Mudit Jindal Email & Phone Number

Evanston, Illinois, United States

• Enhanced scheduling efficiency by 25% by applying Linear Programming (LP) and Genetic Algorithms, combined with advanced predictive models such as Decision Trees, Logistic Regression, XGBoost, and Random Forests, to accurately forecast patient attendance and optimize appointment schedules.• Streamlined care planning and data collection by implementing an AI copilot system utilizing large language models (LLMs), which reduced manual effort and saved time. Additionally, developed LLM agent systems for market discovery and healthcare providers, employing Retrieval-Augmented Generation (RAG) to enhance service delivery.• Engineered highly reliable summarization systems using LangChain and LangGraph, designed to maintain integrity in summaries by incorporating checks throughout the summarization process to prevent hallucinations and information loss.

Chicago, Illinois, United States

• Enhanced clinical documentation accuracy by developing a large language model (LLM) system using Llama 3 and Mistral for automated note summarization, significantly reducing error rates.• Achieved a 98% accuracy rate in Protected Health Information (PHI) data anonymization by leveraging Regular Expressions (Regex), Named Entity Recognition (NER), and the RoBERTa model, ensuring stringent privacy compliance and improving data handling efficiencies.• Optimized data processing workflows by integrating Optical Character Recognition (OCR) and advanced anonymization techniques, boosting operational efficiency and reinforcing robust privacy compliance.

Evanston, Illinois, United States

• Engineered a Reinforcement Learning (RL) agent to streamline the scanning processes of X-ray fluorescence (XRF) scanners, significantly reducing data capture time. This approach leverages a Q-Learning algorithm, inspired by gaming models, to enhance the efficiency and precision of XRF mapping in cultural heritage conservation.• Pioneered a self-supervised denoising method for X-Ray Fluorescence (XRF) data from cultural heritage paintings, utilizing pixel-downsampling and a novel re-visible loss strategy to minimize information loss commonly associated with blindspot-driven denoising techniques.

• Developed object detection and image segmentation models achieving 95% accuracy for precise fish dimension analysis, significantly enhancing measurement reliability in aquaculture.• Built a machine learning model that accurately predicted fish biomass with an error margin of less than 5%, optimizing feeding strategies and growth monitoring.• Led a team to integrate 3D depth mapping using ZED cameras into our analysis pipeline, substantially increasing the accuracy of volume estimation for better resource management.• Conducted a detailed analysis and visualization of factors leading to a 37% student dropout rate in Colombia, providing crucial insights that informed strategies to mitigate educational attrition.• Engineered a career recommendation system utilizing Random Forest, Logistic Regression, and MLP Neural Network models, effectively lowering dropout rates by aligning students with suitable courses based on predictive analytics.

Chennai, Tamil Nadu, India

Chennai, Tamil Nadu, India

Master'S In Ai, Artificial Intelligence

Bachelor Of Technology - Btech, Computer Science

Education record