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AI scientist · Independent founder

Akansha
Singh Bansal.

I take AI from an idea to a working product.

I’m the founder of phirky.ai, a planning tool for Bay Area families. I own the work from problem definition and technical design through product development and launch.

My background spans machine-learning research, teaching, production AI at Bayer, and engineering leadership. Ph.D., Computer Engineering, UMass Amherst.

What I’m building My experience
Akansha Singh Bansal
San Francisco Bay Area
Research foundation
Ph.D., Computer Engineering
UMass Amherst
Industry experience
Production AI at Bayer
LLMs, agents & evaluation
Independent work
Founder, phirky.ai
Product, engineering & launch

01 / Independent work

What I’m building

phirky.ai · Founder · Since July 2026

A family day out.
With the details worked out.

Finding something to do with a toddler and a dog means checking more than whether a place looks fun. Ages, opening hours, travel, cost, and time for a nap all matter.

I built phirky.ai to bring that planning into one place: local activities, a calendar, seasonal guides, and an AI day planner for Bay Area families.

I own the product and engineering, from the underlying data to the experience a parent sees.

Explore phirky.ai

The problem I’m working on

“What can we do before nap,
with the dog?”

A useful answer depends on the local information underneath it. I’m working on both the planning experience and the data that makes it useful.

Why I started it

02 / Experience

Research, systems, and the people using them

My work has moved from smart grids and solar energy to satellite imagery, weather, and scientific AI. Across these settings, I’ve worked on the models, the software around them, and how people use the results.

Bayer Crop Science R&D

June 2023–October 2026

Data Scientist / Applied Generative AI

I built AI tools for scientists working with research data, reports, and unfamiliar codebases. The work included production delivery, model comparison, evaluation, and engineering leadership. Earlier work contributed to digital-twin research using deep learning and satellite imagery to forecast traits from spatiotemporal observations.

Scientific report generation
Developed an LLM report-generation platform through its third major release, with multiple models, semantic search, and built-in evaluation. Used in daily scientific workflows.
Natural-language data access
Built agent interfaces for querying complex R&D datasets in plain English.
Codebase intelligence
Developed tools to help researchers understand unfamiliar code and get started with existing analytics projects.
Conversational memory & team leadership
Led a small engineering team on the design and delivery of hierarchical short- and long-term memory for an organization-wide assistant.

Gro Intelligence

April–May 2023

Research Scientist

Developed machine-learning models using satellite data to identify crop masks and estimate crop yields.

Colorado State University

CIRA · NSF AI2ES · January 2022–March 2023

Machine Learning Scientist / Postdoctoral research

Developed U-Net models using GOES satellite observations to identify atmospheric convection and compared their outputs with ground observations. Explored diffusion-based foundation models for the spatial and temporal structure of satellite data, alongside research on trustworthy AI for environmental science.

Related research

Oak Ridge National Laboratory

June–August 2021

ORISE Research Intern, GeoAI Group

Developed machine-learning models for gravity anomaly mapping and explored explainable AI techniques to analyze their performance.

University of Massachusetts Amherst

2015–2021

Graduate Research Assistant, Computer Engineering

Studied data-driven control, modeling, and forecasting for residential solar power. My research included self-supervised learning on multispectral satellite data, software-defined solar systems, and the costs and carbon emissions of grid defection.

Related research

Nanyang Technological University

Singapore · 2014–2015

Research Associate, School of Computer Science

Worked on real-time operating systems and smart batteries for vehicles.

03 / Education

The academic foundation

2011–2014

M.S. by Research, Information Technology

International Institute of Information Technology Bangalore

Thesis: Health Monitoring for Smart Grid Network for Prevention Against Failures. Research on smart-grid communication, health monitoring, and network resilience.

2007–2011

B.Tech, Computer Science

Maharshi Dayanand University, Rohtak

04 / Research

Selected publications

Research & publications

My research spans trustworthy AI, learning from satellite imagery, solar forecasting, and energy systems. These are some of the questions I’ve worked on with collaborators.

  1. 2023

    Trust and Trustworthy Artificial Intelligence: A Research Agenda for AI in the Environmental Sciences

    Risk Analysis · Coauthor

  2. 2023

    Leveraging Spatiotemporal Information in Meteorological Image Sequences: From Feature Engineering to Neural Networks

    Environmental Data Science · First author

  3. 2022

    A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning

    ACM e-Energy · First author

  4. 2020

    See the Light: Modeling Solar Performance using Multispectral Satellite Data

    ACM BuildSys · Honorable mention, best presentation

05 / Beyond the project

Speaking, teaching, and service

Speaking

I’ve given invited talks at Stanford University, Lawrence Berkeley National Laboratory, PARC, Oak Ridge National Laboratory’s GeoAI Group, and UW–Madison’s Energy Analysis & Policy program.

Other presentations include the ESST Transformers Workshop, the AMS Annual Meeting, and an internal workshop on working with LLM APIs.

Teaching & mentoring

At UMass Amherst, I created and independently taught ENGIN 191, Staring at the Sun: An Introduction to Solar Energy.

I also served as a teaching assistant for graduate and undergraduate courses, completed CIRTL teaching certifications, and have mentored junior scientists and interns.

Research service

Peer reviewer for AI and energy-systems venues. Technical program committee member for FATEsys 2021.

Recognition

  • Honorable mention, best presentation
    ACM BuildSys · 2020
  • Second prize, Data Analytics Lightning Talks
    Duke Energy · 2020
  • Third prize, Three-Minute Thesis
    UMass ECE · 2021