Hello! I am a junior at MIT studying Computer Science and Biology, with an interest in developing machine learning methods for medicine. I am particularly interested in new geometric deep learning and generative modeling methods to enable breakthrough therapeutics for challenging diseases.

As an undergraduate researcher with the Uhler Group @ Broad Institute of MIT and Harvard, I am working on geometric deep learning methods for RNA design. I previously worked with the Coley Group @ MIT Schwarzman College of Computing on graph-based generative models for molecular design.

Feel free to reach out at divnor80 [at] mit [dot] edu!

Previously

My past experience includes:

• Models for antibody binding prediction as an AI Research Intern at Absci, published a paper at NeurIPS 2023 GenBio (Spotlight), MLSB, and AI4D3 Workshops
• Graph-based generative models for PROTAC design, published a paper at NeurIPS 2022 AI4Science Workshop
• Protein engineering tools on the BioML team at Microsoft
• High school research projects combining deep learning and medicine, won grand awards at ISEF

Skills

• Machine Learning and Deep Learning: PyTorch, Jax, LightGBM, Scikit-Learn, OpenCV, H2O, Numpy, Pandas
• Bioinformatics/Cheminformatics: Biopython, RDKit, PyMOL, DeepChem
• General Software Development: Python, Java, Git, Linux