As a child, I learned computers on a blackboard. At school, we were taught Microsoft BASIC on a blackboard. Imagine that. There were only a few computers, and we would be lucky to get just 5-10 minutes a week on an actual computer.
Even exams involved writing MS BASIC codes on the exam paper to generate specific outputs. I remember having to write a code for printing a Nepalese Flag on the final exam. I believe I did it right, but I guess I can never be sure as I did not get to check it on a computer then. It was all in my head. I didn’t have a computer at home until medical school.
Mathematics wasn’t my forte either. I skipped majoring in math in college. Math assignments? Forget it. I avoided it like the plague.
Fast forward to today, and I’m an NIH-funded clinical researcher and physician-scientist, specializing in machine learning and AI. It just all seems surreal considering my background.
So, how did I get here?
Not everything starts out as a promise. Sometimes, you just have to take a leap and see where the journey takes you. My path in clinical research is a testament to this.
Unexpected Beginnings
Sometimes, the journey begins in the most unexpected places.
For me, it was that dusty blackboard. It was far from the sleek, modern computer labs we see today. Yet, I always loved coding in my head, wondering if it would actually work on a real computer.
It ignited a curiosity that stayed with me and set the stage for a career filled with exploration and discovery.
Overcoming Fears
Clinical research often involves complex data analysis. And to get better at it, I had to confront my old adversary: math. But this time, it was through the lens of real-world problems and patient data. It wasn’t just numbers; it was stories, patterns, and solutions.
Facing math again but in a practical, impactful context, transformed my perspective. Instead of abstract equations, I was now dealing with patient outcomes and medical data. This shift made the subject not only palatable but also fascinating. Each data point represented a patient’s story, a piece of a larger puzzle. By connecting these pieces, I could help predict outcomes and improve treatments. This made the effort to overcome my aversion to math all the more worthwhile.
The real-world application of math in clinical research turned a former weakness into a powerful tool for discovery.
1+1=3
My background in traditional epidemiology wasn’t a straightforward path to machine learning and AI. However, it provided a unique perspective.
Epidemiology taught me the importance of studying patterns and determinants of health in populations. It equipped me with the skills to analyze and interpret data in ways that purely tech-oriented researchers might overlook. I could see connections and draw insights that others might miss, leading to a more robust and comprehensive research outlook. My work today combines traditional epidemiology, bioinformatics, human genetics, and AI to advance personalized medicine for arthritis patients.
My clinical and traditional research background became a huge strength rather than a hindrance.
Conclusion: Embrace Your Journey
Not everything starts out as a promise. Sometimes, you just have to explore and see where the road leads you. My journey from learning computers on a blackboard to conducting cutting-edge research in AI and personalized medicine is a testament to this.
As you embark on your path in clinical research, remember: It’s okay not to have everything figured out. The road may be winding, but every step, every obstacle, and every question will help you better understand where you ought to go.
So got out there. Embrace the unplanned path. Let curiosity guide you. You might just find that your unconventional journey leads to groundbreaking discoveries.