جستجوی استاد راهنمای دانشگاهی

Marcus A. Brubaker is an Associate Professor of Computer Science at York University, founder and director of the Computational Vision and Imaging Lab (CVIL), Faculty Affiliate at the Vector Institute, and Adjunct Professor at the University of Toronto Department of Computer Science. He holds additional roles as a Visiting Professor at Samsung AI Center - Toronto, co-founder and advisor of Structura Biotechnology (creators of cryoSPARC), and Academic Advisor at Borealis AI. His research focuses on building rich models capturing fundamental relationships between the world and observations, with recent emphasis on generative models (particularly normalizing flows) and Cryo-EM structure estimation of biological molecules. His broader interests span vehicle localization, physically realistic human motion modeling, probabilistic programming languages, Bayesian methods, MCMC, and forensic ballistics. His publication trends reveal strong specialization in computer vision and generative modeling, with recent ICCV 2023 papers advancing neural radiance field inpainting and photometrically consistent view synthesis using diffusion models. These works demonstrate interdisciplinary integration of computer graphics, deep learning, and novel view generation techniques. Brubaker has served as Associate Editor for IET Computer Vision and Area Chair/Senior Area Chair for major conferences including CVPR, ECCV, NeurIPS, and AAAI. Previously, he was Research Director at Borealis AI (2018-2020), Research Associate at Cadre Research Labs (creators of TopMatch-GS), and an original contributor to the Stan probabilistic programming framework. He directs the Computational Vision and Imaging Lab (CVIL) at York University alongside colleagues Kosta Derpanis and Michael Brown, focusing on cutting-edge computer vision research with applications in biotechnology and AI. As of August 2024, he has commenced a leave of absence from York University to join Google DeepMind as a Research Scientist in Toronto.

• Computer Vision
• Machine Learning
• Statistics
• +۶ مورد دیگر

Xiaoquan (William) Wen is Professor of Biostatistics in the University of Michigan School of Public Health , Department of Biostatistics. Since earning his PhD in Statistics from the University of Chicago in 2011, he has been a continuous member of the Michigan faculty and is an active contributor to the NIH GTEx Consortium and the Center for Statistical Genetics . Education PhD in Statistics, University of Chicago, 2011 Research Focus Wen’s methodological work lies at the intersection of Bayesian statistics , computational genomics , and probabilistic graphical models . He develops scalable algorithms for Bayesian model comparison and false discovery rate control , with particular emphasis on multi-tissue eQTL discovery , causal inference from high-dimensional omics data, and integrative analysis of genome, transcriptome, and proteome to dissect the molecular basis of complex human traits. Applied domains include functional genomics, pharmacogenomics, immunogenomics, and pediatric gene–environment interactions, where he translates statistical innovation into biologically meaningful insight. Publications Snapshot (2022–2025) Recent work spans large-scale GWAS meta-analyses, single-cell multi-omics, metabolome-wide Mendelian randomization, and robust replicability frameworks. Recurring themes are causal gene prioritization, fine-mapping, and rigorous statistical validation across diverse human tissues and disease contexts. Scientific Awards (no awards explicitly listed in provided text) Grant & Team Activity Wen is an ongoing NIH GTEx project investigator and contributes to the Michigan Center for Statistical Genetics . While explicit grant numbers are absent, his sustained publication stream and consortium involvement indicate active federal and collaborative funding. No advisees are named in the supplied material. Laboratory & Software He maintains an active GitHub repository releasing open-source tools such as BLIMP (Best Linear IMPutation), SLAT (gene- and pathway-level testing), and QuASAR (quantitative allele-specific analysis).

• Bayesian statistics
• Computational statistics
• Probabilistic graphical models
• +۵ مورد دیگر

Marleen de Bruijne is Professor of AI in Medical Image Analysis jointly appointed at the University of Copenhagen, Denmark and Erasmus MC – University Medical Center Rotterdam, The Netherlands. Within the Department of Computer Science at Copenhagen she belongs to the Image Analysis, Computational Modelling and Geometry section, where she leads research at the intersection of machine learning and medical imaging. Education MSc in Physics, Utrecht University, 1997 PhD in Medical Imaging, Utrecht University, 2003 Research Interests Her work centers on developing and validating machine-learning algorithms for quantitative analysis of medical images. Key themes include: Transfer learning and domain adaptation across imaging centers Deep learning architectures for segmentation and classification of pulmonary, cardiovascular and neuro images Probabilistic graphical models and Bayesian approaches for robust airway and vessel extraction Computer-aided diagnosis systems for emphysema, bronchiectasis, COPD and calcification Her group translates these techniques into clinical workflows to improve early diagnosis and patient management. Scientific Awards NWO-VENI (Netherlands Organisation for Scientific Research) NWO-VIDI NWO-VICI DFF-YDUN (Danish Council for Independent Research) Advising & Grants She has (co-)supervised 30 PhD students to completion and served as principal investigator on several large personal grants. Her funding record demonstrates sustained support from both Dutch and Danish national science foundations. Labs & Teams She is actively involved in the SCIENCE AI Centre at the University of Copenhagen and maintains strong collaborative links with Erasmus MC Radiology and Pulmonology departments, fostering cross-institutional datasets and multicenter clinical validation studies.

• Medical Image Analysis
• Machine Learning
• AI in Radiology
• +۶ مورد دیگر

Thomas Wim Hamelryck is a Professor at the Department of Computer Science (Programming Languages and Theory of Computation) and the Department of Biology (Computational and RNA Biology) at the University of Copenhagen . With a PhD in Protein Crystallography from the Free University of Brussels (VUB), he specializes in Bayesian modeling , probabilistic machine learning , and statistical structural bioinformatics , focusing on protein structure prediction, evolution, and non-Euclidean data representation.

• Probabilistic Machine Learning
• Deep Probabilistic Programming
• Statistical Structural Bioinformatics
• +۵ مورد دیگر

Andrew Muteti Musau is a Professor in Business Economics at Molde University College, Faculty of Business Administration and Social Sciences. He joined as an associate professor in 2021 and was promoted to professor in 2024. His research spans behavioral economics, energy economics, and macroeconomics, with a focus on replication studies and experimental methods. PhD in Economics from the University of Trento (2021) Master’s in Business Administration from the University of Agder (2014) Postdoctoral research in energy economics at INN University (2014) His research interests include behavioral economics, experimental economics, energy economics, and macroeconomics. He has contributed to econometrics via Stata programming tools and explored inefficiency determinants in energy sectors, emotional responses in sports, and financial literacy. Recent work investigates personality impacts on financial knowledge gaps. Publications span econometrics, energy economics, behavioral and experimental economics, and replication studies. Teaching areas include econometrics, behavioral economics, and decision theory. Musau collaborates with institutions like the University of Agder, INN University, and the University of Trento.

• Behavioral Economics
• Experimental Economics
• Energy Economics
• +۱ مورد دیگر

Alexander Ihler is a Professor in the Department of Information and Computer Science at the University of California, Irvine. His work bridges artificial intelligence and machine learning with a focus on statistical methods for data analysis and approximate inference in graphical models. Applications span sensor networks, computer vision, and computational biology. His research emphasizes graphical models for structuring probability distributions and enabling efficient reasoning. Key contributions include analyzing convergence in belief propagation, extending variational techniques to continuous systems, and developing bounds for marginal MAP and decision-making problems. He also explores adaptive inference algorithms for dynamic problems, such as tracking changes in sensor data or biological systems. Recent publications highlight advancements in AI for protein design and astrophysics UAV mobility modeling deep learning integration with classical algorithms dynamic mode decomposition for forecasting Scientific accolades include the NSF CAREER award and first-place UAI 2014 Approximate Inference Challenge in five categories. He has advised numerous PhD and MS students, with graduates in fields like robotics, computational biology, and statistical learning. His group collaborates with institutions like Microsoft Research, DARPA, and the NIH, focusing on scalable methods for real-world systems. Additional personal details include a PhD from MIT under Alan Willsky and a prior role as co-program chair of the 2016 Uncertainty in Artificial Intelligence (UAI) conference.

• Artificial Intelligence
• Machine Learning
• Graphical Models
• +۵ مورد دیگر

Wen-Chin Chen is a Professor at National Taiwan University in the Department of Computer Science and Information Engineering. With a PhD in Computer Science from Brown University (1984), his expertise spans algorithms , multimedia systems , and quantum computing . Previously at GTE Laboratories (1984–1987), he has taught foundational courses such as Algorithms , Mathematical Analysis of Algorithms , and Quantum Algorithms . Education: BS in Mathematics, National Taiwan University (1976) PhD in Computer Science, Brown University (1984) His research integrates design and analysis of algorithms with applications in multimedia synchronization and quantum computing . Publications reflect deep engagement with data structures , combinatorics , and real-time system design , including work on audio-video synchronization models , heap-ordered trees , and genetic algorithm optimization . Recent projects involve quantum algorithms (e.g., Shor's, Grover's) and linear algebra for geometric and matrix operations, indicating ongoing contributions to theoretical computer science and applied mathematics .

• Design and Analysis of Algorithms
• Multimedia Systems
• Quantum Algorithms
• +۷ مورد دیگر

Ben Moews is a Lecturer in Predictive Analytics at the University of Edinburgh Business School. He serves as Director of the Domain-Driven Machine Learning Lab, PhD Programme in Financial Technology, and Chair of the Centre for Data, Culture and Society's Advisory Board. PhD in Astrophysics (University of Edinburgh) MSc in Artificial Intelligence (University of Edinburgh) Professional Statistician (American Statistical Association) His research bridges artificial intelligence with financial technology and astrophysics , focusing on domain-specific machine learning and privacy-preserving synthetic data . Recent projects include generative modeling for market microstructure , AI-driven cosmological simulations , and geospatial analytics for societal challenges . Research outputs span Physics-informed neural networks (2024), random number generator analysis in financial markets (2024), and synthetic data evaluation frameworks (2024). His work combines high-performance computing with real-world applications in central banking and public health . Fellowship of the Higher Education Academy (2025) McWilliams Fellowship (2021) Principal’s Career Development Scholarship (2017) Professional Statistician accreditation (2023) Winton Thesis Prize (2022) As Director of the Domain-Driven Machine Learning Lab and FinTech PhD Programme, he leads initiatives integrating data science with domain-specific challenges across business, finance, and public policy. He collaborates with the Scottish Centre for Crime and Justice Research and Edinburgh Centre for Financial Innovations.

• Artificial Intelligence
• Machine Learning
• Statistical Inference
• +۷ مورد دیگر

Jan-Willem van de Meent is an Associate Professor at the University of Amsterdam , where he co-directs the AMLab with Max Welling. He holds an Assistant Professor position (on leave) at Northeastern University , continuing to advise students and collaborate remotely. His research focuses on combining probabilistic programming and deep learning to develop models that generalize from limited data. Key areas include inductive biases through physical simulators, causal structures , and symmetries , with applications in robotics , NLP , healthcare , and physical sciences . Recent work includes Variational Flow Matching for graph generation Equivariant neural models for physical systems Entropy coding of complex data structures Goal-contrastive reinforcement learning for robotics Awards NSF CAREER award (2021) Students & Postdocs Robin Walters (Postdoctoral Fellow) Ondrej Biza (Ph.D. Candidate) Babak Esmaeili (Ph.D. Candidate) Sam Stites (Ph.D. Candidate) Hao Wu (Ph.D. Candidate) Xiongyi Zhang (Ph.D. Candidate) Heiko Zimmermann (Ph.D. Candidate) Jered McInerney (Ph.D. Candidate) Eli Sennesh (Ph.D. Candidate)

• Probabilistic Programming
• Deep Learning
• Artificial Intelligence
• +۵ مورد دیگر

Stephan Eckstein is a junior professor in the Department of Mathematics at the University of Tübingen and a member of the university's machine learning cluster. His research bridges probability theory and machine learning with particular focus on stochastic optimization and numerical approximation. Research interests include: Optimal transport theory and its computational aspects Regularization techniques for high-dimensional problems Causal models and probabilistic structures Graphical models in machine learning Graph neural networks Recent publications analyze dimensional stability in optimal transport, exponential convergence rates for Sinkhorn algorithms, and causal modeling in financial time series generation. Contact: stephan.eckstein@uni-tuebingen.de

• Probability Theory
• Machine Learning
• Stochastic Optimization
• +۴ مورد دیگر

Steven Flammia is a Professor of Computer Science at Virginia Tech’s College of Engineering and Director of the Center for Quantum Architecture and Software Development at the Virginia Tech Innovation Campus. His research focuses on quantum error correction, quantum fault tolerance, and learning quantum noise dynamics. He has held prior roles at Amazon Web Services, University of Sydney, and University of Washington, with over 80 peer-reviewed publications and 9,000+ citations. Education & Experience: Postdoctoral Researcher, California Institute of Technology & Perimeter Institute (2000s) Professor at University of Sydney (2010s) Principal Research Scientist at Amazon Web Services (2020s) Research Interests: His work bridges theoretical foundations (e.g., quantum coding theory, representation theory) with practical implementations (e.g., noise modeling, quantum tomography). Key areas include developing fault-tolerant architectures, optimizing quantum error correction codes, and applying statistical learning to quantum systems. Recent Contributions: Recent papers explore Hamiltonian learning with Heisenberg limits, Zauner’s conjecture via number theory, and fiber bundle fault tolerance of GKP codes. His work emphasizes scalable quantum computing solutions under realistic noise conditions. Grants & Leadership: Leads the Center for Quantum Architecture, overseeing faculty hiring and strategic direction for quantum software/hardware integration. Collaborates on projects funded by industry and academic partnerships.

• Quantum Information Theory
• Quantum Error Correction
• Quantum Computing
• +۳ مورد دیگر

Sheida Nabavi is an Associate Professor in the Department of Computer Science and Engineering at the University of Connecticut (UConn) , where she has been since Fall 2015. Prior to this, she was a Research Associate at the Center for Biomedical Informatics at Harvard Medical School. She holds a PhD in Electrical and Computer Engineering from Carnegie Mellon University (2008) and a Master’s in Medical Science (Bioinformatics) from Harvard Medical School (2012). Her research focuses on computational methods for analyzing genomic data and biomedical images , leveraging statistical machine learning and signal/image processing. Key contributions include tools like DCCNV for CNV detection, SEACON for allele-specific profiling, and CAPTURE for image registration. She leads the Nabavi Lab , which develops open-source tools accessible via GitHub. She advises a diverse group of students, including PhD candidates researching AI in medical imaging, single-cell sequencing, and multimodal data analysis. Notable alumni include Jun Bai (Cincinnati University Assistant Professor), Tianyu Wang (Meta Research Scientist), and Fatemeh Zare (Thermo Fisher Scientific Data Scientist). Courses Taught: Medical Imaging ML, Genomics Data Analysis, Probabilistic Graphical Models. Labs/Teams: Nabavi Lab (specializing in bioinformatics and AI for healthcare). Her work bridges machine learning and biomedical applications, with grants and projects funded by NSF and industry partnerships.

• Machine Learning
• Bioinformatics
• Genomics Data Analysis
• +۴ مورد دیگر

Jaakko Lehtinen is a Professor in the Department of Computer Science at Aalto University, affiliated with the School of Science. His research group focuses on computer graphics, computer vision, and machine learning, particularly in generative modeling, realistic image synthesis, and appearance acquisition. They explore combining machine learning with physical simulators to develop robust and interpretable AI systems. Their work contributes to Visual Computing and Human-Computer Interaction. Lehtinen has supervised doctoral researchers including Erik Härkönen and Tuomas Kynkäänniemi. Notable awards include the Best MSc Thesis Award (2020) and Best PhD Thesis Awards (2017). He has contributed to high-impact publications in venues like NeurIPS and Neural Computation, addressing topics like diffusion models, EEG forecasting, and generative adversarial networks. Key Collaborations: International partnerships in machine learning and visual computing. Media Engagement: Regular commentary on AI trends in Finnish and global media. Professional Activities: Served as a PhD thesis committee member for institutions like UC Berkeley and MIT. His research extends to medical imaging, environmental modeling, and neural rendering, with applications in tomography, audio processing, and dynamic scene generation. Lehtinen’s group actively publishes datasets (e.g., for gradient-domain rendering) and engages in open-source software contributions.

• Computer Graphics
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Edmondo Trentin is an Associate Professor of Artificial Intelligence at the Department of Information Engineering and Mathematics (DIISM), University of Siena, Italy. He chairs the IAPR Technical Committee 3 (Neural Networks & Computational Intelligence) and serves as an Associate Editor for Neural Processing Letters . His research focuses on neural networks, probabilistic models, and their applications in bioinformatics, speech processing, and pattern recognition. Trentin has taught extensively at both undergraduate and graduate levels, including courses on Artificial Intelligence, Neural Networks, and Pattern Recognition. He has advised numerous PhD students and contributed to international conferences and workshops. His work integrates machine learning with statistical methods, emphasizing hybrid models for complex data analysis. Teaching Highlights: Artificial Intelligence (MSc. courses since 2001) Neural Networks for Statistical Pattern Recognition Machine Learning for Graphs Supervised and Unsupervised Learning Research Interests: Connectionist density estimation Probabilistic graphical models Hybrid ANN/HMM systems Bioinformatics applications Professional Activities: Former Vice-Chair of IAPR-TC3 IEEE Computational Intelligence Society Secretary (2005-2006) Organizer of international workshops on neural networks and pattern recognition

• Artificial Intelligence
• Neural Networks
• Pattern Recognition
• +۵ مورد دیگر

Bala Krishnamoorthy is a Professor of Mathematics and Statistics at Washington State University (WSU) Vancouver, where he has been since 2014. Previously, he held positions at WSU Pullman (2004–2014). He earned his B.Tech from IIT Madras (1995) and a PhD in Operations Research from UNC Chapel Hill (2004). His research spans applied algebraic topology, geometric measure theory, optimization, and computational biology, with applications in 3D printing, biomedical analysis, and data science. He has secured grants from the NSF, Department of Energy, and WA State Attorney General’s Office, among others. Key research interests include topological data analysis (TDA), algorithmic optimization, and interdisciplinary collaborations with fields like surgery, chemistry, and criminology. Notable awards include the 2019 WSUV Chancellor’s Research Excellence Award, 2022 Yang Liu Teaching Award, and 2023 College of Arts and Sciences Excellence in Teaching Award. His work emphasizes practical applications, such as toolpath optimization in 3D printing, robust feasibility in optimization, and analyzing complex datasets like cancer gene expression. He advises numerous PhD students, many of whom have secured roles in academia, industry, and government. Current projects include steering committees for NSF-funded initiatives like DELTA and collaborations with institutions like Tohoku University and UC Davis. Teaching responsibilities include advanced courses on algebraic topology, optimization, and computational methods. His seminars and workshops foster interdisciplinary dialogue, such as the WSU Vancouver Math/Stats Seminar series and topological data analysis workshops at ACM-BCB and PSB conferences.

• Applied Algebraic Topology
• Geometric Measure Theory
• Mathematical Aspects of 3D Printing
• +۴ مورد دیگر