When: Wednesday, April 18 2018 @ 7:30pm - 9:00pm

Where: Pupin 329, Columbia University

CDSS is hosting our last data science tech talk of the semester with Dataiku! Dataiku’s core product is a complete data science software tool aimed at shortening the time-consuming load-clean-train-test-deploy cycles of building predictive applications. The French-based startup scored a $28 milion Series B investment in late 2017, a super cool office space in downtown Manhattan, and is currently hiring!

At this event, Dataiku’s Lead Data Scientist, Jed Dougherty, will present a project analyzing the largest national dataset on evictions from Kansas City using Dataiku’s platform. Jed will also speak about the full-time and internship opportunities available at Dataiku.

Please RSVP to the event if you can.

For more info.

About:
Since launching in 2009, Foursquare has collected 12 billion global check-ins, which have formed the cornerstone of its location intelligence. Using this data, Foursquare is able to detect a billion new place visits per month via the activity generated by users and business partners around the world. Foursquare now offers its proprietary location technology to hundreds of other companies, including Apple, Samsung, Microsoft, Twitter and AirBnB.

We will give an overview of Foursquare's data and how it has evolved, starting with the check-in and expanding to include continuously-detected visits on millions of smartphones. We will also describe Foursquare's core location technology, Pilgrim, how it works, and some of the data science challenges it has generated.

Speaker:
Adam Waksman is the Director of Engineering and Data Science at Foursquare, with oversight over Pilgrim. He has worked in various startup areas, including healthcare technology, fintech, and locations intelligence. Most notably, he was Chief Technology Officer at Epickk and was a day-one member at Arcesium. Prior to that he earned his Ph.D. at Columbia University; his academic papers in computer science and neuroscience have resulted in close to a thousand citations.

For more info.

Come join McKinsey’s New Ventures, Advanced Analytics, and QuantumBlack practices on March 29th, 2018 at 7:30pm ET for a conversation about our work in data analytics and machine learning. We will explain the work these groups do and then talk through a recent project that leveraged machine learning to predict and prevent injuries for a professional sports team. We will also discuss the data analytics and machine learning roles at McKinsey and QuantumBlack. Following the talk, there will be a panel discussion to answer any questions. We look forward to meeting you!

Please arrive at 7:30pm sharp, as we’ll be beginning the presentation then.

Agenda:

7:30 – 8:30pm: Introductions, Analytics Case Presentation, Recruiting Process Overview

8:30 – 9:00pm: Q&A

Presenter Bios:

Muneeb Alam - Muneeb is an Analytics Fellow in McKinsey’s Public Sector Analytics group. He joined McKinsey right out of university, with a BA in astrophysics from Columbia and a Master’s in Analytics from Imperial College London. He’s served clients in corrections, tax, and education.

Daniel First – Daniel is a Data Scientist at QuantumBlack, a subsidiary of McKinsey that specializes in machine learning. After pursuing graduate studies at Columbia’s Data Science Institute as an NSF research fellow, he joined McKinsey initially as a management consultant, before moving over to his current role at QuantumBlack. His work has centered around collaborating with doctors and hospitals to design innovative, data-driven solutions to improve outcomes for patients, by forecasting and preventing medical risks. He has also published on the social and political implications of Artificial Intelligence. He holds a master’s degree in philosophy from the University of Cambridge and an undergraduate degree in neuroscience from Yale University. 

Ishneet Kaur – Ishneet is a Risk Advanced Analytics Fellow with experience in risk identification and stress testing. She interned with Risk Analytics in the Summer of 2016 before joining McKinsey full time in July of 2017. Ishneet holds a Masters in Applied Economics from Cornell University.

For more info.

Join IBM Research on March 28th, 2018 at 7:30pm ET for a conversation about our work using Data Science for Social Good. Members of IBM Research will give an overview of the program, and walk through recent projects that leveraged machine learning to produce social good. Projects include using natural language processing-based methodology to accelerate the work-flow of policy experts at UNDP, Accelerate Science Discovery. Following the talk, there will be a panel discussion to answer any questions. We look forward to meeting you!

Speakers’ Bios:
Kush R. Varshney
Kush R. Varshney was born in Syracuse, NY in 1982. He received the B.S. degree (magna cum laude) in electrical and computer engineering with honors from Cornell University, Ithaca, NY, in 2004. He received the S.M. degree in 2006 and the Ph.D. degree in 2010, both in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT), Cambridge. While at MIT, he was a National Science Foundation Graduate Research Fellow.

Dr. Varshney is a research staff member and manager with IBM Research AI at the Thomas J. Watson Research Center, Yorktown Heights, NY, where he leads the Learning and Decision Making group. He is the founding co-director of the IBM Science for Social Good initiative. He applies data science and predictive analytics to human capital management, healthcare, olfaction, computational creativity, public affairs, international development, and algorithmic fairness, which has led to recognitions such as the 2013 Gerstner Award for Client Excellence for contributions to the WellPoint team and the Extraordinary IBM Research Technical Accomplishment for contributions to workforce innovation and enterprise transformation. He conducts academic research on the theory and methods of statistical signal processing and machine learning. His work has been recognized through best paper awards at the Fusion 2009, SOLI 2013, KDD 2014, and SDM 2015 conferences.

Yaoli Mao
Yaoli Mao is a Ben Wood Research Fellow affiliated with the Institute for Learning Technologies and Ph.D. student in the Cognitive Science in Education program at Teachers College Columbia University. 
She conducts research using both quantitative and qualitative methods in the intersection of cognitive psychology, human-computer interaction and learning science. Yaoli is interested in social and cognitive-affective aspects of learning (engagement, boredom, and gaming etc.), learning strategies and behavior patterns. Her dissertation concerns collective intelligence, exploring knowledge sharing and learning among diverse expertise and human crowds’ intelligence can be properly evaluated, supported and elevated by machine learning and system design.

Jonathan Galsurkar
Jonathan will graduate in May with an MS in Data Science from Columbia University. He graduated summa cum laude in with a bachelor in Computer Science and Mathematics. Before coming to Columbia, he served as adjunct lecturer as well as developer for a health-tech company. Jonathan main areas of interests are machine learning and natural language processing, especially their utilization for social good. DSI students might recognize Jonathan from the 2017 Columbia Data Science Hackathon, in which his team came in first place. As a Science for Social Good Fellow at IBM, Jonathan’s work was focused on sentence/paragraph embedding and semantic searching techniques & applications.

For more info. 

Applied Data Science and the Emerging Role of Quantum Computing in Machine Learning

Data science has been rapidly growing over the past decade, and its applications have become ubiquitous in our daily lives. As these applications consume more data and need faster response times, new technologies and algorithms are needed to meet the computational demands. Quantum computing is a highly promising emerging technology that could present significant opportunities to accelerate the training of machine learning algorithms and improve data science methods.

This presentation will provide an overview of data science, with a focus on practical applications in industry. The current state of quantum computing technologies will also be explored, including some of the ways that quantum computing can be harnessed to advance machine learning.

ABOUT THE SPEAKERS:

John Kelly, Ph.D.
John Kelly, Director of Analytics at QxBranch, is leading the company’s development of advanced data analytics technologies. Previously, he was the Technical Lead for Corporate Data Analytics at Lockheed Martin. John has experience applying machine learning to a diverse set of domains including healthcare, supply chain optimization, sustainment, and program management. He completed his BS and MS in Electrical Engineering at NC State and his Ph.D. in Electrical and Computer Engineering at Carnegie Mellon University, where his work focused on machine learning and signal processing algorithms for brain-computer interfaces.

About QxBranch
QxBranch (www.qxbranch.com) develops and deploys advanced data analytics models for global companies in finance, insurance, technology, and sports. We have a diverse team of professionals in systems and software engineering, machine learning, quantum computing, and cyber security.
QxBranch is headquartered in Washington, D.C., with offices in London and Australia. For the most up to date career opportunities, please visitwww.qxbranch.com

For more info.

Arthena is a YC-backed art hedge fund based in NYC. Come hear how we gather millions of records on decades of art auctions and quantitatively analyze, price, and invest in art.

We’re looking for full-time summer interns! Data science interns will build experimental models, expand our data pipeline, build data visualization tools, and contribute to the production machine learning systems we use to invest tens of millions of dollars in art. All internships are paid and based out of our office in SoHo, NYC.

About the speakers:

PAUL WARREN manages the data science team, products, and technical roadmap at Arthena. He is currently co-authoring a data science textbook with a Professor of Data Science in South Korea and has several years of professional data science experience. He studied Computer Science at Stanford, where he grew and managed a 100-person space exploration project group.

BASIL VETAS is a data scientist at Arthena and a graduate student at Columbia's Data Science Institute. He previously worked on the data visualization team at JPMorgan and Qualtrics, with an impact investing fund, and with a number of startups. Basil is originally from Salt Lake City and received his bachelor's degrees from the University of Utah. 

CHIKE UDENZE is a Software Engineering intern at Arthena. He studies software engineering at Rochester Institute of Technology and has previously worked at Releaf (YC Spring '17) and Datto.

*Bring in your résumés!



##############
The Broadway Room is located on the 2nd floor of Lerner Hall (the big glass building just to the west of Butler Library). Detailed floor plan:http://lernerhall.columbia.edu/files/lerner/floorplans/2e.jpg?width=600&height=800