Arpita Gang
Staff Machine Learning Scientist
Team: AI Prototyping
Research areas: Machine learning, Distributed Learning
Arpita received her B.Tech (Bachelors’ degree) in electronics and communication engineering from NIT Silchar (silver medalist), and M.Tech (Masters’) from IIIT Delhi in India. She then worked as a research assistant at IIIT Delhi for a year. Following this, she completed her Ph.D. in electrical and computer engineering from Rutgers University, New Jersey in 2022. Her research interests are in distributed optimization and machine learning. She joined Visa Research in June 2022 as a Staff Machine Learning Scientist where she is a part of the AI Prototyping team.
Publications
- Arpita Gang and Waheed U. Bajwa, “The Best of Both Worlds: Distributed PCA That is Both Fast and Exact”, EUSIPCO, 2022.
- Arpita Gang and Waheed U. Bajwa, “FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis”, IEEE Transactions on Signal Processing, 2022.
- Arpita Gang and Waheed U. Bajwa, “A linearly convergent algorithm for distributed principal component analysis”, Signal Processing, 2022.
- Arpita Gang, Bingqing Xiang and Waheed U. Bajwa, “Distributed Principal Subspace Analysis for Partitioned Big Data: Algorithms, Analysis and Implementation”, IEEE Transactions on Signal and Information Processing over Networks, 2021.
- Zhixiong Yang, Arpita Gang and Waheed U. Bajwa, “Adversary-resilient distributed and decentralized statistical inference and machine learning: An overview of recent advances under the Byzantine threat model”, IEEE Signal Processing Magazine, 2020.
- Arpita Gang, Haroon Raja, Waheed U. Bajwa, “Fast and communication-efficient distributed PCA”, ICASSP 2019.
- Arpita Gang, Pravesh Biyani, Akshay, Soni, “Towards Automated Single Channel Source Separation using Neural Networks”, Interspeech 2018.
- Arpita Gang, Pravesh Biyani, “On Disriminative Framework for Single Channel Audio Source Separation”, Interspeech 2016.