Each year, I aim to recruit a select number of PhD students (0-2), master’s students (0-2), and a few full-time research assistants. While we do offer flexibility with remote work arrangements, it’s important to note that we do not hire fully-remote members. Engaging with our group and fostering collaboration is a core part of the research experience, which is difficult to achieve in a fully remote setting. Unfortunately, we are not offering internships at this time.
Given the volume of emails I receive, I spend a substantial amount of time reviewing and responding to inquiries—time that could otherwise be spent advancing my research, working with my collaborators, or mentoring my students. To ensure your email stands out and has the best chance of receiving a response, I encourage you to adhere to the following guidelines.
Emails that are most likely to receive a response:
Follow the application guidelines provided below – This shows that you have taken the time to understand the process and that you are serious about your interest.
Engage thoughtfully with relevant research topics – I appreciate inquiries that go beyond general statements like, “I found your recent paper ‘X’ interesting.” Instead, I value emails that demonstrate depth of understanding and alignment with my research areas. For example, if you mention one of my papers, please highlight specific concepts or contributions in optimization or machine learning theory that intrigued you, and explain how they align with your academic interests or past research experience.
I strongly value intellectual curiosity, a deep engagement with research, and a clear sense of direction in prospective students. By providing a well-informed and thoughtful email, you not only show respect for my time but also demonstrate the qualities that are essential for succeeding in our group.
I look forward to learning more about your interest in optimization and machine learning theory, and I appreciate your understanding as we strive to make the best use of our time and resources.
Please see the following FAQs:
Our group primarily works at the intersection of statistics, applied mathematics, and machine learning theory. We are particularly focused on understanding the fundamental principles that govern the behavior of machine learning algorithms, with a strong emphasis on optimization techniques.
Currently, my research interests lie in the theoretical analysis of stochastic gradient descent (SGD) and gradient descent (GD) as applied to neural networks. We aim to uncover insights into how these optimization methods drive the learning process and contribute to the success of deep learning models. Additionally, we explore sampling from arbitrary distributions, a problem with broad implications in machine learning, including in areas like Monte Carlo methods and Bayesian inference.
To get a clearer idea of the scope and depth of our research, I encourage you to visit my research and publications pages, where you can explore our latest work and ongoing projects.
Given the theoretical nature of the research we pursue, I expect prospective candidates to possess a solid foundation in statistics, applied mathematics, and/or computer science. Successful candidates should demonstrate strong analytical skills, a passion for abstract thinking, and the ability to approach complex problems from first principles. Proficiency in mathematical tools such as statistics and probability theory, linear algebra, and optimization is essential, as is familiarity with the computational frameworks commonly used in machine learning. A background in these areas will enable you to engage meaningfully with the research questions we address and contribute to advancing the theoretical understanding of machine learning algorithms. If you believe you have the qualifications to contribute to our work, you should belong to one of the following groups:
PhD & M.Tech (by research) programs: To join our research group, you must first secure formal acceptance at IISc. If you believe you would be a good fit, I encourage you to apply to the programs offered by the Department of Computer Science and Automation (CSA) and to specify your interest in this department in your application.
You can check the institute-wide application deadline on the IISc website. For any additional questions regarding the admissions process, please refer to the information provided there.
It’s important to note that students from centrally funded institutes, including IITs and NITs, are not required to take national tests (such as GATE) to apply for the PhD program. This policy represents a significant step towards simplifying the admissions process.
I strongly encourage interested candidates to reach out to me via email prior to the admission cycle. This will provide an opportunity for us to discuss our research interests and styles, fostering a better mutual understanding.
Research Assistants (RAs)/Pre-docs: Directly reach out with the following information.
Interns: There are no available internship positions.
I invite you to address me by my first name; there is no need for formal titles. In your email, I would appreciate it if you could provide a brief overview of your background, skills, aspirations, and research interests. This context will help me understand your unique profile and how it aligns with our work.
Please ensure to include the following important information in your email:
Position Details: Clearly specify the position you are applying for, along with your anticipated start date and the desired duration of your engagement. This will assist in aligning your application with our current openings and timelines.
Supporting Documents: Attach your CV and transcripts. Unofficial copies are perfectly acceptable, as they will still provide the necessary insight into your academic achievements and experiences.
Research Engagement: It is essential to mention which of my papers you have read and what insights or understandings you gained from them. This demonstrates your engagement with my research and helps me gauge your intellectual curiosity and alignment with our group’s focus.
Additionally, I encourage you to include any relevant writing or code samples, such as links to your published papers, articles, or GitHub projects. This supplementary material can provide valuable context regarding your capabilities and contributions, showcasing your potential as a member of our team.
I look forward to reviewing your application and learning more about your interests and background!