Software Engineer at SiMa.ai
M.Eng. in Computer Science at Cornell Tech, B.S. in Computer Science at BITS Pilani
News! Paper accepted for publication at the Visual Computer! Paper entitled A Paced Multi-Stage Block-Wise Approach for Object Detection in Thermal Images
News! Accepted for research thesis at Harvard VCG from Jan 2022
Find Out MorePursuing M.Eng. in Computer Science at Cornell Tech. Current GPA: 4.0
B.E. in Computer Science at BITS Pilani. GPA: 8.89
Research in Deep Learning (Object Detection, Video Action Recognition, Image Super-Resolution)
Python, Java
GoLang
PyTorch, TensorFlow, Cloud Technologies (AWS)
Scikit-Learn, Pandas, Numpy
Flask, Docker
DSA, OOP, DBMS, OS, Computer Networks
Machine Learning Engineering, Cryptogaphy, Data Science
Digital Image Processing, IoT, Probability & Statistics
Neural Networks & Fuzzy Logic (TA)
Jan 2022
Investigated the correlation of data quantity and accuracy in the field of Image Super-Resolution. Streamlined the process by achieving similar performance while using 6.5 times less data, in an interpretable manner by calculating image complexities. Ran PyTorch based Deep Learning models using Slurm. Guided by Prof. Hanspeter Pfister of Harvard’s Visual Computing Group.
Dec 2020
Innovating novel architectures using concepts like Attention for improving performance for Human Activity Recognition in Videos. Concurrently working on a multi-modal input methodology for martial arts recognition. Working under the supervision of Dr. Kamlesh Tiwari of BITS Pilani's CS Department, Dr. Hari Pandey of Edge Hill University, UK and Santosh Yadav, Ph.D. Scholar at CSIR-CEERI, Pilani.
August 2020
Currently working with Dr.Jennifer Ranjani J of BITS Pilani's CS Department to analyze and formulate innovative frameworks for Object Detection (specifically pedestrian) in Thermal Images for applications such as security, surveillance and self-driving cars especially in night-time conditions. Concurrrently working under the supervision of Dr.Poonam Goyal and PhD Scholar Divya Bhardwaj on a comprehensive survey on object detection in thermal images. Paper accepted for publication at the Visual Computer!
May 2021 to July 2021
Worked on the PS Smart Hire product, a Resume Parsing and automatic Candidate Screening solution. My focus was to implement new strategies to improve overall parsing capabilities. My team, working under the Agile method, devised new discrete functionalities such as using Object Detection and OCR to capture text, and NLP models like BERT to extricate relevant information. To tackle scalability concerns for large-scale use cases with over 10,000 resumes, we used an ML-ops pipeline composed of AWS services like Lambda, EC2, S3, and SQS. I designed a classifier that attained over 96% accuracy for the given resumes.
June 2020 to August 2020
Worked on Infor's Vendor Rating System for analyzing vendors in the healthcare domain. My task was to develop Supervised and Unsupervised ML based solutions for measuring vendor performance. I focused on several aspects of the pipeline including engineering appropriate features for better classification, selecting and fitting relevant models, deployment using Docker and easy-to-use UI with Flask. Attained benchmarks for over 30 vendors on the basis of 1000's of transactions and produced a ready-to-use rating system for future use.
January 2022 to June 2022
May 2021 to July 2021
June 2020 to August 2020
You can also find my personal email, LinkedIn and GitHub below too.
