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Speed Mentoring Sessions provides a platform for all attendees and diversity scholars to have quick on-spot networking sessions with Mentors about topics like career in ML, navigating through early career Data Science roles, pursuing research, resume reviews, etc.<\/p><\/div>\n <\/div>[\/vc_column_inner][vc_column_inner width=”1\/4″][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner width=”1\/12″][\/vc_column_inner][vc_column_inner width=”10\/12″][vc_column_text]<\/p>\n
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Ravi Yadav<\/b><\/span> – Talks about Data Science roles, resume reviews<\/span><\/p>\n Bio: <\/b>Ravi Kumar Yadav is an experienced senior data scientist. In the last six years he has built data science solutions for fraud detection at Bank of America and text based recommendation engine at Walmart Search.<\/span><\/p>\n LinkedIn:<\/b> https:\/\/www.linkedin.com\/in\/ravi-kumar-yadav-535b268\/<\/span><\/a>[\/vc_column_text][vc_separator color=”peacoc” style=”dotted”][vc_column_text]Sylvia Tran<\/b><\/span> – Talks about Career in Machine Learning, Python communities, Women in Tech<\/span><\/p>\n Bio:<\/b> Sylvia Tran is a Principal ML Engineer at Hypergiant, working on building and implementing ML solutions for space, defense, and critical infrastructure. Previously, she was a Sr. ML Engineer and Sr. Data Scientist at Gracenote working on deep learning solutions utilizing media & entertainment metadata. In her prior career, she was a Director and VP at Wells Fargo Capital Finance LLC and has 10+ years of experience in business & risk analysis, financial forecasting, and mergers & acquisitions.\u00a0\u00a0<\/span><\/p>\n Shortly after her career change, Sylvia chaired the Los Angeles chapter of PyLadies for 5 years, and organized the inaugural PyData Los Angeles conference in 2018. In her spare time, she\u2019s hiking with her pit bull, or reading a book.<\/span><\/p>\n Sylvia holds a M.S. in Computational Analytics from Georgia Tech and a B.S. in Applied Mathematics from UCLA.<\/span><\/p>\n LinkedIn:<\/b> https:\/\/www.linkedin.com\/in\/sylvia-s-tran\/<\/span><\/a>[\/vc_column_text][vc_separator color=”peacoc” style=”dotted”][vc_column_text]Rohit Supekar<\/b><\/span> – Talks about Research, Academia, Machine Learning<\/span><\/p>\n Bio:<\/b> Rohit Supekar is a data\/applied scientist at The New York Times, and he currently works on developing and deploying causal machine learning models to power The Times\u2019s paywall. He is broadly passionate about understanding the world around us using data, building mathematically rigorous models, and deploying them using modern production-quality engineering tools.<\/span><\/p>\n Prior to joining The Times, he obtained a Ph.D. in 2021 and a Master’s degree in 2017 from M.I.T., and a Bachelor’s degree in 2015 from I.I.T. Madras in India. His Ph.D. thesis work involved building mathematical models for active fluids, such as a dense suspension of bacteria, by using a combination of partial differential equations, machine learning, and principles from fluid mechanics.<\/span><\/p>\n