Xiao Li

Ph.D. 2025 — Google

Tania Banerjee

Former Research Scientist / Postdoctoral Researcher — University of Houston

Rahul Sengupta

Research Scientist, 2025–Present; Ph.D. 2024

Yash Ranjan

2024–Present; expected graduation December 2026

Nooshin Hosseini

Ph.D. 2026 — Postdoctoral Researcher, UC Berkeley

Jaemoon Lee

Ph.D. 2025 — Oak Ridge National Laboratory

The photographs above show current and former group members. Current appointments are listed below.

I have graduated more than 55 doctoral students. Unless a co-advisor is listed, I was or am the principal advisor.

Current Doctoral Students

• Yash Ranjan, 2024–Present (Expected Graduation: December 2026)
• Bhanu Vangala, 2024-Present (Expected Graduation: December 2027)
• Surya Majumdar, 2025-Present (Co-chair with Anand Rangarajan)
• Liangi Xu, 2025-Present (Co-chair with Anand Rangarajan)

Research Scientist

• Rahul Sengupta, 2025-Present, Research Scientist

Past Graduate Students

• Todd Heywood, (1991), A Practical Hierarchical Model of Parallel Computation. Technical Staff, IBM Poughkeepsie.
• Anand Rangachari, (1992), Efficient Neural Algorithms for Multiclass Problems. Technical Staff, IBM T. J. Watson Labs. (Co-advisor with Kishan G. Mehrotra).
• Yeh Chin Chung, (1992), Static Mapping and Scheduling Algorithms for Distributed Memory Multiprocessors. Associate Professor, Department of Computer Science, National Taiwan University, Taiwan R. O. C.
• Jhy Chun Wang, (1993), Load Balancing and Communication Support for Irregular Problems. Technical Staff, IBM Poughkeepsie.
• Harpal Maini, (1994), Incorporating Knowledge in Genetic Optimization. Technical Staff, Deutesche Morgan Bank. (Co-advisor with Kishan G. Mehrotra).
• M. C. Yang, (1994), 3-D Object Recognition and Description Using Parallel Geometric Hashing Algorithms.
• David Koester, (1995), Parallel Block-Diagonal-Bordered Sparse Linear Systems for Power Systems Applications.
• Chao-Wei Ou, (1996), Partitioning and Incremental Partitioning for Adaptive Irregular Problems. Technical Staff, Northeast Parallel Architecture Center.
• Ravi Shankar, (1996), Scalable Parallel Algorithms for Random Accesses and Shared Memory Simulation. Technical Staff, Bellcore.
• Maher Kaddoura, (1996), Parallel Computing in Non-uniform and Adaptive Computational Environments. Technical Staff, Architecture Technology Corporation.
• Seungjo Bae, (1997), Runtime Support for High Performance Fortran. Technical Staff, ETRI (Korea).
• Jang Sun Lee, (1997), User Controllable Parallel I/O. Technical Staff, ETRI (Korea).
• Khaled Alsabti, (1998), Efficient Algorithms for Data Mining Primitives, Assistant Professor, King Fahd University, Saudi Arabia.
• Ibraheem Al-furaih, (1998), Optimizing for Memory Hierarchy. Saudi Arabia.
• Hankil Yoon, (2000), Efficient Processing of Large Sparse Datasets. Oracle Corporation.
• Scott Winterstein, (2000), Efficient Association Mining for Data Warehousing and E-Commerce.
• Srijit Kamath, (2005), Efficient Algorithms for Sequencing Multileaf Collimators. (Postdoc at Stanford University College of Medicine; co-advisor with Sartaj Sahni).
• Jang Uk In, (2006), Policy-Based Scheduling for Grid Environments. Microsoft Corporation.
• Jun Liu, (Feb. 2008), Mining Comparative Genomic Hybridization Data. Google Corporation.
• Xiuyao Song, (2008), Novel Change Detection Techniques in Multidimensional Data Mining. Google Corporation.
• Laukik Chitnis, (2008). Fault Tolerance and Scalability of Data Aggregation in Sensor Networks. Google Corporation.
• Jaeyeon Kang, (2008), Energy Minimization Algorithms for Multicore Machines. Samsung Research.
• Parbati Manna, (2008), Detection, Propagation Modeling, and Designing of Advanced Internet Worms. Intel Corporation.
• Manas Somaiya, (2010), Novel Mixture Models to Learn Complex and Evolving Patterns in High Dimensional Data. EBay Corporation.
• Eunsung Jung, (2010), Network Resource Provisioning in Research Networks, Faculty, Hongik University, Korea.
• Yan Li, (2010), Data Structures and Algorithms for Resource Scheduling in High Speed Networks. (Co-advisee with Sartaj Sahni). Google Corporation.
• Bin Song, (2010), New in Silico Approaches for Metabolic Engineering (co-advisee with Tamer Kahveci). Google Corporation.
• Nirmalya Bandyopadhyay, (2011), Modeling Perturbations in Gene Regulatory and Signaling Networks. Broad Institute.
• Arslan Munir, (2012), Modeling and Optimization of Parallel and Distributed Embedded Systems (co-advisee with Ann Gordon Ross), University of Nevada, Reno.
• Abdullah Almutairi, (2012), Efficient Algorithms for Learning Correlations in Large-Scale Wireless Data, Kuwait University.
• Zhe Wang, (2012), Software and Algorithms for Energy and Temperature Minimization, Facebook.
• Saeed Moghhaddam, (2012), Large-Scale Mining of Mobile Online Behavior: Interest-Aware Modeling and Design, Samsung Corporation (co-advisee with Ahmed Helmy), Samsung Research.
• William Chapman, (2013), Multiresolution SAR Image Formation and Change Detection on High-Performance Heterogeneous Architectures.
• Junjie Li, (2013), GPU Computing for Bioinformatics (co-advisee with Sartaj Sahni), Snapchat Corporation.
• Yifan Wang, (2015), Energy Efficient and Thermal-Aware Task Scheduling on Multi-Core Processors.
• Hengxing Tan, (2016), Performance, Energy and Thermal Tradeoffs for Data Parallel Problems, Amazon Corporation.
• Mohamed Gadou, (2018), Performance Energy Tradeoffs for Iterative and Direct Sparse Matrix Solvers on Hybrid Multicore Architectures, Google Corporation.
• Chengliang Yang, (2018), Interpretable Machine Learning with Applications in Health Care, Uber Corporation.
• Yupeng Yan, (2018), Parallelizable Semi-Supervised Dense Labeling Framework for Very High-Resolution Satellite Images (co-advisee with Anand Rangarajan), Airbnb corporation.
• Matin Kheirkhahan, (2018), Real-Time Data Monitoring and Machine Learning Methods for Activity Recognition using Wearable Devices, Google Corporation.
• Adeesha Pathirannahalage Wijayasiri, (2018), Dynamic Data Driven SAR Reconstruction on Hybrid Multicore Systems (co-advisee with Sartaj Sahni), Faculty in Computer Science, University of Moratuwa, Sri Lanka.
• Meng Tang, (2020), GPU Algorithms and Software for Sparse Matrix Factorization, Qualcomm Corporation.
• Keke Zhai, (2020), Load Balancing Sparse Dataset Based Applications on Hybrid Multicore Architectures, Facebook Corporation.
• Xiaohui Huang, (2020), Video-based Machine Learning for Intelligent Transportation Systems, Facebook Corporation.
• Dhruv Mahajan, (2021), Scalable Data Analytics for High Resolution Signalized Intersection Datasets, Proctor and Gamble.
• Patrick Emami (2021), Neural algorithms for Object Centric scene understanding December 2021, National Renewable Energy Lab.
• Sanaz Gheibi (2022), Speeding up Algorithms for Large Datasets, Intel Corporation.
• Yashaswi Karnati (2022), Machine Learning Algorithms for applications in Intelligent Transportation Systems, Nvidia Corporation.
• Pan He (2023), Deep Learning on Spatiotemporal Point Cloud Modeling for Motion and Correspondence, Auburn University.
• Rahul Sengupta (2024), Machine Learning Approaches for Coordinated Management of Urban Traffic Intersections.
• Aotian Wu (2024), A Multi-Sensor Video/Lidar System for Analyzing and Improving Intersection Safety (2024), Nvidia Corporation.
• Xiao Li, Foundational and Generative Machine Learning Methods for Error Bounded Scientific Data Reduction (2025), Google Corporation.
• Jaemoon Lee, (2025), Compressive Autoencoders: Guaranteeing reconstruction error bounds with linear/nonlinear constraint satisfaction, ORNL (co-adviser with Anand Rangarajan)
• Nooshin Hosseini (2026), Graph Theory Concepts for Advancing Intelligent Transportation Systems, Postdoctoral Researcher, University of California, Berkeley.

Former Postdoctoral Researchers

• Manu Sethi, Postdoctoral Researcher, 2016-2017, Spatiotemporal Machine Learning (co-advisee with Anand Rangarajan).
• Tania Banerjee, Postdoctoral Researcher, 2014–2024, University of Houston.