جستجوی استاد راهنمای دانشگاهی

Konstantin Baumanمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Recommender Systems
• +۳ مورد دیگر

Dr. Konstantin Bauman is an Associate Professor in the Department of Management Information Systems at Temple University's Fox School of Business. He holds a PhD in Mathematics (Geometry and Topology) from Moscow State University and dual Master’s degrees in Mathematics and Machine Learning from prestigious Russian institutions. His research focuses on machine learning, data science, and context-aware recommender systems, emphasizing novel methods for predicting customer preferences and designing personalized recommendation frameworks. Education: PhD in Mathematics (Geometry and Topology), Moscow State University MS in Mathematics, Moscow State University MS in Machine Learning, Moscow Institute of Physics and Technology/Yandex School of Data Analysis Research Interests: Data Science and Analytics Machine Learning and Recommender Systems Context-Aware Systems and Text Mining Technology-Enhanced Learning Recent Work Trends: His publications emphasize context-aware recommendation algorithms, privacy concerns in personalized systems, and applications of hyperbolic embeddings. He also explores device impact on employee feedback and cryptocurrency investor behavior using multimodal data analysis. Awards: None explicitly listed in the provided materials. Advising/Grants: No formal advisees listed; his work at Yandex and NYU involved leading machine learning teams and tackling large-scale data science challenges. Labs/Teams: Active in the MIS department at Temple, contributing to research on adaptive learning systems and enterprise machine learning applications.

Per Christian Hansenمشاهده پروفایل

استاد

• Numerical Analysis
• Numerical Linear Algebra
• Iterative Reconstruction Methods
• +۸ مورد دیگر

Per Christian Hansen is a Professor at the Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark (DTU), where he leads the Section for Scientific Computing. He is a VILLUM Investigator and heads the CUQI (Computational Uncertainty Quantification for Inverse Problems) research initiative, aiming to develop accessible computational platforms for uncertainty quantification in inverse problems. His expertise lies in numerical analysis, numerical linear algebra, iterative reconstruction methods, and computational inverse problems, with applications in tomography, signal analysis, and plasma physics. His research integrates theoretical analysis—such as perturbation and convergence analysis—with the development of robust, adaptive, and efficient computational methods. He has co-authored five books, over 100 scientific papers, and several widely used MATLAB software packages, including IR Tools and Regularization Tools. His recent work (2023–2025) emphasizes uncertainty quantification, Bayesian inversion, and high-dimensional tomography in fusion plasmas, reflecting a strong trend toward probabilistic and robust modeling in inverse problems. He is a SIAM Fellow (2015) for his contributions to computational methods for rank-deficient and discrete ill-posed problems and regularization techniques. His scientific leadership is evident in both theoretical advances and practical software implementations. He actively collaborates across disciplines, particularly in nuclear fusion and medical imaging, and continues to supervise PhD students and publish in top-tier journals such as Inverse Problems , SIAM Journal on Scientific Computing , and Nuclear Fusion . SIAM Fellow (2015) VILLUM Investigator He advises PhD and Master’s students in computational mathematics and inverse problems, and his research is supported by major grants, including the VILLUM Investigator award. He leads the CUQI team, which develops open-source tools for non-experts to apply uncertainty quantification in inverse problems. His lab focuses on creating modeling frameworks that bridge theory, computation, and real-world applications in materials science, imaging, and plasma diagnostics.

• Statistical Genomics and Bioinformatics
• High-Dimensional Data Analysis
• Bayesian Statistics
• +۴ مورد دیگر

Shili Lin is a Professor of Statistics at The Ohio State University's Department of Statistics, within the College of Arts and Sciences. She joined the faculty in 1995 after serving as the Neyman Visiting Assistant Professor at the University of California, Berkeley. Her expertise spans statistical genomics, bioinformatics, high-dimensional data analysis, Bayesian statistics, and Monte Carlo methods. Lin collaborates extensively with medical researchers to address challenges in genomic data such as ultra-high dimensionality, complex dependencies, and sparsity, focusing on diseases like cancer, multiple sclerosis, tuberculosis, and diabetes. She has contributed to developing computational tools for analyzing chromatin interactions, methylation patterns, and metagenomic samples. Lin holds a PhD from the University of Washington (1993). Her professional roles include serving as an Associate Editor for Biometrics , Statistical Applications in Genetics and Molecular Biology , and Statistics in Biosciences , as well as an Editorial Board member for Genetic Epidemiology . She is a standing member of NIH's Biostatistical Methods and Research Design Study Section and has served on multiple NSF and NIH grant review panels. Additionally, she is President Elect of the Caucus for Women in Statistics and has been a member of the ASA Committee on AAAS representation for six years. Her research interests emphasize statistical methodologies tailored to genomic data, including model selection, epigenetic analysis, and integrative approaches for multi-omics data. Lin's work often combines theoretical advancements with practical applications, such as predicting relapse in immune-mediated disorders and improving imputation techniques for single-cell Hi-C analysis. She has pioneered software tools like TopKLists and GrammR to facilitate ranked list aggregation and metagenomic data analysis. Lin's scientific accolades include ASA Fellowship (2004), AAAS Fellowship (2009), and membership in the International Statistical Institute (2014). Her contributions to statistical genetics and epigenomics have been recognized through grants and editorial leadership roles. While her research group focuses on cutting-edge methods, no formal advisees or students are explicitly listed in the provided materials.

Markus Bachmayrمشاهده پروفایل

استاد

• Numerical Analysis
• Partial Differential Equations
• High-dimensional Problems
• +۴ مورد دیگر

Prof. Dr. Markus Bachmayr is a full professor at the Institute for Geometry and Practical Mathematics, RWTH Aachen University, holding the chair for Applied Mathematics. His research focuses on nonlinear approximation, high-dimensional partial differential equations (PDEs), uncertainty quantification, and numerical methods in quantum chemistry. He leads the ERC Consolidator Grant project Computational Complexity of Highly Nonlinear Approximations (COCOA) and contributes to CRC 1481 Sparsity and Singular Structures, and RTG 2326 Energy, Entropy, and Dissipative Dynamics. His recent work emphasizes adaptive low-rank and sparse approximation techniques for parametric and stochastic PDEs, including applications in radiative transfer and poroviscoelastic flow modeling. He serves as Editor-in-Chief of Foundations of Computational Mathematics and Associate Editor for multiple journals. Scientific Awards: John Todd Award 2013 Borchers Plakette 2014 Erwin Wenzl Preis 2007 He has taught courses such as Numerische Analysis I/II, Numerische Mathematik für Elektrotechniker, and seminars on numerical methods and approximation theory.

Themistoklis Sapsisمشاهده پروفایل

استاد

• Probabilistic Prediction
• Nonlinear Systems Analysis
• Machine Learning in Fluid Dynamics
• +۳ مورد دیگر

Themistoklis Sapsis is a Professor in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he also holds an affiliation with the MIT Institute for Data, Systems, and Society. He earned his Ph.D. in Mechanical Engineering from MIT in 2011 and previously served as an Assistant Research Scientist at NYU’s Courant Institute of Mathematical Sciences. His research focuses on developing analytical, computational, and data-driven methods to predict and quantify extreme events in high-dimensional nonlinear systems, such as turbulent fluid flows and mechanical systems. Key areas include probabilistic modeling of climate extremes, machine learning for climate simulation corrections, and uncertainty quantification in complex dynamical systems. Recent work emphasizes applications in ocean engineering (e.g., vortex-induced vibrations, wave energy systems) and environmental science (e.g., spatially resolved climate extremes, bias correction in Earth system models). His methodologies combine stochastic emulators, Bayesian experimental design, and neural networks to address challenges in data sparsity and model fidelity. Notable contributions include frameworks for correcting coarse-scale climate simulations using machine learning, real-time ocean temperature reconstruction from satellite data, and data-driven modeling of hydrodynamic interactions in marine risers. His research bridges theoretical developments with practical applications in energy systems, structural monitoring, and autonomous systems. Prof. Sapsis collaborates with interdisciplinary teams and has contributed to initiatives such as FIRSTLING-DIGIMAR (a marine riser digital twin) and multi-fidelity frameworks for autonomous seakeeping. His work is supported by grants focused on advancing machine learning in scientific modeling and extreme event prediction.

Steven Andrew Culpepperمشاهده پروفایل

استاد

• Quantitative Methods in the Social Sciences
• Psychometrics
• Latent Class Models
• +۶ مورد دیگر

Steven Andrew Culpepper is a Professor of Statistics at the University of Illinois at Urbana-Champaign, holding additional appointments as Professor in the Beckman Institute for Advanced Science and Technology, Psychology, and Educational Psychology. He specializes in quantitative methods for social sciences, focusing on psychometric models, latent class analysis, and statistical computing. Education: PhD, Educational Psychology, University of Minnesota, 2006 BS, Economics, Bowling Green State University, 2001 Research interests include advanced statistical methodologies such as latent class models, high-stakes testing analysis, and applications of Bayesian computing in education and organizational research. His work emphasizes improving large-scale assessment systems through innovative modeling approaches. His publications consistently address latent structure modeling, cognitive diagnosis frameworks, and methodological advancements in educational and behavioral statistics. While no scientific awards are explicitly listed, his contributions to psychometric theory and statistical software development are notable. Steven has grants and consulting projects related to statistical methodologies but specific grant details are not provided in the texts. He has no listed advisees/PhD students in the provided information. He collaborates across disciplines through affiliations with the Beckman Institute and maintains active software development projects, including R packages like 'rrum' and 'pathmodelfit'.

• Big Data
• Business Analytics
• Healthcare Analytics
• +۴ مورد دیگر

Haipeng Shen is a Professor of Innovation and Information Management at HKU Business School, The University of Hong Kong, serving as Associate Dean (EMBA and IMBA) and holding the Patrick S C Poon Professorship in Analytics and Innovation. He chairs the Business Analytics and Innovation program and joined HKU in 2015 after previously holding a professorship at the University of North Carolina at Chapel Hill. His academic credentials include: PhD in Statistics, The Wharton School of Business, University of Pennsylvania, 2003 MA in Statistics, The Wharton School of Business, University of Pennsylvania, 2000 BS in Mathematics, School of Mathematical Sciences, Peking University, 1998 Professor Shen's research focuses on data-driven decision making under uncertainty, with expertise spanning big data analytics, business analytics, healthcare analytics, and service engineering. He develops advanced statistical and machine learning methodologies to solve complex operational problems in call centers, optimize stroke care protocols, and enhance financial risk modeling, emphasizing real-time applications in high-stakes environments. Analysis of his recent publications reveals a consistent interdisciplinary approach bridging operations research, statistics, and domain-specific knowledge. His work demonstrates strong methodological innovation in time-series forecasting for service systems, risk assessment frameworks for medical complications, and covariance structure analysis for financial markets, with direct translational impact on business operations and clinical outcomes. His scientific contributions have been recognized with prestigious awards including: Most Influential Publication Award from China Stroke Association (2018) Fellow of the American Statistical Association (2015) Best Advisor of the Year Award from Academy of Asian Business (2018) Elected Member of International Statistical Institute (2015) Cluster Chair for Big Data Analytics at INFORMS International (2015) As an academic leader, Professor Shen has secured significant research funding from organizations including The Xerox Foundation and National Institute on Drug Abuse. He serves as Associate Editor for Management Science, Journal of the American Statistical Association, and Technometrics, while mentoring graduate students in statistical methodology and applied analytics. His current initiatives position HKU Business School at the forefront of healthcare innovation through big data analytics, driving collaborations with medical institutions to transform stroke care and hospital operations in Asia.

Aditya T Siripuramمشاهده پروفایل

دانشیار

• Fourier analysis
• Signal processing
• Machine learning
• +۳ مورد دیگر

Aditya T Siripuram is an Associate Professor at the Indian Institute of Technology Hyderabad (IITH), holding joint appointments in the Department of Electrical Engineering and the Department of Artificial Intelligence. He completed his PhD at Stanford University and holds B.Tech and M.Tech degrees from IIT Bombay. Education: PhD in Electrical Engineering, Stanford University (2017) - GPA: 4.17/4 M.Tech in Electrical Engineering, IIT Bombay (2009) - GPA: 9.79/10 B.Tech in Electrical Engineering, IIT Bombay (2009) - GPA: 9.79/10 Research Interests: His research spans Fourier analysis, signal processing, machine learning, convex and combinatorial optimization, with applications in AI/ML and applied mathematics. His work particularly focuses on computational aspects of Fourier analysis, including fast DFT computation for structured signals, convolution idempotents, and graph-based signal processing techniques. His recent research directions involve developing efficient algorithms for computing Discrete Fourier Transforms for signals with structured frequency support, investigating relationships between additive structures in frequency domains and computational complexity, and exploring graph learning techniques under spectral constraints. Awards and Recognition: Excellence in Teaching Award, IIT Hyderabad (2019, 2022) Stanford Graduate Fellowship Qualcomm Innovation Fellowship (awarded to his PhD student Charantej Reddy P in 2021) Teaching and Service: He has taught courses including AI1110 Probability and Stochastic Processes, EE5609 Matrix Theory, EE5606 Convex Optimization, and EE5328 Introduction to Submodular Functions. He serves as Departmental Undergraduate Committee Chair for the Department of AI at IITH (2020-present) and was MTech Admissions Coordinator for the same department (2019-2022). Research Group: He currently advises three PhD students working on signal processing based graph learning techniques, DFT computation for structured signals, and coded computing problems.

• Partial Differential Equations
• Inverse Problems
• Bayesian Inference
• +۴ مورد دیگر

Dr. Masoumeh Dashti is an Associate Professor in Mathematics at the University of Sussex, UK, affiliated with the School of Mathematical and Physical Sciences. She holds a PhD in Mathematics from the University of Warwick (2008) and prior degrees in Mechanical Engineering from Sharif University of Technology and Tehran Polytechnic. Her research focuses on Partial Differential Equations, Inverse Problems, Bayesian Inference, and their applications in fluid dynamics and epidemiology. Key research interests include: Bayesian approaches to inverse problems, sparsity-promoting estimators, uncertainty quantification, and mathematical modeling of epidemics on networks. She has contributed to foundational work on Besov priors and MAP estimator consistency in nonparametric Bayesian frameworks. Her publications span topics like network inference from epidemic data, contraction rates of posterior distributions, and fluid-structure interaction problems. She has secured grants including 'Two-dimensional stochastically perturbed shallow water equations' (2019-2023) and 'Confronting High Dimensional Network Models With Data' (2018-2022). Currently, she serves as an Associate Editor for SIAM-ASA Journal on Uncertainty Quantification and AIMS Foundations of Data Science . Teaching expertise includes Functional Analysis, Partial Differential Equations, and Calculus of Several Variables at both undergraduate and postgraduate levels.

Santiago Segarraمشاهده پروفایل

دانشیار

• Network Theory
• Machine Learning
• Graph Signal Processing
• +۵ مورد دیگر

Santiago Segarra is the W. M. Rice Trustee Associate Professor in the Department of Electrical and Computer Engineering at Rice University, with courtesy appointments in Computer Science and Statistics. He joined Rice in 2018 and collaborates with Microsoft Research since 2022. His expertise spans network theory, machine learning, graph signal processing, and optimization. Segarra earned his B.Sc. in Industrial Engineering from ITBA (2011), and M.S. and Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2014-2016), followed by a postdoc at MIT (2016-2018). Research Focus: His work integrates algebraic topology, signal processing, and machine learning to analyze networked systems. Key areas include social/technological network clustering, graph-based data analysis, and applications in neuroscience and communication networks. Recent projects address fair graph learning, distributed GNN training, and network topology inference. Awards: Penn’s Wolf Award for Best Dissertation (2017), Argentine National Engineering Honors (2011), and ITBA’s Best Thesis Award (2011). Grants/Sponsors: Supported by NSF, ONR, and industry collaborations. Labs/Groups: Leads the Rice Wireless group and collaborates with Microsoft Research on applied network science. Advises students in interdisciplinary research combining theory and real-world applications.

Süli Endreمشاهده پروفایل

استاد

• Free-discontinuity problems
• Computational modelling of fracture
• Quasi-continuum methods
• +۱۰ مورد دیگر

Endre Süli is a Professor of Numerical Analysis at the University of Oxford, affiliated with Worcester College and Linacre College. He has held various academic roles since 1985, including Fellowships and Tutorships in Mathematics. University Education: B.Sc. in Mathematics, University of Belgrade (1974-1978) M.Sc. in Mathematics, University of Belgrade (1978-1980) Ph.D. in Mathematics, University of Belgrade (1985) M.A., University of Oxford (1985) British Council Visiting Student, Reading University and University of Oxford (1983/84) Süli's research focuses on numerical methods for partial differential equations (PDEs), with expertise in finite element methods, adaptive algorithms, error control, and computational modeling of fractures and non-Newtonian fluids. His work bridges mathematical theory and practical applications in fluid dynamics and material science. His recent publications emphasize finite element approximations, nonlinear PDEs, and stochastic models for polymer dynamics. Themes include multiscale methods, tensor-sparsity for high-dimensional problems, and compressible flow simulations. Scientific Awards: Fellow of the Royal Society (2021) London Mathematical Society Naylor Prize and Lectureship (2021) Pro Urbe Prize, City of Subotica (2021) SIAM Fellow (2016) Member, Academia Europaea (2020) Foreign Member, Serbian National Academy of Sciences and Arts (2009) IMA Service Award (2011) Fellow, European Academy of Sciences (EurASc) (2010) Fellow, Institute of Mathematics and its Applications (2007) London Mathematical Society/New Zealand Mathematical Society Forder Lecturer (2015) Professor Hospitus, Charles University, Prague (2012) Distinguished Visiting Chair Professor, Shanghai Jiao Tong University (2013) Invited Speaker, International Congress of Mathematicians, Madrid (2006) Süli has supervised numerous research projects and held visiting appointments globally. His contributions to numerical analysis span foundational work on error estimation, nonlinear stability, and advanced computational frameworks for complex physical systems.

Yuan Zhongمشاهده پروفایل

دانشیار

• Applied Probability
• Stochastic Systems
• Operations Management
• +۷ مورد دیگر

Yuan Zhong is an Associate Professor of Operations Management at the University of Chicago Booth School of Business . He previously held positions as an Assistant Professor at Columbia University’s Department of Industrial Engineering and Operations Research and was a Postdoctoral Scholar at UC Berkeley’s Computer Science Department. Education: PhD in Operations Research, MIT (2012) MA in Mathematics, Caltech (2008) BA in Mathematics, University of Cambridge (2006) His research focuses on applied probability and stochastic system design , with applications in cloud computing , supply chain management , and e-commerce logistics . Recent work explores multi-period production systems and dynamic resource allocation in data centers and healthcare operations . Recent publications analyze cloud value chains , sparse graph design for delivery networks, and process flexibility in manufacturing. He has contributed to journals like Operations Research , Annals of Applied Probability , and Stochastic Systems . Scientific Awards: 2012 Kenneth C. Sevcik Outstanding Student Paper Award Best Student Paper Award at ACM Sigmetrics (2012) He teaches courses in business process fundamentals and queueing theory , with a future schedule including Operations Management: Business Process Fundamentals (2025–2026). No explicit student advising list was provided.

• Optimization for Machine Learning
• Collaborative Learning
• Federated Learning
• +۶ مورد دیگر

Sebastian U. Stich is a tenured Professor at CISPA Helmholtz Center for Information Security and a member of the European Lab for Learning and Intelligent Systems (ELLIS). His research focuses on optimization for machine learning, collaborative learning (distributed, federated, and decentralized methods), efficient optimization techniques, adaptive stochastic methods, and privacy/security in machine learning. Recent appointments include ERC Consolidator Grant 2024 for the CollectiveMinds project. Key contributions in federated/decentralized learning, uncertainty estimation, and communication-efficient optimization. Active in workshop organization, including the Optimization for Machine Learning workshop at NeurIPS 2024. Teaching modern optimization methods at Saarland University (2023-2025). His work has been recognized with awards such as the Google Research Scholar Award (2023) and Meta Privacy-Enhancing Technologies Award (2022) . His research group includes postdocs Dr. Anton Rodomanov and Dr. Rotem Mulayoff, and PhD students Xiaowen Jiang and Yuan Gao.

Riyadh Baghdadiمشاهده پروفایل

استادیار

• Compilers
• Programming Languages
• Deep Learning
• +۹ مورد دیگر

Riyadh Baghdadi is an Assistant Professor of Computer Science at New York University Abu Dhabi and a Global Network Assistant Professor at the Tandon School of Engineering, NYU. He is also a Research Affiliate at MIT, where he previously completed a postdoctoral fellowship. His academic journey includes a PhD and Master’s from Sorbonne University (INRIA/UPMC) and an engineering degree from Ecole Supérieure d’Informatique in Algiers. Assistant Professor, NYU Abu Dhabi Global Network Assistant Professor, Tandon School of Engineering, NYU Research Affiliate, MIT His research lies at the intersection of compilers, programming languages, and applied machine learning, with a focus on developing advanced compiler techniques for deep learning, high-performance computing, and data-parallel algorithms. He is the lead developer of the Tiramisu compiler , a polyhedral compiler designed to optimize dense and sparse deep learning workloads across diverse architectures including CPUs, GPUs, and FPGAs. Riyadh’s recent publications demonstrate a strong trend toward integrating machine learning into compiler optimization—particularly in cost modeling, loop scheduling, and automatic code generation. His work addresses critical challenges in optimizing sparse neural networks and enabling efficient execution on resource-constrained platforms like smartphones and autonomous vehicles. Outstanding Paper Award, MLSys 2021 He has mentored 18 students and taught core courses such as Computer Systems Organization and Machine Learning at NYUAD. His service to the academic community includes program committee roles at MLSys, IPDPS, ECOOP, and PACT, as well as organizing workshops on polyhedral compilation and machine learning for hardware-software co-design. Riyadh actively contributes to open-source projects and collaborates with industry leaders including Google, Facebook, NVIDIA, and Intel. He leads the development of Tiramisu and collaborates on DSLs like GraphIt and Halide, focusing on performance portability and automation in compiler design.

Chen Liuمشاهده پروفایل

استادیار

• Machine Learning
• Adversarial Robustness
• Privacy-Preserving Machine Learning
• +۹ مورد دیگر

Chen Liu is an Assistant Professor in the Department of Computer Science at City University of Hong Kong and the Principal Investigator (PI) of the Machine Learning and Optimization (MLO) group. His research focuses on building reliable machine learning models, particularly studying robustness and privacy properties of deep neural networks from an optimization perspective. University: City University of Hong Kong Academic Rank: Assistant Professor Students: Supervises multiple PhD, MPhil, and postdoctoral researchers. Education: Holds a Ph.D. (2022) and MSc (2017) in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), and a BSc (2015) in Computer Science from Tsinghua University. Research Interests: Adversarial robustness, privacy-preserving machine learning, optimization algorithms, dataset distillation, generative models, and theoretical analysis of loss landscapes. His work addresses challenges like catastrophic overfitting, architecture overfitting in distilled data, and stable adversarial training methods. Article Trends: Recent publications explore adversarial robustness under l0/l1 norms, gradient inversion for data reconstruction, evolutionary factor searching in finance, and meta-tuning for out-of-domain few-shot learning. These works emphasize optimization techniques to enhance model reliability and generalization. Scientific Awards: Microsoft Research Ph.D. Scholarship Programme (2017–2019) Advising and Grants: Supervises a diverse team of current and former students, with collaborations across institutions like George Mason University and Zhejiang University. Research supported by academic and industry grants. Labs and Teams: Leads the MLO group, which investigates fundamental ML theory and algorithms to improve system reliability. The group's work spans adversarial training, dataset distillation, and generative model optimization.