Vedang A. Waradpande

I am a Machine Learning Engineer II at TikTok, working on the creator monetization integrity team. I have 2.5 years of industry experience working in Data Science and engineering teams at two of the top startups (Faire and Razorpay), and research experience applying and improving Machine Learning models in the domains of Natural Language Processing, Drug Discovery, and Computational Genomics.

I finished my master’s in Computer Science from Rutgers University with a Machine Learning concentration in May 2023 and my bachelor’s in Computer Science from Birla Institute of Technology and Science (BITS), Pilani (Goa campus) in May 2019.

In my industry roles, I have worked in Data Science and engineering teams in social media, fintech, and e-commerce companies. I have worked on challenging industry problems like creator spam detection, fraud detection, last-mile delivery, underwriting, and recommender systems. My work has involved development of the full end-to-end ML pipeline including data curation, feature engineering, model development, deployment, monitoring, backend development, and maintenance.

Resume

Download my resume here

• Aug 2024 – Present: Machine Learning Engineer II – TikTok
• Jun 2023 – Aug 2024: Machine Learning Researcher – FreundlichLab, New Jersey Medical School, Rutgers University
• Sep 2021 – May 2023: M.S. in Computer Science (Concentration: ML), Rutgers University
  • Jan 2022 – May 2022: Research Assistant – Dept. of Genetics, ShahLab, Rutgers University
  • May 2022 – Aug 2022: Data Science Intern – Faire
  • Nov 2022 – May 2023: Research Assistant – FreundlichLab, New Jersey Medical School, Rutgers University
• Jun 2019 – Aug 2021: Data Scientist and Software Engineer – Razorpay
• Aug 2015 – May 2019: B.E. (Hons.) in Computer Science, BITS Pilani
  • Jul 2018 – Dec 2018: Research Assistant – Bioinformatics Research Center, Nanyang Technological University

Writing

I have 2 papers under preparation, and will soon provide links to preprints here.

Publications

1. “Predicting Completeness of Unstructured Shipping Addresses Using Ensemble Models” (2021). Vedang A. Waradpande, Petchetti Vinay Surya Prakash, Nikhil Jhaveri, and Shashank Agarwal. In Proceedings of ACM SIGIR Workshop on eCommerce (SIGIR 2021 eCom). [PDF]
2. “Applications of Deep Graph Matrix Completion Models in Bioinformatics” (2018) [Undergraduate thesis] [PDF]

Posters

1. Graph Convolutional Neural Networks (2019) [PDF]

Blog posts

1. Using Machine Learning to Detect E-Commerce Fraud: Introduction (Apr 2020)
2. About k-Nearest Neighbors (Jul 2018)

Selected Projects

Following are some personal and academic projects I have pursued in the past couple of years related to Machine Learning, NLP, Computer Vision, and Software Engineering.

1. Visual Question Answering with Vision-Language Models (2023)
   [Code], [Paper]
   Adapts the GIT model by Microsoft for the Visual Question Answering task and evaluates it on the challenging AOKVQA dataset by the Allen Institute for AI. The dataset is designed to test the model’s ability to reason using diverse knowledge sources and commonsense.

2. Unsupervised Passage Retrieval for Fact-Checking and Entity Linking (2022)
   [Code], [Paper]
   Builds on the Unsupervised Passage Retrieval method by extending its formulation for Fact-Checking and Entity Linking tasks as well as evaluating it’s performance on the Question Answering datasets from the KILT benchmark by Meta.

3. IQ Tests on Pretrained Vision Transformers (2022)
   [Code], [Paper]
   Evaulates and improves the performance of the pretrained Vision Transformer and BEiT models on the Raven’s Progressive Matrices IQ test using the I-Raven dataset.

4. Gated Graph Neural Networks for Text Classification (2022)
   [Code], [Paper]
   Re-implementation of the TextING model using PyTorch for ease of use. Converts text to graphs and builds a Gated Graph Neural Network model for text classification and evaluates it on four standard datasets, closely replicating the original results.