Daniel De Mello is a CS PhD student at Purdue with nine years of hands-on experience in core machine learning research, specializing in NLP and principled analyses of transformer architectures to interpret and control how models store factual and relational knowledge. His work bridges theory and practice across deep learning topics—adversarial generative models, VAEs, Bayesian neural nets, MCMC, self-/semi-supervised learning, knowledge graphs, and time series—reflecting a breadth that enables cross-pollination of ideas. He holds an M.Sc. in Computer Science and a B.Sc. in Computer Engineering and brings a track record of exploring both model-centric and inference-driven approaches to representation and robustness. Notably, his focus on where factual memory resides in parameter space aims to make language models more interpretable and editable, an angle that informs both experimental design and potential tooling for model debugging. Located in West Lafayette, Indiana, he combines rigorous academic training with practical implementation experience cultivated over a diverse set of ML projects.
9 years of coding experience
Ph.D. Student in Computer Science, Machine Learning, Ph.D. Student in Computer Science, Machine Learning at Purdue University
B.Sc., Computer Engineering, B.Sc., Computer Engineering at Universidade Federal do Amazonas
Master's degree, Computer Science, Master's degree, Computer Science at Universidade Federal de Minas Gerais
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