# Hello! Do you have a question?

This page contains very important information for students (including prospective students) trying to contact me (Daniel Hsu).

For many more answers to frequently asked questions, see the FAQ page maintained by the CS department.

1. I would like to declare Computer Science as my major. Can you sign my major declaration form?
• Congratulations on this decision! Yes, I can sign your major declaration form. Please come to my office hours (see above) to get the form signed.
2. I would like to do study abroad, or take courses at another institution. Will these courses count towards the CS major?
• You will have to find the instructor of a comparable course offered at Columbia, and ask them if the course at the other institution is an acceptable substitute.
• Also see this entry in the FAQ.
3. I would like to choose the Combination track for the CS major. How do I do this?
• You should write a proposal for your desired combination of CS and another academic discipline, using the format given in this document.
• The proposal should be submitted by September 30th in your 3rd year (after having declared the major) to your CS faculty advisor.
• The proposal will be reviewed by a committee shortly after it is submitted.
• The committee will recommend either to approve or reject the proposal; I will generally follow the committee’s recommendation.

## Applying to Columbia

1. 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?
• 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.
2. Do you have any internship positions available?
• I do not have internship positions available.
3. Can I do a research visit with you for $$n$$ months? My company/government/self will cover all of my expenses; I will not require a stipend.
• Due to time constraints, I cannot commit to hosting external students or visitors whom I’ve never met or worked with. If you would like to apply to a degree program at Columbia, see above.
4. Can I do a postdoc with you?

## Advising for Columbia students in MS programs

1. I am a student in the Computer Science MS program interested in machine learning. What classes should I take? Am I on track to graduate?
2. I am a student in a CVN degree program interested in machine learning. Are you my adviser and what classes should I take?
• I am not involved with the CVN programs.
• I do not know if/when courses will be offered through CVN.
3. Do I have sufficient mathematical and computational background to take a machine learning course?

## Curricular Practical Training (CPT) for Columbia students

1. I would like to get your approval for CPT. Can you sign my forms?
• 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).
2. What is the CPT report I have to submit?
• 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.
• Use this Google Docs template for the report.
3. When is the report due?
• It is due two weeks after the last day of your internship.
4. How do I submit the report?
• Your internship supervisor(s) (who is/are listed on the “Fieldwork Instructions for Employers” form you submitted) 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.
• After you have submitted the form, e-mail me alerting me of this fact. (Do not attach the report/coversheet on the e-mail.)
5. How will the report be graded?
• If you follow instructions and make an effort to write a high quality report, you will receive full credit.
6. My supervisors have not approved my report because they are on vacation (or otherwise unavailable). Is it okay to submit the report anyway?
• No.
7. I will submit the report late because my supervisors cannot approve the report until after the deadline. Is this okay?
• The report will be considered late.

## Research opportunities for Columbia students

1. Can I join your lab as a research assistant?
• 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.
• Some examples of projects I’ve done with students at Columbia: lexical representations, analysis of Expectation Maximization, correspondence retrieval, Markov chain choice models, orthogonal tensor decomposition, single-index models.
• 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. (Some statistics background also helps.)
2. 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.