Weather OTG

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:

Weather On-the-Go allows users with visual impairments to build a customized and integrated weather notification system to help them plan their trips. In contrast to a design focused project, this project looked to adopt a more exploratory approach. Our primary goal was not to come up with the perfect design prototype but to further probe into our problem space and look to understand our users. This was a more research heavy project and we placed a lot more focus on coming up with the right questions that would allow us to best study our space. We looked to do this through a lot of literature review and by mainting constant touch with our interviewees through interviews.

Background

Weather plays a central role in all our lives. The conditions around us dictate what we wear, what we carry with us, and what our plans are. Its unpredictable nature means that oftentimes it is crucial for us to know about the various parameters that might affect these decisions. The challenge one faces is to study the various numerical information and extract what matters to them.

Early into our research and interviews, it became increasingly clear to us that visually impaired people face plenty of issues while checking the weather. As a result, this semester, our team looked to explore in-depth how people with visual impairments stay informed about the weather and how we can look to make the experience smoother and more personalized for them.

Some of the probing questions that our research revolved around are: ​​​​​​​

1. How are target users accessing current weather?

2. In what contexts are they checking for current weather?

3. Are they employing the use of any assistive technology or devices to access weather data?

4. Are there any differences in how they access weather data in private and public spaces?

5. Do they have to rely on auditory methods for weather data? Is it multi-modal or does it involve only one sensory stimulus?

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