The School of Information’s May 2023 Commencement presented an opportunity to honor faculty and student achievements.
Head of School Marti Hearst presented awards for outstanding capstone projects from the Master of Information Management and Systems program, the Master of Information and Data Science program, and the Master of Information and Cybersecurity program.
Students in the MIMS, MIDS, and MICS programs voted for their most outstanding instructors and gave awards to their classmates.
James R. Chen Award for Outstanding MIMS Final Project, 2023
Session 1: Tools for Sustainability and Productivity
The Hidden Emissions of Electric Vehicles
Joshua Everts, Astoria Ho, Clara Hu
Advisor: Marti Hearst
As the U.S. begins moving towards a greener future with greater adoption of electric vehicles (EV), “The Hidden Emissions of Electric Vehicles” team examined the sustainability of zero emissions vehicles and highlights barriers to EV adoption and use in California. They created an interactive online tool called Emissions Explorer that allows consumers to explore and understand how different factors affect emissions.
“The project addressed a key challenge with sustainability,” the judges remarked, “aggregating dispersed data into useful parseable information for the average consumer.”
Session 2: Supporting Diverse Populations
MYND
Alyssa Guo, Calvin Lee, Wadzanai Makomva
Advisor: John Chuang, Elliott Adams
At a time where mental health crises are affecting the lives of almost 50 million Americans, the team noticed a need for a mental health resource that would proactively assesses the user's day-to-day mental health. Their product, MYND, was designed to empower users to be proactive about their mental health. Monitoring a user’s mental wellbeing, the application automatically checks in with the user to provide suitable interventions when anxiety or distress is detected.
Judges were excited about the prospect of this project, stating that “MYND addressed a major need in the mental health space and demonstrated a clear differentiator for scalability. They articulated the potential impact of keeping mental health on top of mind.”
Session 3: Algorithmic Fairness and Social Impact
DigitALL: The Data Guide for Low-Resourced Organizations
Ian Mulholland
Advisor: AnnaLee Saxenian
DigitALL compiles resources, tools, and exercises for under-supported and low-resource social impact organizations. Addressing a variety of topics such as community-centered design, security and privacy, and impact assessment, this toolkit has been tailored to support organizations and individuals of all sizes and stages of their impact journey.
Session 4: Innovations with Artificial Intelligence and Machine Learning
GNNIE: GNN-Recommender-as-a-Service
Pratik Aher, Benjamin Fell Costa, Arogya Koirala, Heidi Lin, Aayushi Sanghi, Faezeh Taghva, Michael Yang
Advisor: Daniel Aranki
The team behind GNNIE identified issues with current recommendation systems: they either suffered from popularity bias or from lack of novelty and diversity. Their project aims to remedy this by relying on Graph Neural Networks (GNN), which has shown potential in making recommendations better, faster, and explainable. As a result, GNNIE seeks to help the retail e-commerce sector leverage GNN and rich contextual information to provide better, diverse, and more personalized product recommendations.
MIMS Video Award
Egaleco: Advancing Fairness in Machine Learning, by Gurpreet Kaur Khalsa, Alan Kyle, Mudit Mangal, and Orissa Rose is the winner of the MIMS Video Award. Check out their video here.
Hal R. Varian MIDS Capstone Award, Spring 2023
curie.AI
Steve Hall, Romain Hardy, Joe Klepich, Ryan Mitchell, Jericho Villareal
curie.AI seeks to empower patients’ liver health, offering a suite of applications that assist medical practitioners with liver ultrasound navigation, organ delineation, steatosis severity grading, and liver mass detection. The platform also generates realistic, synthetic liver ultrasound images from scratch for dataset enrichment and training in order to more accurately detect liver disease.
Lily L. Chang MICS Capstone Award, Spring 2023
INHOME
Isabelle Delmas, Joseph Giulianelli, Suraj Ilavala, Andrew Robertson
Noticing an increase in insecure networked devices (IoTs) within home networks, the INHOME team utilized Zero Trust Architecture (ZTA) to make homes safer, more reliable, and available for critical needs like remote work, health monitoring, and conducting online financial transactions.
The judges were particularly impressed with INHOME’s potential reach and impact, writing “Their use of zero trust architecture is an excellent example of innovation: building on-top of work others have done in the industrial context, and looking at what it would take to bring it to the masses -- an excellent example of co-creation and collaboration.”
MICS Video Award
Synthetix, by Arushi Sardana, Jorge Arturo, Ramirez Reyna, Ian Clark, Taylor Rainey, and Danny Rittman is the winner of the MICS Video Award. Check out the video here.
For more information on previous MIDS and MICS Capstone awards, please check out our Summer 2022 winners and Fall 2022 winners.
Faculty Awards
Distinguished Faculty Award (MIMS program): John Chuang, Professor for INFO 234. Information Technology, Economics, Strategy, and Policy; INFO 290. Biosensory Computing; and INFO 290. Climate, People, and Informatics.
Distinguished Faculty Award (MIDS program): Joyce Shen, Lecturer for DATASCI 210: Capstone and D. Alex Hughes, Assistant Adjunct Professor for DATASCI 203: Statistics for Data Science and DATASCI 241: Experiments and Causal Inference.
Distinguished Faculty Award (5th Year MIDS program): Amit Bhattacharyya, Lecturer for DATASCI 207: Applied Machine Learning.
Distinguished Faculty Award (MICS program): Clarence Chio, Lecturer for CYBER 207: Applied Machine Learning for Cybersecurity.
Student Awards
Students with the Most Spirit (MIMS): Michael Yang.
Outstanding Teaching Assistant (MIMS): Rishabh Thakur, INFO 253A: Front-End Web Architecture and INFO 253B: Back-End Web Architecture.
Outstanding Teaching Assistant (MIDS): Mitchell Karchemsky, DATASCI 200: Introduction to Data Science Programming and DATASCI 255: Machine Learning Systems Engineering.
