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.


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

[first name]d@asu.edu

Recent News


Paper accepted at IEEE ICASSP 2023

The following paper was accepted to the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2023

  • A. Rayas, R. Anguluri, J. Cheng, G. Dasarathy Differential Analysis of Networked Systems that Obey Conservation Laws.
Congratulations Anirudh, Raj, and our undergraduate student -- Jiajun! Thanks to the ASU SURI program for introducing us to Jiajun.
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Papers accepted at NeurIPS 2022

Two papers from our group have been accepted at the Neural Information Processing Systems (NeurIPS) 2022 conference:

  • A. Rayas, R. Anguluri, G. Dasarathy Learning the Structure of Large Networked Systems that Obey Conservation Laws.
  • P. Thaker, M. Malu, N. Rao, G. Dasarathy. Maximinzing and Satisficing in Multi-armed Bandits with Graph Information.
Congratulations Parth, Mohit, Anirudh, and Raj!
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NSF Award: Predictive Intelligence for Pandemic Prevention (PIPP)

Very excited to be a co-PI on a fantastic multi-disciplinary project as part of the NSF PIPP program. This is led by Pavan Turaga

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DARPA Award: Geometries of Learning

A multi-disciplinary project has been accepted for support as part of the DARPA Geometries of Learning (GoL) program. This is led by Pavan Turaga and the full team includes Visar Berisha (ASU), Vishal Patel (JHU), Rama Chellappa (JHU), Anuj Srivastava (FSU).

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Best Paper Award: IEEE Journal of Haptics

Our paper titled "A Multisensory Approach to Present Phonemes as Language Through a Wearable Haptic Device" was declared the winner of the 2022 IEEE Transaction on Haptics Best Application Paper Award!

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Distinguished Alumnus Award: VIT University

I am extremely honored to receive the Distinguished Alumni Award (Academics) from my alma mater VIT University, Vellore.

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Paper accepted to Journal of Mathematical Biology

Our paper on phylogenetic inference was accepted to the Journal of Mathematical Biology (JoMB).

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Fulton Schools of Engineering: Top 5% Teaching Award

It is an absolute honor to be recognized for my contributions to ASU's teaching mission. All credit goes to the fantastic students who make teaching stimulating!

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ONR Award: Active Meta Learning

A new project titled "Active Meta Learning" has been accepted for support by Office of Naval Research (ONR). This project will push the frontiers of meta-features in data-starved regimes and is led by Visar Berisha.

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I am honored to have received the NSF Faculty Early Career Development (CAREER) Award for a research program that aims to develop novel theory and algorithms for learning and leveraging the structure of graphs. Thanks NSF!
[Read More]

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AISTATS 2021 (Oral) Paper

A paper titled "Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model" has been accepted to the 2021 International Conference on AI & Statistics (AISTATS) in the oral track (top ~3% of 1500+ submissions).

This is joint work with Nafiseh Ghoroghchian and Stark Draper from the University of Toronto.


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.
[arXiv] [proceedings]


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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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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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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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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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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Fulton Schools of Engineering Top 5% Teaching Award

I am honored to have received the Top 5% Teaching Award from the Fulton Schools of Engineering.
[Read More]

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