WiDS Cambridge Datathon 2025

The WiDS Cambridge Datathon Workshop is an annual workshop in addition to the main event, WiDS Cambridge Conference. The workshop aims to provide mentorship and training for those interested in participating in the WiDS Datathon Challenge, and, more generally, anyone with a strong interest in data science.

Sharut Gupta

What I Do: I'm a third year Ph.D. student in the Machine Learning Group at MIT CSAIL, advised by Prof. Stefanie Jegelka.

My research mainly focuses on building robust and generalizable machine learning systems with minimal supervision. I enjoy working on out-of-distribution generalization, causal inference, self supervised learning and representation learning.

What I Love About Data Science: Data science greatly aids the decision making process and provides insights which are otherwise hard to infer. Its amazing how it brings ease to lifestyle alongside challenging the degree of its involvement.

What Else: I love playing outdoor sports especially baseball and squash, traveling and painting. In my free time, I can be be found trying different cuisines and enjoying by the beach.

Arushi Jain

What I Do: I'm a Applied Machine Learning Scientist at Microsoft in Seattle.

I work on Language Understanding pillar of M365 Copilot in Microsoft Search Assistant & Intelligence (MSAI) Team. Prior to this, I was part of Microsoft's 2-year AI program where I got to work on different paradigms of AI with 4 Microsoft Teams.

What I Love About Data Science: I love that data science allows us to make informed decisions – and to stop guessing. It also helps businesses and industries to know their stakholders closely and find new opportunities.

What Else: I am really passionate about teaching and empowering women in this field and this is the fourth time I am organizing this workshop, really excited to be here!! I am big on traveling to national parks, hiking, swimming and digital art!

Claire Tinker

What I Do: I’m a Sustainability Solution Architect at IBM, focusing mainly on Computer Vision solutions. Recently, I’ve been working with civil infrastructure clients (Railroads, Bridges, Airports, Roads) to train and productionize large vision models for virtual inspections and automated maintenance cycles. My current project focuses on defect detection for the Norwegian Railroad.

What I love about Data Science: Data Science is wide in its application but never diluted in its impact. Data is powerful, and its manipulation begets both rote diligence and creative ambiguity. Also, I love data scientists! They’re a super diverse crowd, and everybody is so thoughtful.

What Else: I love endurance sports, urban agriculture, thrifting, going to concerts, hip-hop dance and playing piano!