ML at Typeface.ai

We are building the future of generative AI for enterprise content generation & personalization.
My current work focuses on building products with massively-pretrained models like GPT & SD.

Previously worked as Applied Scientist II working with Azure OpenAI, Azure Cognitive Services & Microsoft’s AI Development Acceleration Program (MAIDAP) at the Office of Applied Research.

Over the years, my work has spanned the breadth of problems in the areas of Deep Learning and ML. In additition to NLP, I have previously worked on major projects in the domains of Vision, Interpretability and ML for Systems.

Keywords:
Large Language Models, Chatbots, Transformers, Stable Diffusion
GNNs, Interpretability, Deep Learning, VQA

Past Highlights:

Satya Nadella’s GPT-3 Ignite Keynote

Our work on using GPT-3 for summarization and content generation for WNBA was featured as one of 2 demos in Satya’s 2021 Ignite keynote.

Optimal Resource Allocation for Big Data workloads

Our work on petabyte scale resource use prediction from computational graphs was published in EDBT 2022 with associated patent.

Professional Summary:

Publications:

• EDBT 2022 - Towards Optimal Resource Allocation for Big Data Analytics
• ER 2020 - Evaluating Tree Explanation Methods for Anomaly Reasoning
• US Patent - Optimizing job runtimes via prediction–based token allocation
• US Patent app. - Intelligent table suggestion and conversion for text

Experience: 5 yrs

• Applied Scientist II at Microsoft for 3.5 years
• Deep Learning intern - Computer Vision at Mathworks for 8 months
• ML Engineer at SenseHawk for 6 months
• Executive Engineer - Data Analytics at Honda for 6 months

Education:

• MS in CS at UMass Amherst

Misc:

I spend most of my leisure time cooking, learning musical instruments and climbing (with varying degree of success).
I love ranting about terrible urban design and discussing geo-politics.
I am fascinated by all sorts of meta information, which doesn’t help my case of going down random internet rabbit holes.