A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.



GSoC Blog 2- Gen tutorial

4 minute read


Work in Progress Update

The last few weeks have been extremely fulfilling while I learnt more about Gen, Arviz and the entire PPL ecosystem. I’m almost close to the completion of my project and the next 2 weeks I will be focusing on winding up and making any changes required to the code. Since I have been learning to use Gen for some time now I realised it would be helpful to put a tutorial out there.

Some background about Gen

Gen is a platform for probabilistic modeling and inference. Gen includes a set of built-in languages for defining probabilistic models and a standard library for defining probabilistic inference algorithms, but is designed to be extended with an open-ended set of more specialized modeling languages and inference libraries.



IPARC Challenge

The project aimed to address the IPARC Challenge, resembling ARC but with defined background knowledge. My initial attempts involved applying various search methods, such as exhaustive search, greedy search, beam search, and A* search, to Category A simple tasks. Despite experimenting with different similarity metrics for heuristics, the high search space led to poor performance. Subsequently, I used dreamcoder along with inductive logic for solving these tasks . Initially achieving an 11 out of 20 success rate, the model’s performance improved to 15 out of 20 tasks (75% success rate) after experimenting with input pairs and triplets.

Predicting ATP binding sites for protein sequences

Predicting binding sites between ATP and proteins holds significant importance in the realms of Biology and Medicine. Traditionally, extensive research in this field relied on time and resource-consuming ‘wet experiments’ conducted in laboratories. However, in recent years, there has been a shift towards leveraging computational methods, specifically employing advanced Deep Learning and Natural Language Processing (NLP) algorithms.

Code Search and code clone detection

Built a preliminary model for code search with a simple encoder-decoder architecture that computes the cosine similarity of the embeddings for searching. Additionally, fine-tuned the CodeBERT model specifically for Code Search on C/C++. Moreover, trained and fine-tuned a code clone detection model across multiple languages such as Python, Java, C/C++ for detecting plagiarized code.




CTE Instructor-Introduction and Applications of Machine Learning

Introductory course, BITS Pilani, 2021

The objective of this course was to introduce beginners to the feild of Machine Learning. We began by laying the groundwork with fundamental machine learning theories and introduced essential tools such as numpy and pandas. Subsequently, we delved into various machine learning algorithms, providing insights into the underlying mathematics. The course was taken by over 100 students.

Meta Learning CS F441

Teaching Assistant, BITS Pilani, 2022

I was the teaching assistant to Gautam Shroff, Prof. Tanmay for the graded course BITS F441: Meta Learning, conducted by BITS, IIT Delhi, and IIIT Delhi. I conducted Homeworks and Quizzes as a part of the course. I was also responsible for conducting lab sessions, grading assignments and exams, and helping students with their doubts.

Object Oriented Programming CS F213

Teaching Assistant, BITS Pilani, 2022

I was a teaching assistant to Prof. Neena Goveas for the course Object Oriented Programming (CS F213) at BITS. I was responsible for conducting lab sessions, grading assignments and exams, and helping students with their doubts. The course is a 3 credit compulsory course taken by all second year students of the Computer Science department.