Note to Prospective Students

Interested in working with us?

Thank you for your interest! I’m deeply committed to mentoring students and junior researchers who are curious, hardworking, and eager to learn and make an impact. But please read through this page carefully before reaching out.

Our work
I’m passionate about working on hard problems at the interface of theoretical and empirical/applied machine learning to build privacy-preserving and robust AI. We use mathematical tools from AI/ML, optimization, probability, calculus, etc. to develop novel algorithms and approaches to tackle (and ideally solve) challenging real-world problems in sensitive domains such as healthcare. Projects may be theoretical, applied, or a mix. Take a look at our recent publications.

If this excites you, here’s what typically helps students thrive in our group. A solid background in machine learning, linear algebra, probability & statistics, and calculus is enough to get started, together with a strong drive to learn any further math required. Strong coding/experimentation skills (or the drive to develop them) are also a must. Historically, prior degrees in ML/AI, CS, EE/ECE/EECS, data science/statistics, math & computing, etc. typically work well.

Further, we only have full-time in-person positions (with some flexibility around working from home). We do not have any part-time or fully remote opportunities. We support medium-to-long-term engagement (1 year or more): good research takes time and there are no shortcuts.

MS/PhD (by research)
I am looking to hire 1-2 strong and self-motivated fully-funded PhD students. Interested students need to first be admitted to the department of Data Science & AI at IITM. You can apply at https://research.iitm.ac.in. Note that students with GPA > 8 from centrally funded institutes, including IITs and NITs, are not required to take national tests (like GATE) to apply for the MS/PhD program. Those with a Master’s degree (from India or abroad) in engineering/technology (MS/MTech/ME) are also exempt, see the official handbook.

Please feel free to drop me an email after applying with your application details (CV, prior project experience, etc.). The application deadline to get admitted in July (fall semester) is in March, and the deadline for a January admission (spring semester) is around October. Please see here for more details.

Undergraduates at IITM
Your best bet is to take a course I teach and do well. I typically teach an elective on privacy in AI in addition to an undergraduate core course. I also expect a minimum commitment of 1 year to research, preferably 2 or more years: good research takes time, as does building the required technical background. Note that we have an excellent post-bacc (i.e. pre-doctoral) program for full-time research after graduation.

Post-bacc (pre-doctoral) fellowship
WSAI offers a prestigious and highly competitive post-baccalaureate fellowship for full-time research. I typically take 1-2 strong candidates each year. Post-baccs working with me have previously published in top venues (e.g. NeurIPS, ACL).

If you have the right background and are willing to commit at least 1 year (but preferably 2 years), apply here and reach out via email.

Internships
We do not have any openings for short-term engagements, sorry. Please consider applying to the post-bacc or MS/PhD programs.

What to write in an email?

Due to a high email load, I might not be able to respond individually to all queries/emails, my apologies in advance. To increase your chances of eliciting a response, please include the following in your email:

• Describe your academic interests, career goals, and what you hope to achieve from working with me.
• CV
• A short and concise research statement detailing your previous research or course projects in ML/AI, open source contributions, etc. Please write it yourself. Your authentic voice matters more than polished text and LLM-generated content becomes apparent.
• Your transcripts (previous/current degrees, unofficial ones are fine)
• Your timelines: when can you start and how long do you wish to work with me.

It is also helpful to engage deeply with my group’s research before reaching out. You can demonstrate that by sharing some insights gained from our work. (This has to be genuine. A superficial engagement or LLMs-generated content will be obvious and will actually hurt your case.)

In case you do not hear back, please feel free to follow up, but please allow at least a week or two between subsequent emails.

What is my advising style?

I’m in academia precisely for the joy and privilege of mentoring students and junior researchers.

I take my role as a mentor very seriously and invest a lot of time to train and educate my students and ensure their all-round growth, providing both technical support (like brainstorming research problems/directions or working through some proofs/debugging) and non-technical support (like career advice). The process typically involves developing lower-level skills like executing on regular experiments/proofs and improving at writing/presentations to begin with, and moving on to develop higher-level skills like identifying/forecasting timely and impactful research directions, and moving towards full independence. My objective is to nurture research leaders who can think about, develop, and lead independent research agendas.

I expect a similar serious commitment in return, with hardwork and a sincere commitment to excellence and learning. You should be self-motivated and driven, and take ownership of your projects. They will be as successful and impactful as you push them to be — I will assist, guide, and help on each step of the way but most of the effort should come from you.

You will likely be out of your comfort zone often. For example, I may let you figure issues out by yourself without giving the answer directly (assuming I know it). Similarly, I value your overall long-term growth instead, but that might lead to slower near-term progress.

In summary, I expect the overall journey to be intense, but incredibly rewarding with lots of learning.