For graduate students diving into AI and computer science research, growth goes beyond completing coursework or following a supervisor’s lead—it’s about building a foundation for a meaningful, impactful career. Growth here means more than adding to your resume; it’s about continuously setting and achieving new goals, developing your skills, and creating a body of work that defines your unique contribution to the field. The following principles are tailored to help you navigate the ups and downs of research, keep your work focused, and build momentum toward a lasting career in AI and computer science.
A tangible, lasting achievement that represents your unique contribution. Research deliverables take various forms:
Publications 📰: Papers presented at prestigious conferences, impactful workshops, or reputable national conferences.
Code release 💻: Open-source code published on GitHub with a dedicated landing page to enhance visibility.
Resource creation 📑: Resources like blog posts, presentation slides, or wiki pages that make complex ideas accessible.
Every research you begin should reach a clear outcome. Small outcomes are perfectly fine; just don't end with nothing. Whether it’s publishing or sharing code, completing a project brings a sense of accomplishment, boosts confidence, and fuels future research.
“Ideas are easy; execution is everything.” - John Doerr
While the world of ideas is vast, true impact comes from implementation. In today’s fast-paced environment:
Ideas abound 💡: Conferences, arXiv, and academic discussions reveal countless ideas each year. Often, professors will help distill these down to a select few for you to pursue.
Execution is the differentiator 🏆: Good research is defined by the ability to execute well. Quick testing, rapid prototyping, and knowing when to pivot are crucial.
Efficiency in code and experiment management 🗄️: A streamlined approach to managing codebases, dependencies, and experimental environments is critical for effective research.
By focusing on execution, you maximize the chances of turning ideas into substantial, actionable results.
“Success is 70% luck, 30% skill.”
As the saying goes, research, like life, involves a mix of skill and luck.
Absolutely not. Instead, improve your odds by:
Increasing volume 📈: More attempts mean more opportunities for success.
Analyzing failures 🔍: Understanding why an approach didn’t work is just as valuable as finding one that does.
Planning backup ideas 📝: Always keep a list of alternative ideas to avoid emotional burnout and maintain momentum after setbacks.
Success often results from a resilient mindset and a well-stocked arsenal of “next steps.” If plan A fails, move to plan B. If B doesn’t work, switch to C—no disappointment needed; every researcher faces failures. And if C flops? Happily jump to D!
Once experiments are concluded and analyses are completed, the next step is... marketing. This involves:
Presenting your work effectively to highlight its strengths while maintaining honesty. This requires significant effort and practice—sometimes more than the research itself.
Avoiding exaggeration, as it may lead to career-ending issues down the line.
Work hard, market well = Recognition at a good conference 🏅
Work hard, stay silent = Little to no recognition 🔇
Do minimal work, overstate = Risk to credibility 🚫
As your expertise grows, it’s natural to begin evaluating others' work with a critical eye. However, keep your criticism constructive:
Avoid personal criticism or dismissing others’ research outright.
Offer helpful suggestions, such as, “This method might face limitations under these conditions; have you considered this approach?” instead of “This won't work. Let's try that.”
Remember, networking and relationships 🧑🤝🧑 are key in academia and industry alike. Constructive feedback fosters collaboration and strengthens professional ties, while negative criticism can isolate you in a field that relies on collegiality and connections.
“No player is bigger than the club.” - Sir Alex Ferguson
Similarly, impactful research often involves collective effort:
Collaborate across ideation, experiment design, execution, analysis, and writing stages.
Divide tasks, share code and resources, and encourage feedback among teammates.
Remember, a team of three ordinary researchers collaborating effectively can often achieve more than a single genius. To sustain productive partnerships and maintain morale, ensure fair and transparent credit allocation, such as author order.
Growth in research isn’t just about personal achievements—it’s about building skills and networks that support sustained contributions to the field. These principles can serve as a blueprint for building a meaningful and impactful research career. Each small step, be it a publication, a collaborative effort, or a constructive critique, adds to the legacy you’re crafting in the world of AI and computer science.