Sr. Machine Learning Lead

I am a Sr. Machine Learning lead at NVIDIA. My research is focused on Machine Learning and Deep Learning methods for large-scale personalization problems, including large language models and recommender systems. I specifically worked on large transformers and graph attention networks. Before joining NVIDIA, I was a Sr. Staff Machine Learning research engineer at Palo Alto Networks and developed efficient algorithms for Anomaly Detection in Time Series and Causal Inference. I have a Ph.D. in (Deep) Machine Learning from the University of Illinois under the supervision of Prof. Theja Tulabandhula. In recent years, I’ve been collaborating with the tech industry as an ML/AI lead to research and develop personalization and recommender system engines.

I enjoy engaging in challenging and open-ended problems to support customers' success. I love to help users and communities to find relevant content and discover new opportunities in real time. Since my first industry experience, I have found my passion for developing next-generation scalable algorithms using state-of-the-art machine learning models in talented AI teams. Being fascinated by working in interactive environments, I always thrive on collaborating and connecting with AI leaders to extend my professional network.

I'm best reached via Linkedin and email and always open to interesting conversations and collaborations.

Recent News:

2023:

• Our talk "How to use Generative AI to Build Content for Real-World Applications" features at GTC, 2023.

2022:

• I'm leading an in-Person "Mentorship session for Technologists" at WoMENAIT Conference, SF event.
• Our paper "Boosted Embeddings for Time-Series Forecasting" was published at International Conference on Machine Learning, Optimization, and Data Science, 2022.

2021:

• Our paper "Katrec: Knowledge aware attentive sequential recommendations" was published at Springer Discovery Science, 2021.
• Our paper "Knowledge Graph Attention for Sequential Recommendations" was published at ACM RecSys workshop, 2021.
• I joined NVIDIA as Sr. Data Scientist Lead to initiate scaled AI services and lead a team of Data Scientists and business partners.

2020:

• I joined Palo Alto Networks as Sr. Staff Machine Learning Research Engineer to design and develope state of the art ML models for Time Series Anomally Detection and Causal Inference.
• I joined Cambia Health Solutions as a AI scientist Intern to design and develope a Deep Learning based Recommender System for target marketing.

2019:

• I'm chairing a session on "Data Mining in Networks" at INFORMS 2019 Annual Meeting.
• We have an invited talk for our paper "Block-Structure Based Time-Series Models For Graph Sequences" in "Graph Algorithms and Applications".
• Our paper "Managing Adoption under Network Effects" was accepted at NetEcon, 2019.
• Our paper "Managing Adoption under Positive Externalities via Dynamic Pricing" was accepted at INFORMS RM and Pricing conference, 2019.
• I joined Target corporation as a AI scientist to enhance the reorder recommendation system by implementing Deep Machine Learning approaches to big data.