We here at RespAI Lab strive for world-class research, leading the AI research landscape from India. To be a part of a group driven by the will and courage to take on really hard problems and solve them, we look for few qualities from a prospective member desiring to be a part of the lab:

• Paper reading - Nobody is born a talented researcher, they build it out of themselves, and it takes work. While we have regular paper presentations at the lab and you will be assigned papers to present, you are expected to read more beyond that. There are no guidelines on “what to read”, study anything which you find interesting. Good ideas are a composition of little ideas collected over a lot of papers. The more you read, the more polished your ideas are, the better you grasp the current flow of research. You can find good papers on X, arXiv, and quite a few in our general group as well. Read papers thoroughly, ask a lot of questions, question everything the authors have written, understand the math beyond the variables on an intuitive level, think out of the box. We suggest managing a Notion/ Obsidian repository where you store your personal insights about the paper, where you try to explain to yourself what the paper is really trying to tell and what the authors have potentially missed out on.
• Code - Research in computer science without knowing how to code will take you nowhere. You may have amazing ideas, but it’s worth noting that the glory goes to the one who executes an idea, not thinks about it. With the advent of LLMs, coding has gotten a lot more structured, however research code is very sensitive and you cannot rely solely on LLMs to write the entire code for you. LLMs are your coding assistant, not your coders. You should not code with prompts like - “Write this entire python file for me.” for the LLM will only provide a very generic implementation of the function with a lot of assumptions that simply won’t hold for your work. We encourage vibe coding in our lab, for we realise that’s the future, however you must be the coder in charge. Open up the documentations for the libraries you are using, manually verify each and every function, their arguments, data types etc. that the LLM generated code has provided and check if it’s consistent with the latest versions of the library. LLMs hallucinate a lot when it comes to coding with newer libraries like LangChain or HuggingFace which go through major updates very frequently, hence you must cross-verify all LLM codes with the actual latest documentations. Pro tip - Learn to code by looking at the actual implementations of functions (like AutoModelForCausalLM.from_pretrained() by looking at the actual implementations on GitHub! It takes some time, but that’s how you learn how these stuff are implemented). If you are facing some errors/ exceptions in code, more often that not, it’s Google search that’ll help you over asking ChatGPT. Go through Stack Overflow, GitHub issues related to that topic, even YouTube videos. That’s where the real solutions are. Plus, we are always great to help within the lab :)
• Consistency - Research takes a lot of patience. You’ll make a lot more connections, have a lot more job opportunities, only if you are patient and consistent with your work. RespAI Lab aims for the best conferences in the world, and you’ll be competing with the brightest minds from across the globe. Hence, it’s crucial to understand that you need to be in for delayed gratifications. Learn to accept that the work you had put your heart and soul into might get rejected at the conference, but it’s guaranteed that if you are consistently pushing out papers irrespective of the results, you will have levels of success sooner than you can imagine. All you need to do is be consistent. Reach out with new ideas, aim for frequent deadlines. The more you submit, the greater the chances of acceptance. In research, we need quality and quantity.
• Paper Writing - You may have good ideas, you may code well, but if you don’t nail the final step, all of the prior work goes in vain. Paper writing is more of an art in this field of science, and when you read enough papers which have been accepted at CORE A* conferences like NeurIPS, ICML, ICLR, AAAI, CVPR etc., you’ll observe that even the simplest of topics have been beautifully laid out and written. This is a crucial skill, and we expect you to learn it. This is not something that you can learn beforehand, this is something that you learn while in the lab. RespAI Lab has published multiple CORE A* conferences, hence you will be guided with fine-grained feedback, but you are the one who needs to take on the bulk of the responsibility. Seek feedbacks, improvise, write and repeat.

Know that it’s daunting to think how you can compete with people who have decades of research experience when compared to you, but also know that they are trying to solve the same problems you are looking at. You can think of new ideas, run a lot of experiments, code a lot, fail, improve, get better. The lab is with you to provide you with all the infrastructure (GPUs) and backup that you’d need to make impact on a global scale, the only thing we demand of you is the will to make a change and the courage to opt for it. You become a good researcher, only if you want to.