Amey Agrawal

I am a systems engineer at Microsoft Research India, where I work in Dr. Muthian Sivathanu’s team on low-level systems for deep learning infrastructure. Prior to that, I spent a couple of years working at Qubole, a big data platform start-up, where I built several components of the Apache Spark-based data science platform and worked on applications of machine learning for software engineering problems. I graduated with B.E. (Hons.) in Computer Science from BITS Pilani, India in 2018. I am applying for Ph.D. programs this Fall with a focus on ML for systems and systems for ML. For more details, refer to my resume or drop me an email.

Publications

Learning Digital Circuits: A Journey Through Weight Invariant Self-Pruning Neural Networks
Amey Agrawal, and Rohit Karlupiya
Proceedings of New in ML Workshop, NeurIPS, 2019, Vancouver Proceedings of Sparsity in Neural Networks Workshop, 2021, Virtual [pdf]
[code]

Delog: A Privacy Preserving Log Filtering Framework for Online Compute Platforms
Amey Agrawal, Abhishek Dixit, Namrata Shettar, Darshil Kapadia, Rohit Karlupia, Vikram Agrawal, and Rajat Gupta
Proceedings of IEEE International Conference on Big Data, 2019, Los Angeles
[pdf]

Logan: A Distributed Online Log Parser
Amey Agrawal, Rajat Gupta, and Rohit Karlupiya
Proceedings of IEEE International Conference on Data Engineering (ICDE), 2019, Macau
[pdf] [blog]

Select Projects

Efficient Device Sharing in Distributed Deep Learning Training Jobs
Mentors: Dr. Muthian Sivathanu, Dr. Bhargav Gulavani
Creating a proxy layer for GPU drivers that enables transparent checkpointing and time slicing for distributed deep learning training workloads with minimal overhead. Efficient device sharing between data-parallel peers enabled by this system would power-efficient job scheduling and resource management on Microsoft’s next-generation deep learning platform.

Learning Efficient Job Placement Policy for ETL jobs on Big Data Platforms
Mentors: Joydeep Sen Sarma, Rohit Karlupia
A learnt scheduling algorithm that leverages recurrent nature of ETL worloads to minimize operational cost by optimal job placement.

Callisto: Bringing Jupyter notebooks to classroom
Advisor: Prof. Surekha Bhanot
A cross-platform desktop application to host and grade assignments designed in Jupyter notebook. The system strives to lower the barrier to entry in the scientific Python ecosystem for newcomers by providing a one-click setup of development environment and Google Colab like interface for hosted assignments. This work was later presented at PyCon India, 2020. [blog] [code] [demo]

Deep Reinforcement Learning for Autonomous Warehouse Robots
Advisor: Prof. Surekha Bhanot
A framework to create Q-learning agents for autonomous navigation tasks in warehouses. The agents are pre-trained in a custom simulation environment built on top of V-REP, a popular robotics simulation package. [code]

Disentanglement Learning for Iris Image Indexing
Advisor: Prof. Kamlesh Tiwari
An autoencoder architecture to learn representations of normalized Iris images that are robust to geometric variations which occur in real-world Iris samples. [blog] [code]

Automated news-in-shorts
Advisor: Prof. Poonam Goyal
A news aggregation system that collects the latest posts from RSS feeds of multiple news agencies to automatically generate abstracts for top stories. Trending topics on Twitter are mapped to news articles and generate extractive text summaries using a natural language processing pipeline. [code]