Ascending the Research Trail
This is my third blog on Research Experience, check out the previous blogs: Why Research if I can Develop?! and Riding the Noisy Research Track.
Hey there, long time no see! Long hiatus, I know. I have been busy unifying image segmentation, traveling, applying to grad schools, binging web series, reading, and spending time with COCO (mostly my pet dog, the dataset, a little, haha). No worries, I am back (at least for this one :P).
In case you missed it, I shared my experience as a rookie undergrad researcher in a previous blog: Riding the Noisy Research Track. Since then, I have grown from a rookie researcher into a more mature beginner researcher with a better outlook on the bigger picture in research (thinking beyond publishing a paper) owing to my close collaboration with the SHI Labs in the last couple of years. I have had several realizations that I will be sharing through this article. A few of those to get your interest piqued are:
- 🤔 I went from "Ph.D., Me? Nooooooo" to "I primarily applied to Ph.D. programs for grad school." What changed?
- 💭 Is publishing/releasing a paper a good "Why?" for research?
- 📖 Is open-source in A.I. a boon or a bane?
- 🎗 What is the importance of the advisor and collaborators?
- ⚓️ How vital is the authenticity of research?
I faced a lot of confusion and uncertainty while deciding whether to continue with the research. Having chosen research as a career, I hope to provide apt reasoning and factors that could contribute to your decision on your career choice. Right then, it's blog time!
Why did I decide to apply to Ph.D. programs?
If you had asked me a year earlier whether I would go for a Ph.D. afterward, I would have replied, "Nooooo." What changed in a year that I applied mainly to Ph.D. programs for graduate school?
Working on OneFormer made me realize that research is more than solving problems. It's about solving impactful problems. In other words, looking at the bigger picture before starting a project. This realization led me to ask the following questions:
Q1 How to identify impactful problems?
The answer to the above question relates to developing the maturity and foresightedness to reasonably, correctly, and independently imagine the likely impact of solving a problem. Gaining experience through practice is the best way to do it. One of the goals of a Ph.D. is to develop the sort of vision required to lead one's research and create impact. I also emphasize that if someone is new to a field, it is hard to have an impactful work straightway. Having a few mediocre works in an area to collect the ground knowledge and then break the ground with impactful work feels great.
Q2 What one problem should I choose out of all the impactful options?
The answer to this question contributed the most to my decision to apply for Ph.D. programs. I have spent much of my undergrad working on image segmentation (2D vision task). However, do I want to continue researching in the same area or switch fields? Honestly, I do not know 🤷♂. A Ph.D. provides me with the perfect opportunity to freely (I am not entirely a paid employee, although I still get some financial support through RAships) explore various directions and then fixate on a single direction at the end of my first two years. I like that promise of freedom 🤗.
Q3 My work is identified with me.
Working on three first-author papers at SHI Labs taught me that I am the only one responsible for my project. My work is identified with my name. I like that recognition. It happens in any scientist's job where the work is publically released, to be honest. Everything I do during a Ph.D. is mine.
Q4 What if I do not like a Ph.D. degree?
Talking to Ph.D. students in my network, I learned that most programs allow Ph.D. students to leave with a Master's degree after the starting two years of their program. So, I can always do that if something doesn't work out. It's better than going for an M.Sc. first and then for a Ph.D. anyway for me.
The answers to the above questions helped me decide to apply for Ph.D. programs. Now, I am still waiting to hear back about all my applications. Let's see how it turns out 🤞. I also recommend reading Karpathy's blog on surviving a Ph.D.
The Purpose (why?) of my research
It's a good practice to ask "Why?" before doing anything. One only gets a convincing answer sometimes. "Why are professors still teaching us outdated content in most courses?" Who knows, it may be laziness on their part to update the content. "Why do girls have to make an entry every time they leave their hostels at IITR?" Guards following senseless orders 🤷♂.
When researching, one must be convinced about the purpose of investing months, if not years, towards a single goal. I started my first research project (in the exploration mode) with the end goal of releasing a paper. I did not care much about its impact and utility at the time. I was happy with a paper to my name, to be honest. However, after gaining more experience, I realized papers are not everything one needs in A.I. research.
The core principle behind a research project is to push the corresponding field forward. Releasing a paper is lovely. However, research is not only publishing papers, and only publishing papers is not research. I think a single paper with 100 citations and 500 stars on GitHub is better on most occasions than ten papers with 10–20 stars and a few citations. Quality matters much more than quantity in the longer run. As a beginner, it is to work toward completing a paper. Publications are still important (reason).
At the end of the day, the researcher in me wants people to acknowledge my research efforts and contributions. I have experienced that a single impactful work brings more acknowledgment (and a sense of fulfillment) than two or three papers with limited impact. So, why research? To create impact and drive the field forward. Also, check out some awesome tips on research by Dr. Jia Bin Huang.
Note that as a beginner, it's a good idea to work on completing a research paper. In the longer run, focus on the impact, though.
The boon of Open-Source in A.I.
With the spread of the open-source culture in the A.I. community, measuring the quality of work has become more accessible. If a stranger on Twitter shares your work, in most cases, it is a fine piece of research. Recently, I worked on integrating OneFormer into the 🤗 Hugging Face transformers library. I was highly impressed by their vision to democratize the field of A.I. With their various libraries (transformers, diffusers, accelerate, etc.), it is getting easier for anyone to use ML models.
Apart from publishing a paper on arXiv, it is an excellent idea to open-source the codebase on GitHub, release a demo (if applicable) for your model and integrate it into any of Hugging Face's libraries. I practiced this practice with OneFormer recently, and it is effective in obtaining some extra attention from the community and makes it easier to use. An open-sourced GitHub repo is now a necessity in the age of open-source. Also, pay attention to the organization of the README.md file. It's important. A good open-sourced project is also more straightforward for others to use, understand and improve upon, pushing the field forward. Unless for commercial reasons, open-source is the way to do research. Although, one also needs to release any warnings for caution attached to the public model (biases, failures, etc.).
The Importance of Advisors and Collaborators
A good research advisor is arguably the most critical contributing factor to the outlook development of a junior researcher toward research. The advisor's values set a lab's culture. Is publishing papers (anywhere) the end goal or producing quality, impactful works? Is paper rejection the worst thing that can happen? Is independent research encouraged? What are the perks of a good presentation of ideas? Are the other lab members collaborative or just thirsty for credit and papers? Do your lab mates respect you as a researcher? These are a few questions one can ask about their research environment.
The Dependable Advisor
Fortunately, at SHI Labs, Prof. Humphrey Shi practices fair values for research where the end goal is to create an impact with our research and not just publish papers at any conference. In other words, the quality of our work should be such that it has a chance at the top conferences (CVPR, ICCV, NeurIPS, etc.). That's an excellent way to leverage deadlines to produce high-quality work. One of his tweets expresses that idea perfectly.
🥇 Impact is Primary, 🥈 Publication is Secondary
The Awesome Autonomy
Another thing that I value the most when doing research is autonomy. I like to think about what to do first and then take feedback from other people on my ideas instead of just following orders. The ability to think and implement solutions on one's own helps develop independence which is critical when researching. The vision of the research lab is also an important contributing factor to guiding your research path. You can do all the work yourself, but without a vision, the impact will only be limited.
Research continues beyond the thinking and implementing stage. Presenting the observations clearly and comprehensively is necessary to create an impact. I haven't always been the best at delivering my knowledge and ideas in writing. I like others to suggest improvements in my big-stake papers or presentations. I always have one of my friends read my blogs before I publish these. Fortunately, I obtained excellent presentation suggestions while working on all my research projects.
The Charming Collaborators
Research in A.I. witnesses developments at lightning speed. It's easy to fall behind by unfollowing the research news, even for a week. Still, one can only be an expert and up-to-date in some fields. Other expert members and collaborators in a research lab play a considerable role when one needs knowledge and guidance about a new area. The advisor can always direct you to the correct person for every field of research. Collaborations are at the core of successful research. It is also necessary for a researcher to respect other researchers. Everyone demands respect, and it is only humane to provide that respect. When arguments and disagreements happen, it's best to act respectfully and humbly. No one likes a haughty, arrogant researcher.
The High Stakes in A.I.
Research in A.I. is subject to constant scrutiny by the open-source community. Your research work must be solid, reproducible, fair, and well-grounded at the time of its release. Failing to achieve any single one of these traits could put you in a nervous position. With its fierce competition, any dubious trait in your research could jeopardize your image among the A.I. community.
Do not copy/use anything (even an English sentence) from any source without proper citations. Don’t be frugal with the citations; cite profusely (it’s free). Always ensure your results are reproducible (run experiments more than once). Use fair settings when comparing to baseline methods. A few things come to my mind.
Some people will always question your research. Hold your research to the highest standards, and you will always have effective responses for them.
Conclusion
Alright, so that’s the end of another research experience blog. As I approach the end of my undergraduate degree at IITR, I have tried to capture my growth as an undergrad researcher in a series of blogs (including this one). Although I still have much to learn, I hope my blogs will help and motivate future undergrads to explore research during their undergraduate degrees.
And my future as a researcher is still being determined. Where (if at all) will I be going for my Ph.D.? Will I be going for academic research or industrial research? There’s still a lot for me to explore, and I will try to capture my research journey and life experiences here in the future. Till then, Adíos!
You can learn more about me on my webpage.
