Email: dhruv@umn.edu
Research Motto: In theory, there is no difference between theory and practice. But, in practice, there is.
I am a third year PhD candidate under Prof. Abhishek Chandra in the Computer Science Department at the University of Minnesota-Twin Cities. I also collaborate with Prof. Ramesh Sitaraman at University of Massachusetts, Amherst. Broadly, I am interested in Distributed Systems and its application in Data Analytics and Machine Learning. Currently, I am working on problems related to Geo-distributed Streaming Analytics and also looking to apply machine learning based techniques in systems.
I graduated in 2014 with a Bachelors in Computer Science from BITS-Pilani, India. At BITS-Pilani, I was a part of Advanced Data Analytics and Parallel Technologies Lab, where I worked on parallel and distributed algorithms for data mining.
Publications
DeCaf: Iterative Collaborative Processing over the Edge [Paper]
Dhruv Kumar, Aravind Alagiri Ramkumar, Rohit Sindhu, Abhishek Chandra
A TTL-based Approach for Data Aggregation in Geo-distributed Streaming Analytics [Paper]
Dhruv Kumar, Jian Li, Abhishek Chandra and Ramesh Sitaraman
in ACM SIGMETRICS 2019 [Acceptance Rate: 18.1%]
TTL-based Approach for Data Aggregation in Geo-Distributed Streaming Analytics
Dhruv Kumar, Jian Li, Abhishek Chandra and Ramesh Sitaraman
Poster at USENIX OSDI 2018
Education
University of Minnesota-Twin Cities, Minneapolis, United States
PhD in Computer Science (September 2017 - Current)
CGPA: 4.0/4.0
3M Science and Technology Fellowship.
Birla Institute of Technology and Science, Pilani, India
B.E.(Hons.) in Computer Science (August 2010 - May 2014)
CGPA: 9.92/10
Rank 1 in Class of 2014 of Computer Science, comprising of 120 students.
Rank 3 in Class of 2014 of BITS-Pilani, comprising of 800 students.
Work Experience
Google Cloud, Sunnyvale, California
PhD Intern (June 2019 - August 2019)
Project: Learning to prefetch data for disk servers
- Identified an efficient strategy for constructing ground truth and proposed a deep neural network based architecture for predicting the prefetch data.
- Initial evaluation showed an improvement of upto 20% in cache hit rates using the proposed model over the existing approach.
Goldman Sachs, Bengaluru, India
Quantitative Analyst and Software Engineer (November 2014 - April 2016)
- Improved the efficiency of risk-management system by suggesting improvements to the SQL queries going to Sybase IQ database.
- Assisted in migrating from Sybase IQ database to MemSQL database for faster access.
- Implemented a H2-database based server for allowing real-time updates to the tables residing in the servers.
- Learnt about the real life use-cases of databases.
Old Publications
(work was done during undergraduate studies)
Grid-R-tree: a data structure for efficient neighborhood and nearest neighbor queries in data mining
Poonam Goyal, Jagat Sesh Challa,Dhruv Kumar, Anuvind Bhat, Navneet Goyal, Sundar Balasubramaniam
In International Journal of Data Science and Analytics (JDSA), Springer, 2020.
An Efficient method for Batch Updates in OPTICS Cluster Ordering
Dhruv Kumar, Poonam Goyal, Navneet Goyal.
Exact, Fast and Scalable Parallel DBSCAN for Commodity Platforms
Poonam Goyal, Sonal Kumari, Ankit Sood, Dhruv Kumar, Sundar Balasubramaniam, Navneet Goyal
In International Conference on Distributed Computing and Networking (ICDCN), 2017
A fast, Scalable SLINK Algorithm for Commodity Cluster Computing Exploiting Spatial Locality
Poonam Goyal, Sonal Kumari, Sumit Sharma, Dhruv Kumar, Vivek Kishore, Sundar Balasubramaniam, Navneet Goyal.
In IEEE International Conference on High Performance Computing and Communications (HPCC), 2016.
Parallelizing OPTICS for Commodity Clusters
Poonam Goyal, Sonal Kumari, Dhruv Kumar, Sundar Balasubramaniam, Navneet Goyal, Saiyedul Islam, Jagat Sesh Challa.
In International Conference on Distributed Computing and Networking (ICDCN), 2015.
Parallelizing OPTICS for multicore systems
Poonam Goyal, Sonal Kumari, Dhruv Kumar, Sundar Balasubramaniam, Navneet Goyal.
In ACM India Computing Conference (ACM COMPUTE), 2014.