Data Science Fall 2022 Capstone Project Showcase
Capstone projects are the culmination of the MIDS students’ work in the School of Information’s Master of Information and Data Science program.
Over the course of their final semester, teams of students propose and select project ideas, conduct and communicate their work, receive and provide feedback, and deliver compelling presentations along with a web-based final deliverable.
Join us for an online presentation of these capstone projects. Each team will present for twenty minutes, including Q&A.
A panel of judges will select an outstanding project for the Hal R. Varian MIDS Capstone Award.
Taiwo Raphael Alabi, MIDS ’18, received his Ph.D. from Georgia Institute of Technology over a decade ago in materials science and engineering before proceeding to join Intel as a senior Integration engineer working on next-generation computer microchips. At Intel, his love for data science and machine learning blossomed; culminating in him going back to obtain a MIDS degree from UC Berkeley in 2018. After Berkeley, he has also obtained several certifications in machine learning and also in cybersecurity from Stanford, Udacity, and Udemy. He has seven filed patents of which three have been issued and over ten publications. He is currently a lead machine learning engineer at Docusign and is a committee member of the TMLS (Toronto Machine Learning and Operations Society) society.
Anna Bethke (they/she) is a principal data scientist focused on fair, accountable, transparent, & explainable (FATE) AI in Salesforce’s Ethical AI Practice Team, collaborating with product and research teams to create AI responsibly and empower our customers to use it responsibly. They research and implement innovative techniques for assessing and mitigating bias and harm in AI. Anna received their M.S. and B.S. in aerospace engineering from the Massachusetts Institute of Technology concentrating on human factors engineering. Anna was formerly the head of AI for social good at Intel and previously worked at Facebook, MIT Lincoln Labs, Argonne National Labs, and Lab41.
Celia Ludwinski, MIDS ’18, is a program director of data science at SFL Scientific, a Deloitte business; with a demonstrated track record of machine learning development, she advises on best methods, novel ideas, market positioning, and helps formulate team structure and requirements for future management and scaling.
Celia is a formally trained data scientist and sets the strategic and operational direction of client engagements. She has worked with over fifty organizations in the S&P500 to develop AI and digital solutions and is recognized as a subject matter expert in leading information technology teams in large-scale transformation projects, focusing in the areas of data strategy, ETL services, data governance, and AI model development.
Prior to joining SFL Scientific, Celia was a senior scientist at L’Oreal and is named on several patents in the technology incubator for the research & innovation division, where she worked in designing experimental procedures and validation methods for enterprise machine learning applications including machine-implemented virtual health and beauty systems.