👋 I am an award-winning and ambitious B.Eng. in computer engineering passionate about:

• machine learning (in particular, LLMs and computer vision)
• vector, semantic, graph, hybrid search
• distributed systems
• information retrieval & relevance
• agents

My work sits at the intersection of information retrieval, vector search, and applied machine learning — improving relevance for complex global queries, grounding LLMs and multi-agent workflows in governed enterprise knowledge, and turning research prototypes into production-grade agentic retrieval and RAG capabilities.

Earlier, as a Software Engineer II, I was part of the team that took vector search from 1 to N and quantization from 0 to N — from building the capability to scaling it for broad production adoption. My contributions spanned quantization techniques that cut customer costs by 8–32× and latency by up to 20×, a hybrid-search relevance stack, and an extensible facet-aggregation engine built on formal grammar and parsing. I care deeply about performance, correctness, and distributed systems, and I'm often the engineer who root-causes the gnarliest production incidents.

Beyond shipping code, I love building communities. I graduated with a B.Eng in Electrical & Computer Engineering (97% CGPA, 20+ awards worth over $100K), drove a 200+ person conference from vision to reality, and grew a Senior's Program to 180+ volunteers and 650+ participants while delivering technical talks to 250+ engineering students. I also led a student team to train a deep neural network for human pose estimation from randomly initialized weights

• check out my website, complete with a chat experience, here!
• a humourous website that showcases questionably horrible UX designs for the web, the Bad Designs Museum
• currently working on a project exploring harness engineering and agent memory.

• Human Pose Estimation on COCO 2017 Dataset: I led a team to design and train a deep neural network for human pose estimation (labelling various joint locations). Using a modified stacked hourglass network and a newly implemented data ingestion and preprocessing pipeline, we achieved results comparable with state-of-the-art from late 2017, in less than three months of work. Check out our deployed model here

• Road Segmentation Machine Learning Project on KITTI ROAD dataset: A convolutional neural network (CNN) for semantic segmentation of road surfaces within a driving context.

• Embedded C Optimization Project with Discrete Cosine Transform algorithm: Implementation of DCT algorithm in C and optimizing performance using more efficient software algorithms, software-level optimizations, and hypothetical firmware- and hardware-based optimizations.

• Computer Vision Project on Monocular Depth Estimation: A CNN for depth estimation on DrivingStereo dataset using Keras & Python.