Meghana Madhyastha

According to our database, Meghana Madhyastha authored at least 18 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2026

To 2:4 Sparsity and Beyond: Neuron-level Activation Function to Accelerate LLM Pre-Training.

Meghana Madhyastha, Daniel Haziza, Jesse Cai, Newsha Ardalani, Zhiqi Bu, Carole-Jean Wu

CoRR, February, 2026

On Harnessing Idle Compute at the Edge for Foundation Model Training.

Leyang Xue, Meghana Madhyastha, Myungjin Lee, Amos J. Storkey, Randal C. Burns, Mahesh K. Marina

Proceedings of the Sixth European Workshop on Machine Learning and Systems, EuroMLSys 2026, 2026

2025

Composer: A Search Framework for Hybrid Neural Architecture Design.

Bilge Acun, Prasoon Sinha, Newsha Ardalani, Sangmin Bae, Alicia Golden, Chien-Yu Lin, Meghana Madhyastha, Fei Sun, Neeraja J. Yadwadkar, Carole-Jean Wu

CoRR, October, 2025

Towards Decentralized and Sustainable Foundation Model Training with the Edge.

Leyang Xue, Meghana Madhyastha, Randal C. Burns, Myungjin Lee, Mahesh K. Marina

CoRR, July, 2025

Poster: On Harnessing Idle Compute at the Edge for Foundation Model Training.

Leyang Xue, Meghana Madhyastha, Myungjin Lee, Amos Storkey, Randal C. Burns, Mahesh K. Marina

Proceedings of the 31st Annual International Conference on Mobile Computing and Networking, 2025

2024

Masked Matrix Multiplication for Emergent Sparsity.

Brian Wheatman, Meghana Madhyastha, Randal C. Burns

CoRR, 2024

T-Rex (Tree-Rectangles): Reformulating Decision Tree Traversal as Hyperrectangle Enclosure.

Meghana Madhyastha, Tamas Budavari, Vladimir Braverman, Joshua T. Vogelstein, Randal C. Burns

Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

EvoStore: Towards Scalable Storage of Evolving Learning Models.

Robert Underwood, Meghana Madhyastha, Randal C. Burns, Bogdan Nicolae

Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing, 2024

2023

Understanding Patterns of Deep Learning ModelEvolution in Network Architecture Search.

Robert Underwood, Meghana Madhyastha, Randal C. Burns, Bogdan Nicolae

CoRR, 2023

DStore: A Lightweight Scalable Learning Model Repository with Fine-Grain Tensor-Level Access.

Meghana Madhyastha, Robert Underwood, Randal C. Burns, Bogdan Nicolae

Proceedings of the 37th International Conference on Supercomputing, 2023

Understanding Patterns of Deep Learning Model Evolution in Network Architecture Search.

Robert Underwood, Meghana Madhyastha, Randal C. Burns, Bogdan Nicolae

Proceedings of the 30th IEEE International Conference on High Performance Computing, 2023

2022

Prospective Learning: Back to the Future.

Joshua T. Vogelstein, Timothy D. Verstynen, Konrad P. Kording, Leyla Isik, John W. Krakauer, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Carey E. Priebe, Randal C. Burns, Kwame S. Kutten, James J. Knierim, James B. Potash, Thomas Hartung, Lena Smirnova, Paul Worley, Alena V. Savonenko, Ian Phillips, Michael I. Miller, René Vidal, Jeremias Sulam, Adam S. Charles, Noah J. Cowan, Maxim Bichuch, Archana Venkataraman, Chen Li, Nitish V. Thakor, Justus M. Kebschull, Marilyn S. Albert, Jinchong Xu, Marshall G. Hussain Shuler, Brian Caffo, J. Tilak Ratnanather, Ali Geisa, Seung-Eon Roh, Eva Yezerets, Meghana Madhyastha, Javier J. How, Tyler M. Tomita, Jayanta Dey, Ningyuan Huang, Jong M. Shin, Kaleab Alemayehu Kinfu, Pratik Chaudhari, Ben Baker, Anna Schapiro, Dinesh Jayaraman, Eric Eaton, Michael L. Platt, Lyle H. Ungar, Leila Wehbe, Ádám Kepecs, Amy Christensen, Onyema Osuagwu, Bing Brunton, Brett Mensh, Alysson R. Muotri, Gabriel A. Silva, Francesca Puppo, Florian Engert, Elizabeth Hillman, Julia Brown, Chris White, Weiwei Yang

CoRR, 2022

2021

BLOCKSET (Block-Aligned Serialized Trees): Reducing Inference Latency for Tree ensemble Deployment.

Meghana Madhyastha, Kunal Lillaney, James Browne, Joshua T. Vogelstein, Randal C. Burns

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

2020

PACSET (Packed Serialized Trees): Reducing Inference Latency for Tree Ensemble Deployment.

Meghana Madhyastha, Kunal Lillaney, James Browne, Joshua T. Vogelstein, Randal C. Burns

CoRR, 2020

Geodesic Forests.

Meghana Madhyastha, Gongkai Li, Veronika Strnadová-Neeley, James Browne, Joshua T. Vogelstein, Randal C. Burns, Carey E. Priebe

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

2019

Geodesic Learning via Unsupervised Decision Forests.

Meghana Madhyastha, Percy Li, James Browne, Veronika Strnadová-Neeley, Carey E. Priebe, Randal C. Burns, Joshua T. Vogelstein

CoRR, 2019

Inferring Concept Prerequisite Relations from Online Educational Resources.

Sudeshna Roy, Meghana Madhyastha, Sheril Lawrence, Vaibhav Rajan

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2017

Online scheduling of a fleet of autonomous vehicles using agent-based procurement auctions.

Meghana Madhyastha, Sriveda Chevuru Reddy, Shrisha Rao

Proceedings of the 2017 IEEE International Conference on Service Operations and Logistics, 2017