<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Competition on Rauf Ibishov</title><link>http://raufibishov.com/tags/competition/</link><description>Recent content in Competition on Rauf Ibishov</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2026 Rauf Ibishov</copyright><lastBuildDate>Sun, 20 Nov 2022 00:00:00 +0000</lastBuildDate><atom:link href="http://raufibishov.com/tags/competition/index.xml" rel="self" type="application/rss+xml"/><item><title>ActInSpace 2022 — AI Disease Detection from Satellite Imagery</title><link>http://raufibishov.com/posts/actinspace/</link><pubDate>Sun, 20 Nov 2022 00:00:00 +0000</pubDate><guid>http://raufibishov.com/posts/actinspace/</guid><description>&lt;p&gt;&lt;em&gt;Key lesson: with four people and 24 hours, the bottleneck is deciding what NOT to build. The first two hours of scoping discipline are what got us to a working demo.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;In November 2022, I led a team of four at &lt;strong&gt;ActInSpace 2022&lt;/strong&gt; — an international hackathon organized by the French National Centre for Space Studies (CNES) and the European Space Agency (ESA), held simultaneously across &lt;strong&gt;34 countries and 66 cities&lt;/strong&gt; with &lt;strong&gt;2,900+ registrants and 410+ teams competing worldwide&lt;/strong&gt;. Our local edition took place at UFAZ (French-Azerbaijani University) in Baku — local partner: Azercosmos — with 32 teams competing over a 24-hour sprint.&lt;/p&gt;</description></item><item><title>TEKNOFEST 2022 — Deep Q-Learning for Traffic Signal Control</title><link>http://raufibishov.com/posts/teknofest/</link><pubDate>Sat, 28 May 2022 00:00:00 +0000</pubDate><guid>http://raufibishov.com/posts/teknofest/</guid><description>&lt;p&gt;&lt;em&gt;Key lesson: reward shaping and state representation matter more than the choice of algorithm — a well-designed reward with a simple DQN beats a fancy algorithm with a poor reward function.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;In May 2022, our team competed among 40 teams at the &lt;strong&gt;Smart Qarabag Hackathon&lt;/strong&gt;, part of the TEKNOFEST Azerbaijan international technology festival. We built a reinforcement learning model for intelligent traffic signal control.&lt;/p&gt;

&lt;h2 class="relative group"&gt;What we built
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&lt;p&gt;The core idea: treat a traffic intersection as an environment and the signal controller as an agent. We used &lt;strong&gt;Deep Q-Learning&lt;/strong&gt; to train the agent to optimize signal timing — learning when to switch lights based on real-time queue lengths, waiting times, and traffic density across all directions.&lt;/p&gt;</description></item><item><title>2nd Place at UFAZ Hackathon — Visualizing Particle Swarm Optimization</title><link>http://raufibishov.com/posts/pso/</link><pubDate>Tue, 01 Mar 2022 00:00:00 +0000</pubDate><guid>http://raufibishov.com/posts/pso/</guid><description>&lt;p&gt;&lt;em&gt;PSO config: 30 particles · 30 iterations · W = 0.8 · c1 = 0.1 · c2 = 0.1–0.2. The demo of particles converging on a 3D surface won the judges over more than the math did.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;In March 2022, our 3-person team placed 2nd out of 40 teams at the UFAZ (French-Azerbaijani University) Hackathon, winning a 2,500 AZN prize. We built an interactive visualization of Particle Swarm Optimization — showing how a swarm of particles explores a solution space and converges on the global minimum.&lt;/p&gt;</description></item></channel></rss>