Summary
Nathaniel Hobbs is an Assistant Professor of Professional Practice and PhD researcher at Rutgers with 11 years of experience applying machine learning to real-world problems across NLP, computer vision, and applied cryptography. Trained at Tsinghua (MS) and Purdue (BS) and seasoned by four years of industry work in China, he has built high-throughput backend systems, implemented large-scale NLP pipelines (Naive Bayes, HMMs, CRFs) and trained neural nets for image tasks. His research blends learning theory and practical ML—recent work explores learning from partial information while isolating general supervised learning techniques. Fluent in English and Mandarin, he contributes to open source and has a track record of interdisciplinary collaboration spanning wireless networking theory, security/obfuscation, and production ad systems.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Management Science and Information Systems, Doctor of Philosophy - PhD, Management Science and Information Systems at Rutgers Business School
Bachelor of Science (BS), Mathematics and Computer Science, Bachelor of Science (BS), Mathematics and Computer Science at Purdue University
Master of Science (MS), Computer Science, Master of Science (MS), Computer Science at Tsinghua University
English, Chinese