Thinking Critically
It’s not often one finds themself immersed so deeply in the wilderness that any hope of returning to civilization seemingly melts away with the sound of evening crickets.
On a multi-day camping trip with my Boy Scouts troop, an unexpected fog enveloped the forest, obscuring our usual landmarks and leaving us disoriented. With our traditional navigation methods rendered useless, I managed to recall a lesson on celestial navigation, one I had recently learned in my astronomy studies. Recognizing the urgency of the situation, I suggested that we use the positions of the brightest stars as a makeshift guide to determine our location and reorient ourselves. This critical moment underscored the immense value of interdisciplinary thinking. By applying a technique from astronomy in a real-world setting, I demonstrated how scientific reasoning and creativity can combine to solve pressing problems.
The experience not only allowed us to safely navigate back to camp but also reinforced my belief in the practical applications of classroom learning, especially when faced with unexpected challenges.
Since that night in the wilderness, I have embraced the habit of integrating cross-disciplinary approaches into both my academic work and everyday problem-solving. This experience has inspired me to explore innovative solutions in astrophysics research, bridging theoretical knowledge with practical applications. It continues to shape my approach as I prepare for graduate studies and future professional challenges, proving that a well-rounded skill set can be the key to overcoming unforeseen obstacles.
Research Management
In one of my most recent astrophysical endeavors, I find myself working with a research team to train an artificial intelligence model. For context, this model requires thousands of image data to produce reliable outputs, and, in my first couple weeks, I had only managed to provide a measly 46 images. Naturally, the initial results weren’t as accurate as we had hoped, but the experience turned out to be quite memorable. It highlighted how even small data sets and new methods can still lead to valuable insights. Through our conversations and reflections, I came to see that each training round, especially as new team members joined, gave us a great opportunity to enhance both the AI model’s capabilities and our overall research approach. Even though the model’s output wasn’t perfect, we were pleasantly surprised to notice steady improvements and learned just how important it is to adapt our teaching techniques for everyone involved in the project.
This process really brought to light the difference between our expectations of technology’s flexibility and its real-world limitations. By recognizing these limitations, we could tweak our methodology, from how we labeled data to how we documented changes, to achieve better outcomes in the next rounds. It also highlighted how crucial effective onboarding is. Our team really appreciated clear communication about the model’s sensitivity to small training sets, which made our collaboration much smoother. Acknowledging these factors not only helps in shaping the AI model’s journey, but also to foster a collaborative atmosphere where each iteration builds on the last.
Moving forward, I plan to integrate these lessons into a more robust research strategy that leverages continuous feedback loops and thorough documentation of each experiment. By systematically capturing what worked, and what didn’t, we can streamline the learning curve for new collaborators and maintain consistency in our methods. This process has dramatically improved my research management skills, like coordinating the team’s efforts, setting realistic goals, and iterating on findings to keep improving. Ultimately, the aim is to expand the training data set, further refine the model, and foster a culture of collaboration and adaptability in our research endeavors.
Technology and Artificial Intelligence
In years past, the majority of my artificial intelligence usage centered around quick grammar checks or double-checking facts. However, with how much more advanced programs are becoming, I keep finding exponentially greater uses for it in both academic and everyday life. I’ve used it to clean up rough drafts, interpret unclear instructions, revisit challenging or forgotten subjects, spot-check extensive papers, and summarize seemingly interminable documents. Throughout my usage, I’ve learned that AI tends to work best when I approach with a clear purpose and a tangible end goal. As useful of a tool as it can be, I’ve also learned how important it is to stay in control of my own ability to think and write while still making the most of what AI has to offer.
After using AI in and out of the classroom, I plan to continue to utilize it for the phenomenal tool that it is, and stress the significance of being thoughtful and transparent about how and when it’s used, not only to others in my academic or professional settings, but also to myself. It’s important to remember that artificial intelligence can be a powerful tool for learning and development, provided it’s used with intention, and not as a shortcut to avoid engaging with difficult tasks. For me, these experiences have reinforced that AI is most valuable when it enhances my efforts rather than substitutes for them.
Click here for a detailed list of AI, AI assisted, or other programs I am proficient with:
- ChatGPT – AI program used for brainstorming and editing
- RIFBot – AI tool that guides deeper reflection through questions
- ClaudeAi – AI program used for brainstorming and editing
- Gemini – Google AI used for drafting, summarizing, and answering questions
- Deepseek – AI research tool for finding and summarizing academic content
- Goblin.Tools – AI-powered tools for task planning and tone checking
- Grok – AI chatbot on X used for real-time info and Q&A
- Canva – Design tool for making graphics, presentations, and web visuals
- WordPress – Block editor used to build websites like my ePortfolio
- Mathematica – Software for symbolic math and scientific computing
- Excel – Spreadsheet program for data analysis and visualization
- Fusion – 3D modeling tool for design and engineering (Fusion 360)
- Tinkercad – Beginner-friendly 3D design tool for modeling and printing
- PyCharm – IDE used for writing, testing, and debugging Python code
- GIMP Studio – Image editing software used for photo manipulation and graphic design
- FITS Liberator – Tool used to process and visualize astronomical FITS image data
- TunerStudio – Engine management software used for tuning and monitoring standalone ECUs