جست‌وجوی استاد و پژوهشگر

Eiko Yonekiمشاهده پروفایل

پژوهشگر

• Distributed Systems
• Networking
• Databases
• +۵ مورد دیگر

Eiko Yoneki is a Senior Researcher and Affiliated Lecturer at the University of Cambridge's Department of Computer Science and Technology. She holds a Ph.D. in Computer Science from Cambridge (2007) and a Postgraduate Diploma (2003). Her research focuses on distributed systems, networking, databases, and large-scale graph processing, with a recent emphasis on auto-tuning frameworks using machine learning techniques like Bayesian Optimization and Reinforcement Learning. She has extensive industry experience, including roles at IBM across multiple countries. Affiliations: Department of Computer Science and Technology, University of Cambridge Education: Ph.D. (2007), Postgraduate Diploma (2003) in Computer Science, University of Cambridge Her research interests span system autotuning, probabilistic performance modeling, and scalable graph analytics. Notable projects include hierarchical Bayesian optimization for high-dimensional parameter spaces and reinforcement learning for GPU schedule optimization. She has contributed to frameworks like RLFlow and HiBO, advancing efficient resource management in compute systems. Labs/Teams: Leads research groups focused on machine learning-driven system optimization and large-scale graph processing. Collaborates with industry partners on cloud computing efficiency and hardware-aware algorithms. Grants/Advising: No specific grants listed, but her work is supported by academic and industrial collaborations.

Xin Tongمشاهده پروفایل

دانشیار

• Asymmetric Statistical Learning
• Neyman-Pearson Classification
• High-dimensional Statistics
• +۵ مورد دیگر

Xin Tong is an Associate Professor in the Department of Data Sciences and Operations at the Marshall School of Business, University of Southern California. His research focuses on asymmetric statistical learning, Neyman-Pearson classification, high-dimensional statistics, and network analysis. He holds a PhD in Operations Research from Princeton University (2012) and a B.S. in Mathematics from the University of Toronto (2007). Key contributions include developing Neyman-Pearson classification frameworks for imbalanced datasets and advancing spectral clustering techniques. His work has been supported by NSF grants and NIH funding. Tong serves as an Associate Editor for the Journal of the American Statistical Association and Journal of Business and Economic Statistics. Recent research highlights include studies on label-noise-adjusted classification algorithms, citation network analysis, and fairness-adjusted machine learning. He teaches courses on modern statistical learning and GenAI/ML strategies for business transformation. Notable awards include the Zellner Thesis Award (2013) and multiple NSF grants. His lab's work bridges statistical theory with applications in genomics, social networks, and healthcare risk assessment.

Robert Nowakمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Optimization
• +۳ مورد دیگر

Robert Nowak is a Professor of Electrical and Computer Engineering at the University of Wisconsin-Madison, holding the Nosbusch Professorship in Engineering. His research focuses on signal processing, machine learning, optimization, and statistics, with notable contributions to neural networks, active learning, and in-context learning. He is affiliated with the School of Computer, Data & Information Sciences and collaborates on interdisciplinary projects involving AI, human-computer interaction, and vision systems. His work spans theoretical advancements in approximation theory, regularization techniques, and neural network architecture design, alongside applied topics like humor analysis in AI, recommendation systems, and OOD detection. Recent publications emphasize multi-task learning, generative models, and scalable data filtering systems. Nowak’s research intersects machine learning foundations and practical applications, with a focus on improving data efficiency, generalization, and human alignment in AI systems. He leads projects on active learning strategies for open-world scenarios and develops frameworks for label-efficient learning.

Rahul Parhiمشاهده پروفایل

استادیار

• Machine Learning
• Neural Networks
• Signal Processing
• +۹ مورد دیگر

Rahul Parhi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego (UCSD), where he has been since 2024. Previously, he was a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL) from 2022 to 2024, and completed his Ph.D. in Electrical Engineering at the University of Wisconsin–Madison in 2022. His research focuses on the mathematics of data science, particularly the theoretical foundations of neural networks and deep learning, with a strong emphasis on functional/harmonic analysis, signal processing, and nonparametric statistics. Key research themes include the mathematics of computed tomography, sparsity, compressed sensing, and the geometry of Banach spaces. Education: B.S. in Mathematics, University of Minnesota, Twin Cities (2018) B.S. in Computer Science, University of Minnesota, Twin Cities (2018) Ph.D. in Electrical Engineering, University of Wisconsin–Madison (2022) His research interests span the interplay between functional and harmonic analysis with data science, including: Foundations of neural networks and deep learning Approximation properties of neural networks Nonparametric statistical estimation Inverse problems and compressed sensing Wavelet-based signal processing Banach space theory His recent publications explore topics such as neural network optimization, attention mechanisms, and the theoretical limits of learning in high-dimensional spaces. He has contributed to understanding the mathematical principles underlying deep learning's success in breaking the curse of dimensionality. His work bridges abstract mathematical concepts with practical applications in signal processing and machine learning. Parhi's research also extends to collaborative projects with experts in optimization and applied mathematics, aiming to develop novel algorithms with rigorous theoretical guarantees. His lab at UCSD focuses on advancing interdisciplinary research at the intersection of mathematics and data science.

Chun Hanمشاهده پروفایل

دانشیار

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

Chun Han is the Nancy M. and Samuel C. Fleming Associate Professor in the Department of Molecular Biology and Genetics at Cornell University, affiliated with the Weill Institute for Cell and Molecular Biology. He holds membership in three graduate fields: Biochemistry, Molecular and Cell Biology (BMCB); Genetics, Genomics and Development (GG&D); and Neurobiology and Behavior (NB&B). His research focuses on understanding gene function in model organisms using advanced genetic tools like CRISPR and innovative techniques such as MAGIC (gRNA-infused crossing over) to study human disease mechanisms through Drosophila models. Education: B.S. in Cell Biology and Genetics from Peking University (China), Ph.D. in Molecular Biology from Cincinnati Children’s Hospital (under Xinhua Lin), and postdoctoral research at UCSF (Jan Lab). He joined Cornell’s faculty in 2013. Teaches BIOG 4990: Independent Undergraduate Research in Biology. Research interests emphasize morphogen gradient formation, neural circuitry, and systems-level biology. Recent work explores seizure mechanisms in zebrafish, Drosophila locomotion mechanics, and theoretical frameworks for neural representation alignment. His articles bridge molecular genetics with computational neuroscience, applying CRISPR innovations and biomechanical modeling to advance biological understanding. Lab activities involve developing novel genetic tools and analyzing complex biological systems. Collaborates across disciplines to integrate experimental and computational approaches, with notable contributions to fruit fly-based disease research and gait analysis in insects.

Thang Buiمشاهده پروفایل

مدرس

• Probabilistic Modelling
• Machine Learning
• Monte Carlo Methods
• +۳ مورد دیگر

Thang Bui is a Lecturer (equivalent to tenure-track Assistant Professor) in Machine Learning at the School of Computing, Australian National University (ANU) since July 2022. Previously, he was a Lecturer at the University of Sydney (2018–2022) and spent two years (2019–2020) at Uber AI. He holds a PhD from the Cambridge Machine Learning Group at the University of Cambridge, supervised by Richard Turner and advised by Carl Rasmussen. Research Interests: His work focuses on probabilistic modeling and inference, Monte Carlo methods, distributed/continual learning, and model-based reinforcement learning. Current projects include uncertainty estimation in Gaussian processes and neural networks, adaptive models for changing environments, and interpretable machine learning techniques. Awards: Best Paper Award, ACL Workshop on Information Extraction from Scientific Publications (2023) Best Paper Award, NeurIPS workshop on Deep Learning through Information Geometry (2020) Best Paper Award, NIPS Workshop on Advances in Approximate Bayesian Inference (2015) Advising & Grants: Actively recruiting PhD/MPhil students and postdoctoral fellows. His research has been supported by grants focusing on Bayesian methods and scalable machine learning. He has advised students on topics ranging from Gaussian processes to federated learning and continual learning. Labs/Teams: Leads a research group exploring uncertainty quantification, adaptive learning systems, and probabilistic AI at ANU's College of Engineering and Computer Science.

Hua Luمشاهده پروفایل

استاد

• Computer Science
• Data Mining
• Data Science
• +۴ مورد دیگر

Hua Lu is a Professor in the Department of People and Technology Programming, Logic and Intelligent Systems at Roskilde University in Denmark. His research focuses on computer science with specialties in database systems, data mining, spatial data processing, and AI/ML applications for databases. He leads projects on AI-powered spatial databases and data-driven systems. His research interests span multiple domains including data science fundamentals, IoT data streams, location-based services, and database optimization techniques. Recent publications demonstrate strong focus on spatial-temporal data processing, machine learning integration with databases, and practical applications of data science. Professor Lu has received multiple scientific awards including Best Paper nominations and runner-up awards at premier conferences, recognizing his contributions to database and mobile computing research.

Sanghyeon Yuمشاهده پروفایل

استاد

• Plasmonics
• Subwavelength Resonators
• Mathematical Analysis
• +۴ مورد دیگر

Sanghyeon Yu is a Professor in the Department of Mathematics at Korea University. His primary research focuses on plasmonics, subwavelength resonators, and mathematical analysis of physical phenomena. He has contributed extensively to understanding stress concentration in fluid dynamics, non-Hermitian systems in metamaterials, and acoustic waveguide behaviors in bubbly crystals. Education details not explicitly provided in text Key research interests include exploring subwavelength-scale phenomena, exceptional points in parity-time symmetric systems, and sparsity-based spectral estimation techniques. Recent work emphasizes quantitative analysis of Stokes flow dynamics between circular cylinders and guided acoustic modes in structured materials. His publications demonstrate trends in interdisciplinary mathematical physics, combining analytical methods with applications in optics, acoustics, and materials science. Collaborations involve institutions like ETH Zurich and Springer publications. No awards were explicitly mentioned in the provided text. Advising activity details are not listed here, though collaborations with researchers like Habib Ammari and Brian Davies suggest active team involvement. Research outputs include journal articles and book chapters addressing advanced topics in applied mathematics and engineering physics.

Zoltán Szabóمشاهده پروفایل

استاد

• Statistical Machine Learning
• Kernel Methods
• Information Theoretical Estimators
• +۳ مورد دیگر

Zoltán Szabó is a Professor of Data Science at the Department of Statistics, London School of Economics (LSE). His research focuses on statistical machine learning, particularly kernel methods, information theoretical estimators, and scalable computation. He has held roles such as Programme Director of the MSc Data Science program and is actively involved in academic service, including Area Chair positions at top conferences like NeurIPS and ICML. His work bridges theory and applications, with contributions to fields like safety-critical learning, economics, climate data analysis, and natural language processing. Education and Affiliations: While specific educational details are not explicitly listed, his career trajectory indicates advanced training in statistics and machine learning. He is affiliated with the LSE's Department of Statistics and the Turing Institute, contributing to academic leadership and interdisciplinary projects. Research Interests: His work emphasizes kernel-based methods, including kernel Stein discrepancies, Hilbert-Schmidt independence criteria, and shape-constrained prediction. Applications span finance, economics, robotics, and environmental data analysis. Recent projects include developing outlier-robust estimators and scalable algorithms for high-dimensional data. Publications: His recent work includes advancements in kernel methods for dependency testing, shape-constrained regression, and robust estimation. Key themes include improving computational efficiency, theoretical guarantees for kernel approximations, and practical applications in interdisciplinary domains. Awards and Grants: While no explicit awards are listed, his contributions to NeurIPS (e.g., a Best Paper Award in 2017) and his role in securing grants (e.g., Europlace Institute of Finance) highlight his impactful research. He also serves on editorial boards, including JMLR and ACM Transactions on Probabilistic Machine Learning. Students and Labs: Supervises PhD students in areas like functional data analysis and scalable computation. Collaborates with researchers on projects such as distribution regression and safety-critical learning, contributing to both theoretical and applied outcomes.

Francisco A. Gómez Velaمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Bioinformatics
• +۳ مورد دیگر

Francisco A. Gómez Vela is an Associate Professor at Universidad Pablo de Olavide, Spain. His work bridges computational techniques with biomedical and energy data analysis. Education: PhD in Computer Science (Cum Laude) from Universidad Pablo de Olavide; BSc in Computer Science from University of Seville Research interests focus on: Machine learning for genetic and biomedical data Big data and high-performance computing (HPC) in bioinformatics Energy consumption analysis in smart buildings/cities Software tools for gene network reconstruction Recent publications highlight: Ensemble approaches for gene co-expression networks Biclustering methods in biological data mining GPU-accelerated bioinformatics software Applications in cancer biomarker discovery Educational data mining for microcompetency frameworks Teaching duties include: Computer Science (BSc and MSc) Bioinformatics Project Engineering History and Digital Humanities He has participated in national research projects and R+D+I transfer initiatives, developing tools like: bioScience (HPC bioinformatics library) CyEnGNet—App (Cytoscape gene network tool) BIGO (gene enrichment analysis) GNC–app (gene network validation)

Wei Niuمشاهده پروفایل

استادیار

• Real-time Machine Learning
• Artificial General Intelligence (AGI)
• Compiler Optimization
• +۵ مورد دیگر

Wei Niu is an Assistant Professor in the School of Computing at the University of Georgia, affiliated with the Franklin College of Arts & Sciences. His research focuses on real-time machine learning systems, AGI on embedded devices, compiler optimization, and parallel computing. He holds a Ph.D. in Computer Science from William & Mary (2023) and a B.S. from Beihang University (2016). Prior to academia, he worked at Bytedance as a mobile software developer (2016–2018). Research Interests: Dr. Niu’s work emphasizes optimizing deep neural networks (DNNs) for real-time execution on mobile and embedded systems. His contributions include frameworks like SmartMem (memory optimization for DNNs on mobile devices), GCD² (compiler for mobile DSPs), and SoD² (static optimization for dynamic DNNs). He also explores applications in autonomous vehicles, healthcare, and edge computing. Recent Trends in Publications: His articles address DNN efficiency, compiler-driven optimization, and real-time systems. Recent work includes semantic segmentation for autonomous vehicles, food recognition, and lossy data compression for scientific computing. He prioritizes practical solutions for edge devices and embedded systems. Funding: He leads NSF-funded projects totaling $600K, collaborating with Gagan Agrawal. These include MITTEN (memory hierarchy optimizations for transformers) and CropDL (scheduling DNNs on HPC clusters). Teaching & Labs: He offers RA positions for Ph.D. candidates and maintains a lab developing real-time AI applications (e.g., style transfer, object detection). Videos of his work are available on YouTube and Bilibili.

Marianne Liebiمشاهده پروفایل

استادیار

• Materials Science
• Biomedical Engineering
• X-ray Imaging Techniques
• +۳ مورد دیگر

Marianne Liebi is a Tenure-Track Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering’s Institute of Materials. She leads the Structure and Mechanics of Advanced Materials group at the Paul Scherrer Institute (PSI) within the Photon Science Division. Her research focuses on developing advanced X-ray imaging techniques, particularly small-angle X-ray scattering (SAXS) tensor tomography, to study hierarchical materials such as biomimetic composites, cellulose-based materials, and bone tissues. She has held positions at Chalmers University of Technology (Sweden) and Empa, and her work bridges materials science, biomedical engineering, and nanotechnology. Education: PhD in Food Science from ETH Zurich (2013), followed by postdoctoral research at the Swiss Light Source. Professional roles include Assistant Professor at Chalmers University (2017–2020) and Scientific Group Leader at Empa (2020–2021). Research interests emphasize imaging complex structures across scales, with applications in 3D printing, industrial plastics, and biological tissues. Key methods include SAXS, ptychographic tomography, and X-ray fluorescence. Her group collaborates widely, addressing challenges in material design, medical diagnostics, and sustainable polymers. Advising and grants: While specific grants are not detailed, her leadership roles and publications indicate active research funding. Labs/teams: PSI’s Laboratory for Condensed Matter and Materials Science, with ongoing collaborations at synchrotron facilities like ESRF and MAX IV.

• Statistical Inference
• Confidence Intervals
• Model Selection
• +۵ مورد دیگر

Dr. Paul Kabaila is an Associate Professor of Statistics at La Trobe University's Mathematics & Statistics school. He holds a PhD from the University of Newcastle and prior degrees from the University of New South Wales. His research focuses on statistical inference methodologies, particularly confidence intervals affected by preliminary model selection, frequentist methods utilizing uncertain prior information, and model-averaged confidence intervals. He has supervised numerous PhD students across diverse fields including biostatistics, bioinformatics, and business analytics. Dr. Kabaila has held visiting appointments at prestigious institutions including Oxford University and the Australian National University. He secured an ARC Discovery Grant (2002–2005) advancing statistical analysis of count data, with applications in epidemiology and finance. He led La Trobe University's first Statistics Program accreditation by the Statistical Society of Australia. His recent work emphasizes high-dimensional data challenges in statistical/machine learning and post-shrinkage strategies. He received the 2020 Distinguished Author Award from the Journal of Time Series Analysis for sustained contributions. His teaching spans all levels of statistical education, including advanced subjects like Statistical Inference and Theory of Statistics .

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۶ مورد دیگر

Shwai He is a PhD student and Affiliate Assistant Professor at the University of Maryland, advised by Ang Li. Their research focuses on advancing AI systems through innovative approaches in large language models (LLMs), fairness in machine learning, and efficient neural network architectures. Key areas include multi-agent systems, causal modeling for bias mitigation, and optimization techniques for mixture-of-experts models. Research interests span artificial intelligence, machine learning, and natural language processing with emphasis on practical applications like healthcare diagnostics and social pairing systems. Notable work includes developing GNWT-based multi-agent digital twins for social platforms and improving LLM transparency through token analysis. Publications highlight contributions to counterfactual fairness, dynamic-depth transformers, and parameter-efficient methods. Current efforts explore computational efficiency in vision-language models and bio-inspired antibody prediction systems. No scientific awards have been mentioned. Advising and grants: Currently a PhD student under Ang Li's supervision. Research involves collaborations across computer science and bioinformatics domains.

Vincent Vuمشاهده پروفایل

دانشیار

• High-Dimensional Data Analysis
• Statistical Learning
• Statistical Computing
• +۳ مورد دیگر

Vincent Vu is an Associate Professor of Statistics at The Ohio State University, affiliated with the Department of Statistics. He holds a PhD from the University of California, Berkeley (2009). His research focuses on high-dimensional statistics, statistical machine learning, and their applications in neuroimaging, particularly fMRI. He previously worked as a software engineer in Silicon Valley for over six years, blending computational and statistical expertise. His expertise includes statistical computing, multivariate analysis, and computational sufficiency. He has been funded by the NSF and received the 2012 AISTATS Best Paper Award. He serves as an associate editor for the Journal of the American Statistical Association and The American Statistician. His work bridges statistical theory and computation, addressing challenges in high-dimensional data analysis. Collaborations involve neuroscientific applications, combining algorithmic innovation with real-world problem-solving.