جستجوی استاد راهنمای دانشگاهی

Gauri Joshi is an Associate Professor in the Electrical and Computer Engineering (ECE) department at Carnegie Mellon University, with affiliate appointments in the Machine Learning Department and Robotics Institute. Her work focuses on system-aware algorithms for distributed machine learning, combining optimization, probability, and coding theory to address communication and computational constraints in edge networks. MIT Ph.D. in EECS (2016) IIT Bombay B.Tech/M.Tech in Electrical Engineering (2010) Research themes include federated learning with communication efficiency, erasure coding for non-linear computations, and reinforcement learning for heterogeneous queueing systems. She leads the Optimization, Probability and Learning (OPAL) lab , affiliated with the Parallel Data Lab (PDL), CyLab, and FLAME Center. Recent publications highlight advances in: federated fine-tuning with low-rank adaptation, robust PCA for model aggregation, privacy-preserving prediction mechanisms, and adaptive reinforcement learning for job dispatching systems. Her group has received 15+ paper awards across SIGMETRICS, MobiHoc, and NeurIPS workshops. Scientific recognition includes: IEEE Goldsmith Lecturer (2025), MIT Technology Review '35 Innovators Under 35' (2022), ONR Young Investigator Award (2023), NSF CAREER (2021), and ACM SIGMETRICS Best Paper (2020). She has advised 20+ graduate students, many now at tech giants like Google, Apple, and Meta. Service contributions span program co-chair roles (MLSys 2025), associate editorships (IEEE/ACM Transactions on Networking), and workshop organization (ICML, NeurIPS). Her NSF AI-EDGE Institute leadership (2021-present) drives next-generation intelligent edge networks for robotics and aerospace applications.

• Distributed Machine Learning
• Federated Learning
• Stochastic Modeling
• +۷ مورد دیگر

Andy Pavlo is an Associate Professor with Indefinite Tenure in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He is an active member of the CMU Database Group and the Parallel Data Laboratory, where he leads research in database management systems with a focus on self-driving architectures, transaction processing, and large-scale analytics. His work bridges academic research and industry applications through projects like NoisePage, OtterTune (which he co-founded and served as CEO before it ceased operations), and Peloton. Dr. Pavlo's research interests span database management systems with particular emphasis on autonomous database architectures that can self-tune and optimize without human intervention. His work explores transaction processing systems that can handle high-throughput workloads while maintaining consistency, and large-scale data analytics techniques that efficiently process massive datasets. He has made significant contributions to query optimization, database extensibility, and automatic database tuning using machine learning techniques. His recent work on database extensibility revealed critical issues in PostgreSQL's extension ecosystem, showing that approximately 16% of extensions are incompatible with at least one other extension due to API violations and memory errors. His research output demonstrates a consistent focus on practical database systems challenges, with recent publications examining database extensibility, user-defined function optimization, and the cyclical nature of database research. The articles show a strong trend toward making database systems more autonomous, with increasing integration of machine learning techniques for automatic tuning and optimization. His work often combines deep theoretical analysis with practical implementation in open-source systems. Dijkstra Award 2024 for contributions to database systems research Dr. Pavlo actively mentors graduate students, with current advisees including Wan Shen Lim, William Zhang, and Sam Arch (co-advised with Todd Mowry). His former students have gone on to successful careers in both industry and academia. He has secured significant research funding through CMU's affiliate program with major database companies including ClickHouse, DataStax, dbt, Firebolt, MotherDuck, RelationalAI, SingleStore, Spiral, PingCAP/TiDB, Yellowbrick, and Yugabyte. His research is supported by these industry partnerships and likely includes NSF funding given his active participation in the database research community. At CMU, Dr. Pavlo leads the Database Group and organizes several seminar series including "SQL or Death," "Database Building Blocks," and "ML⇄DB Technical Talks." These seminars bring together researchers and practitioners to discuss cutting-edge developments in database systems. He also runs a summer research internship program that has attracted students for multiple consecutive years, indicating a strong research group with ongoing projects and funding.

• Database Management Systems
• Self-Driving Database Architectures
• Transaction Processing Systems
• +۴ مورد دیگر

Zakia Hammal is an Assistant Research Professor with dual appointments at Carnegie Mellon University, holding positions in the Robotics Institute within the School of Computer Science and the Department of Biomedical Engineering in the College of Engineering. Her work bridges computer science, machine learning, artificial intelligence, and social/behavioral psychology to advance computational models for human behavior analysis. Dr. Hammal's educational background includes a PhD in Computer Science, a Master of Artificial Intelligence and Algorithmic with specialization in Image Processing, and an Engineer's degree in Computer Science with specialization in Computer Systems. Her academic journey has positioned her at the intersection of technical expertise and healthcare applications. Her research focuses on multimodal human behavior modeling in social interaction, with particular emphasis on health informatics and affective computing (Emotion AI). Dr. Hammal's work has pioneered computational models for multimodal assessment of psychiatric disorders, including depression severity evaluation, automatic pain intensity measurement, assessment of expressiveness in children with facial abnormalities, analysis of non-verbal communication in mother-infant interaction, and identification of behavioral markers in autism spectrum disorder. Her approach integrates computer vision, machine learning, and behavioral psychology to create systems that can objectively measure human behaviors that are often subjective in clinical settings. Analysis of her recent publications reveals a consistent trajectory toward more sophisticated multimodal approaches to healthcare challenges, particularly in pain assessment and mental health diagnostics. Her work increasingly emphasizes interpretable AI models that can translate complex behavioral patterns into clinically meaningful insights, with growing attention to applications for vulnerable populations including infants, elderly patients, and those with craniofacial abnormalities or autism spectrum disorder. Women in AI Awards North America 2023 – AI Researcher of the Year Award Outstanding Reviewer Award at FG 2015 Best Paper award at ACII 2015 Outstanding Paper award at ICMI 2012 Dr. Hammal has secured significant research funding, primarily from the U.S. National Institutes of Health, including an R01 grant for developing a Multimodal Behavioral AI platform for pain assessment and management, and additional grants for automatic pain assessment in older adults with dementia. Her leadership extends to mentoring through her involvement in organizing workshops and conferences that train the next generation of researchers in affective computing and health informatics. As an active leader in her field, Dr. Hammal serves as ACM ICMI Steering Board Committee Member, Associate Editor for IEEE Transactions on Affective Computing and IEEE Transactions on Multimedia, and has organized numerous influential workshops including the International Workshop on Automated Assessment of Pain and Face and Gesture Analysis for Health Informatics. She is set to serve as Program Chair for FG 2025, ACII 2025, and ICMI 2026, demonstrating her growing influence in shaping the future direction of research in multimodal interaction and affective computing.

• Multimodal Human Behavior Modeling
• Health Informatics
• Affective Computing
• +۷ مورد دیگر

Linda Argote is the Thomas Lord Professor of Organizational Behavior and Theory at the Tepper School of Business, Carnegie Mellon University. She serves as Director of the Center for Organizational Learning, Innovation, and Knowledge. Her academic journey includes prior roles such as David M. Kirr Professor and Carnegie Bosch Professor. She holds a Ph.D. in Organizational Psychology from the University of Michigan and a B.S. in Psychology from Tulane University. Her research focuses on organizational learning, innovation, knowledge transfer, and group processes, with notable contributions to transactive memory systems and team performance. Argote has received prestigious awards like the Joseph E. McGrath Award and an Honorary Doctorate. She actively contributes to academic leadership through roles in editorial boards, grant reviews, and university committees. Her teaching includes courses on organizational learning and strategic management.

• Organizational Learning
• Innovation
• Knowledge Transfer
• +۴ مورد دیگر

Sridhar R. Tayur is the Ford Distinguished Research Chair and University Professor of Operations Management at Carnegie Mellon University’s Tepper School of Business. He holds a Ph.D. in Operations Research from Cornell University and a B.Tech. in Mechanical Engineering from IIT Madras. His research focuses on quantum computing applications in operations research, healthcare systems optimization, and supply chain management. He has held visiting roles at MIT, Stanford, and Cornell, and founded companies like SmartOps and OrganJet. His recent work spans quantum-inspired optimization algorithms, healthcare decision support systems, and fair resource allocation policies. He has contributed to over 110 publications, including high-impact papers in Management Science , Operations Research , and IEEE Transactions . Awards include INFORMS Fellow and NAE membership. He teaches courses in quantum integer programming, healthcare operations, and service management at the Tepper School. Education: Ph.D. (Cornell), B.Tech. (IIT Madras) Research Labs: Quantum Technology Group, OrganJet Key Awards: INFORMS Fellow, NAE Member, MSOM Distinguished Fellow Teaching: MBA Operations Management, PhD Quantum Optimization, Healthcare Systems His interdisciplinary work bridges quantum computing, healthcare policy, and logistics, supported by collaborations with industry and government institutions.

• Operations Research
• Quantum Computing
• Healthcare Operations
• +۳ مورد دیگر

Lin Ma is currently an Assistant Professor at the University of Michigan, Ann Arbor in the Department of Electrical Engineering and Computer Science (College of Engineering). Previously, they served as a Post Doctoral Fellow at Carnegie Mellon University (2021-2022) and as a Software Engineer at Databricks, Inc. (2022-2023). Research Interests focus on the intersection of database systems and machine learning, particularly in developing self-driving database management systems . Key areas include workload forecasting , automated index optimization , query execution acceleration , and machine learning integration for database automation. Their work explores GPU-accelerated analytics, memory optimization, and transactional consistency models. Academic Contributions span 15+ publications in top venues like VKDB , SIGMOD , and CIDR , including recent 2025 papers on Vortex (GPU memory optimization) and Scompression (workload compression). Earlier work introduced QueryBot 5000 , a workload forecasting framework, and explored anti-caching for storage optimization in OLTP systems. Teaching includes courses like EECS 584: Advanced Database Management Systems and EECS 484: Database Management Systems at the University of Michigan (2023-2025), and 15-445/645 Database Systems at Carnegie Mellon University. Service involves program committee roles for SIGMOD (2023-2025), VLDB (2022-2025), and CIDR (2024-2025). They also served on admissions and search committees at both institutions. Advising includes supervising PhD and MS students: Siyuan (Doug) Dong , Zhongwei Xu , and Haotian (Jack) Gong (co-advised with Barzan Mozafari), among others.

• Self-Driving Database Management Systems
• Machine Learning for Databases
• GPU-Accelerated Data Analytics
• +۴ مورد دیگر

Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University , focusing on Data Management , System Efficiency (e.g., Scalable Data Platforms), and Human Efficiency (e.g., LLM-Based Query Interfaces). His work bridges Database Systems , Machine Learning , and Human-Computer Interaction . Co-founder of four startups: Paradise (1997), Locomatix (2007), Quickstep (2015), and DataChat (2017). Member of SIGMOD 2025 (AE) , CIDR 2024 (Co-Chair) , and other program committees. Research Interests include efficient data analysis algorithms , LLM-based data interaction , and systems security . His group develops platforms combining scalability and user productivity . Scientific Awards include Best Paper Awards at SIGMOD and VLDB, and Fellowships from AAAS, ACM, and IEEE. He also received Teaching Awards at CMU. Professional Activities feature co-founding startups , serving on program committees , and teaching courses like Database Systems and Advanced Database Systems at CMU.

• Data Management
• Database Systems
• Machine Learning
• +۳ مورد دیگر

Phillip B. Gibbons is a Professor in both the Computer Science Department and Electrical & Computer Engineering Department at Carnegie Mellon University. He received his Ph.D. in Computer Science from the University of California at Berkeley in 1989 and has held research positions at AT&T Bell Laboratories, Lucent Bell Laboratories, and Intel Research Pittsburgh before joining CMU's faculty. His research spans parallel computing, distributed systems, databases, computer architecture, and machine learning. Gibbons' work bridges theory and systems, with publications in top-tier conferences including SOSP, OSDI, SIGMOD, VLDB, NeurIPS, and many others across computer science and engineering disciplines. His research has been supported by significant funding from NSF, Intel, and other organizations. Gibbons has made substantial contributions to streaming algorithms, parallel computing frameworks, distributed systems security, and large-scale machine learning systems. His work on data stream algorithms with Alon, Matias, and Szegedy has been particularly influential in the field. He has served in numerous leadership roles including Editor-in-Chief of ACM Transactions on Parallel Computing (2012-2018) and on the editorial boards of Journal of the ACM and IEEE Transactions on Cloud Computing. He has also been active on program committees for major conferences in systems, databases, and theory. IEEE Fellow (2014) - For contributions to parallel computing and databases ACM Fellow (2006) - For contributions to parallel computing, databases, and sensor networks Selected for Oral Presentation at NeurIPS '13 (only 20 selected out of 1420 submissions) Co-winner of the best paper award for NSDI '06 Gibbons has advised numerous students and mentored researchers who have gone on to make significant contributions in academia and industry. His research has been supported by major grants including the $15M Intel Science and Technology Center for Cloud Computing (2011-2015) where he served as Co-PI/Co-Director. He currently leads research projects on write-efficient algorithms, big learning systems, and visual cloud systems. His laboratory work focuses on bridging theoretical computer science with practical systems implementation, particularly in the areas of parallel and distributed computing. Current research directions include adapting algorithms for emerging memory technologies and optimizing machine learning systems for large-scale deployment.

• Parallel Computing
• Distributed Systems
• Databases
• +۴ مورد دیگر

Anson Kahng is an Assistant Professor in the Department of Computer Science and the Goergen Institute of Data Science at the University of Rochester. He previously held postdoctoral positions at the University of Toronto and completed his PhD at Carnegie Mellon University under the supervision of Ariel Procaccia, focusing on computational social choice. PhD, Computer Science, Carnegie Mellon University Undergraduate degree, Computer Science, Harvard College His research explores the intersection of computer science and democracy, developing frameworks like virtual democracy and liquid democracy while analyzing fairness in participatory budgeting and voting systems. He combines theoretical analysis with empirical methods, emphasizing interdisciplinary collaboration. Recent work includes advancements in ranked choice voting optimization, fairness metrics for elections, and structural analysis in cryo-electron tomography. He has published in top venues such as IJCAI, AAAI, NeurIPS, and ACM Transactions on Economics and Computation. NeurIPS 2019 Spotlight Presentation (top 2.5% of submissions) Kahng advises PhD students Alina Chadwick and Joe Saber, and has mentored multiple undergraduate researchers. He teaches courses on algorithmic game theory and computational statistics at the University of Rochester.

• Computational Social Choice
• Algorithmic Game Theory
• Fairness in Machine Learning
• +۳ مورد دیگر

Yihan Sun is an Assistant Professor at the University of California, Riverside (UCR) since January 2020. He earned his Ph.D. in Computer Science from Carnegie Mellon University (CMU) , advised by Guy Blelloch , and holds a Bachelor's degree in Computer Science from Tsinghua University . Research Interests: Yihan Sun focuses on the theory and practice of parallel computing , including Parallel algorithms and data structures Write-efficient algorithms for Non-Volatile Memory (NVM) Computational geometry (range trees, Delaunay triangulations) Graph algorithms (SSSP, SCC, cluster-based BFS) Concurrent and persistent data structures Multi-version concurrency control (MVCC) with garbage collection Applications in databases, transactional systems, and computational biology Recent Research Trends: His work on join-based parallel balanced trees has been foundational, supporting four balancing schemes (AVL, red-black, weight-balanced, treaps) and enabling efficient implementations in graph analytics, spatial queries, and dynamic programming. Recent publications focus on output-sensitive algorithms , scalable graph libraries (PASGAL) , and pedagogical approaches to teaching parallel algorithms. Teaching: He teaches CS260 (Parallel Algorithms) at UCR and has served as a guest lecturer for MIT 6.886 (Algorithm Engineering) and CMU 15-859 (Algorithms in the real world) . He also contributed to algorithm education through a tutorial at the ACM Symposium on Principles and Practice of Parallel Programming (PPoPP 2019) . Labs & Collaborations: Yihan is a core contributor to the PAM (Parallel Augmented Maps) library, which has been integrated into systems like Aspen (graph-streaming) and C-trees . He collaborates with teams at CMU-Parlay , PBBS , and Ligra , with his code available on Github for community feedback.

• Parallel Computing
• Algorithms
• Data Structures
• +۱۲ مورد دیگر

Elaine Shi is a Professor at Carnegie Mellon University's Computer Science Department and Electrical and Computer Engineering Department, with an Adjunct Professor appointment at the University of Maryland. Her research spans cryptography, security, blockchain technology, algorithms, and privacy-enhancing techniques. Co-founder of Oblivious Labs, Inc. Co-developer of cryptographic protocols adopted by Signal, Meta, and Google Co-founder of CyLab's crypto seminar series Her work has been recognized with prestigious awards including the Packard Fellowship, Sloan Research Fellowship, ACM Fellow, and IACR Fellow. She has advised numerous PhD students and postdocs, many of whom now hold academic or industry positions. 2023 ACM CCS Test of Time Award 2020 CyLab Distinguished Alumni Award 2016 ONR YIP Award Recent publications focus on advancing cryptographic protocols, privacy-preserving algorithms, and blockchain security, with key contributions in garbled RAM, oblivious computation, and differentially private mechanisms.

• Cryptography
• Security
• Blockchain Technology
• +۴ مورد دیگر

Dr. Cameron N. Riviere is a Research Professor at Carnegie Mellon University (CMU), affiliated with the Robotics Institute, Department of Biomedical Engineering, and Department of Mechanical Engineering. He also holds an Adjunct Professorship at the University of Pittsburgh’s Department of Rehabilitation Science and Technology. His research focuses on surgical robotics, medical devices, and control systems, emphasizing minimally obtrusive robotic systems for microsurgery and minimally invasive surgery. Key projects include the Micron handheld tremor-canceling device and the HeartLander cardiac robot. Education: B.S. in Aerospace and Ocean Engineering from Virginia Tech (1989), Ph.D. in Mechanical Engineering from Johns Hopkins University (1995). Research Interests: Medical robotics, rehabilitation robotics, control systems, human-machine interfaces for biomedical applications, and physiological motion compensation. Recent work includes development of robotic systems for retinal surgery, cardiac therapy, and assistive technologies for individuals with movement disorders. Awards Best Paper of 2016 (IEEE/ASME Transactions on Mechatronics) Finalist for Best Medical Robotics Paper (IEEE ICRA 2016) Best Paper (IEEE MFI 2015, IROS 2014) Advising and Grants: Mentor to PhD students (e.g., Arpita Routray) and masters students (e.g., Aditti Ramsisaria). Projects include the HeartLander epicardial therapy robot and advanced tracking systems for handheld surgical tools. Collaborates with institutions like Mayo Clinic and NIH-funded initiatives. Labs: Director of the Surgical Mechatronics Laboratory at CMU’s Robotics Institute, focusing on interdisciplinary development of robotic systems for clinical applications.

• Medical Robotics
• Surgical Robotics
• Control Systems
• +۵ مورد دیگر

Craig Shultz is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign and co-founder of Fluid Reality . His research focuses on interactive embedded systems and haptic actuation , particularly tactile interfaces for VR/AR, IoT, and wearable devices. He employs an interdisciplinary approach blending psychophysics, electrical and mechanical engineering, and human-computer interaction (HCI) to advance haptic rendering. Specializes in surface haptics , non-contact haptics , and electroosmotic pump arrays . Develops technologies for scalable shape displays , mid-air haptic interactions , and haptic-augmented spatial computing . Teaches Interactive Haptic Systems (since 2025). His research has led to 6 best paper awards and nominations at ACM and IEEE venues, including the IEEE Transactions on Haptics Best Application Paper of the Year (2018). He received the Sony Faculty Innovation Award (2025) and the IEEE Technical Committee on Haptics Early Career Award (2025) . His publications emphasize haptic rendering , low-latency touchscreens , and non-contact tactile actuation . He actively collaborates with institutions like Northwestern University and KAIST HCI . Key research areas: tactile interfaces, scientific engineering, VR/AR haptics, and haptic design practices. Technologies developed: Fluid Reality haptic gloves, DynaButtons, LRAir synthetic jets, and TriboTouch micro-patterned surfaces.

• Interactive Embedded Systems
• Haptic Actuation
• Surface Haptics
• +۷ مورد دیگر

Ramayya Krishnan is the W. W. Cooper and Ruth F. Cooper Professor of Management Science and Information Systems at Carnegie Mellon University, jointly appointed in Heinz College and the Department of Engineering and Public Policy. He served as Dean of Heinz College from 2009 to 2025 and has been a faculty member since 1988. He is a leading expert in digital transformation, data analytics, and AI policy, with extensive collaborations in government and industry. His education includes: B.Tech. in Mechanical Engineering, Indian Institute of Technology Madras (1981) M.S. in Industrial Engineering and Operations Research, University of Texas at Austin (1983) Ph.D. in Management Science and Information Systems, University of Texas at Austin (1987) Krishnan's research focuses on the responsible use of AI, data-driven workforce development, risk management in information systems, and the intersection of technology and public policy. His work bridges operations research, information systems, and policy design, with a strong emphasis on real-world impact. He has pioneered academic programs and research centers that integrate analytics with societal challenges. His recent publications reflect a strong trend in AI measurement science, policy design for emerging technologies, and the ethical deployment of data analytics in public and private sectors. These works emphasize safety, fairness, transparency, and accountability in AI systems, aligning with national priorities and global challenges. His scientific awards and honors include: INFORMS Fellow AAAS Fellow Elected Member, National Academy of Public Administration Commissioner, Geotech Center of the Atlantic Council IIT Madras Distinguished Alumnus Award (2017) Y. Nayudamma Award (2015) GM Outstanding Distance Learning Faculty Award Martcia Wade Teaching Award Two-time recipient of Heinz College IT Teaching Award UPS George D. Smith Prize (awarded to Heinz College under his leadership) Appointed to the National AI Advisory Committee (NAIAC), 2022 Krishnan has been a serial academic entrepreneur, founding the Master of Information Systems and Management program and the Decision Analytics and Systems (DAS) undergraduate minor. He has founded four externally funded research centers, raising nearly $100 million, including the Block Center for Technology and Society. He advises policymakers at the highest levels, including the U.S. President, the Commonwealth of Pennsylvania, the U.S. Government Accountability Office, and international bodies like the Asian Development Bank and the World Economic Forum. His leadership in the CMU/NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC) underscores his role in shaping national AI standards. He leads or is affiliated with several key labs and teams: Block Center for Technology and Society (Director) CMU/NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC) (Lead Research Coordinator) NIST AI Innovation Lab (NAIIL) (Contributing Leader) Global Agenda Council on Data Driven Development, World Economic Forum (Former Member)

• Management Science
• Information Systems
• Operations Research
• +۷ مورد دیگر

Andrew Pavlo is an Associate Professor of Databaseology in the Computer Science Department at Carnegie Mellon University , part of the School of Computer Science . His research focuses on database systems, particularly self-driving architectures, transaction processing, and large-scale analytics. He is a member of the CMU Database Group and Parallel Data Laboratory. His awards include the NSF CAREER (2019), Sloan Fellowship (2018), and ACM SIGMOD Jim Gray Dissertation Award (2014). He co-founded OtterTune, a database tuning startup, though it later ceased operations. Current research interests emphasize autonomous database systems, query optimization, and distributed computing. Recent publications (2024) highlight work on self-driving DBMS, null representation in columnar formats, and UDF optimization techniques. Awards: NSF CAREER Award (2019) Sloan Fellowship (2018) ACM SIGMOD Jim Gray Dissertation Award (2014) Advising: Mentors students in database systems, including Sam Arch, Wan Shen Lim, and William Zhang. Labs/Teams: Leads the Database Group and collaborates with the Parallel Data Laboratory.

• Databases
• Distributed Systems
• Data-Intensive Computing
• +۳ مورد دیگر