جستجوی استاد راهنمای دانشگاهی

Carlee Joe-Wong is the Robert E. Doherty Career Development Associate Professor in the Electrical and Computer Engineering department at Carnegie Mellon University (CMU), part of the College of Engineering. She leads the LIONS research group (Learning, Incentives, and Optimization in Networked Systems), focusing on mathematical and economic aspects of computer and information networks. Her work emphasizes practical system deployments, such as her co-founded startup DataMi, which commercialized smart data pricing (SDP) solutions deployed globally by ISPs like AT&T and Airtel. Previously, she held roles at Princeton University (Ph.D., M.A., A.B. in Mathematics/Applied Mathematics) and served as Director of Advanced Research at DataMi (2013–2014). Her research spans network economics, edge computing, federated learning, and autonomous vehicle policy. Notable contributions include foundational work on burstable cloud instances, dynamic pricing mechanisms, and resilience in mixed-autonomy transportation systems. Awards include the INFORMS ISS Design Science Award (2014), Best Paper at IEEE INFOCOM (2012), and DOE Early Career Award (2024). She has advised numerous industry collaborations and contributed to standards in distributed learning and networked systems through initiatives like the Fog Computing framework and federated learning benchmarks. Education: Ph.D. (2016), M.A. (2013), and A.B. (2011) in Applied Mathematics from Princeton University. Active in policy briefs on autonomous vehicles and energy-efficient computing systems.

• Network economics
• Data pricing
• Resource allocation in computer networks
• +۵ مورد دیگر

Bryon Aragam is an Associate Professor and Topel Faculty Scholar at the Booth School of Business , University of Chicago . His work bridges causality , statistical machine learning , and probabilistic modeling , with applications in AI systems like ChatGPT and DALL-E. Key research themes include: Causal Structure Learning : Extracting latent causal graphs from multimodal data using nonparametric methods. Deep Generative Models : Analyzing overparametrization and variational inference for representation learning. Latent Variable Discovery : Using Markov boundaries and convex subset lattices to uncover hidden dependencies. Algorithm Design : Developing scalable methods like DAGMA for DAG learning and theoretical guarantees for GES/PC algorithms. His paper trends reveal a focus on nonparametric statistics , graphical models , and neural network theory , with recent work on transformer memory dynamics and identifiability in deep latent models . Papers frequently appear in top venues like NeurIPS , JMLR , and AOS , emphasizing theoretical rigor and practical validation.

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +۱۲ مورد دیگر

Cosma Shalizi is an Associate Professor in the Statistics Department and Machine Learning Department at Carnegie Mellon University, and an External Professor at the Santa Fe Institute. His work bridges statistics, machine learning, and complex systems theory, with applications spanning neuroscience, statistical mechanics, and social networks. Shalizi's research focuses on nonparametric prediction of time series, learning theory, information theory, and causal inference. He has made significant contributions to computational mechanics, developing algorithms like CSSR (Causal State Splitting Reconstruction) for identifying optimal predictive states in complex systems. His work extends to heavy-tailed distributions, network analysis, and the statistical foundations of complex systems. He has pioneered methods for quantifying self-organization and developing nonparametric approaches to spatio-temporal prediction. His recent publications reveal a trend toward increasingly interdisciplinary work, connecting network science with causal inference, statistical learning theory with macroeconomic forecasting, and information theory with biomedical applications. His work consistently emphasizes rigorous statistical methodology applied to complex, dependent data structures across diverse scientific domains. Winner of the Best Student Paper and Best Poster awards Shalizi has advised students including Georg Goerg, who extended spatio-temporal prediction techniques to continuous-valued fields, and George Montañez, who developed fast approximate algorithms for prediction and explored information-theoretic explanations for machine learning. His collaborative network spans statistics, physics, neuroscience, and social sciences, reflecting his interdisciplinary approach to complex systems. His work with collaborators has led to significant contributions in network analysis, causal inference in social networks, and the development of nonparametric methods for complex data structures. He maintains active research programs in statistical network modeling, time series analysis, and the application of information-theoretic approaches to diverse scientific problems.

• Nonparametric prediction of time series
• Learning theory
• Nonlinear dynamics
• +۱۵ مورد دیگر

Osman Yagan is a Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with affiliate roles in the School of Computer Science and core membership in CyLab Security and Privacy Institute, IDeaS, and Block Center. His research spans modeling, analysis, and optimization of computing systems using tools from applied probability, network science, and machine learning. PhD in Electrical and Computer Engineering, University of Maryland (2011) BS in Electrical and Electronics Engineering, Middle East Technical University (2007) Research Interests: Dr. Yagan focuses on statistical inference , resilient distributed machine learning , robustness of cyber-physical systems , secure IoT networks , and contagion processes in complex networks, including misinformation spread and epidemic modeling. His work integrates multi-armed bandit algorithms , reinforcement learning , and stochastic geometry . Scientific Contributions: He has received multiple fellowships (CIT Dean’s Early Career, IBM Academic Award), best paper awards (ICC 2021, IPSN 2022, ASONAM 2023), and co-authored 15+ recent articles on topics like federated learning, contagion dynamics, and network robustness. His research is funded by ONR, NSF, ARO, AFOSR, and industry grants. Advising: Dr. Yagan has mentored 23 PhD and Master’s students, including junior faculty at Nanyang Technological University and researchers at Meta, Intel, Bloomberg, and MIT. He has taught courses such as Advanced Probability & Statistics for Engineers and Applied Stochastic Processes. Collaborations: He works with institutions including Princeton, MIT, and industry partners like IBM and Intel, and is a Senior Member of IEEE.

• Communication Systems
• Network Science
• Cyber-Physical Systems
• +۱۶ مورد دیگر

Christian Kästner is an Associate Professor in the Software and Societal Systems Department (S3D) within Carnegie Mellon University's School of Computer Science, where he also serves as the director of the CMU Software Engineering Ph.D. Program. His work bridges software engineering and machine learning, focusing on the practical challenges of building production systems with ML components. His research spans several interconnected areas: software engineering for AI-enabled systems (particularly "Machine Learning in Production"), sustainability and fairness in open source communities, and software-supply-chain security. He investigates the limits of modularity and complexity caused by variability in software systems, with applications in quality assurance, interoperability, and feature interactions. His approach combines rigorous empirical research with program analysis and tool building. His recent publications reveal a strong trend toward ML systems engineering, with numerous papers on building reliable ML products, understanding open source sustainability challenges, and securing software supply chains. His work often involves large-scale empirical studies and the development of practical tools to address real-world software engineering problems. He has received multiple distinguished paper awards at top software engineering conferences including ICSE and FSE for his work on dependency abandonment, supply chain security, and collaboration challenges in ML systems. As an educator, he created and teaches the "Machine Learning in Production" course, which formed the basis for his MIT Press book of the same name. He has advised numerous PhD students and mentored many REU students, with several former students now holding faculty positions or working at major tech companies. His service to the community includes extensive program committee work for major software engineering conferences and editorial roles, demonstrating his significant influence in the field.

• Software Engineering for AI-Enabled Systems
• Open-Source Sustainability
• Software-Supply-Chain Security
• +۷ مورد دیگر

Kanat Tangwongsan is a researcher and educator in computer science, with a PhD from Carnegie Mellon University and prior roles at IBM T.J. Watson Research Center. He has extensive experience in algorithm design, parallel computing, and self-adjusting computation, with publications in top-tier venues like DEBS, ICDE, SPAA, and TOCS. Education: PhD in Computer Science (Carnegie Mellon University), B.S. in Computer Science and Mathematics (Carnegie Mellon University) Research Interests: His work spans algorithms , parallel computing , streaming data structures , and self-adjusting programs , focusing on theoretical improvements and practical implementations for graph problems, convex hulls, and network design. Article Trends: His publications emphasize low-latency streaming , parallel SDD solvers , k-means clustering , and triangle enumeration , with applications in large-scale graph analysis, numerical systems, and cache-oblivious methods. Advising and Collaborations: He has collaborated with prominent researchers like Guy E. Blelloch, Martin Hirzel, and Anupam Gupta, contributing to open-source algorithm frameworks and educational materials.

• Algorithms
• Parallel Computing
• Streaming Algorithms
• +۶ مورد دیگر

Limin Jia is a Research Professor affiliated with Carnegie Mellon University's CIT Department of Electrical and Computer Engineering and the Computer Science Department. He leads research in systems security, formal verification, and programming language foundations. His work emphasizes compositional assurance for cyber-physical systems and secure software development practices. Education background is not explicitly stated in the text, but his research focuses on advanced technical domains requiring doctoral-level expertise. Current research interests span cyber-physical systems assurance, software security, and formal methods for program analysis. Recent work includes groundbreaking studies on compositional assurance for large systems (2025), adversarial attacks on large language models (2024), and Rust-based program verification tools like Crabtree (2024). His team also develops tools such as Nodemedic (Node.js vulnerability analysis) and ProInspector (protocol bug detection). Research outputs bridge theory and practice, with 15+ publications in 2023-2025 alone. Current advisees include Myra Dotzel, Nuno Sabino, and Rafael Goncalves. Research collaborations involve multi-institutional projects in network security, IoT safety, and compiler verification. Active in both ACM and IEEE conferences, his work has been funded through multiple NSF and industry grants (details not specified in text). Operates within the Mehrabian Collaborative Innovation Center, fostering interdisciplinary projects at the intersection of electrical engineering, computer science, and cybersecurity. His lab develops both foundational theories and practical tools for improving system security and reliability across domains.

• Cyber-Physical Systems
• Software Security
• Formal Verification
• +۴ مورد دیگر

William Kuszmaul is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. His research focuses on designing and analyzing randomized algorithms and data structures, with a particular emphasis on hashing techniques, algorithm complexity, and data structure optimization. He holds a PhD from MIT, advised by Charles E. Leiserson, and previously served as the Rabin Postdoc of Theoretical Computer Science at Harvard University. His research interests include theory and algorithms and complexity , with recent work addressing optimal bounds for open addressing, efficient cuckoo hashing, and tight analyses of linear probing. Notable contributions include studies on list labeling, minimal perfect hashing, and distributed load balancing under dependency constraints. Kuszmaul has advised students Jingxun Liang and Renfei Zhou in his current role. His publications span 2023–2025, reflecting a focus on foundational algorithmic problems with applications in memory management, parallel computing, and data structure efficiency. Key themes include optimizing hash table performance, analyzing algorithmic phase transitions, and establishing theoretical limits for dynamic retrieval systems. No scientific awards are explicitly listed in the provided materials. His academic trajectory includes postdoctoral work at Harvard and doctoral research at MIT, supported by the John and Fannie Hertz Fellowship. Current courses listed include 15-151 (Fall 2025) and 15-756 (Fall 2024) , suggesting involvement in both introductory and advanced computer science education.

• Theory
• Algorithms and Complexity
• Randomized Algorithms
• +۳ مورد دیگر