Prashant Pandey

Prashant Pandey

Assistant Professor

University of Utah


I am an Assistant Professor in the School of computing at the University of Utah. I co-direct UtahDB Lab. I am also a core member of Utah Center For Data Science.

My goal as a researcher is to advance the theory and practice of resource-efficient data structures and employ them to democratize complex and large-scale data analyses. I work on building tools for large-scale data management problems across computational biology, stream processing, and storage.

Previously, I did a postdoc at UC Berkeley working with Prof. Aydin Buluc and Prof. Katherine Yelick. Prior to that, I spent one year as a Postdoc at Carnegie Mellon University working with Prof. Carl Kingsford. I obtained my Ph.D. in Computer Science at Stony Brook University, and defended my dissertation, Fast and Space-Efficient Maps: Shrinking Big Data Down to Size. At Stony brook University, I was co-advised by Prof. Michael Bender and Prof. Rob Johnson. (Dissertation committee: Mike Ferdman, Rob Patro, Guy Blelloch.)

I love outdoors and occasionally like to scribble my experience in a blog.

I’m looking for self-motivated Ph.D. students. Drop me a note if you want to do cool research, run/hike in the mountains, and enjoy the world famous ski in Salt Lake City.
Specifically, I am looking for students who want to work at the intersection of theory and systems. In your email, please include two papers that you liked the most and why from the publication list. Try and explain the main thesis of the paper in your own words.

  • Data Structures for Big Data
  • Graphs Processing
  • Computational Biology
  • Scalable Graph Neural Networks
  • PhD in Computer Science, 2018

    Stony Brook University

Recent & Upcoming Talks

Recent Publications

(2024). Gallatin: A General-Purpose GPU Memory Manager. PPOPP 2024.


(2023). BP-tree: Overcoming the Point-Range Operation Tradeoff for In-Memory B-trees. VLDB 2023.

PDF Code Project

(2023). IcebergHT: High Performance PMEM Hash Tables Through Stability and Low Associativity. SIGMOD 2023.

PDF Code Project

(2023). Singleton Sieving: Overcoming the Memory/Speed Trade-Off in Exascale k-mer Analysis. ACDA 23.

PDF Code

(2023). High-Performance Filters for GPUs. PPOPP 2023.

PDF Code Project Project


  • Hunter McCoy PhD (Started Fall 2022)
  • Yuvraj Chesetti PhD (Started Fall 2022)
  • Jinghua Yan PhD (Co-advised with Prof. P. Saday)
  • Benwei Shi PhD (Co-advised with Prof. Jeff Phillips)