I am a first year Ph.D. student in the Interpretable AI group at Northeastern University in Boston, MA. Here, I am fortunate to be advised by Prof. David Bau. Prior to Northeastern, I received my BS (Honors) and MS in Computer Science from Brown University in Rhode Island, USA. During my time at Brown, I researched at AI Lab for Biomedical Informatics, with my advisor as Prof. Carsten Eickhoff.
I have also worked as an InfoSec intern at Brown's Network Security department (2018) and Akamai Technologies (2019, 2020 and Spring 2022). There, I worked on applying state-of-the art AI and NLP research to automate and optimize security processes such as PCI policy compliance fulfillment, risks and vulnerability management and more. You can view more in my CV here.
If you are interested in research or would like to break into the field of Computer Science, feel free to reach out to me through
LinkedIn or through email
! I would be happy to help you however I can.
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Sept 2019 - Present
Jan 2022 - May 2022 (Part-time)
May 2020 - August 2020 (Full-time)
May 2019 - August 2019 (Full-time)
May 2018 - July 2018
April 2016 - Jan 2017
|||K. Pal, S. Adepu and J. Goh, “Effectiveness of Association Rules Mining for Invariants Generation
in Cyber-Physical Systems,” 2017 IEEE 18th International Symposium on High Assurance Systems
Engineering (HASE), 2017, pp. 124-127, doi: 10.1109/HASE.2017.21.
I have served as a teaching assistant for the following courses at Northeastern and Brown University. Semesters marked with double asterisks (**) denote a Graduate Teaching Assistant role that I have undertaken at Northeastern. Semesters marked with an asterisk (*) denote a Head Teaching Assistant role that I performed during my time at Brown.
In addition to the teaching committee, I have been active in organizations such as Women In Computer Science where I have served as a mentor to younger women pursuing CS at Brown. Outside Brown, I have been an instructor at Inspirit AI (Summer 2020, Winter 2021), an outreach program to teach AI to high school students worldwide.
Sept 2022 - Present
Sept 2017 - May 2022
The Effect of Multi-Document Summarizations on User SERP Experience
July 2015 - May 2017
As part of our Deep Learning Final project, my friends and I re-created the mechanism to detect phishing using URLs. Here is a picture of us presenting our implementation.
A picture of my friend and I taken by one of the Hack@Brown team members as we waited for our code to finish running.
A classic photo of an intern in front of her company logo.
A group picture of all the 2019 interns at Akamai after a phenomenal BBQ dinner with the CEO.
Due to COVID-19, our 2020 internship was held remotely. Here is the picture of us ,i.e., the InfoSec interns, meeting virtually in one of our many weekly meetups.
Family picture in post-COVID pandemic in-person graduation for my Master's degree.