جستجوی استاد راهنمای دانشگاهی

Hyejin Kuمشاهده پروفایل

استاد

• Mathematical Finance
• Machine Learning
• Risk Management
• +۴ مورد دیگر

Hyejin Ku is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her research focuses on the intersection of Mathematical Finance and Machine Learning, addressing challenges in risk measurement, portfolio optimization, and quantitative finance. She develops advanced mathematical models to enhance decision-making through reinforcement learning and data analytics. Notable projects include novel algorithms for credit rating prediction using neural networks and sequence-based clustering for credit risk assessment. Her work integrates applied mathematics with real-world financial applications, such as systemic risk reduction in multi-layer networks and option pricing under liquidity constraints. She holds a prominent position in mathematical finance, contributing to both theoretical advancements and practical solutions for financial markets. Her research trends emphasize interdisciplinary approaches, combining machine learning techniques with financial modeling to solve complex problems in risk management and asset valuation. Her publications span over two decades, showcasing contributions to portfolio optimization, derivatives pricing, and computational finance. Dr. Ku is affiliated with York University’s Department of Mathematics and Statistics, where she contributes to academic leadership and research mentorship. Her office is located in DB 2025, and she can be reached at hku@yorku.ca.

Xi Zhangمشاهده پروفایل

استاد

• Wireless Networks
• Quality of Service (QoS) Theory
• 6G/Next-Generation Mobile Networks
• +۷ مورد دیگر

Xi Zhang is a Full Professor in the Department of Electrical and Computer Engineering at Texas A&M University . He is also the Founding Director of the Networking and Information Systems Laboratory. His academic career includes research fellowships at the University of Technology Sydney and James Cook University, as well as prior roles at AT&T Bell Laboratories and AT&T Laboratories Research. Education: B.S. and M.S. in Electrical Engineering & Computer Science, Xidian University, China M.S. in Electrical Engineering & Computer Science, Lehigh University, USA Ph.D. in Electrical Engineering-Systems, University of Michigan, USA Research Interests: His work focuses on Quality-of-Service (QoS) theory, 6G/Next-Generation Wireless Networks , Massive MIMO , Integrated Sensing and Communications (ISAC) , and Network Function Virtualization (NFV) . He has pioneered advancements in statistical delay/error-rate bounded QoS , AI-driven 6G architectures , and mURLLC (massive ultra-reliable low-latency communications) . Awards & Honors: IEEE Fellow (2014) for contributions to QoS theory in mobile wireless networks NSF Early Career Award (2004) Multiple Best Paper Awards (IEEE GLOBECOM, WCNC, ICC) Outstanding Faculty Award from Texas A&M (2020) Leadership Roles: He has held key positions as Technical Program Committee (TPC) Chair for major conferences (e.g., IEEE GLOBECOM 2011, IEEE ICDCS 2026) and serves as Editor for top-tier journals like IEEE Transactions on Communications and IEEE Transactions on Wireless Communications . Labs & Teams: He leads the Networking and Information Systems Laboratory , focusing on 6G mobile networks, ISAC systems, and AI-driven network architectures.

Mohsen Pourahmadiمشاهده پروفایل

استاد

• Time Series Analysis
• Multivariate Statistics
• Covariance Estimation
• +۷ مورد دیگر

Mohsen Pourahmadi is a Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing methodologies for modeling covariance matrices in multivariate and time series data, with applications to financial analysis, longitudinal studies, neuroeconomics, and high-dimensional data. Key tools include graphical lasso algorithms, Cholesky decomposition, and Bayesian approaches. He emphasizes extending generalized linear models (GLM) to covariance matrix estimation, leveraging prediction theory and stochastic processes. Education details are not explicitly provided in the text. His work spans theoretical advancements in covariance estimation, such as sparse VAR models, nonstationary process analysis, and regularized multivariate regression. He has contributed to applications like detecting cyber attacks on infrastructure systems and analyzing breast cancer data through Bayesian networks. Research interests include time series graphical models, antedependence models for longitudinal data, and regularization techniques for high-dimensional covariance matrices. His recent work explores fused-lasso penalties, Bayesian correlation matrix estimation, and stationary subspace analysis. Pourahmadi has authored numerous articles on topics ranging from multivariate volatility modeling to nonparametric covariance estimation, emphasizing both computational efficiency and theoretical rigor.

Ryan Murrayمشاهده پروفایل

استادیار

• Calculus of variations
• Partial Differential Equations
• Machine Learning
• +۵ مورد دیگر

Ryan Murray is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State). His research focuses on developing mathematical tools to address problems in applied analysis, including calculus of variations, partial differential equations (PDEs), and their applications to machine learning, fluid dynamics, and control theory. He holds a PhD in Mathematics from Carnegie Mellon University (2016). His expertise spans regularization methods for machine learning, singular perturbations in materials science, algorithms for distributed optimization, and singularity formation in fluid dynamics. His work is supported by the National Science Foundation (NSF) and the Simons Foundation. He actively collaborates with researchers in data science, PDE analysis, and optimization. Key research areas include adversarial training in classification, geometric data analysis via statistical depths, and the analysis of vortex sheet singularities. His teaching experience includes courses on partial differential equations, optimal control theory, and linear control systems. Ryan has published extensively in journals such as SIAM Journal on Mathematics of Data Science , Archive for Rational Mechanics and Analysis , and Journal of Machine Learning Research . His articles explore topics ranging from graph-based learning to fluid dynamics instabilities.

• Nonlinear analysis
• Scattering theory
• Real and complex analysis
• +۷ مورد دیگر

Ronald Coifman is the Sterling Professor of Mathematics and Professor of Computer Science at Yale University. His research focuses on nonlinear analysis, scattering theory, complex analysis, numerical analysis, and their applications in data science, signal processing, and biomedical imaging. He holds the National Medal of Science and is a member of the National Academy of Sciences and the American Academy of Arts and Sciences. Coifman's work bridges pure mathematics and applied sciences, emphasizing harmonic analysis, manifold learning, and data-driven modeling. His contributions include foundational advancements in wavelet theory, diffusion maps, and nonlinear dimensionality reduction techniques. Key innovations include the development of empirical intrinsic geometry for analyzing complex systems and the use of Wasserstein distances in high-dimensional data analysis. His academic portfolio includes over 250 publications since the 1960s, spanning topics from theoretical mathematics to practical medical diagnostics. Notable applications include methods for stroke detection, medical imaging analysis, and anomaly detection in dynamic systems. Coifman collaborates across disciplines, integrating computational methods with domain-specific challenges in biology, chemistry, and engineering. Education: Ph.D. in Mathematics from the University of Geneva (1965) Awards: National Medal of Science (2001), Member of NAS (1993), Member of AAAS (2006) Key Projects: Development of diffusion maps, manifold learning algorithms, and empirical geometry frameworks Coifman's current research explores the intersection of machine learning and mathematical analysis, with recent focus on intrinsic data organization, emergent dynamical models, and scalable computational methods for large datasets.

Tianyu Guanمشاهده پروفایل

استادیار

• Sports Analytics
• Machine Learning
• Functional Data Analysis
• +۶ مورد دیگر

Tianyu Guan is an Assistant Professor in the Department of Mathematics and Statistics at York University, Faculty of Science. He previously served as an Assistant Professor at Brock University and joined York University in 2024. He holds a PhD in Statistics from Simon Fraser University (2020), an MSc in Actuarial Science from the same institution (2014), and a BSc in Statistics from Jilin University (2011). PhD in Statistics, Simon Fraser University, 2020 MSc in Actuarial Science, Simon Fraser University, 2014 BSc in Statistics, Jilin University, 2011 His research centers on sports analytics, functional data analysis, and nonparametric statistics, with strong applications in machine learning and data science. He applies statistical methodologies to understand sports performance, player behavior, and game dynamics. His work also extends to theoretical developments in sparse modeling and functional regression. The recent publications highlight a clear trend toward integrating advanced statistical techniques with real-world sports and entertainment data. His work combines functional data analysis, machine learning, and probabilistic modeling to extract insights from complex longitudinal and high-dimensional datasets. Topics span soccer, rugby, football, and movie reviews, demonstrating interdisciplinary reach. While no formal scientific awards are listed in the provided text, his publications in high-impact journals such as Annals of Applied Statistics and Statistics and Computing reflect strong academic recognition. Tianyu Guan actively advises multiple graduate students at both MSc and PhD levels, primarily at Brock and Simon Fraser Universities. His teaching portfolio includes advanced courses in nonparametric statistics, sampling theory, and experimental design at the undergraduate and graduate levels. He has not received external grant information in the provided text, but his research output suggests active engagement in funded or independent research projects. He leads methodological and applied research in sports analytics, often co-supervising students with colleagues across institutions. His lab or research group appears focused on developing and applying statistical tools for performance analysis and decision-making in sports, supported by computational implementations such as the R package ngr .

Grant Weddellمشاهده پروفایل

دانشیار

• Database Technology
• Real-Time Applications
• Schema Management
• +۵ مورد دیگر

Grant Weddell is an Associate Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. His research focuses on database technology for real-time applications, including large-scale schema management, information clustering, dependency theory, and query optimization for heterogeneous data sources. He teaches courses such as CS338 (Introductory Databases), CS348 (Advanced Databases), CS446 (Software Engineering), and CS848 (Advanced Database Systems). His research interests emphasize the interplay between description logics and database systems, particularly in optimizing query processing and managing complex schemas. Recent work explores path agreements, functional dependencies, and ontology-mediated querying to enhance data integration and schema management efficiency. Teaching responsibilities include foundational database courses (CS338/348), software engineering (CS446), and advanced topics in information integration (CS848). No scientific awards are explicitly listed, though his contributions to database theory and optimization are extensive.

Kenan Liمشاهده پروفایل

دانشیار

• Spatial Computation
• Environmental Health
• Community Resilience
• +۱۲ مورد دیگر

Kenan Li, Ph.D., is an Associate Professor in the Department of Epidemiology and Biostatistics at Saint Louis University’s College for Public Health and Social Justice. He joined SLU in August 2022 and teaches courses such as Statistical Learning, R for Spatial Analysis, and Environmental Determinants of Health. His research bridges data science, GIS, and public health, focusing on spatial computation, environmental exposures, and community resilience. Ph.D. in Environmental Sciences, Louisiana State University M.S. in Environmental Sciences, Louisiana State University B.S. in Environmental Sciences and Applied Mathematics, Nankai University, China Dr. Li’s research interests lie at the intersection of spatial computation, environmental health, and community resilience . He develops geo-AI frameworks , integrated geo-cyber-infrastructures , and biostatistics algorithms using big data, deep learning, and sensor data. His work emphasizes understanding human-environment interactions, urban sustainability, and health disparities. His recent publications from 2023 to 2015 reveal a strong trend in spatial modeling of population dynamics , machine learning for environmental exposure analysis , and resilience assessment in vulnerable coastal regions. He has pioneered methods like Dynamic Time Warping Self-Organizing Maps and Wavelet-based Shapelet Discovery to extract meaningful patterns from high-frequency sensor data. His scientific awards include the Taylor Geospatial Institute Seed Grant (2023) , the Saint Louis University 2023 Health Research Grant , and selection for the Scholarly Undergraduate Research Grants and Experiences . He has secured funding from NSF, NIH, USC Keck School of Medicine, and the US Army Corps of Engineers. Dr. Li has advised and collaborated on numerous research projects, particularly in interdisciplinary teams studying the Mississippi River Delta and urban health interventions. He has been involved in NIH/NIBIB-funded projects and led research on emergency management of trail systems in Los Angeles County. He is actively involved in building research labs and teams focused on spatial data science and public health analytics , having previously worked at USC’s Spatial Sciences Institute and Population and Public Health Sciences Department.

Christian Plesslمشاهده پروفایل

استاد

• High-Performance Computing
• Reconfigurable Computing
• FPGA Acceleration
• +۳ مورد دیگر

Prof. Dr. Christian Plessl is a W3 Professor of High-Performance Computing at the Institute of Computer Science, University of Paderborn. He leads the Paderborn Center for Parallel Computing (PC²), a national HPC center within the NHR alliance. His roles include Director of PC², Board Member of the NHR association, and member of the Sonderforschungsbereich 901. Education: PhD (Dr. sc. ETH) in Computer Engineering, ETH Zürich (2006) MSc in Electrical Engineering, ETH Zürich (2001) Postdoc at ETH Zürich (2007–2011) Research Interests: Architecture and tools for high-performance parallel and reconfigurable computing, FPGA acceleration, quantum chemistry, scientific computing, adaptive systems, and energy-efficient HPC solutions. Key projects include EKI-App (FPGA-based neural networks), FPGA4XPCS (X-ray spectroscopy), and HighPerMeshes (unstructured grid frameworks). Publications: Over 100 peer-reviewed works, focusing on FPGA acceleration, HPC frameworks, and quantum computing. Recent trends emphasize energy-efficient neural networks, FPGA-based quantum computing, and scalable HPC algorithms. Awards: Best Paper Awards at HEART 2023, ReConFig 2012/2014 Paderborn University Research Awards (2018, 2009) SEW-EURODRIVE Student Award (2001) Grants & Projects: Principal investigator in DFG, BMBF, and EU-funded projects. Collaborates with AMD/Xilinx, Intel/Altera, and Fujitsu. Leads initiatives like PerficienCC (custom computing) and HighPerMeshes. Labs/Teams: Directs the High-Performance Computing group at PC², focusing on FPGA supercomputing and HPC infrastructure. Active in the NHR alliance for national HPC coordination.

Hassan Sajjadمشاهده پروفایل

دانشیار

• Natural Language Processing
• Deep Learning
• Safe and Trustworthy AI
• +۷ مورد دیگر

Hassan Sajjad is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He is also the Director of HyperMatrix, a research group focused on AI and deep learning. His work centers on Natural Language Processing, Safe and Trustworthy AI, interpretability, and robustness of language models. PhD - University of Stuttgart, Germany (2012) Masters - National University of Computer and Emerging Sciences, Pakistan (2007) BSc - National University of Computer and Emerging Sciences, Pakistan (2005) Hassan Sajjad's research focuses on making AI systems more interpretable, robust, and safe. He investigates how deep learning models, particularly transformers, encode linguistic and conceptual knowledge. His work spans language generation , model editing , interpretability , and generalization . He develops tools like NeuroX and NxPlain to analyze neuron-level behavior in NLP models. His research also extends to crisis informatics and multilingual NLP, especially Arabic and Urdu. The recent publications show a strong trend in analyzing and interpreting deep NLP models, with a focus on neuron interpretation , latent concept discovery , and model robustness . His work appears in top-tier venues like NeurIPS, ICLR, ACL, and EMNLP, indicating high impact in the AI and NLP communities. There is a clear emphasis on developing practical tools and frameworks for model analysis. No formal scientific awards are mentioned in the provided text. Hassan Sajjad mentors students and has fellowship opportunities available. While specific grants are not listed, his extensive publication record and leadership roles suggest active research funding. He advises students in AI, NLP, and deep learning, and collaborates widely across institutions. He leads the HyperMatrix research group at Dalhousie University, which focuses on AI, deep learning, and NLP. The group develops tools for model interpretation and works on safe and trustworthy AI. Collaborations extend to institutions like MBZUAI, NYU Abu Dhabi, and various international research centers.

• Federated Learning
• Optimization for Machine Learning
• Privacy-Preserving Machine Learning
• +۱۲ مورد دیگر

Sai Praneeth Karimireddy is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), with a courtesy appointment in the Ming Hsieh Department of Electrical and Computer Engineering. He previously held an SNSF postdoctoral fellowship at UC Berkeley under Michael I. Jordan and earned his PhD at EPFL advised by Martin Jaggi. He co-leads the Federated Learning and Data Quality working group at MONAI (NVIDIA) and collaborates with researchers at Apple Research. His research lies at the intersection of optimization, machine learning, statistics, and economics, with a strong focus on federated learning, privacy-preserving machine learning, data valuation, and AI for healthcare. He investigates how data quality, privacy, and incentives shape collaborative ML systems, especially in high-stakes domains like medicine. His work has been deployed at companies such as Meta, Google, OpenAI, and Owkin. His recent publications span top-tier venues including NeurIPS, ICML, ICLR, and JMLR, with influential contributions such as the SCAFFOLD algorithm for federated learning. His research shows a consistent trend toward building robust, private, and incentive-compatible collaborative learning systems, with increasing emphasis on real-world deployment in healthcare and decentralized data markets. 2023 SNSF Mobility Fellowship 2022 Patrick Denantes Memorial Prize for best thesis in computer science 2022 EPFL thesis distinction (top 8%) 2021 Chorafas Foundation Prize for exceptional applied research Capitol One Fellow (2025) He is actively mentoring PhD students and leads a research group focused on foundational and applied challenges in federated and privacy-preserving ML. He teaches graduate courses at USC, including CSCI 599 on Optimization for Machine Learning and CSCI 699 on Privacy-Preserving Machine Learning. He serves as an area chair for ICLR 2025 and co-organizes major workshops on incentives in data sharing and federated learning. His lab collaborates with institutions like NVIDIA, Apple, and Argonne National Laboratory, and he is building a research program centered on sustainable, equitable, and trustworthy AI ecosystems.

Li-Yang Tanمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Computational Complexity
• Machine Learning Theory
• +۵ مورد دیگر

Li-Yang Tan is an Assistant Professor of Computer Science at Stanford University , focusing on theoretical computer science. His research emphasizes computational complexity, machine learning theory, and algorithm design. Education: Ph.D. in Computer Science from Columbia University , advised by Rocco Servedio His work explores: Boolean function complexity Decision tree learning algorithms Circuit lower bounds Computational-statistical tradeoffs Query complexity Massively parallel algorithms Recent publications analyze computational-statistical tradeoffs via NP-hardness, improve decision tree learning techniques, and establish direct sum theorems for query complexity. His research often bridges complexity theory, learning theory, and algorithm design, with applications in pseudorandomness and correlation clustering. Awards: Best Paper Award at FOCS Best Paper Award at CCC Best Paper Award at SAT Sloan Fellowship Li-Yang collaborates with students and researchers including Guy Blanc, Caleb Koch, and Carmen Strassle. He has delivered invited special issue papers at FOCS, CCC, and SAT conferences.

• Computational Biology
• Single-Cell Genomics
• Machine Learning
• +۶ مورد دیگر

Dana Pe'er is Chair of the Computational and Systems Biology Program at the Sloan Kettering Institute (SKI) and an Investigator at the Howard Hughes Medical Institute (HHMI). She holds the Alan and Sandra Gerry Endowed Chair and leads an interdisciplinary lab combining single-cell genomics, machine learning, and computational modeling to study cancer biology, immunity, and development. Pe'er earned her PhD at the Hebrew University in Jerusalem and focuses on cellular plasticity, epigenetic regulation, and tumor-immune interactions. Her lab develops tools like CellRank , Wishbone , and SEACells to analyze single-cell data and uncover mechanisms in cancer progression and immunotherapy. Key research areas: Computational Biology, Single-Cell Genomics, Cancer Systems Biology, Epigenetics, Immunotherapy Recent trends: Articles from 2025-2024 emphasize spatial transcriptomics, tumor microenvironment mapping, and regulatory network inference using machine learning. Scientific honors include the NIH Director’s Pioneer Award , AACR Academy Induction , and Packard Fellowship . Her work has direct clinical implications for precision medicine and cancer immunotherapy. Labs & Teams: Leads the Dana Pe'er Lab at SKI, directs the Single Cell Research Initiative (SCRI), and collaborates with the SAIL program.

Shahrokh Valaeeمشاهده پروفایل

استاد

• Wireless Networks
• Signal Processing
• Vehicular Networks
• +۴ مورد دیگر

Shahrokh Valaee is a Professor and Associate Chair for Undergraduate Studies in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, part of the Faculty of Applied Science and Engineering. He founded and directs the Wireless and Internet Research Laboratory (WIRLab). Education: BSc and MSc in Electrical Engineering from University of Tehran PhD in Electrical Engineering from McGill University Research Interests: Focuses on wireless networks (vehicular/sensor networks, B5G/6G), signal processing (indoor localization, machine learning for medical imaging), and integrated sensing/communication. His work spans: Localization in GPS-denied environments Machine learning for healthcare with limited/imbalanced data Reconfigurable Intelligent Surfaces (RIS) and drone networks Publications: Recent articles (2014-2016) show strong focus on indoor localization techniques, vehicular network protocols, and network coding, with emerging trends in machine learning applications for wireless systems and healthcare. Awards: Connaught Award (2012, 2013) NSERC Discovery Accelerator Award (2010) MaRS Innovations cPOP Award (2012) IEEE Fellow (FIEEE) Engineering Institute of Canada Fellow (FEIC) Leadership: Advises graduate students at WIRLab, where research combines theory with practical implementation (GPU-based ML, Android localization). Manages projects in integrated sensing/communication, ML for health, and B5G networks. Labs/Teams: Directs WIRLab with focus on wireless signal processing, networking, and ML implementations. Current team includes postdocs and PhD students working on localization, B5G networks, and medical ML applications.

Steven Corcelliمشاهده پروفایل

استاد

• Theoretical Chemistry
• Computational Chemistry
• Biophysical Chemistry
• +۷ مورد دیگر

Steven Corcelli is a Professor and Interim Dean of the College of Science at the University of Notre Dame, with a joint appointment in the Department of Chemistry and Biochemistry. He has held key leadership roles including Department Chair (2022–2025) and Associate Dean for Interdisciplinary Studies (2019–2022). His research is centered in the Computational Molecular Science & Engineering Laboratory (CoMSEL), where he leads a team exploring molecular and biomolecular systems through advanced simulations. Education: Ph.D. in Chemistry, Yale University, 2001 Sc.B. in Chemistry, Brown University, 1997 His research interests bridge theoretical and experimental physical chemistry, with a strong emphasis on aqueous electrolytes, biomolecular binding, and vibrational spectroscopy . He employs GPU-accelerated molecular dynamics and enhanced sampling techniques to study ion transport in aqueous solutions and the mechanisms of protein-DNA and protein-protein interactions, particularly in immunological contexts such as T-cell receptor binding. His work has significant implications for battery technologies and drug design. The 15 most recent publications reveal a consistent focus on molecular simulation, biophysical chemistry, and spectroscopy , with recurring themes in DNA-ligand binding, ion solvation, and immune recognition. His group develops and applies novel computational methods, such as the coupled local mode approach, to interpret vibrational spectra with high accuracy. Scientific Awards: Thomas P. Madden Award (2020) Rev. Edmund P. Joyce, C.S.C. Award for Excellence in Undergraduate Teaching (2019, 2012) Fellow, American Chemical Society (2016) Sloan Research Fellowship (2009) NSF CAREER Award (2009) Camille and Henry Dreyfus New Faculty Award (2005) Kavli Fellow, National Academies of Science (2011) Corcelli has advised numerous graduate students and postdoctoral researchers, many of whom have gone on to successful careers in academia and industry. His lab collaborates extensively with experimental groups, particularly in spectroscopy and immunology. He has secured substantial research funding and published over 85 peer-reviewed articles. His lab, CoMSEL, benefits from high-performance computing resources through the Center for Research Computing at Notre Dame, enabling large-scale simulations of complex molecular systems. Research Labs and Teams: The Computational Molecular Science & Engineering Laboratory (CoMSEL) is a multidisciplinary research group that combines theoretical chemistry, biophysics, and materials science. The team includes graduate students and staff scientists working on projects ranging from fundamental solvation dynamics to applied biomedical simulations.