جستجوی استاد راهنمای دانشگاهی

• Algorithms
• Data Structures
• Approximate Nearest Neighbor Search
• +۳ مورد دیگر

Ravishankar Krishnaswamy is a Principal Researcher at Microsoft Research India. He earned his PhD from Carnegie Mellon University in 2012 under the guidance of Anupam Gupta. From 2012-2014, he held a Simons Postdoctoral Fellowship at Princeton University's Computer Science Department and spent Fall 2014 visiting Columbia University's IEOR Department. He is also an alumnus of IIT Madras. His research lies at the intersection of algorithm design, optimization, and large-scale data processing. Key contributions include the development of DiskANN, a hybrid data structure for scalable high-dimensional vector search. His work addresses technical challenges in minimizing SSD access latency while maintaining accuracy. DiskANN has been deployed extensively within Microsoft for both first-party and Azure-based third-party applications. He is also deeply engaged in approximation algorithms for graph problems (connectivity/flow), clustering, and optimization models that incorporate input uncertainty, including online algorithms and stochastic optimization frameworks. His PhD thesis on Approximation Techniques for Stochastic Combinatorial Optimization remains a foundational piece of his research. Scientific Awards Simons Postdoctoral Fellowship Contact: Vigyan Building, 9, Lavelle Road, Bangalore 560018

Kanat Tangwongsanمشاهده پروفایل

پژوهشگر

• Algorithms
• Parallel Computing
• Streaming Algorithms
• +۶ 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.

Ioannis Koutisمشاهده پروفایل

دانشیار

• Algorithms
• Numerical Linear Algebra
• Combinatorial Optimization
• +۳ مورد دیگر

Ioannis Koutis serves as Associate Professor in the Computer Science Department at New Jersey Institute of Technology and holds an Adjunct Faculty position in the Computer Science Department at Carnegie Mellon University. His research expertise spans algorithm design with specialization in numerical linear algebra , combinatorial optimization , and spectral methods . He leads development of significant computational tools including CMG (Combinatorial Multigrid), SpA (Spectral Algorithms), and SpClu (Spectral Constrained Clustering), which address complex problems in large-scale system solving. Analysis of his publication record reveals focus on efficient solvers for symmetric diagonally dominant systems, with applications spanning computer vision and image processing. His work demonstrates strong integration of theoretical foundations with practical implementations. NSF grant #1018463 as co-PI NSF grant CCF-#1149048 supporting CMG development Dr. Koutis maintains active research collaborations and has established software projects that have gained recognition in the computational research community, with his GitHub repositories showing significant engagement from other researchers.

• Algorithms
• Parallel Computing
• Graph Theory
• +۳ مورد دیگر

Claudson Ferreira Bornstein is an Adjunct Professor at the Computer Science Department of the Federal University of Rio de Janeiro (UFRJ) . He holds a PhD in Computer Science from Carnegie Mellon University. His research and teaching focus on algorithms, data structures, and combinatorial mathematics. Research Interests include: Algorithm design and optimization Parallel computing techniques Graph theory and elimination orders Combinatorial mathematics Contact Information : Email: cfb@cos.ufrj.br Institutional addresses in COPPE Sistemas and Instituto de Matematica at UFRJ Phone: +55-21-290-8091 (UFRJ) and +55-21-712-1504 (home)

Fabio Cozmanمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Probabilistic Reasoning
• +۷ مورد دیگر

Fabio Cozman is a Full Professor at the School of Engineering, University of São Paulo (USP), and Director of the Center for Artificial Intelligence at USP. He holds a PhD from Carnegie Mellon University (1996) and completed his engineering degree at Poli-USP. His research spans artificial intelligence, machine learning, and probabilistic reasoning, with significant contributions to knowledge representation under uncertainty, credal networks, and physics-informed AI. Current research focuses on AI interpretability , AI and societal impact , and ocean hazard prediction . Key publications cover probabilistic logic programming , credal networks , and Bayesian network extensions . He is a founding member and former president of the Society for Imprecise Probability Theory and Applications (SIPTA) and has served as Associate Editor for multiple AI journals. His work integrates theoretical advancements in probabilistic modeling with practical applications in robotics, computer vision, and environmental systems, particularly focused on the Brazilian maritime territory through projects like the Blue Amazon Brain (BLAB) architecture.

Kenji Shimadaمشاهده پروفایل

استاد

• Additive Manufacturing
• Advanced Manufacturing
• Bioengineering
• +۱۲ مورد دیگر

Kenji Shimada is the Theodore Ahrens Professor of Engineering at Carnegie Mellon University's Department of Mechanical Engineering. His research focuses on geometric computing, mesh generation, and advanced manufacturing technologies, particularly in additive manufacturing, robotics, and biomedical applications. He leads projects in autonomous drone navigation, custom-fit medical devices, and virtual factory simulations. Shimada's work bridges computational engineering with practical industry needs, emphasizing design efficiency and cost reduction. Education: Ph.D. (MIT, 1993), M.S. and B.S. (University of Tokyo, 1985/1983). Affiliations include the Engineering Research Accelerator and NextManufacturing Center. Research interests encompass robotics (aerial/medical), computational geometry, bioengineering, and generative manufacturing. His Bubble Mesh method revolutionizes mesh generation for FEM/BEM analysis. Collaborations with YKK AP and clinical trials highlight his translational research impact. Recent projects include drone navigation in dynamic environments, soft tissue measurement devices for CPAP masks, and AI-driven construction safety monitoring. Media mentions highlight innovations like the Moldable Mask and autonomous window installation robots. Grants and industry partnerships fund his work, with a focus on robotics, medical devices, and advanced manufacturing. Advising emphasizes student-led innovation in surgical training tools and additive manufacturing applications.

Ruslan Salakhutdinovمشاهده پروفایل

استاد

• Deep Learning
• Probabilistic Graphical Models
• Large-scale Optimization
• +۲ مورد دیگر

Ruslan Salakhutdinov is a UPMC Professor of Computer Science in the Machine Learning Department at Carnegie Mellon University's School of Computer Science. His research focuses on Deep Learning, Probabilistic Graphical Models, and Large-scale Optimization, with applications spanning artificial intelligence, computer vision, and robotics. His recent publications demonstrate strong focus on multimodal systems, diffusion models, reinforcement learning, and self-supervised techniques. Research trends show consistent innovation in generative AI methods and their application to robotics and decision-making systems. Awards include Microsoft Faculty Fellow Sloan Fellowship Directs research group advising over 30 PhD/Master's students. Secured funding from NSF, Google, and SAP Labs. Leads the Machine Learning Department's research initiatives with international collaborations.

Anupam Dattaمشاهده پروفایل

استاد مدعو

• Privacy Engineering
• Accountable AI
• Fairness in Machine Learning
• +۳ مورد دیگر

Anupam Datta is an Adjunct Professor at Carnegie Mellon University's Electrical and Computer Engineering Department within the College of Engineering. His research focuses on creating accountable systems for privacy, fairness, and security, with notable contributions to audit mechanisms and bias detection in AI. He holds a PhD and MS from Stanford University and a BTech from IIT Kharagpur. Education: PhD in Computer Science (Stanford University, 2005) MS in Computer Science (Stanford University, 2002) BTech in Computer Science & Engineering (IIT Kharagpur, 2000) Research emphasizes scientific foundations of security/privacy, including formalizing policies, developing audit tools, and analyzing machine learning fairness. Notable projects include NSF-funded work on accountable decision systems and the AdFisher tool that revealed gender bias in Google ads. He serves as Editor-in-Chief of Foundations and Trends in Privacy and Security, leads the NSF project on trustworthy AI systems, and contributes to conferences like FAT* (Fairness, Accountability, and Transparency). His work bridges technical security, ethical AI, and policy enforcement. Grants include a $3M NSF grant for accountable decision systems (2017) and a $7.5M Office of Naval Research grant (2018). Collaborations involve Cornell Tech, International Computer Science Institute, and industry partners. Labs/teams: Part of CyLab Security and Privacy Institute at CMU, leading initiatives on AI transparency and cyber-physical systems security.

John Mackeyمشاهده پروفایل

استاد آموزشی

• Combinatorics
• Graph Theory
• Ramsey Theory
• +۳ مورد دیگر

John Mackey is a Teaching Professor in the Department of Computer Science at Carnegie Mellon University, affiliated with the School of Computer Science. His research focuses on combinatorial problems, particularly in Ramsey Theory, graph theory, and discrete mathematics. He investigates mathematical structures like Ramsey numbers, geometric configurations, and tiling conjectures, such as Keller’s conjecture. His work often involves automated reasoning and computational methods to explore extremal cases and bounds in discrete systems. Notable research interests include minimizing geometric configurations (e.g., pentagons), analyzing directed Ramsey numbers, and variations of combinatorial games like cops and robbers. His publications span over three decades, addressing topics from cube tilings to cycle enumeration in tournaments. Despite extensive contributions, awards or fellowships are not explicitly mentioned in the provided materials. Mackey’s teaching and research emphasize theoretical foundations, with a particular flair for unresolved problems like the party problem involving 43 guests and no five mutual acquaintances/strangers. His work bridges pure mathematics and computational techniques, aiming to uncover hidden structures through rigorous analysis.

David Woodruffمشاهده پروفایل

استاد

• Algorithms and Complexity
• Machine Learning
• Theoretical Computer Science
• +۵ مورد دیگر

David Woodruff is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on algorithms, complexity theory, machine learning, and theoretical computer science. He is particularly known for contributions to communication complexity, data stream algorithms, graph algorithms, and numerical linear algebra. His work bridges foundational theory with practical applications in privacy, security, and efficient computation. Research Interests: Algorithms and Complexity Machine Learning Data Stream Algorithms Communication Complexity Sketching and Randomized Numerical Linear Algebra Recent publications emphasize adversarial robustness in sketching, space-efficient streaming algorithms for graph problems, and eigenvalue approximation techniques. His work on privacy-preserving GWAS and transformer optimizations showcases interdisciplinary impact. He advises three PhD students and teaches courses in algorithms (15451, 15651, 15851). Advising and Grants: Active mentor to Honghao Lin, Hoai-An Nguyen, and Madhusudhan Pittu. His research has been supported by grants focusing on streaming algorithms, communication complexity, and machine learning foundations.

Zhihao Jiaمشاهده پروفایل

استادیار

• Artificial Intelligence
• Systems
• Quantum Computing
• +۳ مورد دیگر

Zhihao Jia is an Assistant Professor in the Computer Science Department at Carnegie Mellon University (CMU). He is affiliated with the CMU Catalyst Group and the Parallel Data Lab, focusing on advancing systems for machine learning, quantum computing, and large-scale data analytics. Previously, he was a research scientist at Facebook and earned his PhD from Stanford University (2020), advised by Alex Aiken and Matei Zaharia. His bachelor's degree is from Tsinghua University's Special Pilot CS Class under Andrew Yao. His research emphasizes accelerating deep learning computations on modern hardware and optimizing quantum circuits for intermediate-scale quantum devices. Notable contributions include speculative reasoning techniques, efficient LLM serving systems, and quantum circuit simulators. He teaches advanced courses such as 15418 and 15618 at CMU. Zhihao advises PhD students including Zhuoming Chen, Zikun Li, and Xinhao Cheng. His work bridges systems research with emerging applications, aiming to enhance computational efficiency and scalability. He collaborates on projects like Specexec, Helix, and Atlas, addressing challenges in distributed computing and quantum simulation.

Eric Xingمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Bioinformatics
• +۳ مورد دیگر

Eric Xing is a Professor in the Machine Learning Department at Carnegie Mellon University, with an affiliation to the Computer Science Department. His research focuses on advancing artificial intelligence, particularly in machine learning, bioinformatics, and interdisciplinary applications. He advises three students: Aviv Bick, Lingjing Kong, and Yonghao Zhuang. His recent work spans cutting-edge topics such as graph neural networks, generative AI, biosecurity safeguards, and scalable language model evaluation. Notable contributions include foundational models for RNA prediction, efficient video diffusion systems, and ethical frameworks for AI deployment. He actively bridges AI with fields like genomics, healthcare, and urban planning through collaborative projects. Xing’s research often emphasizes practical applications, such as edge computing-optimized inference systems and open mathematical corpora. His work has implications for both technical advancements and societal challenges, reflecting a commitment to impactful, interdisciplinary AI research.

Ryan O'Donnellمشاهده پروفایل

استاد

• Algorithms and Complexity
• Security and Privacy
• Theoretical Computer Science
• +۶ مورد دیگر

Ryan O'Donnell is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He specializes in theoretical computer science, with a focus on algorithms, complexity theory, quantum computing, and probability. His research includes Fourier analysis of Boolean functions, constraint satisfaction problems, and quantum information theory. He teaches courses such as 15251 (Fall 2025), 15754 (Spring 2025), and others. His work spans quantum tomography, pseudorandomness, and algorithmic design. Recent publications explore topics like quartic quantum speedups, uniformity testing, and explicit expanders. He advises PhD students William He, Noah Singer, and Jingxun Liang. His research interests bridge foundational theory and practical applications, including healthcare informatics and cryptographic tools. He actively contributes to academic conferences and program committees, reflecting his leadership in the field.

Richard Pengمشاهده پروفایل

دانشیار

• Algorithms
• Theoretical Computer Science
• Graph Theory
• +۴ مورد دیگر

Richard Peng is an Associate Professor in the Computer Science Department at Carnegie Mellon University, part of the School of Computer Science. He specializes in designing efficient algorithms for fundamental computational problems, particularly in graph algorithms, dynamic algorithms, and linear algebraic computations. Prior to joining CMU in 2023, he earned his BMath from the University of Waterloo, a PhD from CMU under Gary L. Miller, and completed a postdoc at MIT's Applied Math department. His research focuses on advancing algorithmic efficiency, including work on sparse linear systems, graph convolutions, and flow optimization. He advises PhD students Hoai-An Nguyen, Alicia Stepin, and Junzhao Yang. His teaching includes courses such as 15495 and 15151, reflecting his engagement in both research and education. Peng’s articles span topics like approximate spanning tree counting, dynamic graph algorithms, and Laplacian solvers, emphasizing practical scalability and theoretical guarantees. His contributions bridge theoretical computer science with applied challenges in network analysis and numerical computation.

Hanghang Tongمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Hanghang Tong is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), holding the title of University Scholar. He specializes in large-scale data mining, machine learning, and AI with a focus on graph and multimedia data. His research addresses applications in social networks, healthcare, cybersecurity, and cyber-physical systems. Education: PhD and M.Sc. in Machine Learning from Carnegie Mellon University (2008–2009). Prior to UIUC, he was an Associate Professor at Arizona State University. Research: Leads the IDEA Lab, focusing on network robustification, fair network learning, and multi-network alignment. Key areas include optimizing graph connectivity, algorithmic fairness, and developing tools like FASTEN and Sylvester Equation solvers. Awards: IEEE Fellow (2021), NSF CAREER Award (2017), multiple best paper awards, and the ICDM 10-Year Highest Impact Paper Award (2015). Editor-in-Chief of ACM SIGKDD Explorations and associate editor of ACM Computing Surveys. Teaching: Courses include CS512 (Data Mining Principles), CS514 (Advanced Network Science), and CS412 (Introduction to Data Mining). Authored influential textbooks like Data Mining: Concepts and Techniques (4th ed., 2022). Service: Organized key conferences (CIKM, DSAA) and workshops on adversarial activity modeling. Active in academic leadership and editorial roles. Labs/Teams: Runs the IDEA Lab, collaborating on projects like NetFair (fair network learning) and Network Correspondence Mining. Supervises a vibrant group of PhD and Master’s students in graph algorithms and data science.