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Vinodchandran Variyam

Avery 366
University of Nebraska - Lincoln
Lincoln NE, 68588

Email :
Phone : (402)-472-5002  

Curriculum Vitae


Prof. Vinodchandran Variyam is broadly interested in computer science topics where there is a fundamental computational efficiency concern. Concretely, his research spans multiple areas of core computer science topics including computational complexity theory, machine learning, and large data management. His contributions to complexity theory have played a significant role in advancing the state-of-the-art in several topics including de-randomization, circuit complexity, space-limited computations, and Kolmogorov complexity. In addition to computational complexity, his current research themes also include reproducible computations, sample efficiency in distribution learning and testing, and algorithms for streaming data and their applications. 

Current Research Themes

Reproducibilty/Replicability in Randomized Computations: Can monte-carlo approximation algorithms be made reproducible (output a unique value on all runs of the algorithm)? Current investigations on this question reveal surprising connections to several other notions in foundations of computation such as circuit complexity, hierarchy theorems, Sperner/KKM Lemma and certain partition problems in geometry. Recent contributions on this topic are available at proceeding of  ITCS 2021 and STOC 2022. This research led us to the discovery of a new Euclidean parition problem (arXiv 2022) which is of independent mathematical interest. A recent work applies the techniques that we developed to understanding list and certificate complexity of replicable/reproducible algorithms (arXiv 2023).   

Sample Efficiency in Learning and Testing: Learning distributions from observations is a fundamental problem in machine learning. Establishing optimal bounds on the number of observations needed to learn is a central research question especially for high-dimensional distributions. Our recent research establishes new upper and lower bounds on learning and testing high-dimension distributions including Bayes nets and interventional distributions. Recent publications are available in proceedings of ICML 2020, NeurIPS 2020, ALT 2021, STOC 2021, and AISTATS 2022.

Algorithms for Streaming Data: The data streaming model is one of the well-established models for computation over large data sets. It  is a model for real-time computation that limits storage capacity and processing time. Since the velocity and the volume of the data are expected to be very high, algorithms processing them can not revisit an item or spend too much time on a single item.  Our research on this topic has led to contributions not only in foundations of data analysis but also in applied areas including bioinformatics, databases, and software engineering. Recent publications are available at proceedings of SWAT 2016, ACM BMB 2017, PODS 2021PODS 2022, ICSE 2022, and ESA 2022. A work, appeared in PODS 2021, made a striking discovery that certain algorithms developed independently in model counting and data streaming are the same. This work won several recognitions including Best of PODS 21, SIGMOD Research Highlight Award, and CACM Research Highlight Award. 

Complete publication list at DBLP

A note on the name on publications: In scientific publications, name appears as N. V. Vinodchandran.


CACM Research Highlight Award (Invited)
SIGMOD Research Highlight Award 2022
BEST of PODS 2021
UNL CSE Department Student Choice Award 2016-2017
UNL CAS Distinguished Teaching Award 2005

PhD Students

Jason Vander Woude (Current)
Sutanu Gayen (Graduated in 2019. Currently Faculty at IIT Kanpur)
Derrick Stolee (Jointly with Prof. Stephen Hartke, Graduated in 2012. Currently Principle Software Engineer at GitHub)
Raghunath Tewari (Graduated in 2011. Currently Faculty at IIT Kanpur)
Chris Bourke (Graduated in 2008. Currently Associate Prof. of Practice at UNL)