Buzzpool

Duration

4 Months
January - May 2022

Team Members

2 CGHE Leaders
Lead UX Designer (me) 
Lead UX Researcher
2 Developers
Project Manager

Skills Learned

Literature Review
User Interviews
Design Space Critique
Survey
Interview
Affinity Mapping
Brainstorming strategies
Prototyping
Heuristic & Think Aloud Evaluation

Overview

Encouraging users to have a deep understanding of their data through automation, hierarchial exploration and rich data visualizations

As the volume of data increases exponentially everyday, about 50-80% of a data scientists time is spent on making sense out of large datasets. Despite the slowly increasing trend in automating different parts of the DS lifecyle, and increasing usage patterns of autoML tools such as VertexAI and AzureML, data exploration continues to be a painstakingly time consuming and manual process. My user research has shown the various ways in which data scientists handle these tasks when faced with time pressure, some of which can be detrimental towards the quality of models generated.

By integrating automation in the workflow of a data scientist in a controlled manner, Plato aims to assist and accelerate their knowledge discovery process, while making sure the human remains in the drivers seat. Keeping in mind the curiousity and creativity that is necessary for this exploration, the focus of Plato is to steer away from complete automation and look to augment human capabilities through data visualization and pattern discovery.



Bridging the gap

“...auto insight tools informed by user studies in specific domains is scarce. Without significant understanding of users the new applications identified may be divorced from real world needs.”

Stasko & Endert, Characterizing Automated Data Insights, 2020

Primarily focusing on redesigning the data exploration module of an existing autoML tool, the goal of this project is to leverage user-centered design to augment human capability in a controllable and trustable manner. Through findings from 10 semi-structured user interviews and literature, following are the research areas I am exploring through my design:

Background

Carpooling is one of the more well-known solutions for reducing individual carbon footprints and ensuring a better tomorrow. Yet, it is not very popular in current times. We decided to dive into this topic to find if we could do something substantial with carpooling in GT. When we found out that GT already had a carpool system in place, we endeavored to find out why it was not more visible. We identified the potential stakeholders with the carpool system and went out and conducted interviews with some of them such as PTS (Parking & Transportation Services), GT students, and faculty. This helped us envisage the problem space from multiple perspectives.

The goal of Buzzpool is to serve as an application that encourages and incentivizes GT students to carpool while commuting to campus in order to reduce their carbon footprint and contribute towards a more sustainable solution.

Duration

4 Months
August - December 2021

Team Members

We worked in a team of 4:
1. Sejal Sarkar
2. Kshitij Gupta
3. Linda Lee
4. David Lacy

Role

UX Designer (Lead)
UX Researcher
(Phases 1 to 2)

Skills Learned

Literature Review
User Interviews
Design Space Critique
Survey
Interview
Affinity Mapping
Brainstorming strategies
Prototyping
Heuristic & Think Aloud Evaluation

Duration

4 Months
August - December 2021

Team Members

We worked in a team of 4:
1. Sejal Sarkar
2. Kshitij Gupta
3. Linda Lee
4. David Lacy

Role

UX Designer (Lead)
UX Researcher
(Phases 1 to 2)

Skills Learned

Literature Review
User Interviews
Design Space Critique
Survey
Interview
Affinity Mapping
Brainstorming strategies
Prototyping
Heuristic & Think Aloud Evaluation

Duration

1 year
June '23-'24

Team Members

1. Kshitij Gupta (me)
2. Senior data scientist (mentor)
3. Backend Developer

Role

UX Designer
UX Researcher
Frontend Developer

Skills Learned

Literature Review
User Interviews
Data Viz
Exploratory Data Analysis
Boosting user trust in AI 
Designing for humans-in-the-loop