We are excited to continue our collaboration and learning in the field of data science in this month's meetup with two industry speakers.. Dr. Guilherme Maia, Site Director of the Ameren Innovation Center at the Research Park, will be presenting on his innovative work in preventing gas leaks using explainable machine learning. Then we'll hear from Shubanshu Mishra, Machine Learning Researcher at Twitter working on the Content Understanding Research team, who will be discussing information extraction work in understanding digital social trace.

We'll be hosting the meetup online via Zoom. Please RSVP for the event at the CU-DSUG Meetup Page.

==================================
Dr. Guilherme Maia information

Bio:

Dr. Guilherme Maia is the Manager of Digital Innovation for Ameren Corporation and Site Director of the Ameren Innovation Center at the Research Park at University of Illinois Urbana-Champaign. His team applies data science and software engineering innovations to meet Ameren’s business challenges. Prior to this, he was a Senior Data Scientist for Ameren and led the Analytics team for Anheuser-Busch InBev in North America. Dr. Maia was also a postdoctoral researcher at the University of Illinois Urbana-Champaign in the Department of Biological Engineering, where he designed biotechnologies for air pollution control and used mathematical optimization to increase food security and the resilience of supply chains.

Preventing Gas Leaks Using an Explainable Machine Learning
· Solution by: Ameren Innovation Center, Digital – UIUC Research Park
· Internal Business Partner: Ameren Illinois, Gas Integrity Management, Gas Operations (IL)

What?

Ameren Digital partnered with the Ameren Gas Integrity team in Illinois to create a gas asset management tool that uses machine learning to assign risk probabilities for gas leaks in our territory. The model then ranks these risk area probabilities from highest (most likely to leak) to lowest (least likely to leak).

==================================
Shubhanshu Mishra information

Bio:

Shubhanshu is a Machine Learning Researcher at Twitter working on the Content Understanding Research team. He finished his Ph.D. at the iSchool, University of Illinois at Urbana-Champaign, where he worked as a research assistant with Dr. Jana Diesner and Dr. Vetle Torvik, His PhD thesis was titled Information Extraction from Digital Social Trace Data with Applications to Social Media and Scholarly Communication Data. His current work is at the intersection of machine learning, information extraction, social network analysis, and visualizations. He finished his Integrated Bachelor’s and Master’s degree in Mathematics and Computing from the Indian Institute of Technology, Kharagpur in 2012. He was a fellow of Kishor Vaigyanik Protsahan Yojana (KVPY), a scholarship program funded by the Department of Science and Technology of the Government of India, from 2007 to 2012. Shubhanshu has published multiple open source projects as well as open datasets related to his research. More information about Shubhanshu can be found at: https://shubhanshu.com/

Title: Understanding digital social trace data via Information Extraction

Abstract:

Information extraction (IE) aims at extracting structured data from unstructured or semi-structured data. In this talk we will learn how information extraction from social media data and scholarly communication data can be looked through the lens of an abstraction called digital social trace data (DSTD). This abstraction allows us to utilize the graph structure of the data (e.g., user connected to a tweet, author connected to a paper, author connected to authors, etc.) for developing new information extraction tasks.