جست‌وجوی استاد و پژوهشگر

• Information Theory
• Machine Learning
• Computational Neuroscience
• +۳ مورد دیگر

Dr. James Stone is a Visiting Professor at the School of Computer Science, University of Sheffield. His research focuses on interdisciplinary applications of information theory, machine learning, and computational neuroscience. He has authored numerous books and articles on topics such as Bayesian analysis, neural information processing, and deep learning algorithms. His academic contributions span over two decades, with a strong emphasis on bridging theoretical foundations and practical applications. Key areas include the development of mathematical frameworks for understanding neural systems, optimizing machine learning techniques, and advancing educational resources in computational fields. Stone’s work often integrates methodologies from statistics, signal processing, and quantum mechanics, reflecting his broad expertise. Despite extensive publications, no specific awards or grants are explicitly listed in the provided texts. He maintains an active presence in academic outreach through publications and educational materials.

• Signals and Systems
• Adaptive Systems
• Learning Theory
• +۶ مورد دیگر

José C. Príncipe is a Distinguished Professor at the University of Florida , where he holds the Don D. & Ruth Eckis Endowed Distinguished Professor position in the Department of Electrical and Computer Engineering. His primary research area is Signals and Systems , with a focus on adaptive systems and learning theory. Education Agregado, University of Aveiro (1985) Ph.D., Electrical Engineering, University of Florida (1979) M.S., Electrical Engineering, University of Florida (1974) B.S., Electrical Engineering, University of Porto (1972) Príncipe’s research spans machine learning, computational neuroscience, and biomedical signal processing. He has pioneered work in kernel adaptive filtering, information-theoretic learning criteria, and neural network architectures. His recent publications emphasize uncertainty quantification, spiking neural networks, and robust filtering techniques. His 15 most recent articles reflect trends in machine learning (transformers, predictive coding, reinforcement learning), signal processing (kernel methods, entropy-based filtering), and biomedical applications (electrophysiology, pain prediction). Key subfields include neural interfaces, nonlinear system identification, and statistical dependence measures. Scientific Awards : Fellow, American Association for the Advancement of Science (2021) Fellow, National Academy of Inventors (2021) IEEE SP Society Claude Shannon-Harry Nyquist Technical Achievement Award (2021) Life Fellow, IEEE (2017) SEC Faculty Achievement Award Winner (2016) IEEE Neural Network Pioneer Award (2011) Honorary Doctorate, Aalto University (2010) Gabor Award, International Neural Network Society (2006) Príncipe leads the Computational NeuroEngineering Laboratory (CNEL) at the University of Florida, fostering interdisciplinary research at the intersection of neuroscience and engineering. His work includes grants and collaborations in biomedical systems, brain-computer interfaces, and machine learning theory.

Jouke Jan Hottengaمشاهده پروفایل

پژوهشگر ارشد

• Genetics
• Genomics
• Molecular Biology
• +۵ مورد دیگر

Jouke Jan Hottenga is a Visiting Research Fellow at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Behavioural and Movement Sciences (Biological Psychology department) and the Amsterdam Public Health (APH) research institute's Health Behaviors & Chronic Diseases and Personalized Medicine divisions. His work focuses on genetic and epigenetic mechanisms underlying complex traits, including psychiatric disorders, neurodevelopmental outcomes, and environmental interactions. He has contributed to large-scale genome-wide association studies (GWAS), twin studies, and polygenic risk score analyses. Key research interests include the genetic basis of developmental milestones, mental health, and chronic diseases. Hottenga has co-authored over 498 publications, with recent work emphasizing the role of genetic variants in income-related health disparities and the epigenetic influences of asthma polygenic burden. He teaches courses on genome-wide association methodologies and collaborates internationally on projects like the Netherlands Twin Register. Notable contributions include studies on twin zygosity assessment, ADHD polygenic risk scores, and the genetic architecture of female fertility. His datasets and software contributions highlight expertise in molecular genetics, metabolomics, and epigenetic analyses.

Krzysztof Bartoszekمشاهده پروفایل

عضو هیئت علمی

• Statistics
• Data Science
• Machine Learning
• +۳ مورد دیگر

Krzysztof Bartoszek is a Senior Associate Professor at Linköping University's Department of Computer and Information Science (IDA), affiliated with the Division of Statistics and Machine Learning (STIMA). His research focuses on statistical and computational methods in evolutionary biology, stochastic processes, and mathematical modeling. He contributes to interdisciplinary projects combining data science with biological systems analysis. Research interests include phylogenetic comparative methods, trait evolution models, and the application of stochastic operators to population dynamics. Recent work emphasizes multivariate analysis of evolutionary processes, with contributions to understanding bacterial genome organization and trait dynamics across phylogenies. Publications span topics like Ornstein-Uhlenbeck models for large phylogenies, statistical methods for trait evolution, and the interplay between genetics and environmental adaptation. Collaborations involve international teams in computational biology and mathematics.

Michael Hörnquistمشاهده پروفایل

استاد

• Systems Biology
• Gene Regulatory Networks
• Computational Biology
• +۴ مورد دیگر

Michael Hörnquist serves as Professor and Deputy Head of the Department of Science and Technology (ITN) at Linköping University, concurrently leading the Physics, Electronics and Mathematics (FEM) Division. His responsibilities encompass undergraduate education oversight and management of research activities within FEM, which focuses on communication electronics, mathematics, engineering didactics, physical electronics, and nanotechnology. Hörnquist's research bridges systems biology and computational physics, specializing in gene regulatory network analysis through machine learning techniques including Elastic Net and LASSO regression. His work extends to nonlinear dynamics in aperiodic electronic structures, reflecting FEM's interdisciplinary approach to physics, electronics, and mathematics. This dual focus enables innovative cross-domain applications in biological modeling and electronic systems. Publication trends since 2003 reveal consistent contributions to systems biology, particularly in reverse-engineering biological networks through integrated data approaches. His 2010 DREAM3 challenge-winning work on gene expression prediction exemplifies practical applications of computational methods, while later research (2016) connects molecular oncology with network dynamics. This trajectory demonstrates sustained innovation in translating theoretical models to biological insights across diverse systems. As an academic leader, Hörnquist emphasizes learning as the university's core mission: 'Learning is the essence of what we do as a university, whether it involves teaching, research, or collaboration. It is learning that allows us to grow and mature as individuals.' He actively fosters this philosophy through mentorship and institutional leadership.

Kai Shimagakiمشاهده پروفایل

پژوهشگر

• HIV research
• virology
• immunology
• +۴ مورد دیگر

Kai Shimagaki is a Postdoctoral Associate at the University of Pittsburgh. His research focuses on HIV-1 evolution, coevolutionary processes between viruses and antibodies, and quantitative analysis of genetic interactions. He holds a Ph.D. from Sorbonne University (formerly Pierre et Marie Curie University), where his work centered on protein structure prediction and pathogen-immune system interactions. Education: Ph.D. in Computational Biology, Sorbonne University Research Interests: Quantitative analysis of viral evolution and immune system dynamics Computational modeling of fitness landscapes and epistasis Development of algorithms for deep mutational scanning and protein sequence analysis His work bridges virology, immunology, and computational methods to understand HIV adaptation mechanisms. Publications Overview: Recent work emphasizes HIV evolution modeling, epistasis detection, and computational tools for genetic fitness prediction. Key methods include Bézier interpolation, Boltzmann machine models, and higher-order covariance analysis. Scientific Awards: None listed. Advising & Funding: Supported by the Honjo International Scholarship Foundation during his Ph.D. No current advising roles noted. Labs/Teams: Affiliation details not specified in text.

Samuel Touchardمشاهده پروفایل

استادیار

• Bayesian Statistics
• Statistical Modeling
• Biostatistics
• +۳ مورد دیگر

Samuel Touchard is an Assistant Professor (Teaching Focused) at the University of Warwick, Department of Mathematics. He coordinates the IM Dissertation/Project module and teaches courses like Games, Decisions and Behaviour, Applied Statistical Modelling, and Statistical Laboratory. Previously, he worked as a Teaching Associate at the University of Bristol and a Research Associate in the School of Medicine at Cardiff University, focusing on biomarkers in dementia. His PhD (University of Sheffield, 2015) explored Bayesian inference of gene-miRNA regulatory networks. Education: PhD in Statistics from the University of Sheffield (2010–2015), Diplome d’Ingenieur (MSc equivalent) from ENSAI, France (2007–2010), and a Master of Research from University of Rennes 1 (2009–2010). Research interests include Bayesian methodologies, statistical applications in medicine, and data-driven approaches in biological systems. Contact via email or Microsoft Teams.

Jason Xuمشاهده پروفایل

دانشیار

• Biostatistics
• Artificial Intelligence/Machine Learning
• Epidemiology
• +۳ مورد دیگر

Jason Xu is an Associate Professor of Biostatistics at the University of California Los Angeles (UCLA), with a courtesy appointment in Duke University's Statistical Science department. He holds a Ph.D. in Statistics from the University of Washington (2016) and a BS in Mathematics from the University of Arizona (2012). His research focuses on statistical methodologies for stochastic processes, with applications in epidemiology, cancer biology, and machine learning. Xu has led NSF-funded projects investigating epidemic dynamics on adaptive networks and cancer latency modeling through semi-Markov processes. He teaches advanced courses in Bayesian inference, time series analysis, and statistical computing. Notable recognitions include the NSF Postdoctoral Fellowship and NDSEG Fellowship. Research Interests: Stochastic modeling of biological systems Bayesian computational methods Epidemiological network analysis Cancer stem cell dynamics Optimization algorithms for statistical inference Grants & Awards: RAISE: IHBEM Grant (NSF, $2M, 2022-2026) NSF Postdoctoral Research Fellowship (2018-2021) NDSEG Fellowship (2013-2016) Labs/Teams: Core member of UCLA's Biostatistics Computational Biology Group, collaborating with Duke's Statistical Science group on multi-institutional projects.

Jehoshua Bruckمشاهده پروفایل

استاد

• Information Theory
• Distributed Storage Systems
• DNA Data Storage
• +۴ مورد دیگر

Jehoshua (Shuki) Bruck is the Gordon and Betty Moore Professor of Computation and Neural Systems & Electrical Engineering at the California Institute of Technology. He leads the Distributed Information Systems Group and serves as the founding director of Caltech's Information Science and Technology (IST) program. His work bridges information theory, storage systems, and computational biology. Bruck holds B.Sc. (Summa cum Laude) and M.Sc. degrees from the Technion-Israel Institute of Technology (1982-1985) and a Ph.D. in Electrical Engineering from Stanford University (1989). He has held academic positions at Caltech since 1994, advancing from Associate Professor to Moore Professor. Research Focus: His dual-track research explores Physical implementations of coding schemes for flash memories (rank modulation, rewriting codes) Biological computation mechanisms, particularly DNA sequence evolution via replication Secure distributed storage systems and RAID schemes Awards & Recognition: Recipient of the Feynman Teaching Prize (2008), Sloan Fellowship, NSF Young Investigator Award, and multiple IEEE best paper awards. Holds over 40 patents and authored 350+ publications. Industry Contributions: Co-founder of storage startups Rainfinity (acquired by EMC) and XtremIO, which pioneered flash storage solutions. Collaborates with NASA-JPL on non-volatile memory for space missions.

• Computational Neuroscience
• Statistics
• Machine Learning
• +۴ مورد دیگر

Robert E. Kass is the Maurice Falk University Professor of Statistics and Computational Neuroscience at Carnegie Mellon University, with affiliations in the Department of Statistics & Data Science, Machine Learning Department, and Neuroscience Institute. His career spans statistics, machine learning, and neuroscience, focusing on statistical methods for analyzing neural data, particularly spike trains and brain connectivity. Research highlights include: Developing statistical frameworks for neural data analysis Investigating neural oscillations and cross-regional interactions Contributing to neuroscience education through Neuromatch Academy Exploring intersections between statistics, machine learning, and scientific inference Scientific awards and recognitions include: Outstanding Statistical Application Award (ASA) Distinguished Achievement Award (COPSS, 2017) Election to National Academy of Sciences (2023) Fellowships in ASA, IMS, AAAS His recent publications focus on: Neural circuit oscillations and phase-amplitude analysis Latent dynamic modeling of high-dimensional neural recordings Population-level neural interactions and connectivity Educational frameworks for computational neuroscience training Methodological bridges between statistics and machine learning Advanced graphical models for neural synchronization studies

Bernie J. Daigle, Jr.مشاهده پروفایل

دانشیار

• Genomic data integration
• Post-traumatic stress disorder (PTSD)
• Single-cell gene expression
• +۲ مورد دیگر

Dr. Bernie J. Daigle, Jr. is an Associate Professor in the Departments of Biological Sciences and Computer Science at the University of Memphis. He received his B.S. in Biology from Cornell University with a concentration in Genetics and Development, followed by a Ph.D. in Genetics at Stanford University. His postdoctoral work was conducted at the University of California, Santa Barbara, focusing on bioinformatics and stochastic biochemical systems modeling. Dr. Daigle's research spans genomic data integration , single-cell gene expression analysis , and computational systems biology . His lab develops advanced statistical and computational tools to analyze noisy, heterogeneous biological datasets, aiming to extract meaningful insights from single-cell experiments to whole-organism studies. Affiliated with the University of Memphis Bioinformatics Program and Data Science Research Cluster, Dr. Daigle leads projects integrating genome-scale datasets for PTSD biomarker identification and applies computational methods to infer gene regulatory networks from single-cell data. His lab collaborates with the Systems Biology of PTSD Biomarkers Consortium (SBPBC), working with institutions including New York University, Harvard University, and Walter Reed Army Institute of Research. Current research involves machine learning applications to multi-omics data for improved PTSD diagnosis and prognosis.

Pradeep Ravikumarمشاهده پروفایل

استاد

• Machine Learning
• Statistical Learning
• Probabilistic Graphical Models
• +۵ مورد دیگر

Pradeep Ravikumar is a Professor in the Machine Learning Department at Carnegie Mellon University's School of Computer Science. He leads the Statistical & Symbolic Learning (Neuro-Symbolic AI) Group and serves as co-Editor-in-Chief of the Journal of Machine Learning Research (JMLR). Previously, he was Associate Editor-in-Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). His research focuses on machine learning theory, probabilistic graphical models, neuro-symbolic AI, and causal representation learning. He has taught advanced courses such as Advanced Machine Learning (PhD level), Probabilistic Graphical Models, and Convex Optimization. His awards include the Sloan Research Fellowship, NSF CAREER Award, and Siebel Scholarship. His work emphasizes theoretical foundations and practical applications, with recent contributions in neuro-symbolic systems, causal inference, and robust machine learning. Key research themes include differentiable structure learning, interpretable AI, and invariant representation learning. He has published extensively in top venues like NeurIPS, ICML, and JMLR, addressing challenges in optimization, causal discovery, and model interpretability.

Ankur Parikhمشاهده پروفایل

استادیار مدعو

• Natural Language Processing
• Machine Learning
• Spectral Learning
• +۳ مورد دیگر

Ankur Parikh is a staff research scientist at Google NYC and an adjunct assistant professor at New York University. His research focuses on natural language processing, machine learning, and spectral learning methods with applications to graphical models and biological networks. He holds a PhD from Carnegie Mellon University’s Machine Learning Department (2014), advised by Eric Xing, and an undergraduate degree in Computer Science and Applied Math from Princeton University. His work bridges theoretical advancements in machine learning with practical applications, including decomposable attention models for NLP, kernel graphical models, and network analysis of biological systems. Notable contributions include the TREEGL algorithm for gene network inference and spectral methods for latent structure discovery. He has been recognized with best paper awards at EMNLP and ISMB conferences. Education: PhD, Machine Learning, Carnegie Mellon University (2014) BS, Computer Science & Applied Math, Princeton University Parikh has taught advanced courses on spectral learning and probabilistic graphical models, including guest lectures at CMU and tutorials at Microsoft Research. His research is supported by an NSF Graduate Fellowship and has led to open-source implementations like the PLRE language modeling toolkit. Current affiliations include NYU’s machine learning community and Google’s NYC research team, with ongoing collaborations in NLP, computational biology, and statistical network analysis.

• Machine Learning
• Artificial Intelligence
• Probabilistic Modeling
• +۳ مورد دیگر

Aditya Ravuri is a Researcher affiliated with the Department of Computer Science and Technology at the University of Cambridge. His work focuses on advancing machine learning and probabilistic modeling techniques with applications in computational biology, chemistry, and data science. He contributes to foundational research in geometric learning, scalable Gaussian processes, and dimensionality reduction methods. Key research interests include developing algorithms for single-cell transcriptomics analysis, chemical reaction prediction, and uncertainty quantification. His recent work explores unifying probabilistic frameworks for graph and manifold learning, as well as applying self-supervised learning for zero-shot prediction tasks. Ravuri collaborates on interdisciplinary projects such as ice core dating and catalyst design, demonstrating the applicability of machine learning across diverse scientific domains. His publications reflect a strong emphasis on methodological innovation while addressing real-world challenges in biology, chemistry, and environmental science. No formal teaching role or student advising is explicitly listed, though his research contributions likely involve collaboration with graduate students and postdoctoral researchers. The lack of award mentions suggests his work is in early career stages or focused on technical contributions over award-seeking activities.

• Cognitive-Computational Neuroscience
• Threat Avoidance
• Human Behavior
• +۳ مورد دیگر

Prof. Dr. Dr. Dominik Bach holds the Hertz Chair for Artificial Intelligence and Neuroscience at the University of Bonn within the Transdisciplinary Research Area - Life and Health . His research focuses on understanding the computational architecture of the human mind and brain, particularly in threat avoidance and human behavior, using serious games, virtual reality, and associative learning setups. His work bridges cognitive neuroscience with artificial intelligence, aiming to unpack mechanisms of psychiatric conditions like anxiety disorders and PTSD through computational modeling and experimental platforms. Recent projects involve decoding aversive memory inhibition, threat memory maintenance, and developing novel treatments through transcranial stimulation and neural network analysis. Research Trends : Dominik Bach's publications emphasize computational characteristics of escape decisions , threat learning , and neural circuitry in anxiety , with methodological innovations in VR-based behavioral experiments and neuropharmacology. His 2025 work explores experiment-based calibration and transdiagnostic psychiatric symptom dimensions. Advising and Grants : While no specific students or grants are listed in the provided texts, his leadership in TRA Life and Health and collaborations with institutions like University Hospital Bonn highlight his role in cross-disciplinary research initiatives.