Mr. Göktuğ Öcal | Network Systems | Best Researcher Award
Göktuğ Öcal at Bogazici University, Turkey
Mr. Göktuğ Öcal is a skilled data scientist and computer engineer with expertise in machine learning, time series forecasting, and AI-driven solutions. He holds an MSc in Computer Engineering from Boğaziçi University, where he focused on developing robust time series forecasting models and explored federated neural architecture search. His academic journey began with a BSc in Control and Automation Engineering from Istanbul Technical University, where he volunteered in AI research and led student robotics initiatives. In his professional career, Mr. Öcal has made significant contributions to the air conditioning, automotive, and energy management industries by developing predictive maintenance systems, driver evaluation algorithms, and energy-saving models. His technical proficiency includes Python, TensorFlow, SQL, and cloud platforms, enabling him to build scalable machine learning solutions. Beyond his professional work, he has a deep interest in cinema, communication, and graphical design, which he pursues through personal projects and blogging.
Profile:
Education:
Mr. Göktuğ Öcal holds an MSc in Computer Engineering from Boğaziçi University, Istanbul, where he studied from 2021 to 2024. His coursework included advanced subjects such as Deep Learning, Testing and Verification Techniques in Machine Learning, Natural Language Processing, Cloud Computing, and Operating Systems. During his studies, he conducted research on “Robust Time Series Forecasting Models against Adversarial Attacks,” which led to the development of LSTM-based robust forecasting models. His thesis focused on “Network-Aware Federated Neural Architecture Search,” showcasing his deep engagement with cutting developmments in the field. Before this, Mr. Öcal completed his BSc in Control and Automation Engineering from Istanbul Technical University in 2020. During his undergraduate studies, he volunteered at the Artificial Intelligence and Intelligent Systems Laboratory (AI2S) for two years, where he focused on robotics and AI-based time-series forecasting models. He also actively contributed to the OTOKON student club, where he organized robotics and coding courses and events.
Professional Experience:
Mr. Göktuğ Öcal has a rich professional background in data science, with experience spanning several key industries. In 2024, he worked as a Data Scientist at Daikin Europe, where he developed machine learning-powered predictive maintenance systems for residential air conditioning units and implemented MLOps pipelines using AWS and Databricks. From 2022 to 2024, he was a Data Scientist at Ford Otosan, a leading automotive manufacturer, where he created a driver evaluation algorithm using Python and PySpark to assess and train fleet drivers. He also served as a Scrum Master for the Data Analytics Center of Excellence (CoE), overseeing the development, coding, and deployment processes. Earlier, from 2020 to 2022, Mr. Öcal was a Data Scientist at Reengen, a company specializing in sustainability and energy management. There, he developed a time series analysis algorithm that identified energy-saving opportunities during non-operating hours for retail businesses, leading to an average energy cost reduction of 7%. He also enhanced operational efficiency by reducing the workload of customer teams by 40% through the implementation of advanced energy analysis tools and anomaly detection algorithms for IoT devices. His career began as a Data Science Assistant at Reengen, where he developed LSTM-based time series forecasting models to predict energy consumption across various sectors with a 6% error margin.
Research Interests:
Mr. Göktuğ Öcal’s research interests lie at the intersection of machine learning, time series forecasting, and AI-driven optimization techniques. He is particularly focused on developing robust models that can withstand adversarial attacks, as demonstrated by his work on LSTM-based time series forecasting. His interests also extend to federated learning, where he has explored network-aware federated neural architecture search to optimize distributed machine learning models. Additionally, Mr. Öcal is keen on advancing predictive maintenance systems, anomaly detection algorithms, and energy management solutions through the application of data science and machine learning methodologies. His work reflects a strong commitment to bridging the gap between theoretical research and practical, industry-relevant applications.
Skills:
Mr. Göktuğ Öcal is proficient in a diverse array of technical skills that are essential for data science and machine learning. He is highly skilled in programming languages such as Python, SQL, C++, MATLAB, and Java, and has extensive experience with machine learning frameworks like TensorFlow and Scikit-Learn. His expertise extends to big data and distributed computing, where he utilizes tools like PySpark and Databricks for processing large datasets. Mr. Öcal is also adept at time series analysis, statistical modeling, and implementing software development practices such as object-oriented programming and MLOps. His cloud computing skills are highlighted by his experience with AWS, and he is well-versed in using development tools like Git, Docker, Jupyter, and VS Code. Additionally, he holds certifications in Big Data with PySpark and SQL Fundamentals from Datacamp, and in Machine Learning from Stanford University on Coursera. Proficient in English, Mr. Öcal achieved an IELTS score of 7.0/9.0 in 2021, further demonstrating his strong communication abilities.
Conclution:
Given his solid academic background, demonstrated research abilities, and impact-driven professional experience, Mr. Göktuğ Öcal would be a compelling candidate for a research-focused award, particularly if the focus is on practical applications in data science and machine learning.
Publication Tob Noted:
Title: Network-aware federated neural architecture search
- Authors: G. Öcal, A. Özgövde
- Published In: Future Generation Computer Systems
- Year: 2024