Jodh Singh Email : jetjodh@gmail.com jetjodh.com Phone: +1-8484371268 Education • Rutgers University New Brunswick, USA MS Computer Science Sept.2021–Present • Guru Gobind Singh Indraprastha University Delhi,India Bachelor of Technology; GPA: 67.9% Aug.2016–June 2020 Experience • Shiryam Technologies New Delhi, India Software Developer October 2020 - July 2021 ◦ Worked as a backend developer on various projects: : ∗ Built a completely modular and scalable CMS backend which can be used by any number of websites. This project is deployed and being used by FHM India. Optimized and reduced the image fetching times and media loading times from 500-800 ms to under 200 ms. Technology used: Flask, Python, AWS EC2, AWS SES, Elasticsearch, S3, PostgreSQL. : ∗ Worked on a complete end to end subscription based service for magazines, newspapers, and other multiple media formats. Implemented multiple features like user management, coupons, scheduled releases, subscription management, cart management, etc. Technology used: Flask, Python, AWS EC2, AWS SES, Elasticsearch, S3, PostgreSQL, RDS, Celery. : ∗ Worked on a algoritmic trading platform. Wrote code for creating and maintaining scrips for different trading algorithms and schemes. Also used boilerplate from previous projects, to create the authentication flow for all the users. Technology used: Flask, Python, AWS EC2, AWS SES, Elasticsearch, S3, PostgreSQL, RDS, Celery. : ∗ Designed the backend of a Neobanking solution for Salt. It is designed to accept and send payments across the world without any restrictions on cross-border transactions. Technology used: Python, AWS EC2, PyOTP, AWS SNS, AWS SES. : • BlueStacks Gurgaon, India R&D Intern June 2019 - Sept 2019 ◦ BlueStacks App Player : Worked on a computer vision, deep learning solution for implementation in C#, JS and in C++ using PyTorch and Tensorflow as the frameworks of choice. The project involved a recognition task involving low resolution and low contrast images and work on all the aspects of ML pipeline: data collection, preprocessing, model architecture design, training, evaluation, deployment, and integration. The project had many challenges like low memory and low latency constraints. The final accuracy of the recognition solution observed was 98.78% accuracy with the latency of the solution being 60 ms in real-world tests. The solution is now live on the BlueStacks App Player. • BlueStacks Gurgaon, India R&D Intern June 2018 - Aug 2018 ◦ BlueStacks App Player : Worked on an OCR System using CRNN and CTPN approach for scene images using Pytorch, Tensorflow, and various image processing techniques to achieve 77% overall accuracy in image to text accuracy in real-world test in languages of English, French, German, Korean, Mandarin, and Japanese. Enabled many users to be able to use applications in foreign languages. The solution is now live on the BlueStacks App Player. • Central Scientific Instruments Organisation Chandigarh, India Research Trainee Dec 2017 - Feb 2018 ◦ Autonomous Carts : Worked as a research trainee to develop a Wi-Fi enabled communication system for multiple Raspberry Pi’s to communicate with each other in close proximity for autonomous carts using Python, Socket Programming, Bash Scripting, Pandas. Research Projects • Tuberculosis Detection Using Anti-Aliasing CNNs (August 2019-December 2019) : Led a team of 3 students under the guidance of Assistant Prof. Anupam Kumar. The research was done on the ImageCLEFmed Tuberculosis dataset which is part of the CLEF initiative labs 2019 with the segmentation task being the main focus. Our work was presented in ICITETM has been published in Elseiver Procedia.(https://www.sciencedirect.com/science/article/pii/S1877050920315374) • Voice-Based Gender Identification Using qPSO Neural Network (January 2020-May 2020) : Worked on development of a baseline neural network and a Quantum behaved Particle Swarm Optimized Neural Network for classifying the voices of each subject as being that of a male or female on the basis of feature of their voice samples. Our work was presented in the proceedings of ICDAM 2020, and will be published in Springer Lecture Notes on Data Engineering and Communication Technologies series. Projects • Hateful Meme Detection : This is an ongoing project in Facebook’s Hateful Memes competition in which using multi-modal techniques the memes are being classified as hateful or non-hateful. Highest rank achieved was 1, currently at 45. • Logo Detection : In this project, logos of different brands had to be detected in a video clip. The dataset used was Openlogo dataset. The object detecion algorithm used was EfficientDet implemented in PyTorch and resultant IoU was 70.5% and label accuracy was 85%, just after 1 day of training on the dataset. • Speed Tracking using YOLO-v3 : In this project, A speed estimation program was created using vehicle detection program with YOLO-v3(using PyTorch) and a centroid tracker. Due to camera being at an oblique angle to the road, there were some mathematical calculations and estimates to be made as calibration was not possible. The solution can make predictions in real-time at 30 fps. • Reddit Post Flair Predictor : This is a ML + Data Science project. In this project,reddit post data from r/india was scraped, analyzed and cleaned. Then the data was used to train a multi input neural network which was then deployed via Heroku. • DeepFake Detection : Made while competing in the the Kaggle’s DeepFake Detection competition. An automated script extracted the frames from the videos in order to train a 2D image classifier. Then the frames where fed into a face extraction algorithm and then to a binary classifier which led to a much better accuracy than the just predicting on the whole frame. A very significant data imbalance in the training set was handled with various data augmentation methods and random sampling. • UrbanSound8k : This is a unique approach to multi label classification in the urban sound dataset. The original dataset consists of sound clips. These clips were visualized and used as image so that image classification techniques could be applied to this dataset and an inception net architecture model trained on ImageNet was retrained on the dataset using 10 fold technique. The resulting average categorical accuracy is 79.178 % on 10-fold dataset split. • Signature Verification system : Applied various DL and ML techniques to get a high precision model for verifying signatures. The final accuracy was 96.6% on real world tests. • Recommendation System : Books and Movie cross domain recommender systems using collaborative filtering on public datasets. • NLP analysis of popular novels : NLP analysis of the novel Moby Dick extracted from the Gutenberg Project website using web scraping. The aim of the project was to learning to mine data from webpages and applying NLP analysis on the text data using the NLTK library. • Creating Customer Segments : In this project, unsupervised learning techniques were applied to product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data. Data were explored using various data visualization techniques. With the good, clean data, PCA transformations were applied to the data and implemented various clustering algorithms to segment the transformed customer data. • Battle Ship Game : Made the classic battleship game in text form in C. Skills Technologies : Python, Tensorflow, Keras, PyTorch, Numpy, ONNX, C++, C, JS, Go, Swift, OpenCV, PostgreSQL, jQuery, Java, HTML, Linux, AWS Sagemaker, AWS Cloud, AWS Lambda, LibTorch, Azure Cloud, Google Cloud, JavaScript, Bash, Socket Programming, GitHub, Scikit-learn, NLTK, spaCy, Twitter APIs, SQL, Visual Studio.