teaching and services

Teaching and other services.

teaching

As a graduate teaching assistant in-charge of grading, I formulated the rubrics as well as performed the grading for Prof Justin Domke’s course COMPSCI 688: Probabilistic Graphical Models in Spring 2020. I also performed grading for COMPSCI 688: Research Methods in Empirical Computer Science taught by Prof. David Jensen in Spring 2019. During my final year at IIT Madras, I provided mentorship to 10 groups of sophomore students for Advanced product development Lab., performed grading for Mechanics and design of mechanisms class and presented as a guest lecturer in two classes.

mentorship

I have been fortune to have found great collaborators and mentees. I have provided mentorship to the following M.S. students through various projects and independent studies at UMass Amherst.

  1. Tejas Chheda (2021)
  2. Purujit Goyal (2021-22)
  3. Trang Tran (2021)
  4. Elliot Tower (2021)
  5. Hitesh Golchha (2022)
  6. Eunjeong Hwang (2021)
  7. Alan Sempruch (2021)
  8. Varad Pimplekhute (2023-2024)
  9. Sahil Yerawar (2024)

services

reviewing

top reviewer at NeurIPS 2023, Reviewer for ARR December 2023 (NAACL, ACL 2024), ICML 2024, Reviewer for AAAI 2025, Reviewer for ICLR 2025

A note for student collaborators

I’m in charge of collecting student information of independent study projects at IESL. If you are an undergraduate or masters student at UMass interested in doing research at IESL, please fill out this profile form and drop me an email. If you are interested in working with a specific person at IESL, please mention that in your email.

Every semester we get a lot of requests for independent study projects. We use the profile form to match students with potential mentors from the lab. Following are some helpful tips for making your application stand out.

  1. Coursework: It is good to have a minimum of two relevant graduate-level data science courses from UMass, or their equivalent from another institution. Following is the list of relevant courses:

    1. Reinforcement Learning (COMPSCI 687)

    2. Advanced Natural Language Processing (COMPSCI 685 or 690N or 690D)

    3. Optimization (COMPSCI 690OP or 651)

    4. Computer Vision (COMPSCI 670)

    5. Neural Networks (COMPSCI 682 or COMPSCI 691NR)

    6. Machine Learning (COMPSCI 689)

    7. Visual Analytics (COMPSCI 690V)

    8. Intelligent Visual Computing (COMPSCI 674 or 690IV)

    9. Algorithms for Data Science (COMPSCI 514)

    10. Information Retrieval (COMPSCI 646)

  2. Projects and relevant experience: While filling the profile form, we encourage students to talk about their relevant experience. You are encouraged to be as specific and technical as possible while describing your research experience and interests. Vague descriptions meant for non-technical audiences may leave an unfavorable impression. In the description of the relevant experience, we are looking for several key characteristics. Following are some examples.

    1. Whether the student has demonstrated that they take initiative when required
    2. Whether they can step up to take complete ownership of a project if required
    3. How does the experience relate to the research done at IESL?

A strong project experience will demonstrate contribution to several critical steps of a project’s journey like identifying a problem, identifying suitable data and modeling approach through a literature survey, and presenting the results to the stakeholders through a paper, report, or presentation.

Note that we don’t necessarily care about the outcome of the projects in terms of impact, but we care more about what relevant skills you may have learned through your previous projects.