This page contains very important information for (prospective) students trying to contact me (Daniel Hsu).
If you're already on campus, feel free to come talk with me during office hours.
I try my best to keep up with e-mail, but sometimes that is not enough. So on this page, I have provided some answers to questions I frequently receive. I will likely not reply to questions already answered on this page; I apologize in advance.
Applying to Columbia
- I am interested in applying to a computer science degree program at Columbia. Should I do so and what are my chances of getting in?
- Please see the CS department's webpage on admissions. There, you'll find information about all aspects of the application process (e.g., deadlines, your chances of being admitted), as well as information about the academic programs themselves.
- Some frequently asked questions, already answered for your convenience: M.S. Program Application FAQ, Ph.D. Application FAQ.
- You may also be interested in academic opportunities through the Data Science Institute, as well as the NSF IGERT program "From Data to Solutions".
- I work on research in algorithmic statistics and machine learning. This work involves algorithmic design and mathematical analysis, as motivated by problems in (applied) machine learning and statistics. I am looking to recruit highly-motivated PhD students with strong backgrounds in algorithms, probability, and statistics to work with me on these research topics. If you are such a student, please mention this in your application; this is the best way to draw my (and others') attention to your file.
- Unfortunately, I do not generally have time to reply to inquiries from prospective students.
- Do you have any internship positions available?
- I do not have internship positions available.
- Can I do a postdoc with you?
Curricular Practical Training (CPT) for Columbia students
- I would like to get your approval for CPT. Can you sign my forms?
- First, please read the CS department's webpage on CPT.
- If I am your faculty advisor, and you are seeking approval for CPT, submit a Fieldwork application form, along with the official employment offer letter and a short proposal, to me over e-mail. In particular, the employer must have signed off on the "Fieldwork Instructions". If everything is in order, then I will approve (over e-mail).
- What should I write in the CPT report? When is it due?
- You must write and submit a five-page report at the end of the internship, describing your work and what you learned in the process.
- The report is due two weeks after the end of the internship.
- Your internship supervisors MUST approve the report before it is submitted to me. Use the coversheet provided on the CS department's webpage on CPT.
- It is up to you to obtain approval from your supervisors before the report is due.
- My supervisors have not approved my report because they are on vacation (or otherwise unavailable). Is it okay to submit the report anyway?
- I will submit the report late because my supervisor cannot approve the report until after the deadline. Is this okay?
- The report will be considered late.
Research opportunities for Columbia students
- Can I join your lab as a research assistant?
- I don't have a "lab" in a traditional sense; I advise a group of students who work on research in algorithmic statistics and machine learning. This work involves algorithmic design and mathematical analysis, as motivated by problems in (applied) machine learning and statistics.
- If you are a current (or already admitted) Columbia student and are interested in discussing a project with me, please feel free to get in touch with me (via e-mail or office hours). Please send/bring (i) an up-to-date CV, (ii) a transcript highlighting your background in algorithms, probability, statistics, and learning theory, and (iii) a brief description of your research interests and the types of problems you are interested in working on.
- If you do not have at least an advanced undergraduate background in algorithms and probability, I will likely ask you first to acquire this background knowledge before working with me.
- I am interested in applying (deep) machine learning to application \(X\). Can I work with you on this?
- I would suggest you find a faculty member who is an expert in \(X\) to serve as your primary advisor on this research. I would be happy to facilitate this search. Depending on the application and alignment with my research interests, I may also be interested in collaborating.