award (2022) Tencent Faculty Award (2021) Outstanding Reviewer – ICLR (2021) AMIA Doctoral Dissertation Award Finalist (2020) Amazon AWS Research Award (2020) Tencent AI-Lab Faculty Award (2020) Innovator Award – Pittsburgh Business Times (2018) Siebel Scholarship (2014) Advising and Grants: He currently advises PhD students, postdocs, and master’s students. He has received major grants including the NIH MIRA and NSF CAREER awards, and actively mentors Schmidt AI in Science postdocs and graduate students. Teaching and Labs: He teaches ECE285 Deep Generative Models and ECE175B Probabilistic Reasoning and Graphical Models . His lab focuses on foundational and translational AI research with applications in biomedicine and healthcare.

• Nuclear Magnetic Resonance (NMR)
• In vivo imaging
• MRI pulse sequence development
• +۷ مورد دیگر

Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.

• Time Series Analysis
• Big Data Analytics
• Predictive Modeling
• +۳ مورد دیگر

Yasushi Sakurai is a Professor in the Department of Translational Datability at Osaka University's Institute of Scientific and Industrial Research, co-leading the Sakurai and Matsubara Laboratory within the Center for Industrial Science and AI. His research mission focuses on transforming society through real-time prediction of natural and social phenomena using large-scale data analytics, with emphasis on practical technological implementation. His research spans time-series big data analysis, dynamic learning systems, and real-time information provision. Key areas include tensor stream mining, EEG-based healthcare applications, cybersecurity anomaly detection, and multi-omics cancer subtyping. The lab specializes in developing deployable technologies that optimize social activities through predictive modeling of evolving data streams. Recent publications (2023-2025) reveal concentrated innovation in time-series data stream processing, with dominant themes in tensor analytics, frequency-domain forecasting, and causal modeling. His team produces high-impact work accepted at premier AI venues (ICLR, AAAI, KDD, WWW), consistently featuring oral presentations that highlight technical novelty and societal relevance. Scientific Awards: FY2024 Minister of Education, Culture, Sports, Science and Technology Award for Science and Technology (Research Category) for dynamic learning and real-time data stream analysis Professor Sakurai mentors graduate students including Naoki Chihara (DEIM2024 Outstanding Paper Award winner), Yuka Tamura (DEIM2024 Student Presentation Award winner), and Ren Fujiwara. His lab maintains active industry-academia partnerships focused on practical technology deployment, with research directly addressing real-world challenges in healthcare monitoring and cybersecurity. The Sakurai and Matsubara Laboratory operates as a dynamic research unit within Osaka University's Center for Industrial Science and AI, structured around specialized teams for tensor stream analysis, medical data mining, and network dynamics. Current projects emphasize real-time prediction systems with immediate societal applications, supported by strong industry collaboration frameworks.

Hongtu Zhuمشاهده پروفایل

استاد

• Biostatistics
• Medical Image Analysis
• Precision Medicine
• +۴ مورد دیگر

Dr. Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science, and Genetics at the University of North Carolina at Chapel Hill (UNC). He holds affiliations with the Gillings School of Global Public Health and leads the Biostatistics and Imaging Genomics Analysis Lab. His expertise spans statistical learning, medical imaging, AI, and big data integration, with a focus on precision medicine and biomedicine. Dr. Zhu earned his PhD in Statistics from The Chinese University of Hong Kong (2000) and has held prior roles including DiDi Fellow/Chief Scientist (2018-2020) and Bao-Shan Jing Endowed Professor at MD Anderson Cancer Center (2016-2018). He has published over 345 peer-reviewed articles in top-tier journals like Nature, Science, and JASA, and actively contributes to editorial roles including Coordinating Editor of JASA. His research interests include neuroimaging analysis, knowledge graphs, and AI applications in healthcare. Notable awards include the COPSS Snedecor Award (2025), IEEE Fellowship (2025), and IMS Medallion (2027). He has mentored over 80 PhD students/postdoctoral fellows and serves on NIH grant review panels and professional organizations like the ASA's Section on Statistics in Imaging. Key Contributions: Imaging genomics, brain connectivity studies, ridesharing market optimization, medical AI frameworks Lab Innovations: Brain Imaging Genetics Knowledge Portal, Biomedical Knowledge Graph Interface Teaching: Advanced biostatistics courses (Generalized Linear Models, Deep Learning in Biomedicine) Recent work explores causal inference in healthcare, X chromosome's role in neurobiology, and AI ethics in medical vision-language models. His interdisciplinary projects bridge statistics, computer science, and clinical practice to address complex biomedical challenges.

Lucas Paoliمشاهده پروفایل

پژوهشگر

• Microbiome Research
• Microbial Immunity
• Ecological Genomics
• +۴ مورد دیگر

Lucas Paoli is a Researcher and Course Instructor at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He leads the Paoli Lab (Microbiome Immunity and Ecology) within the Global Health Institute (GHI) under the School of Life Sciences (SV). His roles include scientific collaboration and teaching responsibilities in life sciences engineering. Paoli holds a PhD in Microbiome Research from ETH Zürich (2018-2023), an M.Phil. in Environmental Policy from the University of Cambridge (2017-2018), and an M.Sc. in Ecology and Evolution from École normale supérieure (2015-2017). Research Focus: His work bridges microbial immunity and ecology, investigating how microbes defend against viral infections across ecosystems like oceans and human microbiomes. Techniques include global-scale metagenomics and functional genomics to study immune strategies and ecological interactions. Key themes involve microbial community dynamics, viral defense mechanisms, and the impact of environmental factors on microbial immunity. Lab & Education: The Paoli Lab explores microbiome-immunity interactions with a focus on ecological contexts. Paoli supervises PhD students in Life Sciences Engineering. His research outputs span microbial ecology, metagenomic tool development, and marine microbiome studies. Contact: lucas.paoli@epfl.ch, AAB 1 39, +41 21 693 16 99.

Lawrence Goodridgeمشاهده پروفایل

استاد

• Food Safety
• Antibiotic Resistance
• Foodborne Pathogens
• +۶ مورد دیگر

Lawrence Goodridge is a Professor and Director of the Canadian Research Institute for Food Safety (CRIFS) at the University of Guelph's Ontario Agricultural College. He holds the Leung Family Professorship in Food Safety and leads research at the intersection of food safety, antibiotic resistance, and One Health principles. His work focuses on applying genomic technologies to study foodborne pathogens (E. coli, Salmonella, Listeria, Cronobacter) and leveraging wastewater surveillance for infectious disease outbreak prediction. Academic History: BSc Microbiology (University of Guelph, 1995), MSc Food Microbiology (2003), PhD Food Microbiology (2002), followed by post-doctoral training in Food Safety at the University of Georgia (2002). Joined CRIFS in 2003. Research Interests: Genomic analysis of pathogen emergence, wastewater-based epidemiology, bacteriophage applications, and consumer education strategies for food safety. His lab develops innovative methods for rapid pathogen detection in food systems and environmental samples. Articles Trends: Over 100 peer-reviewed publications emphasize genomic surveillance of foodborne pathogens and SARS-CoV-2, with a focus on wastewater sampling innovations. Recent work explores multi-modal data integration for public health forecasting and ethical data protection frameworks for surveillance programs. Awards: While no specific prizes are listed, his $50M+ research funding from Canadian/international sources underscores recognition of his impactful work. Grants support projects like phage-based sanitization and antimicrobial resistance tracking. Advising & Labs: Leads CRIFS laboratory operations and collaborates globally on food safety initiatives. His research has informed food industry guidelines and policy frameworks for mitigating pathogen risks in agricultural and environmental systems.

Dane Morganمشاهده پروفایل

استاد

• Computational materials science
• Ab initio electronic structure
• Multiscale methods
• +۴ مورد دیگر

Dane Morgan is a Professor in the Department of Materials Science & Engineering at the University of Wisconsin-Madison, College of Engineering. His research focuses on computational materials science for materials design, including ab initio electronic structure modeling, multiscale methods, and machine learning applications in materials discovery. His work spans nuclear materials, battery and fuel cell electrodes, and electronic materials. Education : PhD, 1998, University of California, Berkeley MS, 1994, University of California, Berkeley BA, 1992, Swarthmore College Research Interests : Computational materials science, ab initio methods for electronic structure and thermokinetics, machine learning for materials discovery, electrochemical systems modeling, and applications in nuclear materials, batteries, and electronic materials. His work integrates advanced computational techniques with experimental validation. Scientific Awards : 2024 APL Materials, Editors Pick 2023 Microscopy and Microanalysis Best Paper Award (Instrumentation and Software category) 2023 IEEE Transactions on Plasma Science Best Paper Award 2023 Kellet Mid-Career Award 2015 TMS Materials Genome Initiative Ambassador 2006 3M Technical Nontenured Faculty Grant

Mona Singhمشاهده پروفایل

استاد

• Computational Biology
• Machine Learning
• Algorithms
• +۳ مورد دیگر

Mona Singh is a Professor of Computer Science at Princeton University, with affiliations to the Lewis-Sigler Institute for Integrative Genomics and the Department of Molecular Biology. She has been a faculty member since 1999. Ph.D., Massachusetts Institute of Technology, 1995 A.B. and S.M. degrees in Computer Science from Harvard University Her research focuses on computational molecular biology, integrating machine learning and algorithms to analyze biological networks, protein interactions, and mutational impacts. Key areas include DNA/RNA binding prediction, protein structure analysis, and network-based disease gene discovery. Her recent work highlights trends in protein language models, kinase-substrate prediction, and equitable MHC binding algorithms. These span sub-fields like structural bioinformatics, network biology, and functional genomics. Scientific Awards: Presidential Early Career Award for Scientists and Engineers (PECASE) Rheinstein Junior Faculty Award ACM Fellow (2019) ISCB Fellow (2018) She has taught an introductory computational biology course with Professor Coleen Murphy, covering sequence analysis, phylogenetics, and network reconstruction. Her group has developed tools like dPUC , nCOP , and DiffMut . Her lab collaborates with institutions including Carnegie Mellon, Duke University, and the Broad Institute, advancing applications in cancer genomics, metabolic disease, and precision medicine.