Columbia University's Data Science Institute

09/09/2026

NEWS: The National Science Foundation (NSF) has selected Columbia University to establish a National Synthesis Center for Organismal Resilience (NSCORE).

This new multidisciplinary research center will catalyze an international community of biologists and computer scientists to better understand, predict, and identify actions to improve organismal resilience.

NSCORE will employ cutting-edge approaches in artificial intelligence and data science, leveraging expertise from Columbia’s Faculty of Arts and Sciences, Columbia Engineering, Columbia University's Data Science Institute, and other university and external partners. By combining organismal biology with computer science into the emerging field of computational organismal biology, NSCORE will enable unprecedented insight into one of the most critical and growing challenges of our time.

Read more about the announcement: https://fas.columbia.edu/news/columbia-lead-20-million-national-research-center-organismal-resilience

Leadership for the center will include:
➡️ NSCORE Executive Director Dustin Rubenstein, Thomas Hunt Morgan Professor of Conservation Biology in the Faculty of Arts and Sciences
➡️ NSCORE Associate Director & Integration Officer Itsik Pe’er, Professor of Computer Science and Systems Biology at Columbia Engineering
➡️ NSCORE Director of Data Science Eugene Wu, Associate Professor of Computer Science at Columbia Engineering
➡️ NSCORE Director of Education, Outreach and Engagement Florence Hudson, Executive Director of the Northeast Big Data Innovation Hub and Interim Executive Director for Sponsored Research in Columbia University's Data Science Institute

09/09/2026

Welcome to New York🗽

Our incoming MSDS students got the chance to explore their new city during orientation last week, including visits to some iconic locations like the National History Museum, the MET Cloisters, and the Statue of Liberty! NYC fosters exploration, curiosity, and community - something our students can enjoy in between classes and coursework!

09/08/2026

The first day of the fall semester 📚

Classes have officially begun for students at Columbia University's Data Science Institute. We are so excited to see what you’ll accomplish this year.

Columbia University Columbia Engineering

09/03/2026

Where Data Takes Us: Nami Jain MSDS '26

This summer, Nami worked as a Data Science Summer Analyst at JPMorganChase, where she built an automated, end-to-end test framework to help ensure compliance with the firm’s data-usage policy, replacing manual validation with scalable testing across thousands of policy scenarios to improve reliability and release confidence.

We caught up with her about her internship experience:

➡️ How did your experience at Columbia University's Data Science Institute contribute to your internship?

DSI gave me so many opportunities to connect with JPMorganChase through career fairs, networking events, and hackathons, which ultimately helped me land this internship. Once I got there, the collaborative, hands-on environment at DSI gave me the confidence to take ownership of projects and make meaningful contributions from day one.

➡️ How will this internship shape the rest of your time at DSI and your future career?

This experience completely changed how I think about building technology. Instead of creating solutions that only solve today’s problem, I learned how to design systems that are scalable, sustainable, and built for long-term impact. I’m excited to bring that mindset back to DSI and into my future career in AI and data science.

➡️ What was your favorite part of the internship?

Seeing the impact of the work I was doing! I had the opportunity to present my project to senior leadership, meet people across the organization, and even meet Chairman and CEO, Jamie Dimon. It was an incredible reminder that interns can make a real difference when they’re trusted with meaningful work.

Nami is pictured here with her fellow JPMorganChase intern and DSI student, Emily Ramond.

09/03/2026

The Center for Sustainable Futures at the Teachers College, Columbia University is taking climate education to the next level with a new TC Academy online module: the 2026 Climate Education Knowledge Sharing Convening 🌎

The self-guided, online resource is an evolution of the Center’s multi-year research-practice partnership with NYC Public Schools and LEAP at Columbia University, which has reached more than 600 NYC educators.

DSI Member Tian Zheng, who is the Deputy Director, Education Director and Chief Convergence Officer at LEAP and a Professor with the Department of Statistics, says: “What's exciting about this convening is that it turned three years of on-the-ground work with NYC teachers into a resource the whole field can build on. That kind of knowledge-sharing is exactly what LEAP makes possible.”

Read more:

A new asynchronous online course from the Center for Sustainable Futures presents years of insights and resources

08/31/2026

It's the exciting first week of their Master's degree - and their future careers - for these 248 DSI students! 🙌 🌎

This week, the incoming students in Columbia University’s Master of Science in Data Science program start their orientation. These future data scientists represent the best and brightest students from across the country and the world.

Their time at Columbia University's Data Science Institute will give them foundational knowledge & skills, hands-on experience, and a strong grounding in ethical practice through the Data Science Institute’s commitment to Data For Good.

Welcome to Columbia! 🦁

08/21/2026

Where Data Takes Us: Diya Bedi MSDS '26

🛞 This summer, Diya worked as a Data Science Intern at Bridgestone Americas Technology Center, where she built machine learning models to predict how different tires perform in winter conditions. She also helped design a simulation tool that brought together several separate testing environments into one interface, and built out a statistical pipeline for analysing tire wear over time. 🛞

We caught up with her about her internship experience:

➡️ How did your experience at Columbia University's Data Science Institute contribute to your internship?

The coursework at DSI, especially statistical inference and applied deep learning, meant I could walk in and start building instead of catching up. Beyond that, DSI gave me opportunities to open up, as an introvert, the push I got through networking events, hackathons, and career fairs gave me just the confidence I needed, which turned out to be exactly what the internship and the people at the company loved about me.

➡️ How will this internship shape the rest of your time at DSI and your future career?

It taught me that a good model isn't enough on its own, it has to be something people actually trust and use. I want to carry that mindset into the rest of my time at DSI and into whatever I build next, whether that's in ML engineering or research.

➡️ What was your favorite part of the internship?

My favorite moment was watching a model I built actually influence a real design decision, and knowing something I made on my laptop turned into something real is a feeling I'll carry with me long after this summer. I also loved that I never felt imposter syndrome there, everyone from interns to senior engineers was learning and growing right alongside each other, and being part of that was one of the best feelings.

08/07/2026

As a DSI Scholar this Spring term, Nitanshi Bhardwaj researched the capacity of data science in creative technology 🎵

The Columbia University Computer Music Center has advanced speakers and VR equipment that can create realistic 3D sound and visual experiences. These devices communicate through Open Sound Control, which allows different music and media software to send information to each other. However, there is a gap in technology that can convert data from a spreadsheet, and send it to these systems in real time. This creates repetitive work, slowing the creative process.

Nitanshi applied her data science skills to develop a Python tool that bridges this gap, with the ability to read and convert Excel and CSV data into live messages. This not only reduces friction in the creative process, but lowers the technical barriers for students, researchers, composers, and artists to creatively interact with data.

She completed her project with faculty mentor Seth Cluett of the Columbia University Computer Music Center.

08/06/2026

DSI Student Scholar Spotlight ⭐ Tanish Patel partnered with the Lamont-Doherty Earth Observatory of the Columbia Climate School to research source-level data valuation for sparse ocean carbon prediction.

Machine learning is used to predict how much carbon dioxide the ocean absorbs or releases. To make these predictions, data is combined from various locations, such as ocean observations and climate model simulations.

The problem: not all sources of data contribute equally to model performance. Some may improve regional generalization, while others can be harmful. Patel’s project applied his data-science skills to determine which data is truly worth collecting, and which data sources help prediction the most.

Tanish measured each data source as a player in a cooperative game, using Shapley values to determine the average contribution of each source. This helps scientists determine which datasets are most valuable for ocean prediction.

Throughout his DSI Scholar project, he worked with several mentors - Lamont-Doherty’s Galen McKinley, Amanda Fay, Thea Hatlen Heimdal.