Ninad Dixit

EEE & Data Analytics @ NTU | NPI Product Engineering @ AMD | Panasonic/HPE/Temus/Shopee

Singapore, Singapore

About

As a passionate student in NTU, an unwavering dedication to the field of AI/ML has resided in me in the last 5 years, rising from the wonder and awe of emerging technologies. I approach this journey through the tool of learning, one step at a time, not just from knowledge, but from experiences. All the way from high school to University, I have been an enthusiastic participant to various events in this journey. Being part of multiple clubs has improved my socialising and teamwork skills, and has helped me develop meaningful connections. I hope to improve my skills in these relevant fields to help harness the power of emerging technologies to make a positive social impact.

Experience

  • Computer Vision Engineer at Chalmers University of Technology
    May 2026 - Present · 2 mos

  • Head of Technology and Research at Machine Learning and Data Analytics Lab at NTU EEE
    Mar 2025 - May 2026 · 1 yr 3 mos

  • NPI Product Engineer Intern at AMD
    Jul 2025 - Dec 2025 · 6 mos

    Crafted a Quantitative Model and a Neural Network approach to predict the test time of various chips based on chip designs and test program architectures to improve tester usage efficiency by 70%. • Explored a CNN to read Automated Testing Equipment (ATE) outputs and classify failure signatures into device or software issues and effectively automate engineering judgement. • Implemented optimized test programs to effectively test various IP Blocks of new chips on Automated Testing Equipment (ATE) using Object Oriented Programming and Data Structures.

  • Data Analyst Intern at Shopee
    May 2025 - Jul 2025 · 3 mos

    - Developed a Graph Neural Network based on Spatio-Temporal Architectures to forecast incoming delivery numbers in SPX warehouses regardless of delivery path changes with an accuracy of 70%, significantly over performing baselines. - Worked on implementing and optimising operational algorithms to improve courier productivity and speed. - Analysed the need for Feeder Vehicles to reduce courier back and forth and optimised the best possible timing and location of such feeders.

  • AI/ML Intern at Temus
    Aug 2024 - Jan 2025 · 6 mos

    Developed AI-powered solutions to improve carbon credit evaluation of industrial equipment and outlets as part of a Carbon Finance Project. • Implemented AWS S3 Sensors and Directed Acyclic Graphs in Apache Airflow to optimize functionality of Data Pipelines in the Data Engineering Team and enhanced storage efficiency by 333%. • Contributing to a research paper on cutting edge Evolution of Heuristics LLM Technology by leveraging Claude, Gemini and OpenAI LLMs for Temasek Foundation.