Research Field Guide
How to Get a Machine Learning Research Position
To get a Machine Learning research position, find professors who are actively publishing in Machine Learning, read what they actually work on, and email one of them a short, specific note. Much of the work is computational, so you can offer to contribute remotely.
Below are 12 professors publishing in Machine Learning right now, what each is working on, and how to reach out. Every name and topic is pulled from real, recent publication data, not a generic list.
Machine Learning professors who are actively publishing
| Professor | Institution | Recent research focus |
|---|---|---|
| David Lowe | Memorial University of Newfoundland | Neural Networks and Applications |
| Trevor Hastie | Stanford Medicine | Neural Networks and Applications |
| Yann LeCun | Courant Institute of Mathematical Sciences | Neural Networks and Applications |
| Jiawei Han | Zhengzhou University | Topic Modeling |
| Yoshua Bengio | Centre Universitaire de Mila | Neural Networks and Applications |
| Christopher D. Manning | Stanford University | Natural Language Processing Techniques |
| Douglas M. Bates | University of Wisconsin–Madison | Data Analysis with R |
| Philip S. Yu | University of Illinois Urbana-Champaign | Topic Modeling |
| Hadley Wickham | Posit Science (United States) | Data Analysis with R |
| Andrew Y. Ng | Stanford University | Topic Modeling |
| Jürgen Schmidhuber | University of Applied Sciences and Arts of Southern Switzerland | Neural Networks and Applications |
| Quoc V. Le | Ton Duc Thang University | Topic Modeling |
Sourced from OpenAlex publication records. Click a name to see their full profile and recent papers.
What Machine Learning research involves
Machine learning research builds and studies the models behind modern AI. The active areas you will see most are neural networks and deep learning, natural language processing, and the statistics and topic-modeling methods that make models work and stay interpretable. The work is overwhelmingly remote-friendly: it is code, datasets, experiments, and papers, run on shared compute. That is good news for students, because you can prove yourself with a GitHub repo or a clean reproduction of a result instead of needing to be in a specific building. Most labs care far more about whether you can implement and debug than where you sit.
How to email a Machine Learning professor
An ML professor's inbox is full of generic praise, so lead with evidence instead. Offer to contribute remotely with code: reproducing one of their results, running an ablation, or cleaning and analyzing a dataset they work with. Name your real stack (PyTorch, JAX, Python, strong linear algebra) and link a repo or project if you have one. Reference a specific recent paper, ideally on the exact subarea they work in, like NLP or a particular architecture, and ask a sharp technical question about a choice they made. Skip the word passionate. One concrete, verifiable thing you can do is worth more than a paragraph of admiration.
Machine Learning overlaps with nearby fields. If you are casting a wider net, look at research positions in Computational Biology, Bioinformatics, Cognitive Science, and Biomedical Engineering.