My research group studies the theory and practice of large-scale distributed sensing. This includes the design of efficient streaming data structures, distributed/decentralized data collection, and data compression. We’re also really interested in studying novel applications of these algorithms in data governance and network security (i.e., tracking how information flows through an organization).

I’ve also spent time in industry in the quantitative finance space building large-scale machine learning systems.


9/10/2022 New paper on streaming approximation to be presented at ICDE 2023:

9/01/2022 Lab Alumni Stavros Sintos joins the University of Illinois-Chicago as an Assistant Professor

3/23/2022 Lab Alumni Xi Liang joins Databricks

1/12/2022 New Initiative on Ubiquitous Sensing for Healthcare

Recent Publications

Bruno Barbarioli, Gabriel Mersy, Stavros Sintos, Sanjay Krishnan. HIRE: Hierarchical Residual Encoding for Multiresolution Compression in Time-Series Data. In progress.

Xi Liang, Stavros Sintos, and Sanjay Krishnan. JanusAQP: Efficient Partition Tree Maintenance for Dynamic Approximate Query Processing. ICDE 2023 pdf

Ted Shaowang, Xi Liang, Sanjay Krishnan. Sensor Fusion on the Edge: Initial experiments in the EdgeServe System. Big Data in Emergent Distributed Environments 2022. pdf

Ted Shaowang, Nilesh Jain, Dennis D. Matthews, and Sanjay Krishnan. “Declarative data serving: the future of machine learning inference on the edge.” VLDB 2021 pdf

Past Selected Publications in Relevant Areas

Data Structures for Approximation

Xi Liang, Stavros Sintos, Zechao Shang, Sanjay Krishnan. Combining Sampling and Aggregation (Nearly) Optimally. SIGMOD 2021 pdf

John Paparrizos, Chunwei Liu, Bruno Barbarioli, James Hwang, Ikrudya Edian, Aaron Elmore, Mike Franklin, and Sanjay Krishnan, VergeDB: A Database for IoT Analytics on Edge Devices. CIDR 2021 pdf

Xi Liang, Zechao Shang, Aaron J. Elmore, Sanjay Krishnan, Mike Franklin. Fast and Reliable Missing Data Contingency Analysis with Predicate-Constraints. SIGMOD 2020 pdf

Zongheng Yang, Eric Liang, Amog Kamsetty, Chenggang Wu, Yan Duan, Xi Chen, Pieter Abbeel, Joseph M. Hellerstein, Sanjay Krishnan, and Ion Stoica. Deep Unsupervised Cardinality Estimation. VLDB 2020. pdf

Distributed and Decentralized Systems (Digitial and Human)

Siyuan Xia, Zhiru Zhu, Chris Zhu, Jinjin Zhao, Kyle Chard, Aaron Elmore, Ian Foster, Michael Franklin, Sanjay Krishnan, Raul Castro Fernandez. Data Station: Delegated, Trustworthy, and Auditable Computation to Enable Data-Sharing Consortia with a Data Escrow. VLDB 2022 pdf

Nalin Ranjan, Zechao Shang, Sanjay Krishnan, and Aaron J. Elmore. “Version Reconciliation for Collaborative Databases.” SoCC 2021 pdf

Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, and Michael I. Jordan. Communication-efficient distributed dual coordinate ascent. NeurIPS 2014. pdf

Sanjay Krishnan, Jay Patel, Michael J. Franklin, and Ken Goldberg. Social Influence Bias in Recommender Systems: A Methodology for Learning, Analyzing, and Mitigating Bias in Ratings. Under Review: ACM Conference on Recommender Systems (RecSys). Foster City, CA, USA. Oct 2014 (pdf)

Machine Learning Applications

Vanlin Sathya, Adam Dziedzic, Monisha Ghosh, and Sanjay Krishnan. Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT. ICNC 2020 pdf

Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron Elmore, and Michael Franklin. Band-limited training and inference for convolutional neural networks. ICML 2019. pdf

Sanjay Krishnan, Zongheng Yang, Keng Goldberg, Joe Hellerstein, and Ion Stoica. Learning to Optimize Join Queries with Deep Reinforcement Learning. 2018. pdf

Roy Fox, Richard Shin, Sanjay Krishnan, Ken Goldberg, Dawn Song, and Ion Stoica. Parametrized hierarchical procedures for neural programming. ICLR 2018.

Roy Fox, Sanjay Krishnan, Ion Stoica, and Ken Goldberg. Multi-level discovery of deep options. 2017.