Jodh Singh

Currently shaking things up as an ML Engineer @ picture.studio. MS in CS from Rutgers. Former intern @ Thrasio and Bluestacks. Always on the hunt for the next big challenge.

Research Interests

Stable Diffusion: harnessing chaos in latent space
LoRA Training
2nd Order Optimizers
CUDA Optimization: Wrestling with GPUs ^^

Work Experience

AI @ pictrue.studio, NYC (Present)

The ai guy in a startup, pushing the envelope with everything Stable Diffusion. Technologies: Stable Diffusion, Python, TS, Svelte, PyTorch, CUDA, GCP, Docker, Kubernetes.

Intern @ Thrasio (June 2021 - September 2021)

Crafted backend magic as an SWE Intern on the Product Catalog team. Technologies: Django, Python, GraphQL, AWS, React, TypeScript, Kubernetes, Docker, PostgreSQL, Celery.

Product Catalog Service: Exposed existing PC service APIs over GraphQL and set up comprehensive unit tests.
Bulk SKU Update Service: Built a robust backend service over GraphQL, integrated with S3 for file uploads, and processed SKUs across multiple tables. Enhanced the UI with React and TypeScript.
Barcode Verification Flow: Developed a service to verify barcode data for SKUs, with a sleek GraphQL query to check verification statuses. Led the design of the entire flow, table definitions, and POC.

Software Developer @ Shiryam Technologies (Oct 2020 - July 2021)

Backend architect at a dynamic startup. Mentored interns, streamlined processes, and delivered high-impact solutions. Technologies: Flask, Python, AWS EC2, AWS SES, Elasticsearch, S3, PostgreSQL, RDS, Celery.

Modular CMS Backend: Engineered a scalable CMS backend, slashing image fetching and media loading times from 500-800 ms to under 200 ms.
Subscription Management Service: Built a comprehensive service for managing subscriptions, user interactions, and transactions.
Algorithmic Trading Backend: Developed modular components for a trading platform, crafted efficient DB mechanisms, and standardized user authentication flows.
Neobank Backend: Designed the backend for a neobanking solution to facilitate unrestricted global transactions.

Intern @ BlueStacks (June 2019 - September 2019)

Delved into the depths of R&D to create a deep learning solution for C, JS, and C++. Tackled low-res, low-contrast image recognition with PyTorch and TensorFlow. Achieved 99.78% accuracy with 60 ms latency in real-world tests.

Intern @ BlueStacks (June 2018 - August 2018)

Developed an OCR system using CRNN and CTPN for scene images, achieving 77% accuracy in real-world tests. Enabled multilingual application use. The solution is now live on the BlueStacks App Player.

Research Trainee @ CSIR-CSIO (December 2017 - February 2018)

Pioneered a Wi-Fi enabled communication system for Raspberry Pi's. Ensured seamless interaction between autonomous carts using Python, Socket Programming, Bash Scripting, Pandas, and SQL.

Projects

Speed Tracking using YOLO-v3

Engineered a speed estimation program using YOLO-v3 (PyTorch) and a centroid tracker. Real-time predictions at 30 fps.

Logo Detection

Mastered logo detection in video clips using the Openlogo dataset and EfficientDet (PyTorch). Achieved 70.5% IoU and 85% label accuracy in a single day of training.

Reddit Post Flair Predictor

Analyzed and cleaned r/india post data. Trained a multi-input neural network to predict post flair, deployed via Heroku.

DeepFake Detection

Competed in Kaggle's Deep Fake Detection challenge. Extracted frames with ffmpeg, identified faces with RetinaFace, and classified them using EfficientNet models.

Cross-Domain Recommendation System

Developed cross-domain recommender systems for books and movies using collaborative filtering on public datasets.

Urbansound8k Classification

Pioneered an image classification approach to urban sound dataset, achieving a 79.2% average accuracy.

Signature Verification System

Created a high-precision model for verifying signatures, boasting 96.6% accuracy on the test set using advanced DL and ML techniques.