Women in AI Reception schedule

Dr. Usha Jagannathan is a Principal Engineer & Digital Innovation Leader at McKinsey & Company within Technology & Digital. She brings twenty years of digital transformation experience in building new technology ecosystems, and empowering teams to build user-centric products that meet customer needs. Usha leads domain level AI/ML applications for cloud transformations, drives innovation initiatives and support the domain strategy to accelerate innovation, and coach product development teams to deliver custom digital cloud-native solutions.

Before McKinsey, Usha worked at a digital innovation lab for Marsh & McLennan and spearheaded the IT graduate degree program as a program chair at Arizona State University. Following her Ph.D. dissertation, she pioneered the concept of virtual IT lab immersion for online engineering degree programs to facilitate experiential learning.

Her research interests include machine learning, ethical AI, decarbonization and AI, measuring carbon footprint in cloud instances. Usha is a passionate STEM advocate and works to increase industry diversity through apprenticeship and work-study programs.

Jigyasa Grover is a Senior Machine Learning Engineer at Twitter working in the performance ads ranking domain. Recently, she was honored with the 'Outstanding in AI: Young Role Model Award' by Women in AI across North America. She is also one of the few ML Google Developer Experts globally. 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. She graduated from the University of California, San Diego, with a Master’s degree in Computer Science with an Artificial Intelligence specialization and has 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 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 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, co-leads the [email protected] BRG at Twitter, and co-chairs the Financial Aid Committee for Python Software Foundation. She 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, which she has conducted in 10+ countries and countless cities across the US. 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).

MLOps/Trusted AI Summit: Closing General Session: Complexity vs Simplicity in ML and AI Projects

Women in AI Reception: Pivoting into AI

Supreet is an AVP at Morgan Stanley. Prior to Morgan Stanley, she was a management consultant at ZS Associates where she automated different workflows and built data driven solutions for fortune 500 clients. She is extremely passionate about technology and AI and hence started her own community called DataBuzz where she engages the audience by sharing the latest AI and Tech trends and also mentors people who want to pivot in this field.

Brenda is an Executive Director of Applied AI & ML within Commercial Banking at JPMorgan Chase (JPMC), and the DE&I Lead at JPMC's Silicon Valley Tech Center. Prior to joining JPMC, she led the Machine Learning Group at Lawrence Livermore National Laboratory (LLNL) and served as PI on ML projects that applied deep learning towards multimodal data fusion for energy grid health prediction, surrogate development for complex physics-based carbon capture models, and interpretable natural language processing for document classification. Her projects to date are tied to a single theme: how do we exploit our knowledge and sensing capabilities to take optimal actions in a world plagued with uncertainty. Her research interests include machine learning, uncertainty quantification, and decision-making under uncertainty. Brenda received her Ph.D. in computer science from Harvard with specialization in artificial intelligence.