Hi, I’m Hunter 👋

I’m a machine learning engineer with experience in both AI research and practical applications. Over the past 5+ years in deep learning, I’ve focused on natural language processing, time-series forecasting, and scientific AI—particularly exploring how machine learning can help with problems in chemistry and materials science.

Education

  • M.S. Computer Science - Harvard University
  • B.S. Computer Science - Drexel University

Selected Publications

“Page Stream Segmentation with LLMs: Challenges and Applications in Insurance Document Automation”
Hunter Heidenreich, Ratish Dalvi, Nikhil Verma, Yosheb Getachew
31st International Conference on Computational Linguistics: Industry Track (COLING ‘25)
📄 Paper

“The earth is flat and the sun is not a star: The susceptibility of GPT-2 to universal adversarial triggers”
Hunter Scott Heidenreich, Jake Ryland Williams
Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES ‘21)
📄 Paper

“Latent semantic network induction in the context of linked example senses”
Hunter Heidenreich, Jake Williams
5th Workshop on Noisy User-generated Text (W-NUT 2019)
📄 Paper

These publications represent my research across different AI areas—from insurance document processing and AI safety to building knowledge graphs from noisy text data. My work explores making AI systems more robust, interpretable, and practically useful.

For a complete list of publications including preprints and working papers, see my Research page.

Experience & Expertise

My work in machine learning has taken me from academic research to practical applications, where I enjoy exploring what’s possible with AI.

🤖 Machine Learning & AI

Over the past 5+ years, I’ve built and deployed deep learning algorithms in academic research settings, with 4+ years exploring NLP through semi-supervised learning and large language models. More recently, I’ve spent 2+ years developing models for scientific simulation and time-series forecasting, plus 1+ year working with Graph Neural Networks for chemistry and materials applications.

💻 Technical Skills

  • Core Languages: Python, SQL, C/C++
  • ML Frameworks: PyTorch, TensorFlow, Hugging Face
  • Specializations: NLP, Generative Modeling, Time-Series Forecasting, Transformers, RNNs, GNNs
  • Application Domains: Scientific Computing, Molecular Dynamics, Materials Science

What I Write About

On this blog, I share thoughts and insights about:

  • Deep learning architectures and techniques
  • Natural language processing advances and applications
  • Generative AI and its practical uses
  • Time-series forecasting methods
  • Scientific computing with AI in chemistry and materials science
  • ML engineering practices

Let’s Connect

I’m always interested in discussing ML research, exploring collaboration opportunities, or chatting about developments in AI. Whether you’re a fellow researcher, industry practitioner, or just curious about machine learning—feel free to reach out!