Q00. I am an international student and I want to apply to your PhD program. Are you taking students?

We get 5+ emails of this type per week through the fall. As a once international student, I understand the anxiety of being on the other side. First, these emails are not effective unless you’ve read the faculty’s papers and have something intelligent to say. So, don’t bother wasting your time. Second, let me explain a common issue with PhD admissions for international students. Typically, CS programs tend to fund their PhD students through the length of their program (5 years). This means faculty tend to be risk averse. Having sat on PhD admissions committees, most faculty find it challenging to assess the background of an international student because they don’t often know your school or your advisor. It’s also difficult to gauge whether your grades are highly competitive or not. As a result, most committees pass on international students unless they have an *obviously* strong application. If you’re serious about research and getting a PhD, and don’t have a strong research background (i.e. published papers in top conferences and strong recommendation letters), apply to the masters program. Very often, we take our strong masters students as research assistants after a semester or two. This gives you a chance to build credibility. And, very often, you can recover the cost of your masters through industry internships which pay quite a bit. Also, at a place like Hopkins, there are many faculty outside of computer science that are looking for strong programmers for a research project. That funding can tide you over until you find a lab. But, in the long run, it’s more fruitful to apply to a strong masters program with the goal of switching to a good PhD program rather than going to a PhD program with a poor fit.

Q0. I’m primarily interested in machine learning. But, I’m unsure of the application area. Do I need to have determined this ahead of time?

No. There are a number of faculty including myself that work on machine learning problems applicable to multiple domains. Look through ML@JHU. Also, look through application areas at Human Language Center of Excellence , and IDIES .

Q1. I’m a student at Hopkins and I’m interested in working with you. How can I get involved?

Please take a look at my papers. If you still remain interested, please send me an email. It’s often also helpful to speak with the students in the research group to get a flavor of the problems you could get involved in.

Q2. I’m not at Hopkins currently. Can I apply to your lab for a PhD?

Yes, we are looking for creative and brilliant students to join us. However, you must formally apply to the PhD program for me to be able to consider you. It might be helpful to read through this site on how to put together a strong graduate school application. To gain a better understanding of the types of problems I work on, please read my papers. I soon plan to put up an active projects page but if you’re interested, feel free to send my students or I a note.

Q3. I’m an undergraduate and I am looking for internship opportunities. Can I visit your lab?

Yes, we started a new internship program called the Summer Research Expeditions (SRE) in 2013. The program brings together faculty from multiple departments in engineering and is a great opportunity to gain exposure to multidisciplinary applications of computing.

Q4. I’m looking for postdoctoral or research scientist positions. Are there positions in your lab?

We are always looking for great people to join our group. There is flexibility in terms of the projects you can get involved with. Please send me a copy of your CV if you’d like to learn more.

Q5. I’m interested in machine learning and your work but I have never worked in medicine/biology/healthcare. Do I need a medical background to work on healthcare projects?

No. In my own work, we’ve made significant progress from bringing in a fresh machine learning perspective to existing problems in healthcare. See my recent article to get a flavor of the kinds of interesting computational problems that machine learning researchers can help solve in healthcare. You can learn most of what you need to know about the domain through your readings and interactions with your collaborators. Our healthcare expenses are upwards of 2.5 trillion dollars and we’re in desperate need of better approaches for improving outcomes and lowering cost. Our health system produces vasts amount of messy and heterogeneous data that we need smarter modelers to be looking at and gleaning insights from.

Q6. Why Hopkins?

If you’re interested in solving difficult computational problems in healthcare, Hopkins is one of the best places to join. We have more than two dozen faculty across Computer Science, Statistics, and Biomedical Engineering who are studying novel ways to improve medicine and healthcare using computational techniques. See Institute for Computational Medicine , Lab for Computational Sensing and Robotics , inHealth, ML@JHU and Institute for Data Intensive Science and Engineering for related work by faculty.