About Me
I'm a developer with three years of industry experience in building Machine Learning models. I have a solid background in end-to-end development, with a focus on building generalizable, scalable and efficient solutions.
I take pride in my ability to innovate and think out of the box. I am an expert user of Python and I am familiar with a wide range of tools and frameworks to solve any problem at hand. For me, tools are mediums to put creative throughts into actions. I am a strong advocate of open source and I feel that sharing knowledge is the best way to contribute to the community.
I am currently pursuing my Master's in Computer Science at Arizona State University, where my goal is to upskill myself in all the subfields of AI. This includes Classical ML, Generative AI, Natural Language Processing, Computer Vision, and Robotics. Parallel to academics, I am working as a Data Scientist and Researcher at different laboratories to apply learned theory to real-world problems.
My Journey
M.S. Computer Science
@ Arizona State UniversityTopics in Generative AI •
Cloud Computing •
Data Mining •
Semantic Web Mining •
AI in Robotics •
Data systems for ML •
Data Processing at Scale•
Knowledge Representation •
- Top 5% in 6/6 courses taken
- 4.0 GPA
- Engineering Graduate Scholarship (rewarded to top 10% candidates).
- Working at Kerner Lab in Partnership with NASA: Ran 20 k+ computer-vision experiments on Mars data using ASU HPC clusters (Slurm + Python) & developing foundational models based on orbital/rover data.
- Working at Decision Theater @ ASU on analytical projects and dashboards with partner organizations
- Worked at ACME lab @ ASU towards developing efficient solutions for uncertainty estimation in deep learning models
Machine Learning Engineer
@ O9 Solutions- Productionised hybrid clustering to optimise inventory for 10 Fortune 500 clients, cutting E2E runtime by 20%.
- Delivered ARIMA-based line-speed forecasts for AB InBev, raising accuracy by 10% & plant throughput 5%.
Machine Learning Engineer
@ Head Digital Works- Won "Innovator of the Year" for card-arrangement optimisation; boosted engagement by 30%, cut fraud by 8%.
- Fast-tracked to MLE in 15 months; shipped 3 revenue-bearing features in year 1.
- Built MLService (AWS event-driven model deployment service), enabling ML-driven marketing campaigns, lifting daily active users by 10% and revenue by 0.25%.
Associate Data Scientist
@ Head Digital Works- Deployed a real-time retention model combining XGBoost & fuzzy logic, raising daily transactions 5%.
- Reshaped A23's loyalty program, segmenting 5.8M+ users through Python and advanced pivot table analytics to compute Lifetime Value (LTV) and balance cost-benefit allocation, enhancing customer retention by 15%
B.E. Electronics & Communication
@ BITS PilaniProjects
Showcasing some of my favorite work and personal projects.
Mars-Bench
Mars-Bench brings 20 standardized datasets for classification, segmentation, and detection of Martian features like craters, cones, boulders, and frost, aiming to make evaluation consistent and to catalyze Mars‑specific foundation models.
Adaptive Learning Platform
An adaptive learning system driven by a Multi-Armed Bandit controller to optimize question difficulty in real-time. The architecture uses a Streamlit frontend, a Groq API-based question generator, and JSON for persistent progress saving.
DishCovery
DishCovery is a food discovery app that analyzes restaurant menus and enables natural language query-based filtering of dishes and restaurants.
Silkot Silicones
Designed and developed a responsive website for Silkot Silicones, a leading manufacturer of silicone products. The website features a clean, modern design with a focus on usability and SEO.
CounterEcho AI
A vulnerability-based strategic counter-narrative system that analyzes social network data to generate targeted counter-narratives based on user vulnerability scores.
Earnings Call RAG
Earnings Call RAG Bot is a Retrieval-Augmented Generation system designed for analyzing and querying financial documents (Currently supports earnings call transcripts)
Candidate Recommendation System
AI-powered resume ranking system that intelligently matches candidates to job requirements.
Prompt Engineering
Best‑practice prompts and tutorials for large language models.
Machine Learning from Scratch
From-Scratch implementations of classic machine‑learning and deep learning algorithms.
Auto Storage Categorization
NLP‑powered bot that auto‑sorts Google Drive files into logical folders.
Movie Recommendation System
Hybrid recommender merging content clues, ratings, and composite ranking.
Predictive Modelling for Composites
Regression models to optimize composite manufacturing parameters.
Recent Updates
A chronological log of my professional journey and accomplishments.
- Submitted my first paper at CVPR 2026 under the guidance of Dr. Hannah Kerner (Nov 2025)
- Participated in Sunhacks 2025 to develop DishCovery, a food discovery app (Sep 2025)
- Recieved acceptance letter for my paper at NeurIPS under the Dataset & Benchmarks track (Sep 2025)
- Completed a summer 2025 internship at the ACME lab at ASU with Dr. Krishendu Chakrabarty towards developing efficient solutions for uncertainty estimation in deep learning models (Jun 2025)
- Submitted my first paper at NeurIPS 2025 under the Dataset & Benchmarks track with Dr. Hannah Kerner (May 2025)
- Completed the Spring 2025 semester with a GPA of 4.22, achieving top 5%ile in Data Intensive Systems for ML and Knowledge Representation (May 2025)
- Started working at Decision Theater @ ASU (April 2025)
- Started volunteering at the Kerner Lab @ ASU (March 2025)
Let's Connect!
Feel free to reach out for collaborations, opportunities, or just to say hello. I'm always open to connecting with fellow data enthusiasts and developers!
Alternatively, drop me an email at ihmehta@asu.edu