About Me

I am an Assistant Professor in the School of Electrical, Computer, and Energy Engineering at the Arizona State University. Before this I was a Postdoctoral Fellow in the Electrical and Computer Engineering department at Rice University where I worked with Rich Baraniuk. And prior to that, I was at the Machine Learning Department at Carnegie Mellon University where I worked with Aarti Singh.
I received my M.S. and Ph.D. in Electrical Engineering in 2010 and 2014 respectively from the University of Wisconsin - Madison, where I was advised by Rob Nowak and Stark Draper. Before that, I received my B. Tech in Electronics and Communication Engineering in 2008 from VIT University.
My research interests span topics in Machine Learning, Statistics, Signal Processing, Networked Systems, and Information Theory. More on my research can be found here.



Coordinates

Goldwater Center (GWC) 324
650 E Tyler Mall
Tempe, Arizona 85281

[first name]d@asu.edu

Recent News

09.20

NeurIPS 2020 Paper

A paper on active learning algorithms for estimating topological properties of decision boundaries accepted has been accepted to NeurIPS 2020.

This is joint work with Weizhi Li, Visar Berisha from ASU and Karthikeyan Ramamurthy from IBM.

NeurIPS

NSF Grant on Machine Learning + Wireless

A new project titled "Distributed Learning over Multi-Access Channels: From Bandlimited Coordinate Descent to Gradient Sketching" has been funded by the National Science Foundation.

This is a collaboration with Junshan Zhang (ASU), Na Li (Harvard), Zhi Ding (UC Davis). Thanks NSF!

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NSF Grant on Loss Functions for Robust, Accurate, and Fair ML

A new project titled "Alpha Loss: A New Framework for Understanding and Trading Off Computation, Accuracy, and Robustness in Machine Learning " has been funded by the National Science Foundation.

This is a collaboration with Lalitha Sankar (ASU). Thanks NSF!
[Read More]

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08.20

NIH Grant on Graphical Model Selection from Partial Measurements with Biomedical Applications

A new project titled "Graphical Models from Partially Observed Interactions with Biomedical Applications" has been funded by the National Institutes of Health.

This is a collaboration with Genevera Allen (Rice University). Thanks NIH!

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07.20

ECCV 2020 Paper

A paper titled "Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model" has been accepted to the 2020 European Conference on Computer Vision.

This is joint work with John Janiczek, Parth Thaker, Christopher Edwards, Phil Christensen, Suren Jayasuriya

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04.20

NSF RAPID grant on Graph-based Methods for COVID Predictions and Interventions

A new project titled "Active Tracking of Disease Spread in COVID19 via Graph Predictive Analytics" has been funded by the National Science Foundation under the RAPID program.

This is a cross-disciplinary collaboration at ASU with Doug Cochran (Math), Huan Liu (CS), Patricia Solis (Geography), and Pavan Turaga (AME). Thanks NSF!
[Read More] [Local News Coverage]

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03.20

Two Papers at ISIT 2020

Two paper have been accepted to the IEEE International Symposum on Information Theory:

  • Sypherd, T., Diaz, M., Sankar, L., and Dasarathy, G. On the alpha-loss Landscape in the Logistic Model.
  • Thaker, P., Dasarathy, G., and Nedić, A. On the Sample Complexity and Optimization Landscape for Quadratic Feasibility Problems.

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01.20

Two Papers at AISTATS 2020

Two paper have been accepted to the International Conference on AI & Statistics:

  • LeJeune, D., Dasarathy, G., and Baraniuk, R. Thresholding Graph Bandits with GrAPL.
  • Li, W., Dasarathy, G., and Berisha, V. Regularization via Structural Label Smoothing.

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