جستجوی استاد راهنمای دانشگاهی

Joy Arulrajمشاهده پروفایل

دانشیار

• Data Systems
• Machine Learning
• Database Systems
• +۲ مورد دیگر

Joy Arulraj is an Associate Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research focuses on data systems, machine learning, and database systems, with a particular emphasis on video analytics and adaptive query processing. He leads the Data Systems and Analytics Group and is developing the EVA AI-Relational Data System. Dr. Arulraj's research interests span data systems, machine learning, database systems, video analytics, and adaptive query processing. His work centers on developing systems that efficiently process complex queries, particularly for video analytics and machine learning workloads. He has made significant contributions to GPU database systems, non-volatile memory database management, and adaptive query processing techniques. His research often bridges the gap between theoretical database principles and practical implementations for modern hardware architectures. His recent publications show a strong trend toward video analytics systems, adaptive query processing for machine learning workloads, and GPU-accelerated database systems. The EVA system represents a major focus of his recent work, providing end-to-end exploratory video analytics capabilities. His research also addresses fundamental database concepts like buffer management, query optimization, and storage management, adapting these principles for modern hardware and application requirements. Dr. Arulraj has advised numerous graduate students including Pramod Chunduri, Gaurav Tarkok Kakkar, Jiashen Cao, and Sayan Sinha. His graduated students have gone on to work at companies like ServiceNow, Meta Research, and the Korean Army. He actively teaches database system courses at Georgia Tech, including Database System Implementation (CS 4420/6422) and Advanced Database System Implementation (CS 4423/6423), where students build database systems from scratch using C++ and the BuzzDB framework. He maintains an active research program with consistent publication output across top database and systems conferences. His work spans from theoretical database principles to practical system implementations, with a recent emphasis on video analytics, machine learning integration with database systems, and leveraging modern hardware like GPUs and non-volatile memory for database applications.

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

George H. Chenمشاهده پروفایل

دانشیار

• Trustworthy Machine Learning
• Time-to-Event Prediction
• Survival Analysis
• +۳ مورد دیگر

George H. Chen is an Associate Professor at Carnegie Mellon University , with dual affiliations in the Heinz College of Information Systems and Public Policy and the Machine Learning Department . His research focuses on trustworthy machine learning methods for temporal reasoning , particularly in health applications such as time-to-event prediction (survival analysis) and electronic health records analysis . He has extensive experience in nonparametric methods requiring minimal data assumptions. Educational Background PhD in Electrical Engineering and Computer Science, MIT (2015) SM in Electrical Engineering and Computer Science, MIT (2012) BS in Electrical Engineering and Computer Sciences & Engineering Mathematics and Statistics, UC Berkeley (2010) His work spans survival analysis , deep learning , and time series modeling , with applications in neurological prognostication , medical adherence , and health equity . He has developed self-contained educational resources including a 2024 monograph on deep survival analysis and tutorials at CHIL and SIGMETRICS. His 2025 course 95-865: Unstructured Data Analytics focuses on practical unstructured data analysis techniques. Notable projects include advising the AgriTech startup CoolCrop , which provides cold storage and market forecasts for Indian farmers serving 9,000+ farmers across 7 states. His Google Scholar publications reveal a strong focus on temporal modeling in healthcare, with recent advancements in neural survival analysis and fairness-aware temporal prediction.

Tze Meng Lowمشاهده پروفایل

استاد پژوهش

• Parallel and High Performance Computing
• Formal Methods
• Graph Algorithms
• +۳ مورد دیگر

Tze Meng Low is an Associate Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. His research focuses on high-performance algorithms, formal methods, and hardware-software co-design, with an emphasis on achieving performance portability across architectures. He holds a Ph.D. and M.S. in Computer Science and dual B.A./B.S. degrees in Economics and Computer Science from the University of Texas at Austin. His research interests span parallel computing, graph algorithms, machine learning, and cyber-physical systems. He has contributed to projects like the DARPA BRASS initiative, collaborating on adaptive software systems for resource-challenged environments. His work also involves developing tools such as SMaLL and qLD, which address challenges in machine learning library instantiation and genomic analysis. Low has received the Dean’s Early Career Fellowship (2022) and led the $2.7M DARPA-funded BRASS project (2016–2020), supported by SpiralGen, Inc. and academic collaborators. His contributions include code generation frameworks like SPIRAL and advancements in linear algebra-based graph algorithms, emphasizing analytical models and automated code optimization. His research bridges theoretical formal methods with practical implementations, aiming to enhance software reliability and scalability in emerging domains. Collaborations include work on fault-tolerant coded computing and high-assurance systems for cyber-physical applications.

John Paul Shenمشاهده پروفایل

استاد

• Modern Processor Design
• Instruction-Level Parallelism
• Dependable Computing
• +۳ مورد دیگر

John Paul Shen is a Distinguished Service Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), based at the Silicon Valley campus. With over three decades of experience in academia and industry, he has held prominent roles including founding director of Nokia Research Center-North America Lab (2007–2012) and director of Intel’s Microarchitecture Research Lab (2000–2006). His academic career includes a tenured professorship at CMU’s ECE Department from 1990–2000 and a return to CMU in 2015 as a tenured professor. Education: Ph.D. in Electrical Engineering, University of Southern California M.S. in Electrical Engineering, University of Southern California B.S. in Electrical Engineering, University of Michigan Research Focus: Modern Processor Design: Specializes in superscalar processors, instruction-level parallelism (ILP), and microarchitecture validation tools like VMW (Visualization-Based Microarchitecture Workbench). Neuromorphic Computing: Develops energy-efficient temporal neural networks (TNNs) and CMOS implementations for sensory processing. Edge AI: Focuses on low-precision accelerators, sparsity exploitation, and real-time data analytics for edge devices. Dependable Computing: Leverages idle processor resources for error-checking and fault tolerance. Awards & Impact: Recipient of multiple teaching awards during his CMU tenure. Supervised 17 Ph.D. students and dozens of M.S. students, with notable contributions to compiler design and parallel computing. Authored two influential textbooks, including Modern Processor Design: Fundamentals of Superscalar Processors , used at Stanford. Generated over 100 patents and 200+ papers at Nokia and Intel, with a focus on mobile computing and hardware architecture innovations. Labs & Collaborations: Founded and led the Nokia Research Center-NAL, advancing mobile Internet and computing. Directed Intel’s Microarchitecture Research Lab, advancing IA32/IA64 processor designs. Current research at CMU Silicon Valley focuses on neuromorphic systems, wearable computing, and real-time data analytics.

Victor Akinwandeمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Victor Akinwande is a doctoral researcher at Carnegie Mellon University's Computer Science Department under advisor J. Zico Kolter. His research focuses on robust machine learning systems, causal inference for global health applications, and advancing vision-language models through generative modeling techniques. He holds an MSc in Information Technology (CMU, 2016–2018) and a BSc in Computer Science from the University of Ilorin (2011–2015). His work spans key areas including adversarial robustness, generalization bounds for prompt-based learning, and causal effect estimation in public health. Notable contributions include HyperCLIP (2024), AcceleratedLiNGAM (2024), and foundational work on subset scanning for anomaly detection in health data (2020–2022). Victor has been recognized with an Outstanding Paper Award (ICLR 2024 workshop) and a Distinguished Paper Award Nomination (AMIA 2022). His research bridges theoretical machine learning advancements with practical applications in global health systems and AI safety.

Shahriar Noroozizadehمشاهده پروفایل

پژوهشگر

• Representation Learning
• Machine Learning for Healthcare
• Multimodal Machine Learning
• +۴ مورد دیگر

Shahriar Noroozizadeh is a Ph.D. student at Carnegie Mellon University, jointly affiliated with the Machine Learning Department (School of Computer Science) and the Heinz College of Information Systems and Public Policy . Advised by Prof. George Chen (Heinz/MLD) and Prof. Jeremy Weiss (National Library of Medicine at NIH), he focuses on interpretable representation learning for temporal data in healthcare , particularly for patient risk progression modeling and hospital discharge delay prediction. Education : Ph.D. in progress (ML & Public Policy, CMU); M.S. in Machine Learning (2022) and Biomedical Engineering (2020) from CMU; B.A.Sc. in Engineering Physics (EECS specialization) from UBC. His research bridges machine learning methodology with high-stakes decision-making systems in healthcare, emphasizing interpretability, contrastive learning, and multimodal representation. He has pioneered supervised contrastive frameworks that outperform traditional models in clinical outcome prediction and synthetic dataset recovery, while also exploring embodied AI and large language models for molecular design and robotics. Recent work includes a patent-pending mRNA language model (mRNA-LM) for structure prediction and translation efficiency analysis, developed during his A.I. Research Scientist Internship at Sanofi . His publications span venues like NeurIPS , AAAI , and ML4H , with notable improvements in MIMIC-III and ADNI datasets . Key Awards : TCS Presidential Fellowship, NSERC CGS-D Doctoral Fellowship, CMU CMLH Digital Health Innovation Fellowship, Suresh Konda Memorial Award, and multiple undergraduate honors.

Phil Gibbonsمشاهده پروفایل

استاد

• Big Data
• Parallel Computing
• Databases
• +۵ مورد دیگر

Phil Gibbons is a Professor in the Electrical & Computer Engineering and Computer Science Departments at Carnegie Mellon University. He holds a Ph.D. from UC Berkeley (1989) and has extensive industry experience at AT&T Bell Labs, Lucent Bell Labs, and Intel Research. His research focuses on parallel computing, distributed systems, databases, and machine learning, with a emphasis on algorithmic and systems-level innovations. He has led major initiatives like the Intel Science and Technology Center for Cloud Computing and contributed to projects such as IrisNet (a planetary-scale sensor network). Education : Ph.D. in Computer Science, University of California at Berkeley (1989) Research Interests : Gibbons' work spans big data analytics , high-performance computing , and cloud systems . He develops scalable algorithms and systems for emerging memory technologies, distributed ML, and robotics. Notable contributions include processing-in-memory (PIM) optimizations, pipeline parallelism for DNN training, and system architectures for robotic processors. Awards : IEEE Fellow (2014) ACM Fellow (2006) ACM Paris Kanellakis Theory and Practice Award (2019) Best Paper Award at NSDI 2006 Grants & Leadership : Co-PI of the $15M Intel STC for Cloud Computing (2011-2015) Editor-in-Chief of ACM Transactions on Parallel Computing (2012-2018) Leadership roles in conferences like SPAA, EuroSys, and MLSys Teams & Labs : Active in robotics computing (RobotPerf benchmark), distributed ML systems, and hardware-software co-design initiatives.

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.