Ananya Mondal

I've built data analytics and machine learning systems for transportation, health, climate risk, and product decisions. At TRIPC, IIT Delhi, I engineered real-time telemetry infrastructure across 7,471 roads in 79 districts, identifying 363 traffic hotspots for Hajj 2027 planning.

I also investigated resilience across 21+ Gulf cities with TU Dresden, connecting geopolitical shocks to 2,000+ telemetry vectors using deep reinforcement learning and hierarchical clustering. For Ujjain's major religious events, I processed GPS traces to identify bottlenecks, public-transport stress, and optimized routing opportunities.

At Mealistik, I analyzed 10K+ user interaction records across clickstream, survey, and session data. My segmentation, cohort, funnel, and statistical analysis informed MVP validation, feature prioritization, and four-plus product decisions.

My projects include an LLM-to-pandas data intelligence engine with 93.8% operation-classification accuracy, flood-risk modeling with LightGBM and LSTM, a ReAct research agent, and explainable population-scale health risk stratification using 400K+ records. I work primarily with Python, SQL, machine learning, data visualization, and deployment tools.