About Me

Currently graduated from AGU, Computer Engineering; and working in TurkNet. Interested with network-based deep data analytics and big data system architecture.

Education & Professional Experiences

Abdullah Gul University / Graduated Computer Engineering

2016 - 2021, Kayseri, Turkey

Lectures, that I took, are Art of Computing, Object Oriented Programming, Exploring Computer Engineering, Data Structure, Mobile Programming, Digital Design, Database Management System, Software Engineering, Artificial Intelligence, Social Network Analysis, Operating System, Computer Organization, Linear Algebra, Discrete Math, Probabilities and Statistics, Korean 101 and so on.

University Of LA Verne / Student in English Language School

July 2017 - August 2017, La Verne, California, USA

ELS La Verne University is located in the state of California in the United States and I joined the ELS program which is one of the America's leading language schools.

University College Dublin / Visiting Researcher Intern

June 2019 - September 2019, Dublin, Ireland

Mainly, studied on human actions recognition through video processing by using neural network models. Also, met the requirements for summer internship programme.

TurkNet / Data Scientist Intern

July 2020 - September 2020, Istanbul, Turkey

• Analyzing and creating a visualization of the Big Data given within the company and making statistics.

• Implemented machine learning algorithms to drive process optimization and improve efficiency.

• Training given Data on the practical use of Python and Machine learning algorithms within the company.

• Being able to use several Big Data visualization and editing tools, like AWS, Spark, GraphViz, Kepler.gl, Plotly.

TurkNet / Data Scientist Junior Full-time

September 2020 - August 2022, Istanbul, Turkey

• Conducted extensive data analysis and visualization using SQL queries for various projects.

• Demonstrated proficiency in utilizing different analysis tools, particularly Tableau, with a focus on churn applications.

• Contributed to the backend development of web applications using Django.

• Employed ETL processes to write SQL scripts, ensuring efficient extraction, transformation, and loading of data.

• Generated ad-hoc reports tailored to specific business needs.

TurkNet / Data Scientist Mid-Level Full-time

August 2022 - August 2023, Istanbul, Turkey

Expertise in Big Data Processing Technologies and Ongoing Analysis Tasks:

• Possess extensive expertise in big data processing technologies, including handling large volume datasets (exceeding 250 GB per day on some projects).

• Successfully performed ongoing analysis tasks on various complex datasets, including complex customer and service-related CRM data, DNS data, netflow data, and geospatial data.

Building and Maintaining Analytic Reports, KPIs, Metrics, and Dashboards:

• Developed and maintained analytical reports, key performance indicators (KPIs), metrics, and interactive dashboards.

• Utilized ETL processes to write SQL scripts, ensuring efficient data extraction, transformation, and loading.

• Scripted and generated automated email reports for efficient reporting and communication.

• Leveraged BI tools such as Tableau and Microsoft Power BI for data visualization and reporting.

End-to-End Project Experience:

• Contributed to various end-to-end projects, including geographic, demographic, and behavioral customer segmentation.

• Developed an intelligent network monitoring system using ISP central POPs, enhancing network performance and reliability.

• Generated ad-hoc reports to meet specific business needs 

• Supported academic projects.

Marketing Performance Analysis and ROI Optimization:

• Thoroughly examined marketing performance, user behaviors, and user conversion data funnels.

• Identified opportunities to enhance ROI, understand trends, and provided actionable recommendations for business improvement.

Self-Led with Strong Problem-Solving Skills and Emphasis on Product Development:

• Demonstrated ability to work self led, taking ownership of projects and driving them to successful completion.

Technologies: Tableau, Microsoft Power BI, MS SQL, NoSQL, MySQL, PostgreSQL, MongoDB, ElasticSearch, Python, PyTorch, PyG, NetworkX, Scikit-learn, Keras, TensorFlow, Flask, Git, Pandas, NumPy, Matplotlib, Seaborn, Spark, version control (GitFlow), CI/CD

Istanbul Technical University / PHD

September 2021 - Ongoing, Istanbul, Turkey

Will be working on:

• Data-driven Network Management for 6G systems

• Data analytics based Network Management

• Big data system architecture

Wingie Enuygun Group / Data Scientist Full-time

August 2023 - Ongoing, Istanbul, Turkey

Optimization Expertise:

• Successfully addressed a complex shift management optimization problem as part of the Shift Management Project.

• Leveraged Google OR-Tools to optimize shift scheduling efficiently.

• Employed Reinforcement Learning-based penalizing techniques to enhance shift management outcomes.

• Developed a Flask application to streamline project operations.

• Utilized Google BigQuery to fetch and process relevant data for optimization.

End-to-End Knowledge Management Agent Development:

• Led the development of a comprehensive Knowledge Base system.

• Consolidated and organized a vast collection of internal and external documents.

• Utilized cutting-edge technologies, including Autogen, to streamline knowledge retrieval.

• Implemented the Group Chat with Retrieval Augmented Generation for efficient information access.

• Leveraged the LangChain framework to facilitate seamless knowledge management.

• Employed the Vector DB for improved data storage and retrieval capabilities.

• Proficient in working with various AI models and APIs, including ChatGPT, Claude 2 (for input up to 100k tokens), AutoGen, and MeMGPT, further enhancing knowledge management capabilities.

Analytic Report Development for Anomaly Detection:

• Designed and implemented a robust anomaly detection system for website access.

• Utilized deep temporal neural networks to analyze user behavior and historical data.

• Developed advanced algorithms to identify anomalies effectively.

• Enhanced website security and performance through precise anomaly detection.

• Successfully fetched and processed data from Kibana and ElasticSearch to support anomaly detection applications.

Projects

Similarities of Human Actions / Neural Network Models & Analysis / GitHub

June 2019 - May 2020, Remote, UCD

Splitted videos by using (I3D) neural network model, python and its libraries like opencv. Then, found similarities between human behaviours such as standing, walking, siting etc. from video data in Google Cloud Platform and created tree by hierarchical clustering technique. Currently, working to publish an academic paper. To get more information, please contact with me.

Global Recruitment Tool / Developing Online Tool for Nokia

December 2019 - June 2020, EPIC Erasmus Project with Nokia 

It's an Erasmus project, run by Nokia and aims to create an effective tools to recruit top talents from the market.

Quadcopter Drone / Python & Dronekit / GitHub

November 2018 - September 2019, IEEE AGU

Finished this project for competition, organised by Tubitak, with 7 people group; and played role in software part of drone project by using Linux environment, Python and its libraries like DroneKit, Numpy, Pandas etc.

Predicting Cast and Director Collaboration / Social Network Analysis / GitHub

October 2019 - January 2020, AGU Social Network Analysis Lecture

It's a term project about experimental study on social network analysis and an approach that combines the graph analysis and machine learning implementations between IMDB’s cast and directors by using analysis techniques like Node2Vec, Logistic Regression, Neural Network models; data collection techniques like web scraping; and Python libraries like Pandas, NumPy.

SnapPuzzle / Android

March 2019 - May 2019, AGU Software Engineering Lecture

Finished an android app for mandatory course in AGU and applied Android's advanced components such as notifications, services, broadcast receivers, content providers, API, Database, Firebase etc. with 3 people group and one customer from the sector.

Non-Academic Transcript Project / Php, JavaScript / nonacademic.agu.edu.tr

2018 - 2019, AGU Youth Factory

It's a new project that is conducted by AGU and worked with 4 people group. Created web page by using Php, JavaScript and MySQL.

Publications

Teach A Human Activity Recognition Machine Inductive Reasoning Through Hierarchical Labelling / Published

Mahsun Altin, Furkan Gursoy, Lina Xu

Many advanced machine learning (ML) models for video processing have been proposed for human activities recognition (HAR), along with a large number of publicly available datasets. Most existing work dedicates to improve the prediction accuracy through either creating new model structures, increasing model complexity or refining model parameters by training on larger datasets. In this paper, we have proposed an alternative idea, differing from existing work, to increase model accuracy through creating higher level summarizing labels for groups of human activities.

Intelligent Network Monitoring System Using an ISP Central Points of Presence / Published

Yousef Alkhanafseh, Mahsun Altın, Altan Çakır, Esra Karabıyık, Ekrem Yıldız, Sencan Akyüz 

The proliferation of both internet usage and users have been remarkably increased due to certain situations that influenced face-to-face communications, which in turn have created high pressure on Internet Service Providers (ISPs). This research mainly aims to boost ISP services by conducting near real-time analysis for customer’s behavior movements based on their score of central Points of Presence (POP). In addition, this study focuses on establishing special Recurrent Artificial Intelligence (RNN) architecture to make daily sales predictions based on various central POPs. The process utilizes different RNN architectures, Long Short Time Memory (LSTM) and Gated Recurrent Unit (GRU), and compares them in order to make smart scoring measurements for customers’ high-dimensional data. As a result, it can be concluded that LSTM architecture has achieved much better Mean squared Error (MSE) than GRU architecture. LSTM outperforms GRU in forecasting less sensitive outliers, with an average Mean Absolute Error (MAE) of 1.354 for LSTM and 1.554 for GRU. Additionally, LSTM performs better in forecasting outliers, with an average MSE of 3.592 compared to GRU’s average of 4.8. Thereafter, the obtained results are merged over private Application Programming Interface (API) and monitored over smart reports. Eventually, the outcomes of this research can be summarized in providing several benefits for customers such as increasing internet performance, reaching promised speed, and shortening activation times. ISP-related benefits such as gaining reputation, promoting sales, and reducing customers’ negative support tickets can be achieved as well.

Volunteering Experiences

Erasmus+ / Volunteer

May 2018 - May 2018, Montenegro

Erasmus+, 2018 Erasmus+ activity in Montenegro, which has communication training courses. (See below)

Project Title:​ ​#OnACT

Identifier:​ ​589976-EPP-1-2017-1-ME-EPPKA2-CBY-WB

IEEE AGU Student Branch / Board Member / www.aguieee.org

September 2018 - June 2019, AGU

Completed semester as board member in IEEE AGU student branch.