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Electrical and Computer Engineering
Anderson Hall 200
Lisa Johnston
Administrative Assistant
School of Engineering & Computer Science
3601 Pacific Avenue
Stockton, CA 95211

Vivek Pallipuram

Assistant Professor


Phone: 209.946.3072

Office Location

Anderson 204


Ph.D., Computer Engineering, Clemson University, Clemson, SC, 2013

M.S. Computer Engineering, Clemson University, Clemson, SC, 2010

Bachelor of Technology, National Institute of Technology, Tiruchirapalli, TN India, 2008

Teaching Statement
I have a strong passion for teaching - with my exceptional classroom students, I strive to create an energy-filled environment of high-quality learning, hands-on experience and collaboration. My goal in the classroom is to be a facilitator by engaging students using active-learning techniques. In addition to receiving information from me, students interact with their peers via in-class group activities and gain valuable perspective. This process increases the net influx of knowledge per student, promoting well-rounded and comprehensive learning. I am always excited to introduce new courses that make students shine in the ever-demanding job market. Some of these courses include graduate-level high-performance computing (HPC), graduate- and undergraduate-level digital image processing (DIP). Some of my students from these classes have pursued further research and even moved on to pursue industrial careers in these fields.   My teaching is not limited to classroom and research lab. I actively work with my students to prepare them for industry interviews by conducting mock-interview sessions. These sessions include exhaustive technical interview questions and discussions, making them steel-plated for their actual interviews.    

Research Statement 
My research group actively investigates the fields of high-performance computing (HPC), computer systems architecture, and digital image processing (DIP). My group's research on HPC focuses on developing performance-modeling tools using statistical signal processing techniques. These tools enable researchers to predict software performance prior to large-scale deployment, write efficient software codes, and forecast computing resource utilization for smart scheduling decisions. My group's research on DIP aims to build sophisticated products such as sign language detector and multimedia processing systems.   I highly enjoy working with motivated undergraduate researchers. My HPC undergraduate students successfully presented research titled, Make it big, Make it fast: A parallel image amplification technique for producing high-resolution images at the National Conference for Undergraduate Research 2016. My DIP undergraduate students will present their research titled, A best-features based digital rotoscope in IEEE cosponsored Asilomar Conference on Signals, Systems, and Computers in October 2017. I am highly interested in promoting HPC education for undergraduate students. With a faculty colleague in SOECS, I submitted a revised book chapter on GPGPU computing for undergraduate students to NSF/IEEE-TCPP curriculum initiative. I also use this book chapter in my HPC classes.


High performance computing
Digital signal processing
Random Signals


High-performance computing
Heterogeneous systems
Cloud computing
Performance Modeling and analysis
Parallel and distributed systems
Machine learning
Predictive modeling in science


Iain Murphy, Tyler Norlund, Vivek Pallipuram (2017). A Best-Features Based Digital Rotoscope. To appear in: 51st Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA

Vivek K. Pallipuram, Jinzhu Gao (2016). The Realm of Graphical Processing Units (GPU) Computing. Submitted to NSF/IEEE-TCPP Curriculum Initiative.

V.K. Pallipuram, M.C. Smith, N.C. Sarma, R. Anand, E. Weill, and K. Sapra (2014). Subjective versus Objective: Classifying Analytical Models for Productive Heterogeneous Performance Modeling. Journal of Supercomputing, DOI:10.1007/s11227-014-1292-9  

V.K. Pallipuram, M.C. Smith, N. Raut, and X. Ren (2013). A regression-based performance pre- diction framework for synchronous iterative algorithms on GPGPU clusters. Concurrency and Com- putation: Practice and Experience, Wiley Publications, pp. 29, DOI: 10.1002/cpe.3017  

V.K. Pallipuram, M.A. Bhuiyan, and M.C. Smith (2011). A comparative study of GPU program- ming models and architectures using neural networks. Journal of Supercomputing, pp. 46, DOI: 10.1007/s11227-011-0631-3  

M.A. Bhuiyan, M.C. Smith, and V.K. Pallipuram (2010). Performance, optimization, and _tness: connecting applications to architectures. Concurrency and Computation: Practice and Experience, 32/10, 1066-1100, DOI:10.1002/cpe.1688