Hi, I am a rising sophomore in North ATL working on a Robotics/Systems and Software project. I’ve seen a lot of projects that have very clear, significant real-world impact that performed well at ISEF, but how do more academic framed projects do? My research is in scaling Reinforcement Learning agents and much of the research in this field is purely academic. My project takes the same direction, so I wanted to know if projects MUST have real world impact to get to and perform well at ISEF. My project does have real-world impact, but it is not as obv as other projects because Reinforcement Learning is used with robotics so my research could help us make larger models to improve robot learning and make them bigger. Do you recommend that I actually collab with a robotics lab to build something physical with this to show the impact, or is it fine to keep it purely theory and just speak about the application?

Context:

Hi everyone,

I'm a high school rising sophomore in Northeast Georgia, and I'm currently working on a research project to make Kolmogorov-Arnold Networks more computationally efficient. I'm aiming for publication, but I recognized that I'm at a very early stage in my academic research journey, and I really need experienced mentors to help guide me through the research process. I'm looking to work on this project until late December 2026.

Problem I'm addressing:

The known bottleneck with KANs is that they have a significantly higher total wall clock time during training compared to other traditional feed-forward networks. I was looking to take a pruning-based direction to address this problem, with an approach that, to my knowledge, has not been explored in past literature.

Current Background:

I'm relatively new to Deep Learning as I have started to take it seriously about a few months ago. I'm familiar with Python and C++ (probably irrelevant), and I have self-taught myself PyTorch. Most importantly I'm incredibly passionate about Deep Learning and willing to learn.

Where I Need Mentorship:

I'm exploring a pruning-based approach to KAN efficiency that I haven't seen in the literature, and I'd love to work with a mentor who could help validate this direction. I'm primarily looking for some with Deep Learning experience (pruning or experience with KANs would be nice). I'm looking for a mentor who can guide me through experimental design, help me understand the mathematics I encounter, and provide feedback on paper writing. I plan to do as much of the work as possible and reach out thoughtfully when I need guidance.

I'm genuinely open to collaborate if there is mutual interest, but I'm primarily looking for a mentor who can guide me through the research progression and some of the mathematics. I'm happy to share more project details via DM if anyone is interested on hearing more about it.

I would like to thank everyone who spend their time to read this post, I really appreciate it. If anyone is not able to assist me on my project I would incredibly appreciate it if you could leave any advice you may have regarding my research. Thanks for any guidance or mentorship opportunities.

Context:

Hello! I am a rising Sophomore in the North ATL area, and I was looking for advice on what makes a good ISEF Project idea. I am interested in Artificial Intelligence and Deep Learning, and I was looking for advice on whether to go with more of a real-world impact project or a more pure AI research type project. I was looking to participate in the Systems and Software category or Robotics. I want to place high in my category and possibly win a category award; however, that is a stretch, as it would be my first time participating in ISEF if I qualify.

Questions:

1. Real-World Impact vs Pure Research

From your experience, do real-world impact projects or more pure scientific research projects perform better in Systems and Software and Robotics? To expand on what I mean, do judges prefer that your project have a significant and clear real-world impact (e.g. cancer detection app or an AI designed to detect microplastics) or do they prefer projects that are more similar to what real PhDs work on (e.g. knowledge transfer b/w neural networks or improving pruning methods in Deep Learning)? Currently, my project is more of a pure research type of project; however, there is a case of it making AI more efficient, but it is less obvious.

2. What do judges care the most about?

Do judges prefer projects that push a very small field within AI forward and highly novel ideas, or do they prefer general, broader impact? Additionally, I was looking to develop my own novel proof with the help of a PhD student, and I wanted to know if that is something that is going to impress a judge. To summarize, what factors do judges in Systems and Software and Robotics really weigh a lot and actively look for within projects?

Thank you very much to whoever took the time to read this post and provided me with any advice.

Context:
Hey guys, I am currently a rising Sophomore with no previous competitive programming experience and looking to get to Plat by Senior year. I am doing USACO because I want to major in CS and hopefully get into a prestigious school like MIT, CMU, Princeton, GT, Cornell, Columbia, or UC Berkeley. I was introduced to USACO and started practicing USACO around mid-May. I have been taking it more seriously since summer started (around late May for me), and have been consistently logging 4-6 hours per day since around the beginning of June. I am working through the USACO Guide, and I am almost done with all the modules; however, I still struggle a lot with the problems (especially the modern Bronze problems). It has been frustrating as I feel that I fully understand the problems and have the general right structure in my answers, but I still get many of the questions incorrect because of not considering a specific edge case, using data structures that slow down my implementation speed, or missing a "key insight" regarding the problem. To learn from previous mistakes, I have been writing an error log in Notion with what I did incorrectly with each question. Given my situation, it would be much appreciated if you guys could give me any advice that could help me perform better in Bronze and hopefully be able to comfortably solve modern Bronze questions by early to mid July.

Questions:

1. Tutoring vs. Self-Study

Given the hours I have been putting towards USACO and my stagnant progress, would you recommend that I take tutoring? I am aware that developing the "competitive programmer mindset" takes a significant amount of time, but I am afraid that my progress is too slow, given the hours I am putting in. I have heard mixed reviews about taking tutoring in USACO (especially at such an early stage), as many people recommend grinding problems out.

Would you guys recommend that I:

• Keep on practicing past Bronze problems (focusing on problems post 2020) on my own?
• Consider tutoring?

I have heard of:

• CP Initiative
• AlphaStart

But if I were to take tutoring, I would lean toward CP Initiative because they are within my budget.

2. Activities That Transfer to USACO

Other than strictly practicing USACO, are there any other activities that you recommend I start doing that could translate into USACO and potentially help me increase my progress?

I have heard that the problem-solving skills in math competitions, such as:

• AMC
• AIME

translate into USACO, but I wanted to know how seriously I should be taking them, as I have other ECs that I am participating in.

Additionally, I have seen that for higher divisions like Gold or Plat I should be reading about different data structures and algorithms in my free time.

Are there any particular:

• Online resources
• Books

that you all recommend, or any books that I can invest in?

3. Study Method

Is there a particular study method that I should be applying to increase the rate at which I learn in USACO?

Currently:

• I read through a section in the USACO Guide and solve the problems at the end.
• With each problem, I spend the first 20-25 mins reading the question and coming up with a general plan to solve the problem.
• Then, I began implementing my devised solution and set a strict time limit of 80 mins (counting the 20 mins I spent coming up with a strategy), where if I am not done with the code, I immediately open up the editorial and analyze where I went wrong.
• I then log the question in my Notion notebook and use AI to really reinforce the lesson I learned from the question and what I should do next time.

Additionally, would you all recommend me to:

• Follow the USACO Guide
• Use the self-study pathway with CP Initiative

I am more of a visual learner, but I sometimes do like the USACO Guide as it really forces me to think harder to understand the question and the solution since it does not have a visual explanation.

4. Timeline to Plat / Camp

As previously mentioned, in the summer, I am spending approximately 4-6 hours per day on USACO, and I am planning on spending about 15+ hours per week on USACO when the school year starts. To set my expectations correctly, in approximately how many months would you say that I could reach Plat?

My main goal is to get Platinum by my senior year; however, I also have a stretch goal of getting USACO Finalist/Camp.

I am aware that many of the programmers who reach Camp started coding at 10 and were already Platinum by freshman year. I wanted to know what my chances are, given that I started later.

I know that this post was extremely long, and I really appreciate anyone who read this post and provided any kind of feedback on how I can improve.