🎓 Graduate student in Analytics | 🧠 Applied Machine Learning, Forecasting & Generative AI Enthusiast
📍 Passionate about building data-driven systems that are practical, explainable, and impactful.

🌐 My Website : https://tejaskhandwekar.github.io/

• 📈 Time Series Forecasting: Combining statistical and foundational models for long-horizon prediction
• 🧠 Applied ML & NLP: Building production-ready systems for demand planning, Q&A, and classification
• ✨ Generative AI: Working on LLM-powered tools for search, summarization, and automation
• 🔍 Explainability and Performance: Building ML models that are both interpretable and reliable

🥈 2nd Place – National Hackathon by Myntra & Dare2Compete
Real-time fashion trend detection system using Instagram scraping, semantic segmentation (Mask R-CNN), and attribute classification to track emerging styles from social media.

Repository with in-depth analysis and implementation of intermittent exponential smoothing (iETS) models across various datasets for robust intermittent demand forecasting.

Benchmarking time series LLMs (like TimeGPT, Moirai) against classical and statistical forecasting models. Includes replication of public models and evaluation pipelines.

A deep learning approach using CNNs to accelerate computational fluid dynamics simulations in porous media. Built using TensorFlow/Keras with custom physics-informed training.

• 🥈 2nd Place, National Hackathon by Myntra – Built a fashion trend detection system using ML and social data
• 🏅 Winner, India Inc. Award for innovation in demand forecasting
• 💬 Multilingual: English, Hindi, Marathi, German (basic)

• 🎓 Currently doing a masters in Analytics at Georgia Institute of Technology
• 📘 Advancing in Generative AI, forecasting architectures, and interpretable ML

• 🔗 LinkedIn
• 🧠 Open to collaboration, research, and internship opportunities in applied AI and Forecasting
• 📬 tejas5589@gmail.com , tkhandwekar3@gatech.edu