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Collaboration Inquiries

Thank you for your interest in working with me! I am always open to collaborations. To work with me as a mentee, I kindly request that you take the following steps in drafting an email to me.

  1. Introduction. Please, always attach your CV or a link to your portfolio.
  2. What is an example of things you read? Please write to me about kind of problems you find interesting broadly, within mathematics and applied science. A good example is to mention one recent articles that you have studied, and discuss in one paragraphs why it is of interest to you.
  3. What is an example of things you did before? Please take one instsance of a "problem" that you had worked on before. This can be a blogpost, a substantial homework question, a course project, or a research paper. Ideally, this is something that you hold credit for, and are authorized/ready to discuss in greatest details. Next, please introduce me to this problem in input/assumptions/output format, e.g., the input is a dataset of animals, the assumption is that they are all cats/dogs, and the output is a classifier, and then discuss your approach in one paragraph.
  4. What is an example of things you would like to do (optional)? If you have a concrete problem formulation or a target literature within machine learning, causal inference, or applied sciences, I would be happy to hear your ideas. The standard practice for me is to use one concrete/numerical example to ground the idea first. Crafting this example is often the hardest component in the initial phase of a project, and it is critical since it serves as the skeleton throughout. I believe that the most imprecise example would enable our communication more effectively than the most polished discussion of generalities; I'm afraid the latter would take us further away from finding a common ground.
  5. Do you want to work on Causal AI? To ground our conversations, please take a look at The Book, read the introduction, and try to describe any problem of your interest (within broader machine learning, statistics, and applied sciences) in terms of figure 1.12. I work on machine generalizations, so I encourage interested readers to also check out Part IV of the book. The introduction to statistical generalizations (Section 11.1) is a great way to get to know the tasks that I am interested in within machine learning, and would helps us find common ground more smoothly.
  6. Read about our work. I encourage you to pick one of our papers that interests you, and try to understand the technical component of it to the best of your ability. The goal is not to master the techniques or extend that particular work, but to see the kind of tools (mathematics/engineering) we use to tackle our problems. Please include one paragraphs about the above.

Please shoot me the email with the subject line including your name. I am committed to respond to all collaboration inquiries even if they only partially meet the above. So please do send me reminders if you feel that I might've missed your message! :)