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With Ned Martorell (Data Scientist, Dataiku) & Karthik Ramasamy (Machine Learning Enggr, Google Cloud).
Thursday, March 26, 2020 at 07:00 PM   Absolutely Free
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EVENT DETAILS
Thank you for your interest this Dataiku NY Meetup! The health & safety of our attendees & speakers is our primary concern. While this currently proves to be a tricky time for public gatherings, Dataiku is still committed to providing great tech content & facilitating discussions in the data science space. As such, weve decided to pivot towards online webinars via our partner platform, Brighttalk. The entire Meetup in the same format will be held virtually, which allows for a live presentation & Q&A session after.

In order to gain entry, you must RSVP through this BrightTalk link: https://www.brighttalk.com/webcast/17108/391367?utm_source=Dataiku&utm_medium=brighttalk&utm_campaign=391367

Tentative Schedule:
4:00pm: Intro
4:05pm: Determining Voter Preference: From Straw Polls to MRP by Ned Martorell, Data Scientist at Dataiku
4:35pm: Looking Beyond Human Labeling & Supervised Learning by
Karthik Ramasamy, Machine Learning Engineer at Google Cloud.
5:30pm: Q&A

Talk Abstracts:

Determining Voter Preference: From Straw Polls to MRP:

November is fast approaching, & that can only mean one thing: polls. And were not just talking Presidential Election polls, but Democratic primary polls, economic sentiment polls, & and even polls for elections to your local Board of Education or Traffic Court Judge. With so many polls, & so many pollsters, one may start to wonder has it always been like this? Did Cicieros campaign manager spend sleepless nights trying to estimate sentiment among the Roman curia? Were Cromwells fundraising efforts hampered by negative BBC coverage? And all thats before we even consider the validity & trustworthiness of polls! Can a dubiously worded poll from a semi-reputable website actually settle the Friends vs. Seinfeld conundrum? (Hint: it cant. And for the record, its Seinfeld, hands-down). Or can an established pollster, drawing on years of experience & a trove of data, actually predict the outcome of this years presidential election?
If any of these questions have piqued your interest, or you simply want to kill some time before Karthiks talk, then please tune in to Determining Voter Preference with Ned Martorell. Amongst other topics, hell be discussing the history of electoral polling, ways to limit (or not) bias is polling outcomes, & a selection of polling methods.

Speaker Bio:

Ned Martorell is a Data Scientist at Dataiku, where he helps organizations break down knowledge silos & democratize data-driven insights. Prior to Dataiku, he worked as a research scientist at Thales, & as a high school physics teacher at Teach First. He has a masters degree in aerospace engineering, & a keen interest in politics & social policy.

Looking Beyond Human Labeling & Supervised Learning:

Typically most companies have been using supervised learning with human labeled dataset. However, thirst for more labels is never satisfied, especially for deep learning models. In this talk I will talk about technologies to overcome lack of abundance of human labeled dataset with specific examples in computer vision & NLP domains. I will talk about how synthetic dataset can be used to train a model that can be generalized to operate on real world data. Then I will cover self-supervised learning that is becoming an emerging trend in large scale deep learning tasks. I will focus on a couple of use cases of self-supervised learning techniques that are general enough to be applicable in the majority of computer vision tasks.

Speaker bio:

Karthik is a machine learning engineer in Google Cloud AI team working on TensorFlow for enterprise efforts. His team's latest release was https://tensorboard.dev, a free to use hosted TensorBoard service. Before Google, Karthik was part of machine learning platform team at Uber self driving team & before that led a team of data scientists in Uber, & LinkedIn risk teams. He is passionate about hackdays, self driving cars, & tiny computers.

 
 
 
 
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