Exploring the Depths of Statistics and Machine Learning Studies: A Discussion with Jake Snell
For undergraduate students interested in delving into the world of Statistical Machine Learning (SML) research at Princeton University, here are some strategies to consider, as shared by DataX postdoctoral researcher Jake Snell in a recent interview.
1. **Engage with the Center for Statistics and Machine Learning**
Participate in projects, attend seminars, and network with faculty and students involved in SML research at the Center for Statistics and Machine Learning. Reach out to faculty members affiliated with the center to explore potential research projects or volunteer to assist them in ongoing studies.
2. **Pursue Relevant Coursework**
Enroll in the minor in Statistics and Machine Learning to gain foundational knowledge in statistical methods and machine learning techniques. Take data science courses that incorporate machine learning and statistical analysis to build practical skills.
3. **Join Research Groups and Projects**
Look for research groups focused on machine learning and statistics at Princeton University. Many groups welcome undergraduate students to contribute to ongoing projects. Participate in summer research programs or internships that focus on machine learning and statistics to gain hands-on experience and exposure to research environments.
4. **Network and Seek Mentorship**
Attend seminars, workshops, and conferences related to SML at Princeton University to meet researchers and learn about current projects. Find a mentor who is involved in SML research to provide guidance, recommend opportunities, and help navigate the research environment.
5. **Apply for Research Positions or Assistants**
Look for research assistant positions in departments or labs focused on machine learning and statistics. Explore any available funding opportunities or grants that support undergraduate research in SML at Princeton University.
Jake Snell, who is currently serving as a lecturer for SML 310: Research Projects in Data Science at Princeton University, recommends finding ways to get plugged into the research community, such as attending conferences, joining research groups, or taking courses with a research project component. He also advises undergraduate students to find and connect with researchers whose work they find interesting.
Jake Snell's research focuses on making deep learning models more adaptive and reliable to changing environments. He develops novel deep learning algorithms by drawing insights from probabilistic models and takes insights from statistical models to create better deep models. He is also interested in using Large Language Models (LLMs) for modeling probabilistic models.
The current trends in ML research include LLMs and their ability to be queried, opening up new directions of research. Jake Snell's interview was published in "Post-Princeton Life: An Interview with Bennett McIntosh '16". His postdoc adviser is Tom Griffiths. It is important to note that the interview did not discuss any human or animal subject research or mention any Junior Paper (JP).
By following these strategies, undergraduate students can effectively engage with SML research opportunities at Princeton University and build a strong foundation for future academic or professional pursuits in the field.
- To complement traditional coursework, undertake independent work on a junior paper focusing on data-and-cloud-computing, technology, and education-and-self-development, particularly in the context of Statistical Machine Learning (SML) research.
- Dedicate time to exploring SML research topics outside of the classroom, such as Large Language Models (LLMs) and their relation to probabilistic models, as highlighted by DataX postdoctoral researcher Jake Snell.
- Seek out opportunities to gain hands-on experience in SML research by contributing to ongoing projects or participating in research groups focused on data science, application of machine learning techniques, and statistical analysis.
