Hi! I'm Koyena Pal đź‘‹

I am a second year Ph.D. student in the Interpretable AI and DATA Lab at Northeastern University in Boston, MA. Here, I am fortunate to be co-advised by Prof. David Bau and Prof. Renée Miller. 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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 Recent News


Student Researcher Providence, RI

Sept 2019 - Sept 2022

  • Advisor: Prof. Carsten Eickhoff
  • AI + Healthcare: Developed a patient-centric literature summarization mechanism by implementing NLP models on biomedical texts.
  • AI + HCI: Conducted user-study on the effect of multi-document summarizations on User Search Results Page (SERP) experience.

Information Security Intern Cambridge, MA

Jan 2022 - May 2022 (Part-time)

  • Built and integrated Machine Learning components into a threat-intelligence dashboard.

Information Security Intern Cambridge, MA

May 2020 - August 2020 (Full-time)

  • Designed and deployed UI features to achieve consistent language interpretation in Technical Risk Illuminator, a tool built to support executive decisions.
  • Conceptualized and modeled an NLP-based multiple-tag generator to identify key terms and types of incidents from cyber-incident reports.

Information Security Intern Cambridge, MA

May 2019 - August 2019 (Full-time)

  • Optimized the yearly audits process duration by building a systems register, which generates answers that business units require to show that they are PCI Compliant.
  • Revamped the PCI Gap Analysis Template to streamline the process of products entering the PCI Compliance program for the first time.

Information Security Analyst Intern Providence, RI

May 2018 - July 2018

  • Substantially improved copyright ticketing process automation through network expansion in support of internal IP address, MAC address, and public IP username identification.
  • Delivered code improvements through the establishment of quality assurance (QA) environment in DeskPro (REST API), enabling daily running of specific tickets by agents.

Research Intern Singapore

April 2016 - Jan 2017


[1] Koyena Pal, Jiuding Sun, Andrew Yuan, Byron C. Wallace, and David Bau. "Future Lens: Anticipating Subsequent Tokens from a Single Hidden State." SIGNLL Conference on Computational Natural Language Learning (CoNLL) (2023).
[Paper]   [Website]
[2] Meyer, C., Adkins, D., Pal, K., Galici, R., Garcia-Agundez, A., & Eickhoff , C. (2023). Neural text generation in regulatory medical writing. Frontiers in Pharmacology, 14. https://doi.org/10.3389/fphar.2023.1086913
[3] 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.
[Paper]   [Presentation]

Teaching Experience

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.

CSCI 7150: Deep Learning

Fall 2022**

Course Description:
Introduces deep learning, including the statistical learning framework, empirical risk minimization, loss function selection, fully connected layers, convolutional layers, pooling layers, batch normalization, multilayer perceptrons, convolutional neural networks, autoencoders, U-nets, residual networks, gradient descent, stochastic gradient descent, backpropagation, autograd, visualization of neural network features, robustness and adversarial examples, interpretability, continual learning, and applications in computer vision and natural language processing. With Prof. David Bau.

CSCI 1470/2470: Deep Learning

Fall 2020*

Course Description:
This course intends to give students a practical understanding of deep learning as applied in these and other areas. It also teaches the Tensorflow programming language for the expression of deep leaning algorithms. With Prof. Daniel Ritchie.

  • Interviewed, hired, trained, and coordinated staff of 35 undergraduate and graduate TAs.
  • Developed course materials, managed course logistics, led weekly labs, graded student work and held office hours.

CSCI 1010: Theory of Computation

Fall 2019

Course Description:
The course introduces basic models of computation including languages, finite-state automata and Turing machines. Proves fundamental limits on computation (incomputability, the halting problem). Provides the tools to compare the hardness of computational problems (reductions). Introduces computational complexity classes (P, NP, PSPACE and others). With Prof. Lorenzo De Stefani.

CSCI 0220: Introduction to Discrete Structures and Probability

Spring 2018

Course Description:
Seeks to place on solid foundations the most common structures of computer science, to illustrate proof techniques, to provide the background for an introductory course in computational theory, and to introduce basic concepts of probability theory. Introduces Boolean algebras, logic, set theory, elements of algebraic structures, graph theory, combinatorics, and probability. With Prof. Caroline J Klivans.

CSCI 0170: Computer Science - An Integrated Introduction

Fall 2018

Course Description:
CSCI0170/0180 is an introductory sequence that helps students begin to develop the skills, knowledge, and confidence to solve computational problems elegantly, correctly, efficiently, and with ease. The sequence is unique in teaching both the functional and imperative programming paradigms--- the first through the languages Scheme and ML in CSCI0170; the second through Java in CSCI0180. All of the following fundamental computer science techniques are integrated into the course material: algorithms, data structures, analysis, problem solving, abstract reasoning, and collaboration. With Prof. Philip Klein.

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.


Ph.D in Computer Science Boston, MA

Sept 2022 - Present

  • Empirical Research Methods for Human Computer Interaction
  • Special Topics in Data Science
  • Seminar in Human-Computer Interaction
  • Seminar in Database Systems
  • Readings

M.Sc and B.Sc in Computer Science (Honors) Providence, RI

Sept 2017 - May 2022

Master Thesis:
Summarization and Generation of Discharge Summary Medical Reports

Undergraduate Thesis:
The Effect of Multi-Document Summarizations on User SERP Experience

Graduate-Level Coursework:

  • Computer Systems Security
  • Topics in Software Security
  • Privacy-Conscious Computer Systems
  • Advanced Topics in Deep Learning

Undergraduate Coursework:

  • Computer Science: An Integrated Introduction
  • Introduction to Engineering
  • Honors Calculus
  • Principle of Economics
  • Discrete Structures and Probability
  • Cybersecurity and International Relations
  • Linear Algebra
  • Statistical Inference
  • Introduction to Computer Systems
  • Theory of Computation
  • Introduction to Software Engineering
  • Machine Learning
  • Financial Accounting
  • Intermediate Microeconomics
  • Deep Learning
  • Design and Analysis of Algorithms
  • Software Security Exploitation
  • Distributed Computer Systems
  • Logic for Systems (Formal Methods and Verification)
  • Entrepreneurial Process
  • Social Pyschology
  • Introduction to Video Game Studies
  • CS for Social Change
  • User Interface and User Experience
  • Computer Graphics
  • Computer Vision

International Baccalaureate (IB) Diploma Singapore

July 2015 - May 2017

Higher-Level Coursework:

  • Physics
  • Chemistry
  • Math

Standard-Level Coursework:

  • Economics
  • English Literature
  • French Ab Initio

 Photo Gallery

Daebak Kpop Dance Group at Brown

Fun photo taken with my dance crewmates in the dance group that I was part of for all 4 years of my undergraduate program. Click any of the following links below to see some of our dance covers:


Sometimes, I play the piano during my free time. I professionally learned and completed all levels of Piano ABRSM certification. Feel free to click the following link to see a song I covered for fun!

Summer Love, One Direction

Shallow Forgetters

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.

Akamai Internship 2019

A classic photo of an intern in front of her company logo.

Akamai Intern Picture

A group picture of all the 2019 interns at Akamai after a phenomenal BBQ dinner with the CEO.

Akamai Internship 2020

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.

Brown Master's Graduation 2022

Family picture in post-COVID pandemic in-person graduation for my Master's degree.

Lab Retreat Spring 2023

Bau@NEU and Torralba@MIT lab meetup for a mini-conference/retreat at Cape Cod.