Jigyasa Grover
Senior Machine Learning Engineer at Twitter
10-time award winner in the field of Artificial Intelligence and Open Source, Jigyasa Grover is a powerhouse brimming with passion to make a dent in this world of technology and uplift the weaker strata. Currently working at Twitter as a Senior Machine Learning Engineer in the ads prediction domain, she is spearheading a variety of projects spanning ML model development, feature engineering, user tracking transparency remediations, and monetizing new Twitter products. She is also one of the few ML Google Developer Experts globally. Most recently, she won the Next Generation Leader of the Year and Outstanding Women in AI Award for her contributions to this field and efforts in inspiring and empowering the future generation. Jigyasa co-authored a book titled ‘Sculpting Data for ML’ that campaigns for a data-centric approach to ML and 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 titled ‘Do not fake it till you make it!’ is a synopsis of trending fake news detection methodologies on social media using deep learning and has been published in a world-renowned Springer book series. Having graduated from the University of California, San Diego, with a Master’s degree in Computer Science with an Artificial Intelligence specialization, her journey is also highlighted by a myriad of experiences from her brief stints at Facebook, the National Research Council of Canada, and the Institute of Research & Development France involving data science, mathematical modeling, and software engineering.
Red Hat ‘Women in Open Source’ Academic Award winner and Moxie Women in Tech Award Winner, Jigyasa is an avid proponent of open-source and credits the access to opportunities and her career growth to this sphere of community development. She is an Advisory Board Member of VigiTrust where she collaborates with experts from worldwide to quantify and combat privacy risks in ML and also for the Las Positas College to help build the curriculum for budding computer engineers. She is also the proud recipient of multiple scholarships for her research and travels like Mitacs Globalink, Linux Foundation, Facebook GHC, ESUG, GHC India, etc. She currently leads the open-source and ML track for Anita Borg’s IWiC group and co-leads the Women@ML BRG at Twitter, to provide a safe circle for professional growth, collaboration, and advocacy. She is the Program Chair for PyBay, co-chair of the Financial Aid Committee for Python Software Foundation, and has also served as the Director of Women Who Code and Lead of Women Techmakers for a handful of years to help bridge the gender gap in technology. 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 has 100+ conference talks, panels, keynotes, technical workshops, and podcasts to her name, with renowned publishers like United Nations, Red Hat, Python Software Foundation, Systers, Lead Dev, Women in Science & Engineering, IEEE, 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).
Watch live: March 7, 2023 @ 12:30 – 1:00 pm ET
Sculpting Data for Machine Learning
In the contemporary world of machine learning algorithms – “data is the new oil”. For the state-of-the-art ML algorithms to work their magic it’s important to lay a strong foundation with access to relevant data. Volumes of crude data are available on the web nowadays, and all we need are the skills to identify and extract meaningful datasets. This talk aims to present the power of the most fundamental aspect of Machine Learning – Dataset Curation, which often does not get its due limelight. It will also walk the audience through the process of constructing good quality datasets as done in formal settings with a simple hands-on Pythonic example. The goal is to institute the importance of data, especially in its worthy format, and the spell it casts on fabricating smart learning algorithms.
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