I am a senior scientist in the Security Expert Analytics and Learning (SEAL) team of Splunk. Before recently joining Splunk, I spent 3 years in
AT&T Data Science and AI Research (erstwhile part of AT&T Bell Labs).
Throughout my career, I have worked on data-driven solutions that pushed the boundaries for a variety of challenging and cross-product problems.
My current focus is on trustworthy machine learning in the wild
: not only proposing novel solutions to technical problems
that ensure qualities such as fairness, transparency, privacy, and robustness, but also implementing them in real-world use cases.
I deeply believe in the power of data to bring about positive changes in the world, and have collaborated with stakeholder organizations such as
UNICEF Office of Innovation and Chicago Department of Public Health
to devise solutions towards achieving these changes.
Recently, I co-founded the Trustworthy ML Initiative as an effort to build a community and facilitating dialogue
among researchers and practitioners in this field.
Before joining AT&T, I was a Postdoctoral Researcher at University of Florida Informatics Institute.
I received my PhD in Statistics
from The University of Minnesota Twin Cities.
- Trustworthy ML
- Privacy and security
- Statistical Machine Learning
- Natural Language Processing
- Computational chemistry
- Applied statistics