Curriculum vitae

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Education

• Ph.D in Computer Science, University of Illinois Chicago, 2027 (expected)
• B.Tech in Computer Science, Manipal University Jaipur (2019)

Work experience

• August 2021 - Present: PhD Student
  • University of Illinois at Chicago
  • Location: Chicago
  • Duties included: Partially studied masters here, and transferred to the PhD program. Research projects include: Solving combinatorial multi-armed bandits, exploring theoretical reinforcement learning
  • Supervisor: Aadirupa Saha
• May 2023 - August 2023: Data Science Intern
  • Dell
  • Location: Austin, Texas
  • Duties included: Used ChatGPT and prompt engineering to summarize customer support conversations. Speeding up customer support escalations, by allowing senior customer support to only read the summarized version of the conversation
  • Supervisor: Will Wilson
• May 2022 - August 2022: Data Science Intern
  • Dell
  • Location: Austin, Texas
  • Duties included: Created an embedding based search to find the best answers to customer questions. This was designed to give answers to the customers using dell’s expansive knowledge base. This project was deployed on the live dell support website
  • Supervisor: Ashutosh Singh
• November 2019 - March 2021: Deep Learning Engineer
  • Sensight Labs (Friendly Brands now)
  • Location: Bangalore
  • Duties included: Created an intelligent shopping cart to make checkout queues obsolete. Developed software to identify grocery products through computer vision. Projects include: Detecting when products are put into a cart, Webscraping for Grocery App, Automated data analysis, Colour based item selection, Created a Visual image Text-based search engine, Created a cropping tool to segment videos to generate still image databases
  • Supervisor: Prashant Maurice
• August 2019 - November 2019: Machine Learning Engineer
  • IceCream Labs
  • Location: Bangalore
  • Duties included: Created a model that can identify brands of products from only the front image with support for zero-shot learning, an accuracy of 97% on 250 brands, and no training requirement. Supported zero-shot learning was infinitely extendible
  • Supervisor: Bharat
• January 2019 - April 2019: Analytics Intern
  • Spinnaker Analytics
  • Location: Mumbai
  • Duties included: Worked in predictive analytics. Implemented numerous machine learning and deep learning models to provide actionable insights to the clientele. Created a website and time-series forecasting to predict Boston Children’s Museum’s footfall. Created a model capable of identifying whether a customer would return a product before placing an order
  • Supervisor: Ashwin Meshram
• June 2018 - August 2018: Machine Learning Intern
  • IceCream Labs
  • Location: Bangalore
  • Duties included: Worked on CNNs, using Keras and OpenCV, to cluster visually similar products for e-commerce recommendation engines. Create pipelines to extract tabular nutrition data from images of nutrition tables of groceries. Developed a synthetic data generation pipeline. Projects include: Visual Search, Fashion Design Overlay, Variable Multiple Instance Learning, Nutrition Table Mapping
  • Supervisor: Prashant Maurice
• June 2017 - August 2017: Automation Intern
  • CSS Corp (Movate)
  • Location: Chennai
  • Duties included: Developed tools in python with the automation team that employees used to boost efficiency by automating manual tasks. Implemented tools like VLookup, through python. Added plugins for open source tools like NVDA
  • Supervisor: Kiran Marri

Skills

• Convex Optimization
• Theoretical Machine Learning
• Pytorch
• Reinforcement Learning

Publications

A deep reinforcement learning technique for bug detection in video games

Rani, G., Pandey, U., Wagde, A.A. et al. A deep reinforcement learning technique for bug detection in video games. Int. j. inf. tecnol. 15, 355–367 (2023). https://doi.org/10.1007/s41870-022-01047-z