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Learning about learning — Junior at Saratoga High — — Writer for Towards Data Science

How being an AI developer, hacker, and researcher lead to some of my lowest moments

From Paul Bulai on Unsplash

Helplessness. Panic. Hopelessness

My best attempt to describe a burnout. For programmers, developers, and researchers, burnout is an all too common experience that can completely halt productivity and ruin mental states. Traditional problems can be addressed if the source is known. As programmers, we all know that if we understand why our code is throwing errors, we can quickly fix them. With burnout, however, even if you know exactly how you got there, recovering is still an extremely challenging task. For me personally, I’m a work-oriented person, so coming to terms with being “unproductive”(making free time) has been very difficult.

Coding, winning prizes, and proving ourselves

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Authored by Ayaan Haque, Adithya Peruvemba, Viraaj Reddi, Sajiv Shah, and Ishaan Bhandari.


Let’s start with a quick disclaimer: We say $9.5k, but most of the prizes are actually virtual credits from various tech companies. In terms of tangible prizes,(e.g. AirPods), we won about $3k.

But enough about our inflated numbers! Let’s get to the story.

We go to Saratoga High School. As a Bay Area school and top-15 STEM high school nationwide, it’s known for its competitiveness and high academic levels.


Thanks for your response.

On the idea that theory is not as important, I do agree that this is certainly true. For most ML engineer jobs, you will not really need to understand a lot of the conceptual theory. However, a concern I have developed is that if so many new developers see this option, they will gravitate towards it. This means that the long-term progress of AI will be slowed, as we will have less people focused on research and producing the top papers on ArXiv. However, the degree of importance of this concern is debatable.

I definitely…

An opinion and discussion piece on whether AI democratization is on net beneficial

Image from Unsplash by Markus Winkler

In the modern age of education, almost anyone with an internet connection can learn anything they want to. This is also true for learning AI, and now, anyone with the requisite background has the opportunity to learn AI and build AI programs. When I say “democratization,” I mean the easy access to AI education and learning, and more importantly, the easy access to building scalable AI applications. In an article I wrote earlier this summer, I discussed my personal experience with AI ethics and how I paid little regard to the implications of my work. That article is here:


My story of entering hackathons and how I learned to win at a high rate

Image by Juanjo Jaramillo from Unsplash

Hackathons have been my life for the last year. Staying up until 4 AM every weekend building advanced projects under a time crunch produces an unmatched adrenaline rush. While hackathons are surely enjoyable no matter the outcome, it is not the same without a win. The prizes and swag help validate the hard work and lack of sleep over a weekend. However, winning is not easy, and learning how to do so can take a long time. As a result, many new hackers become discouraged and don’t continue. …

Why being a great hackathon contestant can help with a research career

Image from Alex Kotliarskyi on Unsplash

At first, Hackathons and AI research seem like they have few similarities, mainly the fact they are both CS and AI-related. However, after being involved in both for over a year now, I’ve learned to use hackathons to improve my research skills and vice versa (similar to how multi-task learning works). While research is a real occupation as opposed to competing in hackathons, there are lots of portable skills between the two that are important to highlight. …

A story and message about understanding the impact and ethics behind AI projects

Image by Nathan Dumlao on Unsplash

The democratization of AI is seemingly beneficial — anyone, including young developers like me, can easily become involved in AI by taking one Coursera course or watching a Youtube playlist. Unfortunately, many, including me in the recent past, don’t understand how AI plays a role in the real world. For programmers who start with basic object-oriented programming, it is hard to envision the apps or programs we create to have any effect on anyone else. Generally, this is true, as creating an Alien Invader game with Java surely has no real implications. …

Thoughts and Theory

A review of our recent paper on generating simulated data for deep learning-based trajectory modeling

A schematic of FCE-NN (Image by Authors)

This article was authored by Ayaan Haque and Sajiv Shah

In this article, we will review our recent work titled “Simulated Data Generation Through Algorithmic Force Coefficient Estimation for AI-Based Robotic Projectile Launch Modeling” by Sajiv Shah, Ayaan Haque, and Fei Liu. In this paper, we present FCE-NN, a novel method of modeling robotic launching of non-rigid objects using neural networks which are trained with supplemental simulated data, generated from algorithmic force coefficient estimation. This work has been accepted to ACIRS 2021. The paper is available in ArXiv, and the project website is here. …

Tips and examples to teach AI concepts at a party

Image from Yutacar on Unsplash

As AI enthusiasts, when friends or acquaintances ask what we do all day, we respond with a general statement such as “I work on AI applications/research/etc.” Since AI is becoming such a household term these days, they might follow up and ask for more details, which makes the environment somewhat dreadful. This leaves you in a tough situation. Obviously, AI is very complex, and if you are at a party or a dinner, explaining it properly in a short time is tough. …

A review of our recent work for using deep learning for suicide and depression classification in the presence of noisy labels

Image by Authors

This article is authored by Ayaan Haque and Viraaj Reddi

In this article, we will review our recent work titled “Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction” by Ayaan Haque, Viraaj Reddi, and Tyler Giallanza. This paper is a method that uses deep learning for suicide vs depression identification in the presence of noisy labels, and is currently under review at a top conference. Our paper is available on ArXiv, the code is available on Github, and the project website is here. …

Ayaan Haque

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