The QMSS Innovation Lab was established with  the goal of generating bold ideas; providing an interdisciplinary perspective; developing thoughtful methods; and working with data for the public good.

 

QMSS partners with organizations in the public and private sectors on projects that enable students to apply their research and data science skills while providing value to the partner organization. Innovation Lab projects typically synthesize the statistical, computational, engineering and social challenges involved in solving complex real-world problems. Projects typically progress through the following phases:

 

  1. Background and problem definition
  2. Data wrangling, munging and cleaning
  3. Exploratory Data Analysis
  4. Coding prototypes of algorithms and models
  5. Data Visualization
  6. Reporting, communicating and ethics discussion
  7. Productising any models or algorithms if applicable

For more information about partnering with QMSS, find out how it works HERE.

This Lab is also designed to have a strong student-focused orientation and support individual or team student projects and research ideas that they would like to explore and work on independently while pursuing their degree. In addition, QASR, the QMSS student organization, works to explore specific topics of interest to students through panels, book discussions, and workshops to expand on class-based learning and research.

We have worked with a number of organizations on critical projects. A partial list is below:

The UN Joint SDG Fund

CASE STUDY Spring 2022

Measuring the Integration and Network Effect of the SDGs

Challenge: The 17 Sustainable Development Goals (SDG) are defined in a list of 169 SDG Targets and 231 unique SDG indicators. The 17 SDGs were designed not as separate and isolated goals, but as a network, in which links among the goals exist through targets and indicators that refer to multiple goals. With less than 10 years remain to achieve all Sustainable Development Goals (SDGs) globally, there is a growing need for integrated implementation and measurement.  In this project, the team was tasked to devise a model/tool that define and measure linkages and networks of SDGs and see how such SDG linkages/networks progress over time.

Student Team: Peishan Li, Qinyue Hao, Jasmine Hwang, Dan Li, Rina ShinConnie XuHanyu ZhangLizabeth Singh

<CLICK HERE TO READ ABOUT THE PROJECT>

Lovelytics

CASE STUDY Spring 2022

Visualizing Customer Segmentation

Challenge: Conduct comprehensive customer segmentation analysis using credit card data and present possible insights for marketing purposes.

Student Team: Andrew Lai, Edmund Lam, Yixuan Li, Jinghan Ma, Nicole Neo, Xudan Wang

<CLICK HERE TO READ ABOUT THE PROJECT>

The Black List

CASE STUDY Fall 2021

Developing a Dashboard for Producers

Challenge: The Black List is a platform for TV and film writers to showcase their screenplays for industry members and get their work evaluated by professional readers. Having accumulated a large number of scripts over the years, the challenge is to use these data in ways that help the mission of the organization, as well as provide service to the industry.

Student Team: Pruthvi Panati, Lavanya Narayanan, Xintong (Maxxie) TangArielle Herman, Tianqing Zhou

<CLICK HERE TO READ ABOUT THE PROJECT>

The Opportunity Project (US Census)

CASE STUDY Fall 2021

R-Story; Facilitating Rural Economic Development in partnership with the Census Bureau and the Environmental Protection Agency

Challenge: Create digital tools that help rural communities access and use data to implement solutions to economic, environmental, and human health challenges, taking care to reach places that have limited professional capacity and small budgets.

Student Team: Alisha GurnaniMichelle A. ZeeGretchen StreettAlison RylandKyung Suk Lee, and Asahi Nino, in partnership with the US Census Bureau and the Environmental Protection Agency

<CLICK HERE TO READ ABOUT THE PROJECT>

KPMG

CASE STUDY Fall 2021

COVID-19 Forecasting Tool

Challenge: Improve on previous modeling efforts to forecast COVID-19 infection rates based on daily time series data. Evaluate how to forecast daily COVID-19 time series data in one geography (e.g. New York) based on daily COVID-19 time series data from both that geography and other geographies (e.g. South Korea, Italy).

Student team: Sydney (Bolim) Son, Andrew Thvedt, Louisa Ong, Ariel Luo, Jen Woo

<CLICK HERE TO READ ABOUT THE PROJECT>

Introducing Innovation Lab Mentors!

Michael Haupt

PhD Candidate in Cognitive Science, UC San Diego

Interests: Media Consumption and Susceptibility to Misinformation
Michael Haupt is a doctoral research fellow at the Global Health Policy and Data Institute and a PhD student in Cognitive Science at University of California, San Diego. His current research involves using machine learning and social network analysis to investigate political mobilization, communication dynamics, and misinformation spread on social media.
 
Michael is interested in mentoring QMSS students interested in working on the spread of conspiracy theories and misinformation. For more information on the projects Michael is working on and how to get involved, email <qmss@columbia.edu>.

Kevin C. Wang

Vice President at .406 Ventures

Interests: Data Science Entrepreneurship

Kevin is an early stage investor focused on data & cloud at .406 Ventures and has 8 years of technology advisory and investment experience working closely with software and data companies ranging from start-ups to F1000 enterprises. He spends a disproportionate amount of time within data science & ML given his personal interests and learnings developed through the QMSS program. A few companies that he’s actively involved with include: ClosedLoopPromethiumLineaChaosSearch, and Telmai. Prior to joining .406, Kevin was an investor at BV Investment Partners where he focused on technology investments serving financial services, healthcare and the enterprise. Kevin began his career in technology investment banking at J.P. Morgan, and spent most of his time within enterprise software and tech-enabled services. He received his BA in Economics, Political Science, and Mathematics from The University of Connecticut, and his MA in Quantitative Methods from Columbia University. During his free time, he enjoys backpacking, scuba diving, skiing, and traveling, and is actively involved within Big Brothers Big Sisters as a big brother.

Kevin is interested in mentoring students who are thinking about using their data science skills in starting new ventures.

Adam Arenson

Professor of History and Director of the Urban St at Manhattan College

Adam Arenson is professor of history at Manhattan College. He is the author of two award-winning books: The Great Heart of the Republic: St. Louis and the Cultural Civil War (Harvard University Press, 2011) and Banking on Beauty: Millard Sheets and Midcentury Commercial Architecture in California (University of Texas Press, 2018).  He is co-editor (with Andrew Graybill) of Civil War Wests: Testing the Limits of the United States (UC Press, 2015), and (with Jay Gitlin and Barbara Berglund) of Frontier Cities: Encounters at the Crossroads of Empire (University of Pennsylvania Press, December 2012). 

Arenson has published a half-dozen scholarly articles, as well written for The New York Times Disunion series, The AtlanticThe Washington Post, and History News Network. He has spoken about his Civil War Era research at the Library of Congress, the Jefferson National Expansion Memorial, Lincoln University Founder’s Day, the Autry National Center, Yale’s Gilder Lehrman Center for the Study of Slavery, Resistance, and Abolition, York University’s Harriet Tubman Institute for Research on Africa and its Diasporas, and the Buxton Homecoming, as well as at academic annual meetings. 

Arenson holds an A.B. in History and Literature from Harvard and a Ph.D. in History from Yale. More about his research on U.S. history, memory, and visual culture can be found at http://adamarenson.com and https://manhattan.edu/campus-directory/adam.arenson