Pavan Chhatpar

I am a graduate student at Northeastern University. I'm working towards my Master's degree in Computer Science specializing in Data Science and Artificial Intelligence.

Currently, I'm walking into my last semester of grad school. I did Data Science internships at Honeywell and Wayfair while enrolled in grad school, where I contributed to projects using NLP and Survival Analysis using Deep Learning.

Outside of internships and curriculum, I have a keen interest in research topics related to NLP and Deep Learning. I try to explain concepts to myself by coding the methods proposed in various research papers.


  • Python
  • C++
  • C
  • Java
  • Julia
  • TensorFlow
  • PyTorch
  • sklearn
  • XGBoost
  • Spark
  • Airflow
  • Hive
  • Vertica
  • MongoDB
  • MySQL
  • MS SQL
  • HTML
  • CSS
  • JavaScript
  • TypeScript
  • Angular
  • Node.js
  • PHP

Areas of Interest


I dive into various NLP techniques, and focus on Natural Language Generation. I have experience using transfer learning for fine-tuning task specific deep neural nets.

Machine Learning

ML is way more than being able to use packages. I code the algorithms to understand its nuances and how the packages get to an efficient implementation. I find that the math becomes easier along the way of implementing it.

Competitive Coding

Every now and then I find myself spending hours improving efficiency of my code and its always a fun exercise. The end result of seeing the execution time reduce by a very high rate brings a feeling of satisfaction.


I try to incorporate, for dev and prod environments, containerization tools like Docker, conda, and venv just so that I don't have to scream "But it worked on my laptop!" in the end.


In the absence of a discourse marker, splitting a sentence at the point of discourse is tricky and such discourse based splitting is quite useful in many NLP tasks. I fine tuned ELECTRA with an appropriate head using transformers library to achieve 91.8% test accuracy in 2 epochs.

Used SQuAD 1.1 to train a seq2seq model that employs copy mechanism to generate questions given a pair of context and answer. All code for the model architecture was written using TensorFlow 2.2. Published copynet-tf which can be trained for any seq2seq task that would benefit from copy mechanism. Questions generated could predict answers with 18% lesser F1 score compared to original questions.

An interdisciplinary research work where ML was used to automate the referral decision of endodontic cases, which was deployed as a mobile app to use at a busy Nair Dental Hospital, Mumbai. Published in Clinical Oral Investigations, Aug. 2019

Explored the generalizability of an attention-driven GAN model by trying latent space interpolations and understanding the role of the latent vector. The model was also tightly dependent on a particular sentence syntax. It was for an individual project work of CS 7180

Employed various ensemble model techniques to make a tri-class predictor of traffic density in a given location at a given time using over a half a year of crawled data. It was my team's final year undergrad project which was published in ICSCET, an IEEE conference and IJRASET in 2018

A playground repository for various ML algorithms which I implemented as I was learning about them. They may not be optimized like sklearn but it laid a very strong base about my understanding of how things work under the hood.

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