👋 Hello! I'm Sam, a Full Stack Data Scientist with a background in Chemical Engineering, currently pursuing an M.Sc. in Polymer Science at Freie Universität Berlin. I'm passionate about solving complex problems at the intersection of data science, chemical physics and engineering.

• 🔬 Full Stack Data Scientist with experience in NLP, medical entity extraction, and patient-study profile matching
• 🎓 Currently pursuing M.Sc. in Polymer Science at FU Berlin
• 👨‍🏫 Regular corporate trainer in Generative AI, Causal Discovery and Inference, Linear Algebra, and Machine Learning
• 💻 Proficient in Python, with experience in Pandas, NumPy, Langchain, and FastAPI
• 🧮 Strong background in mathematical modeling of physical systems and mathematical optimization
• 🤖 Experience with Large Language Models (RAG and Agent)
• 🌍 Multilingual: Fluent in English, Hindi, Malayalam; Proficient in German, Tamil, and Telugu; still dabbling with French and Spanish!

I'm currently working on my Master's thesis that combine my expertise in data science with my studies in Polymer Science:

• 🧪 Molecular Dyanmics study of PFAS interaction with candidate PEI variants for water purification
• 🔍 Searching dominant molecular interactions for PFAS removal
• 🔗 Integrating knowledge from computational chemistry & physics with data science to explore molecular interactions
• 📊 Utilizing data-driven approaches to accelerate materials discovery and optimization

• Python (Pandas, NumPy, Langchain, FastAPI)
• Mathematical modeling and optimization
• Large Language Models (RAG and Agent)
• Natural Language Processing
• Azure DevOps and AWS
• Data reconciliation and process optimization
• Image processing and deep learning

Here are some projects I've worked on earlier in the industry:

1. Medical Entity Extraction

   • NLP project for extracting medical entities from clinical texts

2. Patient-Study Profile Matching

   • AI-powered system to match patient profiles with suitable clinical studies

3. Water Network Management Optimization

   • Large-scale integer optimization for efficient water network scheduling

4. Blast Furnace Data Reconciliation

   • Data reconciliation project for improving blast furnace efficiency

• 💼 LinkedIn: linkedin.com/in/sammathewai
• 🐙 GitHub: github.com/samiit

• 📚 I'm deeply interested in causal inference and its applications in data science. Judea Pearl's "The Book of Why" has been a significant influence on my thinking in this area.
• 🧠 I love exploring the intersection of machine learning, causal inference, and materials science.
• 📖 My reading interests span history, philosophy, technology, and scientific advancements.
• 🧗 In my free time, you can find me hiking or cycling.
• 🌍 I've lived and studied in India, Germany, and the Netherlands.
• 🧬 I'm fascinated by the potential of combining materials science with machine learning and causal inference to solve real-world problems.

Feel free to explore my repositories and don't hesitate to reach out if you'd like to collaborate on a project, discuss the exciting world of polymer science and machine learning, or explore the depths of causal inference!