KEMAL KILIÇASLAN

KEMAL KILIÇASLAN

AI Developer

I am Kemal, I live in Ankara, Turkey. I graduated from Kastamonu University, Department of Mathematics in 2022 with a GPA of 2.8/4. Currently, I am focusing on improving my technical skills in areas such as artificial intelligence, machine learning, deep learning, and image processing by applying the knowledge from my mathematics background. To achieve this, I am exploring AI algorithms through personal projects, online courses, and practical applications specific to image processing in line with my interest. These projects include tasks such as image detection, recognition, and interpretation. Through this hands-on work, I gain experience that helps me deepen my understanding of AI concepts and further develop my technical skills. My goal is to continue developing these skills and contribute to AI solutions in real-world contexts.



Education

Kastamonu Üniversitesi

KASTAMONU ÜNİVERSİTESİ FEN EDEBİYAT FAKÜLTESİ MATEMATİK BÖLÜMÜ

2022 Graduate - Bachelor's Degree

Personal Initiatives

Kastamonu Üniversitesi Matematik ve Bilim Toplulugu

KASTAMONU ÜNİVERSİTESİ MATEMATİK & BİLİM TOPLULUĞU


(Founding President)

[December 2021 - June 2022]

 Kemal Kılıçaslan - My speech on the meaning and importance of the day

'14 March π Day'



Türkiye Matematik Kulübü

Türkiye Matematik Kulübü


(Member)

[December 2020 - ]

 I took an active role in the organisation team of 6 events, 4 of which were academic and 2 of which were popular, where we addressed more than 1500 people in total.

Bilgisayar Kavramları Topluluğu

Bilgisayar Kavramları Topluluğu


(Member)

[September 2025 - ]

Talks & Workshops

Niğde Ömer Halisdemir Üniversitesi Bilgisayar Mühendisliği Kulübü

Niğde Ömer Halisdemir Üniversitesi Bilgisayar Mühendisliği Kulübü
(Speaker)

[15 May 2025]

At the TeknoKonferans NÖHÜ'25 event held within the scope of the "Training, Cooperation and Competition Event for the Software Industry" organized at Niğde Ömer Halisdemir University, I gave a speech titled "From Mathematics to Artificial Intelligence: An Interdisciplinary Career Journey".

Experiences

BİLGİ TEKNOLOJİLERİ VE İLETİŞİM KURUMU

BİLGİ TEKNOLOJİLERİ VE İLETİŞİM KURUMU


AI Instructor

[July 2024 - ]


Calorin

Calorin


Computer Vision Developer

[August 2024 - September 2024]


Ultralytics

Ultralytics


AI Content Creator — Brand Partnership

[March 2026 - April 2026]

Produced a sponsored LinkedIn post and a YouTube tutorial for the Ultralytics Platform launch, demonstrating the full computer vision pipeline (annotation → training → export → deployment) using YOLO on two original real-world datasets.

🔗 Try the Ultralytics Platform




Tech Skills



Python C++ MATLAB Wolfram Mathematica PostgresSQL HTML5 CSS JavaScript Swift LaTeX

scikit-learn TensorFlow PyTorch OpenCV BootStrap FFmpeg






PROJECTS



6174 Kaprekar Constant with Python Programming

This project implements the fascinating Kaprekar Constant (6174) discovered by Indian mathematician D.R. Kaprekar in 1949. Features iterative arithmetic operations that converge any four-digit number to 6174 within 7 steps, plus three-digit variant (495). Built with Python for educational exploration of number theory.

Face Detection and Person Recognition System

This project implements a versatile Face Detection and Person Recognition System using computer vision and deep learning. It provides a modular toolkit for detecting faces and identifying specific individuals in both static media (images, videos) and real-time webcam streams.

Road Lane Lines Detection System

This project implements a real-time Road Lane Detection System using computer vision techniques. Features Canny edge detection, ROI masking, and Hough Transform to identify road lane markings in video streams. Processes visual data for accurate lane boundary detection with OpenCV and Python for ADAS and autonomous driving applications.

Garbage Classification with Convolutional Neural Network (CNN)

Garbage Classification Class
Garbage classification class

Garbage Classification Predicted Class
Garbage classification predicted class

This project implements a Garbage Classification System using Convolutional Neural Networks (CNN) with TensorFlow/Keras. Classifies waste into 6 categories (cardboard, glass, metal, paper, plastic, trash) using the TrashNet dataset. Achieves 62.2% accuracy with real-time image prediction capabilities for automated recycling applications.

Object Detection and Segmentation with YOLOv8

This project implements comprehensive Object Detection and Segmentation using YOLOv8. Features real-time detection and instance segmentation for images, videos, and webcam streams. Supports 80+ object classes with both CLI and Python API interfaces. Built with Ultralytics framework for computer vision applications.

Vehicle Recognition with Segmentation Training on a Custom Dataset

This project implements a custom Vehicle Recognition and Segmentation system using YOLOv8 trained on a specialized dataset. Classifies and segments three vehicle types (cars, pickups, trucks) with 99.5% mAP50 accuracy. Complete pipeline from data annotation with Roboflow to model training and evaluation for traffic monitoring applications.

Pose Detection with YOLOv8 using Wolfram Mathematica

This project implements Pose Detection using YOLOv8 within Wolfram Mathematica. Features real-time human pose estimation with 17 keypoint detection, skeleton visualization, and comprehensive analysis tools. Leverages pre-trained models from Ultralytics for accurate body pose recognition and movement tracking.

Facial Expression Recognition System

This project implements a Facial Expression Recognition System using deep learning with VGG19 architecture. Classifies 7 emotions (angry, disgust, fear, happy, neutral, sad, surprise) from grayscale facial images. Achieves 66.33% accuracy on FER-2013 dataset with transfer learning and data augmentation techniques.

Vehicle Speed Estimation System

This project implements a real-time Vehicle Speed Estimation System using YOLOv8 object detection and perspective transformation. Tracks multiple vehicles within a defined ROI, calculates speeds in km/h using geometric principles, and provides annotated video output for traffic monitoring and analysis applications.

Data Visualization of Turkey Population with Plotly

This project implements comprehensive data visualization of Turkey's population from 1927 to 2023 using Plotly and Folium. Features interactive charts, geographic maps, and demographic analysis across 81 provinces with gender distribution trends and historical comparisons.

Traffic Signs Recognition, Vehicle Plate and Person Blurring System

This project implements a comprehensive computer vision system for real-time traffic sign recognition (24 classes) with automatic privacy protection through vehicle plate and person blurring. Built with YOLOv11 models, it's designed for autonomous vehicles and GDPR-compliant surveillance applications.

Safety Lane Violation Detection System

This project implements a real-time Safety Lane Violation Detection System using YOLOv12 object detection and tracking. Monitors emergency lane usage, detects vehicles crossing safety boundaries, and provides live violation statistics with color-coded visualization for traffic enforcement applications.

Vehicle Distance Measurement System

This project implements a real-time Vehicle Distance Measurement System using YOLOv12 and perspective geometry. Calculates distances to surrounding vehicles in three ROI lanes with adaptive warning thresholds, automatic license plate blurring for privacy, and color-coded safety indicators for dashcam applications.

Video to Audio Converter

This project implements a professional video-to-audio conversion application for macOS using SwiftUI and FFmpeg. Features support for 19 audio formats, real-time video preview, drag-and-drop interface, customizable metadata editing, and 10-language localization for multimedia processing applications.





CERTIFICATES