I am a Research Engineer at IBM Research (MIT-IBM Watson AI Lab), where I develop and train the Granite family of LLMs at up to 200B scale. I lead the RL infrastructure for Agentic RL of Granite LLMs, enabling multi-scaffold, multi-task, and multi-turn reinforcement learning.
I did my M.S. in Computer Science at UIUC (Siebel Scholar) and my B.Tech (Honors) at IIT Bombay with dual minors in CS and AI. My interests lie at the intersection of mathematics, low-level systems engineering, and machine learning.
Outside of work, I enjoy singing, listening to music, and travelling.
University of Illinois Urbana-Champaign
M.S. in Computer Science
Aug 2023 – May 2025 | GPA: 3.79 / 4.0
Indian Institute of Technology Bombay
B.Tech (Honors) in Mechanical Engineering
Jul 2019 – May 2023 | GPA: 9.1 / 10.0
Research Engineer
IBM Research — MIT-IBM Watson AI Lab
June 2025 – Present
Researching & developing Granite LLMs (up to 200B scale) & Agentic RL systems
Applied Scientist Intern
Amazon
Summer 2024
Fine-tuned LLMs & developed end-to-end pipeline for product catalog matching using LoRA
Software Engineer Intern
Wells Fargo & Co.
Summer 2022
Developed a Trading Platform in Flask & ReactJS; received Pre-Placement Offer
Towards Improving Breast Cancer Detection through Multi-Modal Image Generation
DSPy: Compiling Declarative Language Model Calls into State-of-the-Art Pipelines
IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languages
ANGEL: Enterprise Search System for the Non-Profit Industry
Fused Triton kernels for ColBERT MaxSim with dimension tiling (d>128) and fused PQ scoring. Reached 80% peak HBM bandwidth on H100 via roofline-guided tiling, 1.9× over PLAID's GPU kernel, 469× exact-scoring throughput over WARP (SIGIR'25 Best Paper) and 8.5× over torch.compile.
GPU-native inverted index and fused scatter-add kernel scaling exact SPLADE retrieval to 8.8M docs. 235× speedup over Pyserini CPU and 23–270× over SPARe's GPU scatter-add kernel. Characterized the work- vs. bandwidth-efficiency tradeoff governing GPU sparse retrieval kernel design.
Designed a metric for quantifying ordering anomalies in batch-ordered logs. Implemented Windowed Temporal Reordering using Lamport timestamps, reducing causal anomalies by 80% while preserving throughput.
Implemented a decentralized total ordering algorithm for transaction processing with fault-tolerance via heartbeat-based failure detection and reliable multicast.
Developed a Rate Monotonic Scheduler for real-time task scheduling. Optimized memory allocation with a Slab Allocator and implemented a kernel module for profiling page fault rates.
Modified attention mechanisms using dependency parsing to encode syntax. Deployed a dual-encoder neural retriever with Elasticsearch, achieving 44.5% MAP@10 and 49.7% MRR@10 gains over ColBERTv2.
Implemented a custom TCP protocol over UDP with ACK-based retransmission and AIMD congestion control including Slow Start, Congestion Avoidance, and Fast Recovery.
Developed a music analyzer extracting nearest frequencies using Fast Fourier Transform. Parallelized with OpenMP and CUDA for CPU and GPU acceleration.