Hi, I am Shasvat Desai
Hi, I am Shasvat Desai
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Staff Applied Scientist, Sponsored Search, Walmart
Staff Applied Scientist, Sponsored Search, Walmart
Turning pixels and letters into insights, one gradient descent at a time 😀
Turning pixels and letters into insights, one gradient descent at a time 😀
ABOUT ME
ABOUT ME
I am a Staff Scientist at Walmart, where I lead machine learning and LLM initiatives for sponsored search across intent understanding, product retrieval, and large-scale evaluation. I work across the full ML lifecycle—from data strategy and model training to distillation, low-latency serving, evaluation, and online experimentation, partnering closely with Engineering, Product, Analytics, and Business teams to turn open-ended ideas into production systems with measurable impact.
One thing this work has taught me is that technically strong systems do not always lead to better user outcomes. I try to think beyond offline model performance and ask how a system will actually behave in front of customers, where its assumptions might break, and how relevance, engagement, and product goals should be balanced. That perspective has shaped how I approach both system design and experimentation.
I also enjoy the ambiguity that comes with large, cross-functional projects. Requirements evolve, user behavior challenges assumptions, and the right technical solution is not always obvious at the outset. My approach is to make assumptions explicit, think through likely failure modes early, and build systems that can adapt without requiring major redesign.
Technical leadership, to me, is also about communication. I work across people with different levels of seniority, technical backgrounds, functions, and cultures, and I have learned to adapt the level of abstraction, context, and directness while preserving the core technical argument. That has been central to aligning teams and moving complex projects forward.
Previously, I was a Senior Computer Vision Scientist at Walmart’s Intelligent Retail Lab, where I worked on scene understanding, object detection and tracking, multi-view geometry, and multimodal retrieval for retail environments. Before Walmart, I was a Computer Vision Scientist at Orbital Insight, working on satellite, aerial, and drone imagery across object detection, land-use segmentation, domain adaptation, super-resolution, and semi-supervised learning.
I care deeply about applied research: work that begins with a real problem, requires a technically interesting solution, and can eventually be tested at scale. Over the years, I have published more than a dozen articles across peer-reviewed venues, and I encourage the scientists I work with to publish and patent their work as well.
I completed my Master’s in Computer Science at the University of Massachusetts Amherst, specializing in machine learning and computer vision.
Outside of work, I enjoy hiking, photography, and board games!
News
News
June 2026: Gave a virtual talk at the Toronto Machine Learning Summit on my work on user intent understanding for search systems [Video].
June 2026: Gave a virtual talk at the Toronto Machine Learning Summit on my work on user intent understanding for search systems [Video].
May 2026: Five papers accepted to ACM Special Interest Group on Information Retreival, 2026 (SIGIR 2026).
May 2026: Five papers accepted to ACM Special Interest Group on Information Retreival, 2026 (SIGIR 2026).
(Industry Track) Unified Supervision for Walmart's Sponsored Search Retrieval via Joint Semantic Relevance and Behavioral Engagement Modeling
(eCommerce Workshop) INSPIRE: Intent-aware Neural Sponsored Product Retrieval for E-commerce
(eCommerce Workshop) AutoRelAnnotator: Calibrated Model Cascades for Cost-Efficient Relevance Evaluation in Sponsored Search
(eCommerce Workshop) Scaling Dense Retrieval with LLM-Annotated Training Data: Structured Mining and Progressive Curriculum for E-Commerce Sponsored Search
(eCommerce Workshop) Unified Multi-Task Relevance Modeling for E-Commerce: Comparing Task Routing Architectures Across LLMs and Cross-Encoders
May 2026: Served as a panelist at the Data Infrastructure Summit organized by the AI Accelerator Institute talking about "Build, buy, or borrow: How engineering teams are making data stack decisions that don't haunt them at renewal time"
May 2026: Served as a panelist at the Data Infrastructure Summit organized by the AI Accelerator Institute talking about "Build, buy, or borrow: How engineering teams are making data stack decisions that don't haunt them at renewal time"
June 2025: Patent granted for my prior work at Orbital Insight on Fine-Grained Aircraft Classification from Aerial Imagery.
June 2025: Patent granted for my prior work at Orbital Insight on Fine-Grained Aircraft Classification from Aerial Imagery.
May 2025: Recognized with Bravo Award at Walmart for establishing the ground work in the domain of intent understanding for sponsored search.
May 2025: Recognized with Bravo Award at Walmart for establishing the ground work in the domain of intent understanding for sponsored search.
February 2025: Started a Staff Scientist position in Walmart's Sponsored Search team.
February 2025: Started a Staff Scientist position in Walmart's Sponsored Search team.
October 2024: Recognized with Impact Award at Walmart. This award reflects a commitment to innovation, collaboration and exemplifying values while driving meaningful business impact on fundamental work on temporal aggregation of multiple signals for real-time decision making.
October 2024: Recognized with Impact Award at Walmart. This award reflects a commitment to innovation, collaboration and exemplifying values while driving meaningful business impact on fundamental work on temporal aggregation of multiple signals for real-time decision making.
September 2024: New Medium blogposts on Paradigms on Object Detection and Object Tracking with DeepSORT.
September 2024: New Medium blogposts on Paradigms on Object Detection and Object Tracking with DeepSORT.
July 2024: My team's work on developing scene understanding algorithm utilizing spatio-temporal scene graph representation learning to model interactions and identify shopping behaviors and patterns was released to Walmart customers, making the checkout experience even more smoother.
July 2024: My team's work on developing scene understanding algorithm utilizing spatio-temporal scene graph representation learning to model interactions and identify shopping behaviors and patterns was released to Walmart customers, making the checkout experience even more smoother.
April 2023: My work on using synthetic data for pre-training object detection models got accepted for presentation at Walmart AI Summit Conference 2023. I demonstrated this work across the board to engineers, data scientists, and executives, showcasing the impact of synthetic data on improving detection accuracy and efficiency, as well as its potential for scaling AI solutions across Walmart's vast retail ecosystem. This presentation highlighted the technical depth and strategic value of leveraging synthetic pre-training for large-scale retail applications.
April 2023: My work on using synthetic data for pre-training object detection models got accepted for presentation at Walmart AI Summit Conference 2023. I demonstrated this work across the board to engineers, data scientists, and executives, showcasing the impact of synthetic data on improving detection accuracy and efficiency, as well as its potential for scaling AI solutions across Walmart's vast retail ecosystem. This presentation highlighted the technical depth and strategic value of leveraging synthetic pre-training for large-scale retail applications.
May 2022: Started working as a Senior Computer Vision Scientist at the Intelligent Retail Lab, Walmart, Hoboken, NJ
May 2022: Started working as a Senior Computer Vision Scientist at the Intelligent Retail Lab, Walmart, Hoboken, NJ
October 2021: Our paper titled Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images got accepted to the IEEE Winter Conference on Applications of Computer Vision 2022, to be held at Waikoloa, Hawaii.
October 2021: Our paper titled Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images got accepted to the IEEE Winter Conference on Applications of Computer Vision 2022, to be held at Waikoloa, Hawaii.
Spring 2019: Graduated with a Masters in Computer Science from the University of Massachusetts, Amherst
Spring 2019: Graduated with a Masters in Computer Science from the University of Massachusetts, Amherst
April 2019: Our paper Pedestrian Detection in Thermal Images using Saliency Maps accepted to the IEEE Workshop on Perception Beyond the Visible Spectrum at CVPR, 2019 (Long Beach, California)
April 2019: Our paper Pedestrian Detection in Thermal Images using Saliency Maps accepted to the IEEE Workshop on Perception Beyond the Visible Spectrum at CVPR, 2019 (Long Beach, California)
Summer 2018: Received the DAAD RISE Professional Fellowship to pursue an internship at Bayer, Leverkusen, Germany
Summer 2018: Received the DAAD RISE Professional Fellowship to pursue an internship at Bayer, Leverkusen, Germany
Fall 2017: Started a Masters program in Computer Science at the University of Massachusetts, Amherst, USA
Fall 2017: Started a Masters program in Computer Science at the University of Massachusetts, Amherst, USA
Spring 2017: Graduated from Veermata Jijabai Technological Institute (VJTI), Mumbai, India with a Bachelors in Computer Engineering
Spring 2017: Graduated from Veermata Jijabai Technological Institute (VJTI), Mumbai, India with a Bachelors in Computer Engineering
Get in touch at shasvat.desai@gmail. com
Get in touch at shasvat.desai@gmail. com
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