جست‌وجوی استاد و پژوهشگر

Daniel J. Hsuمشاهده پروفایل

استاد

• Machine Learning
• Foundations of Data Science
• Theory of Computation
• +۴ مورد دیگر

Dr. Daniel J. Hsu is a Professor of Computer Science at Columbia University, affiliated with the Foundations of Data Science Center and TRIPODS Institute. His research focuses on algorithmic statistics, machine learning theory, and their applications in public health informatics. He has advised numerous students and postdocs, and his work bridges foundational theory with practical systems like foodborne illness detection via social media analysis. Key roles: Associate Editor (ACM Transactions on Algorithms), Program Chair (ICML 2025, COLT 2019) Research areas: Foundations of Data Science, Machine Learning Theory, Fairness, and High-dimensional Statistics His work on detecting foodborne illnesses using Yelp reviews has been deployed by NYC Health departments. Recent contributions include advancements in transformer architectures, group fairness algorithms, and multi-group learning frameworks. Scientific awards include the Sloan Fellowship and multiple NSF grants. He has pioneered interactive machine teaching methods and developed algorithms for robust parameter estimation in high-dimensional settings.

Anna Gottardمشاهده پروفایل

دانشیار

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

Anna Gottard is an Associate Professor of Statistics at the University of Florence, where she leads the Department of Statistics, Computer Science, and Applications. She directs the Florence Center for Data Science (FDS) and participates in the Technical Scientific Committee of the Tuscan Center for Big Data, Data Science, and AI (CBDAI). Her research focuses on multivariate statistical models, particularly graphical models, and extends to statistical machine learning, fair models, and directional data analysis. She is an Associate Editor for the Journal of the Royal Statistical Society Series A (JRSSA) and Statistical Methods & Applications (SMA). Her recent work includes Bayesian approaches for mixed graphical models, uncertainty-aware classification trees, and methodological advancements in latent uncertainty models. Her contributions span theoretical developments and applied research in interdisciplinary areas like biostatistics and sustainability. Her research interests emphasize bridging statistical theory with practical applications, including fairness in machine learning, interpretable models, and tree-based methodologies. She has actively contributed to open-source software, notably the Mix3Trees R package for mixed-effect tree models. Her work addresses challenges in variable selection, graphical model inference, and ethical AI practices. Current projects explore Bayesian frameworks for complex data structures and methodological improvements in graphical model interpretability. Anna has advised on interdisciplinary collaborations, such as studies on GDPR compliance in biobanking and epidemiological modeling of the SARS-CoV-2 pandemic in Tuscany. She collaborates with institutions like the CBDAI to advance data science applications in regional policy and healthcare. Her research trajectory reflects a commitment to both foundational statistical theory and real-world problem-solving across diverse domains.

Xiaolei Fangمشاهده پروفایل

دانشیار

• Statistical Learning
• Deep Learning
• Federated Learning
• +۳ مورد دیگر

Xiaolei Fang is Associate Professor in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. His research develops advanced statistical learning, deep learning, and optimization methods for industrial applications involving high-dimensional data, with particular focus on condition monitoring, failure prognostics, and system performance optimization. He holds a PhD in Industrial Engineering and MS in Statistics from Georgia Tech. Professor Fang's research integrates machine learning with industrial engineering to solve complex problems in predictive maintenance, quality control, and energy systems. His methodological innovations include federated learning approaches for privacy-preserving prognostics, distributionally robust machine learning models, and tensor-based statistical methods for manufacturing quality diagnostics. He has received multiple prestigious awards including the ISE Outstanding Research Award (2024), Sigma Xi Best PhD Thesis Award (2019), and SAS Data Mining Best Paper Award (2016). His research has been funded by NSF, Cisco Systems, and the US Department of Energy. Professor Fang teaches courses in Quality Design & Control, Statistical Models for Systems Analytics, High-Dimensional Data Analytics, and Optimization Models. He has supervised 9 PhD students to completion and currently advises 7 graduate students working on projects spanning federated learning for prognostics, tensor-based quality control, and machine learning applications in manufacturing and energy systems.

Jeff Linderothمشاهده پروفایل

استاد

• Large-Scale Numerical Optimization
• Mixed-Integer Nonlinear Programming
• Operations Research
• +۵ مورد دیگر

Jeff Linderoth is the Harvey D. Spangler Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on large-scale numerical optimization, mixed-integer nonlinear programming, and stochastic programming, with applications in energy systems, global routing, and industrial processes. Education: BS in General Engineering (highest honors) from University of Illinois at Urbana-Champaign, MS in Operations Research from Georgia Institute of Technology, PhD in Industrial Engineering from Georgia Institute of Technology. Linderoth's work addresses theoretical and applied challenges in optimization, including developing algorithms for mixed-integer programming, analyzing knapsack polytopes, and creating tools like the Minotaur optimization toolkit. His recent publications explore integer programming techniques for subspace clustering, complementarity constraints, and customized coverage instrumentation. Selected trends in his research include advancements in stochastic programming, orbital branching for symmetric integer programs, and congestion analysis in power systems. His group contributes to optimization software and data-driven libraries like MIPLIB. Scientific Award: Harvey D. Spangler Professor.

Karthika Mohanمشاهده پروفایل

استادیار

• Causal Inference
• Graphical Models
• Missing Data
• +۳ مورد دیگر

Dr. Karthika Mohan is an Assistant Professor of Computer Science in the College of Engineering at Oregon State University, affiliated with the School of Electrical Engineering and Computer Science. Her research bridges artificial intelligence and causal inference, focusing on graphical models, missing data, and non-IID data challenges. Her work has been recognized with the Google Outstanding Graduate Research Award. She serves as an associate editor for the Journal of Causal Inference and has secured NSF funding for research on incomplete data. Dr. Mohan mentors students in causal inference methods and maintains collaborations with institutions like UC Berkeley and UCLA. Her laboratory develops innovative approaches for causal reasoning in AI systems.

Jim Luedtkeمشاهده پروفایل

استاد

• Operations Research
• Stochastic Optimization
• Integer Programming
• +۲ مورد دیگر

Jim Luedtke is a Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on operations research, integer programming, and stochastic optimization methods for solving discrete and uncertain decision problems. Educational Background: BS in Industrial Engineering from University of Wisconsin-Madison MS in Operations Research from Georgia Institute of Technology PhD in Industrial and Systems Engineering from Georgia Institute of Technology Postdoctoral Research at IBM T.J. Watson Research Center His work spans applications in power systems optimization, healthcare analytics, and network design, with particular emphasis on developing cutting-edge algorithms for chance-constrained and multistage stochastic programming problems. Recent publications demonstrate strong focus on Benders decomposition techniques, Lagrangian dual methods, and distributionally robust optimization frameworks. Scientific Awards: NSF CAREER Award (2010) for "Risk Management via Stochastic Programming: Models, Computation, and Applications"

• Machine Learning
• Graph Neural Networks
• Time Series Analysis
• +۷ مورد دیگر

Filippo Maria Bianchi is an Associate Professor in the Department of Mathematics and Statistics at UiT The Arctic University of Norway, where he conducts research at the intersection of machine learning, dynamical systems, and complex networks. He is also a Senior Researcher at NORCE Norwegian Research Centre and actively contributes to the IEEE Task Force on Learning for Structured Data and the ELLIS Society. Department: Department of Mathematics and Statistics School: Faculty of Science and Technology University: UiT The Arctic University of Norway Adjunct Position: Senior Researcher, NORCE Education: Bachelor’s in Computer Engineering, Sapienza University of Rome Master’s in Artificial Intelligence & Robotics, Sapienza University of Rome (cum laude, 2012) PhD in Machine Learning, Sapienza University of Rome His research focuses on graph machine learning, time series analysis, reservoir computing, and probabilistic forecasting , with applications in energy analytics and remote sensing. He has led and contributed to numerous projects involving Arctic power grids, satellite-based environmental monitoring, and deep learning for sustainability. The recent publications reflect a strong trend in graph neural networks —particularly pooling mechanisms, spatiotemporal modeling, and explainability—alongside applications in energy forecasting, avalanche detection, and remote sensing . His work combines theoretical innovation with real-world impact, especially in Arctic and remote environments. Scientific Affiliations and Leadership: Vice-Chair, IEEE Task Force on Learning for Structured Data Member, ELLIS Society Co-founder, Northernmost Graph Machine Learning group Member, IEEE Task Force on Reservoir Computing Visiting Professor, Politecnico di Milano (2024–2025) He actively mentors students and collaborates on interdisciplinary research. He has led projects in power grid reliability, solar fault detection, and unsupervised change detection in satellite imagery . His work is supported by open-source implementations and reproducible research practices. Laboratories and Research Groups: Northernmost Graph Machine Learning group (co-founder) ARC Research Group, UiT Graph Machine Learning Group, Lugano

Gauthier Gidelمشاهده پروفایل

دانشیار

• Deep Learning
• Adversarial Machine Learning
• Generative Modeling
• +۵ مورد دیگر

Gauthier Gidel is an Associate Professor at the Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Science at Université de Montréal, where he also holds the prestigious Canada CIFAR AI Chair position. He is a core faculty member of Mila, Quebec's AI research institute, and maintains active research collaborations with leading institutions. His academic journey includes a PhD in Computer Science under the supervision of Simon Lacoste-Julien, with internships at Sierra, ElementAI, and DeepMind during his doctoral studies. Dr. Gidel's research spans multiple critical areas in machine learning, with particular emphasis on generative modeling , adversarial machine learning , and variational inequalities for machine learning. His work explores the intersection of optimization theory and practical AI systems, focusing on challenges like LLM safety alignment, multi-agent cooperation, and robustness against adversarial attacks. He is particularly known for his contributions to understanding the theoretical foundations of generative adversarial networks through variational inequality frameworks. His recent publications reveal a strong trend toward addressing critical challenges in large language model safety and alignment, with numerous 2024-2025 papers focusing on adversarial robustness, safety evaluation methodologies, and alignment techniques for LLMs. Simultaneously, his foundational work continues in optimization theory, particularly in variational inequalities and performative prediction, demonstrating his dual focus on practical AI safety concerns and theoretical machine learning foundations. Canada CIFAR AI Chair Core member of Mila Organizer of popular NeurIPS workshops on smooth games Co-founder of the ICLR blog post track Dr. Gidel actively supervises an extensive research group with approximately 10 current graduate students and numerous alumni who have secured positions at leading institutions including Inria Lyon, Oxford, and industry research labs. His research is supported by multiple substantial grants from CRSNG, MITACS, and IVADO, including the prestigious CRSNG Discovery Grant program and MITACS Acceleration Québec projects focused on fraud detection in music streaming and conditional generation. His laboratory maintains strong connections with both academic and industry partners, fostering a collaborative environment focused on advancing AI safety and theoretical understanding.

• Statistics
• Learning Theory
• High-dimensional statistics
• +۴ مورد دیگر

Jaouad Mourtada is an Assistant Professor in the Department of Statistics at ENSAE/CREST, École Nationale de la Statistique et de l'Administration Économique since September 2020. Previously, he was a postdoctoral researcher at the Laboratory for Computational and Statistical Learning at the University of Genoa (2019-2020). He completed his PhD in Statistics at École Polytechnique under the supervision of Stéphane Gaïffas and Erwan Scornet. His educational background includes a Master's degree in Mathematics with specialization in Probability and Random Models (2016), a Master's degree in Fundamental Mathematics (2015), and a Bachelor's degree in Mathematics from Pierre and Marie Curie University and École Normale Supérieure (2013). Dr. Mourtada's research focuses on the intersection of statistics and learning theory, with particular interest in high-dimensional statistics, online learning, and density estimation. His work explores the complexity of prediction and estimation problems through rigorous theoretical analysis. His research spans statistical learning theory, robust statistics, and the theoretical foundations of machine learning algorithms. His publication record since 2017 demonstrates consistent contributions to top-tier venues in statistics and machine learning, with recent work focusing on universal coding, aggregation methods, robust regression, and the theoretical analysis of kernel methods and random forests. His research shows a progression from online learning and expert aggregation to more complex statistical learning problems involving high-dimensional data and model misspecification. He teaches courses including Statistical Learning Theory for Master 2 Data Science students and Probability Theory at ENSAE. His teaching spans theoretical foundations of machine learning and core probability concepts for advanced statistics students.

George C Tsengمشاهده پروفایل

استاد

• Genomics
• Bioinformatics
• Statistical Learning
• +۹ مورد دیگر

Dr. George C Tseng serves as Professor and Vice Chair for Research in the Department of Biostatistics at the University of Pittsburgh School of Public Health, with secondary appointments in Human Genetics and Computational and Systems Biology. His educational background includes a BS (1997) and MS (1999) in Mathematics from National Taiwan University and an ScD (2003) in Biostatistics from Harvard School of Public Health. Dr. Tseng's research focuses on developing statistical methodologies for genomic and bioinformatic applications to advance precision medicine. His work spans multiple high-impact areas including multi-omics data integration, machine learning for high-dimensional data, cluster analysis for disease subtyping, and statistical methods for experimental design in omics studies. His approach emphasizes close collaboration with biological and clinical researchers to ensure methodological relevance to real-world problems. His publication record demonstrates consistent contributions to top statistical and bioinformatics journals, with recent work focusing on congruence analysis between animal models and humans, outcome-guided clustering methods, and high-dimensional causal mediation analysis. Elected Fellow, American Statistical Association (2017) Statistician of the Year, ASA Pittsburgh Chapter (2017) Provost's Award for Excellence in PhD Mentoring, University of Pittsburgh (2019) Clinical Research Scholar (K12) Award, NIH (2007-2009) Elected Member, International Statistical Institute (2012) Dr. Tseng has successfully mentored over 25 PhD students who have secured positions in academia, industry, and government agencies. His laboratory has maintained continuous NIH funding as principal investigator since 2012, including current grants R01CA285337 (2025-2030) and R01LM014142 (2023-2026). The Tseng Lab operates as a collaborative research environment focused on translating statistical innovations into practical solutions for biological and medical challenges, with strong connections to multiple research centers and clinical departments at the University of Pittsburgh.

Garvesh Raskuttiمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Optimization Theory
• Graphical and Network Modeling
• +۵ مورد دیگر

Garvesh Raskutti is an Associate Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. He holds joint affiliations with the Departments of Computer Science, Electrical and Computer Engineering, and the Wisconsin Institute of Discovery Optimization Group. His research focuses on statistical machine learning, optimization, graphical/network modeling, and information theory, with applications to systems biology and neuroscience. Education: MEng from the University of Melbourne (2008), PhD from UC Berkeley (2012, advised by Martin Wainwright and Bin Yu), and postdoctoral work at SAMSI. Teaching awards include Honored Instructor (2014, 2017) and Madison Teaching and Learning Excellence Fellow (2015). He advises multiple PhD and undergraduate students, including Yuan Li, Hyebin Song, and Lili Zheng. His grants include NGA HM0476-17-1-2003 (Co-PI), ARO W911NF-17-1-0357 (Co-PI), and NSF-DMS 1407028 (Sole PI). Research interests span large-scale statistical inference, computational-statistical trade-offs, and applications in systems biology and neuroscience. His work bridges optimization (e.g., gradient descent, convex regularization), high-dimensional regression, and network analysis. Recent trends in publications emphasize methods for non-convex optimization, sparse models, and network structure learning. Awards include teaching recognition and grants in statistical methodology. Advising spans theoretical and applied projects, with former students like Gunwoong Park (now at University of Korea). Collaborations include interdisciplinary teams in machine learning and signal processing.

Naim U. Rashidمشاهده پروفایل

دانشیار

• Precision medicine
• Genomics
• Statistical computing
• +۷ مورد دیگر

Naim U. Rashid, PhD, is an Associate Professor with tenure in the Department of Biostatistics at the UNC Gillings School of Global Public Health and holds a joint appointment as Research Associate Professor at the Lineberger Comprehensive Cancer Center. He serves as Associate Director of the Lineberger Biostatistics Shared Resource and co-directs the Biostatistics Cores of the UNC Pancreatic and Breast Cancer SPOREs. His work bridges statistical methodology development with collaborative cancer research, focusing on translating genomic discoveries into clinical applications. Dr. Rashid's research spans precision medicine, genomics, statistical computing, and machine learning with specific applications to pancreatic and breast cancers. His lab develops novel statistical methods for high-throughput genomic data analysis, cancer subtyping, missing data problems in deep learning, and clinical trial design. Recent work includes developing an AI tool that recommends optimal clinical trials to pancreatic cancer patients, funded by a $311,000 Department of Defense grant in 2024. His methodological contributions focus on improving replicability in gene signature selection and clinical prediction, with emphasis on addressing racial disparities in cancer outcomes. His publication record shows consistent output in top statistical and medical journals, with recent work focusing on high-dimensional statistics, missing data methods, and cancer genomics. His research demonstrates a clear trajectory from methodological innovation to clinical implementation, particularly in pancreatic cancer where his PurIST classifier has gained recognition. The work increasingly incorporates machine learning approaches while maintaining strong statistical foundations. Delta Omega Faculty Award (2021, UNC Chapel Hill) IBM and R.J. Reynolds Junior Faculty Development Award (2017, UNC Chapel Hill) Barry H. Margolin Dissertation Award (2013, UNC Chapel Hill) Training Grant recipient (2006-2011, Genomics and Cancer) Dr. Rashid actively mentors graduate students and serves as trial statistician on multiple cancer clinical trials. He teaches BIOS 735, a doctoral-level course on statistical computing, and is involved with the Translational Breast Cancer Research Consortium Statistical Working Group. His lab collaborates extensively with clinicians at UNC Lineberger and beyond, with recent work including the PROCLAIM Study examining mHealth apps to improve diverse recruitment in pancreatic cancer trials. The Rashid Lab focuses on developing computational tools that directly impact clinical decision-making while addressing methodological challenges in genomic data analysis.

Anru Zhangمشاهده پروفایل

دانشیار

• Tensor Learning
• High-Dimensional Statistics
• Machine Learning
• +۳ مورد دیگر

Anru Zhang is the tenured Eugene Anson Stead, Jr. M.D. Associate Professor with joint appointments in Biostatistics & Bioinformatics, Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University. He holds a Ph.D. from the University of Pennsylvania (2015, advised by T. Tony Cai) and a B.S. in Mathematics from Peking University (2010). Current roles: Associate Professor at Duke (2024–present), previously Assistant Professor at UW-Madison (2018–2021) Research focus: Tensor learning, high-dimensional statistics, EHR analysis, and healthcare applications Mentorship: Supervises active research team including postdocs (Jianbin Tan, Qiuyi Wu) and PhD students (Runshi Tang, Yinrui Sun) Research Trends : His recent publications emphasize tensor methods in biomedical data (EHR, microbiome, Alzheimer’s), Riemannian optimization for high-dimensional problems, and hybrid statistical-computational approaches. Key themes include healthcare AI, EHR analysis, and non-convex optimization. Scientific Awards : COPSS Emerging Leader Award (2024) IMS Tweedie New Researcher Award (2022) ASA Gottfried E. Noether Junior Award (2021) NSF CAREER Award (2020) AMIA Data Science Outstanding Paper Award (2023) Advising & Grants : Mentored 16+ students/postdocs, including Yuetian Luo (IMS Lawrence D. Brown Award) and Yuchen Zhou (IMS Hannan Travel Award). Current grants include NIH-funded projects on sepsis detection, mental health AI, precision genetic testing, and telehealth interventions, plus NSF CAREER funding for statistical inference in high-dimensional structures. Labs & Teams : Leads a research group at Duke focusing on tensor learning, statistical theory, and healthcare AI applications. Collaborates with Duke’s AI Health initiative and serves as Associate Editor for leading journals like Annals of Statistics and JASA.

• Neural Networks
• Genetic Algorithms
• System Identification and Control
• +۷ مورد دیگر

FURUZUKI, Takayuki is a Professor at Waseda University's Faculty of Science and Engineering, Graduate School of Information, Production, and Systems. He holds a PhD from Kyushu Institute of Technology and maintains the NCLab research group (http://nclab.w.waseda.jp/nclab/). His professional memberships include the Institute of Electronics, Information and Communication Engineers, Institute of Electrical Engineers of Japan, Society of Instrument and Control Engineers, and IEEE. His educational background includes a PhD in Information Science from Kyushu Institute of Technology (1994-1997), a Master's degree in Electronics Engineering from Sun Yat-Sen University (1983-1986), and a Bachelor's degree in Electrical & Electronic Systems Engineering from Sun Yat-Sen University (1979-1983). Professor FURUZUKI's research spans neural networks, genetic algorithms, system identification and control, complex systems, bioinformatics, and combinatorial optimization. His work integrates soft computing techniques with medical informatics and control systems engineering, developing innovative approaches for problems ranging from brain tumor segmentation to music information retrieval. His research methodology often combines theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His recent publication record shows a strong focus on sequential recommendation systems, graph neural networks for medical imaging, music information retrieval, and power systems analysis. The consistent thread across his work is the application of advanced machine learning techniques to solve complex, real-world problems across diverse domains, with particular emphasis on handling missing data, improving model efficiency, and enhancing prediction accuracy. His scientific achievements have been recognized with several prestigious awards: ISCIIA2008 Excellent Paper Award (2008) Institute of Electrical Engineers of Japan Excellent Paper Award (2001) Guangdong Province Higher Education Science and Technology Progress Second Prize (1991) Guangdong Province Higher Education Science and Technology Progress Second Prize (1989) Chinese Ministry of Education Scientific and Technological Achievements Second Prize (1986) Professor FURUZUKI has maintained continuous research activity since joining Waseda University in 2003, with significant contributions to multiple research projects spanning neural computation, soft computing, and system engineering. His work demonstrates strong international collaboration, with publications involving researchers from various institutions worldwide. His research output includes over 410 papers with 3,623 citations and an h-index of 23 according to Scopus data. The NCLab research group, which he leads, focuses on neural computation and related areas, providing a platform for interdisciplinary research that bridges theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His laboratory environment fosters innovation in machine learning algorithms and their application to real-world problems, with particular emphasis on developing efficient, robust solutions for complex data analysis tasks.

Chenang Liuمشاهده پروفایل

دانشیار

• Smart Manufacturing
• Data Analytics
• Machine Learning
• +۲ مورد دیگر

Dr. Chenang Liu is an Associate Professor in the Department of Industrial Engineering & Management at Oklahoma State University's College of Engineering, Architecture and Technology (CEAT). Their research focuses on smart manufacturing systems, real-time quality monitoring, and machine learning applications in manufacturing and healthcare. Ph.D., Industrial and Systems Engineering, Virginia Tech, 2019 M.S., Statistics, Virginia Tech, 2017 B.S., Mathematics (Statistics track), Zhejiang University, China, 2014 B.S., Environmental and Resource Sciences, Zhejiang University, China, 2014 Research Interests: Dr. Liu develops advanced sensing and data analytics methodologies for smart manufacturing, statistical frameworks for real-time quality control, and mathematical models integrating machine learning with healthcare applications. Their work bridges industrial engineering principles with cutting-edge data science techniques. Publication Trends: Recent articles demonstrate expertise in diabetic retinopathy prediction via interpretable AI, supply chain coordination mechanisms, EHR analytics for disease progression modeling, and combinatorial optimization algorithms. Key themes include healthcare data science, resilient manufacturing systems, and stochastic resource allocation. Scientific Recognition: Featured Article in ISE Magazine, IISE, 2019 Gilbreth Memorial Fellowship, IISE, 2018-2019 Best Poster Award, INFORMS Annual Meeting, 2018 Best Student Paper Finalist, IISE Annual Conference, 2018 Best Paper Awards at INFORMS (2017) and IISE (2017)