10-time award winner in Artificial Intelligence and Open Source and the co-author of the book 'Sculpting Data For ML', Jigyasa Grover is a powerhouse brimming with passion to make a dent in this world of technology and bridge the gaps. AI & Research Lead, she has years of ML engineering & Data Science experience in deploying large-scale low-latency systems for user personalization and monetization on popular social networking apps like Twitter and Facebook, and e-commerce at Faire, particularly ads prediction, sponsored content ranking, and recommendation with a recent focus on Generative AI. She is also one of the few ML Google Developer Experts and Google Women Techmaker Ambassadors globally. As a World Economic Forum's Global Shaper, she ensures to leverage of her technical skills and connections for solution-building, policy-making, and lasting change.
At present, she is spearheading the research and development of a domain-agnostic explainable and context-aware recognition AI powered by LLMs to fuel data-driven conversational solutions at Bordo AI.
Previously at Faire, she architected the ads bidding and prediction engine from the ground up to diversify revenue streams focusing on e2e ML model pipeline development including orchestrating data pipelines, designing model baselines, and launching the alpha to 30+ brands with context-aware XGBoost models. During her time at Twitter as a Senior Machine Learning Engineer in the online ads prediction and ranking domain, she worked towards boosting the revenue and other advertising metrics of the platform using Deep Learning. She led app install and web ads ranking with a focus on foundational infrastructure and core ML modeling for conversion and click prediction rate across multiple display locations like Timeline Feed, Tweet Replies, Profile, and Search. She has driven projects spanning ML model quality and performance improvements (viz. conversion rate, cost per conversion, revenue), feature engineering (feature set extension, imputation, and importance pipelines), privacy-preserving, Apple App Tracking Transparency (ATT) remediation, and SKAdNetwork mobile attribution and measurement.
Jigyasa campaigns for a data-centric approach to ML, and her book is a practical guide on curating quality datasets that lay a strong foundation for an ML pipeline. Hinged on this ideology of throwing the limelight on the mindful practices of dataset curation, she has been proactively sharing her views and best practices in the form of technical talks, panels, podcasts, blog posts, and so on. Her latest research 'Keeping it Low Key' is focused on igniting the public dialogue regarding privacy impacts, ethical consequences, fairness, and real-world harms of non-privacy-compliant ML systems. She also co-authored a chapter 'Do not fake it till you make it!', which is a synopsis of trending fake news detection methodologies on social media using deep learning in a world-renowned Springer book series. To help budding authors and writers, she is also serving as a technical book reviewer for O'Reilly Media and Packt Publishing House.
Having graduated from the University of California, San Diego, with a Master's degree in Computer Science and an Artificial Intelligence specialization, her journey is highlighted by a myriad of experiences from her stints at Faire, Twitter/X, Facebook/Meta, National Research Council Canada, Institute of Research & Development France, San Diego Supercomputer Center involving data science, mathematical modeling, and software engineering.
Jigyasa is an avid proponent of open-source and credits the access to opportunities and her career growth to this sphere of community development. In her spirit to build a powerful community with a strong belief in "we rise by lifting others", she mentors aspiring developers and ML enthusiasts in various global programs. She shares her experiences and knowledge as an Advisory Board Member at Bezoku AI, Las Positas Community College, AI Forum, Corinium Global Intelligence, and VigiTrust. She has also led the open-source and ML track for Anita Borg's IWiC group, chaired the PyBay committee, and served as the Director of Women Who Code. At Twitter, she led the Women@ML BRG, to provide a safe circle for professional growth, collaboration, and advocacy.
Jigyasa is a proud recipient of multiple grants for her research and travels globally from Mitacs Globalink, Linux Foundation, Facebook, European Smalltalk Users Group, Python Software Foundation, Twitter, and many more. She has 150+ media features, keynotes, conference talks, panels, workshops, and podcasts to her name, with renowned entities like Google, United Nations, NTD Business, International Business Times, SiliconAngle, CubeTV, etc. Her love for tinkering has led her to win 5+ hackathons, sponsored by Microsoft, Google, Github, etc. and she now gives back to the community by serving on the judges' panel of hackathons.
Apart from her technological ventures, she enjoys exploring hidden gems in her city, hanging out with friends and family, and has been recently having fun with baking. You can visit her online at jigyasa-grover.github.io or on Twitter (@jigyasa_grover).
