Presentation: Machine Learning from Theory to Practice

Track: Modern CS in the Real World

Location: Plymouth - Royale, 6th fl.

Duration: 4:10pm - 5:00pm

Day of week: Wednesday

Level: Intermediate

Persona: Architect, CTO/CIO/Leadership, Developer


With recent advances in computational power, machine learning is positioned to change the way we interact with the world around us. Likewise, a surge of well-maintained machine learning libraries has made it possible for engineers to use machine learning models with minimal background. However, many find that using machine learning responsibly in your company can be harder than it seems. Here, we will discuss some of the challenges that can arise when working with data.

Speaker: Deborah Hanus

PhD candidate at Harvard University

Deborah Hanus is a PhD candidate studying machine learning in Harvard University's Computer Science Department. She graduated from MIT with a Master's in Electrical Engineering & Computer Science and a dual-degree in Computer Science and Brain & Cognitive Sciences. She has worked as an engineer at a San Francisco startup. She has been awarded the Fulbright Student Fellowship, NSF Graduate Research Fellowship, and the Intel/ACM SIGHPC Computational Data Science Fellowship.

Find Deborah Hanus at

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