RnD Projects Archive

Name	Professor In-charge	Project Topic	Research Area	Prerequisites
Rupesh	Suyash Awate	Neural Network Single image super resolution	Deep Learning	Basic Deep learning
Devansh Garg	Kameswari Chebrolu	Analysis of political data on WhatsApp	Natural Language Processing
Vijaykrishna G	Suyash Awate	Gaussian Processes, MCMC Sampling and applications in classification	Statistics, Sampling	Good knowledge of CS215 material should be sufficient and some enthusiasm to pick up new things along the way
Drumil Trivedi	Preethi Jyothi	Unsupervised disfluency removal in text	Deep Learning, Natural Language Processing	DL ,coding in Pytorch, rest prof provides. A good intro to NLP/ASR is also beneficial
Yash Khemchandani	Sharat Chandran	Pose Classify and Regress – A novel approach to 3D human pose estimation	Computer Vision, Deep Learning	Basic Deep Learning and Computer Vision knowledge required beforehand.
Pranay Reddy S	Ajit Rajwade	Compressed sensing for video wave removal	Machine Learning, Lasso Regression, Image Processing	Although I had knowledge in compressed sensing, it was not required. The professor supplies the necessary content to learn.
Syamantak Kumar	Suyash Awate	Stain Normalisation and Cell Segmentation of Microscopic cell images	Machine Learning	Basic DIP/MIC knowledge required. Papers etc. provided by prof
Manas Shukla	Kameswari Chebrolu	BodhiTree devlopement using React	Web development(frontend)	Basics of tools used in web development like HTML, CSS, Javascript and strong grasp on React framework.
Nitish Joshi	Preethi Jyothi	Accent adaptation for speech recognition	Speech Recognition, Deep Learning
Sahil Shah	Arun Jain	Robustness in human pose estimation	Computer Vision, deep learning	Basic CV and ml
Deep Tejas Karkhanis	Shivaram Kalyanakrishnan	Policy Iteration for POMDPs	Reinforcement Learning (Planning)	Basic Machine Learning knowledge.
Abhro	Bernard Menezes	Analysing SubSieve Algorithm to break Lattice Cryptography	Cryptography, Cybersecurity	Courses which helped me : Introduction to Modern Cryptography (CS 406), Numerical Analysis (MA 214). Read papers based on Sieving algorithms and an improved SubSieve Algorithm, provided an understanding and proof of mathematical complexity of the algorithm.
Ishan Tarunesh	Preethi Jyothi	Language Models for Morphologically Rich Languages	Natural Language Processing
Shreyas Pimpalgaonkar	Pushpak Bhattacharya	Emotion Analysis	Natural Language Processing, Deep Learning
Riya Baviskar	Purushottam Kulkarni	GPU enabled Serverless Software Architecture	Systems
Yash Shah	Preethi Jyothi	Alternate loss functions for language modeling	Natural language processing, Deep Learning	Prof recommended papers/blogs to read. I had not done any formal ML course before starting it.
Onkar Deshpande	Krishna S Narayanan	Reachability Verification of k-CAS programs	Formal Verification, Theoretical CS	Logic for CS is a course prerequisite. Basic understanding of Formal methods and complexity classes
Kumar Saunack	Suyash Awate	Malaria detection using segmentation	Deep Learning	Neural networks for images
Aman Bansal	Manoj Prabhakaran	Extending the Foundations of Differential Privacy	Cryptography, Statistics	Necessary material was provided by the professor.
Rupesh	Umesh Bellur	Auto configuration of Spark	Machine Learning, Docker	Machine Learning, Docker
Shreyas Pimpalgaonkar	RK Shyamasundar	Blockchain Anomalies	Blockchain
Riya Baviskar	Pushpak Bhattacharyya	Knowledge Graph for Product Manuals	Natural Language Processing	Basic Natural Language Processing
Yash Shah	Arjun Jain	Human motion forecasting	Computer vision, Deep learning	I directly jumped into the domain without any prior knowledge. Prof suggested papers to read. I guess prior research experience (R&D-1 and seminar, although in a different field) proved useful.
Aman Bansal	Amitabha Sanyal	Garbage Collection using Finite Domain Liveness	Programming Languages	Basic Functional Programming