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What is the most interesting thing about your project and why?

The most interesting part of my project is how it merges artificial intelligence with localized deformation analysis in materials science. Traditional Digital Image Correlation (DIC) techniques are powerful but often limited in terms of resolution, speed, and adaptability. By incorporating AI/ML approaches, we can enhance precision, automate pattern recognition, and potentially uncover deformation behaviors that were previously difficult to detect.

This fusion of machine learning and material science is exciting because it opens up new possibilities for real-time analysis, predictive modeling, and even applications beyond materials—such as structural health monitoring and biomechanics. It’s fascinating to explore how AI can push the boundaries of what we currently understand about material behavior under stress.

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Overcome the new technologies

Before starting my project, I had no prior experience with TensorFlow and Keras, and learning them initially felt overwhelming. The syntax, concepts like tensors, and the overall workflow of building and training neural networks were completely new to me. At first, I struggled with understanding how layers work, tuning hyperparameters, and debugging errors related to shapes and dimensions. However, through consistent practice, experimenting with different architectures, and following tutorials, I gradually became more comfortable. Now, I can confidently build and train models, preprocess data effectively, and optimize performance. Looking back, the learning curve was steep, but the progress I’ve made has been incredibly rewarding, and I now appreciate how powerful and flexible these tools are for deep learning.

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Should & Avoid

Practices I want to do more often:

Paying close attention to detail and writing code with care: The book emphasizes that clean code looks like it was written by someone who cares. This means not only focusing on getting the code to function correctly, but also ensuring it is easy to read, understand, and maintain.

Example: Instead of quickly writing a function with a generic name like processData(), I would take the time to give it a descriptive name such as validateUserInputAndSaveToDatabase(). This demonstrates care in making the code’s purpose clear.

Example: When writing a conditional, instead of using a complex and hard-to-read logic, I would use explanatory variables to break it down into easier-to-understand parts. For instance, instead of if ((user.getAge() > 18) && (user.getLocation().equals(“US”) || user.getLocation().equals(“CA”))), I would use boolean isAdult = user.getAge() > 18; boolean isUSorCA = user.getLocation().equals(“US”) || user.getLocation().equals(“CA”); if (isAdult && isUSorCA).

Leaving the code cleaner than I found it (Boy Scout Rule): This means making small improvements whenever I work with existing code.

Example: If I encounter a function with duplicated code, instead of ignoring it, I would refactor it into a separate method. This reduces redundancy and makes the code easier to maintain.

Example: If I see a variable with a cryptic name like tmp, I would rename it to something meaningful such as customerName, improving readability.

Example: If a conditional statement is overly complex, I might break it up into multiple smaller if statements to clarify the intent.

Practices I want to avoid:

Writing messy code to meet deadlines: The sources clearly state that messy code will slow you down and cause you to miss deadlines.

Example: I will avoid skipping unit tests or writing them poorly just to finish a feature faster. The book highlights that clean code has unit and acceptance tests, and I understand that tests are important to not only find bugs, but also for documentation and design purposes.

Example: I will avoid creating long, complex functions and instead aim to keep functions small and focused. The book also emphasizes that functions should do one thing, and should not mix levels of abstraction. If I write a function and realize that it does more than one thing I would refactor it to smaller more focused functions.

By applying these principles, I can contribute to creating code that is not only functional but also well-designed, easy to maintain, and enjoyable to work with. These examples are based on the concepts described in chapter one of the book, such as writing clean code, paying attention to detail, and avoiding messy practices, and they align with the key themes

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My Balancing

Life Hacks: Balancing Stress, Teamwork, and Classwork

Managing life as a student or professional can feel overwhelming at times, especially when juggling stressful moments, team dynamics, and class deadlines. Through trial and error, I’ve discovered a few hacks that keep me grounded and productive. Here’s how I handle it all—and maybe these ideas can help you too!


1. Practicing Hot Yoga: The Power of Mind and Body

Hot yoga has become my go-to strategy for maintaining a positive mindset. The heated environment helps me relax, while the physical effort builds resilience—both mentally and physically. This practice strengthens my ability to stay calm under pressure, which is crucial when programming for hours. Plus, having a strong backbone (literally!) makes sitting at my desk more comfortable during those marathon coding sessions.


2. Scheduling and Alarms: Staying Ahead of Deadlines

Procrastination used to be my biggest enemy until I discovered the magic of alarms. Now, I schedule specific blocks of time for starting assignments and set alarms for their deadlines. This approach ensures I don’t fall into the last-minute panic trap. Breaking tasks into smaller milestones also helps me stay on track without feeling overwhelmed.


3. Communication: The Secret Weapon in Teamwork

In any team project, clashing ideas are inevitable. But I’ve learned that open and honest communication is the key to navigating conflicts. Listening to everyone’s perspective and finding common ground ensures our team can move forward productively. Clear communication also helps us align on goals and reduces misunderstandings that could derail the project.


4. Dealing with Mental Blocks: Take a Step Back

Getting stuck on a problem—whether it’s debugging code or brainstorming ideas—can feel frustrating. My trick? Step away for a while. Go for a walk, do a quick workout, or even sleep on it. Often, a fresh perspective reveals solutions that seemed impossible earlier.


5. Prioritizing Self-Care: Work Smarter, Not Harder

Burnout is real, and I make it a priority to avoid it. Hot yoga is one way I recharge, but I also carve out time for hobbies, family, and friends. A clear mind is a productive mind, and taking breaks helps me stay efficient and focused when I return to work.


Final Thoughts

Life as a student or programmer can be demanding, but with the right strategies, it’s manageable. By practicing yoga, staying organized, communicating effectively, and prioritizing self-care, I’ve found a rhythm that works for me. Remember: small changes to your routine can lead to big improvements in how you handle stress and challenges.

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Introduction

I am Van To, and I am currently living in Portland OR with PST timezone. I am marriage and have two boys. Since I have a full-time job at Intel as a Manufacturing Equipment Technician (MET) and full-time school, I don’t have much free time. Anytime, when I have free time, I often play with my kids, watching movies with my family. I also like coffee and can make a good cup of expresso coffee at home. Additionally, I like reading books, especially technical and software books. Even though, I am a MET, but my dream job is to work at Intel as a software engineer in ML/AI field. I found that E-Campus at OSU is suitable for me since I can attend school without going to in person class. Because I like ML/AI, I would like to work on many projects that apply ML/AI in solving the problems to help improve the productivities. Couple of projects that I am interesting in working on including:

  1. Enhancing Localized Deformation Analysis in Materials Science Using AI/ML: A Comparative Study of Traditional DIC and AI/ML Approaches
  2. Leveraging AI for Improved Public Transit
  3. Scaling Artificial Intelligence using the Chapel Programming Language