I am a dedicated and self-taught AI Research Engineer, driven by a profound interest in the intersection of Artificial Intelligence and healthcare, particularly within neuropathic pain and neuroscience. My expertise spans AI research in Neuroscience, NLP - Large Language Models (LLM) engineering, and Cognitive Data Science. My career is defined by a commitment to leveraging cutting-edge AI methodologies to address complex challenges in biological and cognitive domains.

My journey into this field was sparked by a personal connection to the challenges of chronic pain, which deepened my resolve to apply analytical and computational skills to understand the complexities of the nervous system.

My overarching long-term goal is to pioneer AI-driven diagnostic and predictive models for neuropathic pain. I aim to develop tools that can identify at-risk patients early and forecast treatment responses, thereby offering a new paradigm in personalized pain management. I am eager to contribute to interdisciplinary research and development that pushes the boundaries of what is possible in this critical area of healthcare.

My academic foundation and specialized training reflect a multidisciplinary approach to AI and neuroscience.

• MicroMasters in Artificial Intelligence | Columbia University
  • 90% Scholarship
  • Duration: September 2020 – August 2021
• Bachelor of Engineering in Computer Engineering | Altınbaş University
  • Full Scholarship
  • Duration: September 2014 – August 2019

My research and project portfolio highlights my practical skills in applying AI to complex problems, particularly in medical and cognitive domains.

• Predictive Modeling of Multiple Sclerosis Risk in CIS Patients
  • Description: An ongoing NeuroQuantix research initiative focused on developing advanced predictive models for the early identification and risk stratification of Multiple Sclerosis in Clinically Isolated Syndrome (CIS) patients. This project utilizes multimodal data analysis to enhance prognostic accuracy, aiming to improve patient outcomes through timely intervention.
  • Status: 🟡 Ongoing
• Distinguishing Cognitive Reading States using Advanced NLP and Transformers
  • Description: A NeuroQuantix research project that applied advanced Natural Language Processing (NLP) techniques, including BERT and Multi-Layer Perceptron (MLP) models, to classify cognitive states during reading tasks. The project achieved high accuracy in distinguishing various cognitive states, demonstrating the power of LLMs in understanding complex human cognitive dynamics.
  • Status: ✅ Completed | GitHub
• Optimization of Human Sensory Neuron Differentiation for Pain Research
  • Description: Conducted during a virtual internship at LifeArc, this project aimed to optimize a fibroblast-to-sensory neuron differentiation protocol. By analyzing multimodal data, I identified key factors influencing neuron count and the functional ratio of sensory pain receptors, contributing to more effective in vitro models for analgesic drug testing and understanding pain pathways.
  • Status: ✅ Completed | GitHub
• Low-Grade Glioma Segmentation
  • Description: A capstone research project from Up School focused on developing deep learning models for accurate segmentation of low-grade glioma from medical imaging data. This work contributes to improving diagnostic precision for brain tumors.
  • Status: ✅ Completed | GitHub
• Prediction of the Progressive Levels of Alzheimer’s Disease with MRI data
  • Description: An optional research project from Up School utilizing MRI data to predict the progression of Alzheimer's disease. The project involved advanced image analysis and machine learning techniques to develop models capable of forecasting disease stages.
  • Status: ✅ Completed | GitHub
• Personalized Medicine: Redefining Cancer Treatment
  • Description: The final project for the Healthcare AI Program, exploring the application of AI in tailoring cancer treatments. This initiative focused on developing personalized therapeutic strategies based on patient-specific data.
  • Status: ✅ Completed | GitHub
• Estimating the Probability of Confirmed COVID-19 Cases in ICU
  • Description: A Capstone Project from "Data Science for the Public Good" focusing on predictive modeling for ICU admissions related to COVID-19. The project developed statistical models to estimate the probability of confirmed cases requiring intensive care.
  • Status: ✅ Completed | GitHub
• Pneumonia Detection on Chest X-ray Images with Deep Learning (Keras)
  • Description: A Capstone Project from "Data Science for the Public Good" involving the application of deep learning models (using Keras) for the diagnosis of pneumonia from chest X-ray images. This project demonstrates expertise in medical image analysis and deep learning for diagnostic support.
  • Status: ✅ Completed | GitHub

My professional trajectory demonstrates a consistent commitment to applied AI research and development across diverse roles.

• Founder & AI Research Engineer in Neuroscience | NeuroQuantix
  • January 2025 – Present
  • Leading independent research at the forefront of AI and neuroscience, focusing on developing novel diagnostic and analytical tools for neurological conditions.
• AI Coding Specialist | NLP Engineer | Outlier
  • November 2024 – August 2025
  • Specialized in applying advanced NLP techniques to optimize AI systems and improve language model performance.
• Prompt Engineer | Outlier
  • July 2024 – November 2024
  • Focused on designing and refining prompts for large language models to enhance their effectiveness and precision in various applications.
• NLP Engineer | hevi.ai
  • May 2023 – June 2024
  • Developed and implemented NLP solutions for real-world problems, contributing to significant advancements in text processing and understanding.
• ML & Deep Learning Engineer | UpSchool & Google Developers
  • July 2022 – January 2023
  • Engaged in practical applications of machine learning and deep learning, including model development and optimization for various projects.
• Machine Learning Project Team Lead | Kodluyoruz
  • September 2021 – November 2021
  • Managed and guided a team through the complete lifecycle of machine learning projects, from conception to deployment.
• Artificial Intelligence Engineer (Internship) | Coff R&D
  • January 2019 – September 2019
  • Gained foundational experience in AI research and development within an R&D environment, contributing to early-stage projects.

My commitment extends beyond professional roles, encompassing significant contributions to scientific and technical communities.

• Data Mining Analyst | AYA: Açık Yazılım Ağı
  • February 2023 – May 2023
  • Contributed to open-source initiatives through data mining and analysis, enhancing community-driven projects.
• Research Student | Arterys
  • October 2020 - September 2021
  • Participated in research activities focused on medical imaging and AI applications in diagnostics.

These virtual internships provided valuable practical experience and exposure to diverse industry applications, enhancing my skills in data science, analytics, and biological research.

• Global Markets Sales and Trading Analyst | Bank of America (May 2025) - GitHub
• Biology Research Internship Program | Life Arc (April 2025 – May 2025) - GitHub
• Data Science & Analytics Internship Program | Lloyds Banking Group (March 2025 – April 2025) - GitHub
• Data Science Virtual Internship Program | Commonwealth Bank (February 2025 – March 2025) - Certificate
• Data Visualization: Empowering Business with Effective Insights | TATA (February 2025) - Certificate
• Data Analytics Virtual Internship Program | Deloitte (February 2025) - Certificate
• NLP - GenAI Virtual Internship Program | Boston Consulting Group (BCG) (February 2025) - GitHub
• Data Science Virtual Internship Program | Boston Consulting Group (BCG) (February 2025) - GitHub
• Data Analytics Virtual Internship Program | Quantium (February 2025) - GitHub
• Data Science Virtual Internship Program | British Airways (January 2025 – February 2025) - GitHub
• Power BI Virtual Internship Program | PwC Switzerland (January 2025) - GitHub

My accomplishments underscore a strong aptitude for AI and research, validated through competitive programs and rigorous evaluations.

• Awarded an AWS AI & ML Scholarship for the "Future AWS AI Engineer" Nanodegree program after placing in the top 3% of a global pool of 50,000 participants.
• Achieved a 94% score on the IBM Advanced Machine Learning Specialist Exam.
• Awarded 8th out of 64 global participants (top 12%) in the Google ML Olympiad 2023 Breast Cancer Diagnosis Competition.
• Awarded a Bertelsmann Technology Scholarship for the Udacity AI Product Manager program after placing in the top 10% of an initial 50,000 participant challenge.

My commitment to continuous learning is demonstrated through a robust portfolio of certifications in key AI and data science domains.

• IBM Machine Learning Specialist Advanced Badge | IBM / Credly (January 2025) - Certificate
• AI for Healthcare Specialization | DeepLearning.AI / Coursera (October 2024) - Certificate · Transcript
• Computational Neuroscience | University of Washington / Coursera (September 2024) - Certificate
• Machine Learning Engineering for Production Specialization | DeepLearning.AI / Coursera (September 2024) - Certificate
• Machine Learning Specialization | Stanford / Coursera (September 2024) - Certificate
• TensorFlow Developer Certificate | TensorFlow (December 2022) - Certificate
• TensorFlow Developer Professional Certificate | DeepLearning.AI / Coursera (December 2022) - Certificate
• Deep Learning Specialization | DeepLearning.AI / Coursera (November 2022) - Certificate
• Fundamentals of Neuroscience XSeries Program | Harvard University / edX (May 2022) - Certificate

I am proficient in a diverse range of tools, frameworks, and programming languages, enabling me to execute high-level research and develop robust AI solutions.

• Programming and Language Technologies: Python, R, SQL, MATLAB, Bash
• Data Science & Machine Learning: PyTorch, TensorFlow, Keras, Scikit-Learn, Pandas, NumPy, Hugging Face, LangChain
• Computational Neuroscience: Neuroimaging Analysis (MNE-Python, Nilearn, NiBabel, FSL), Medical Image Analysis, Statistical Modeling
• Cloud Technologies: Google Cloud Platform (GCP), AWS, Azure , Compute Engine, Load Balancing
• DevOps & Version Control: Docker, Kubernetes, Git, MLflow , Infrastructure as Code (IaC)
• Web & API Development: FastAPI, Gradio, Ollama
• Research Skills: Experimental Design, Statistical Analysis, Data Synthesis, Benchmarking, Model Validation, Scientific Communication

I regularly share my insights and research findings on my personal blog, covering topics at the intersection of AI, neuroscience, and healthcare.

• From Genomes to Neurons: The role of Bioinformatics in Neuroscience Research
• Decoding the Genome: How AI Is Revolutionizing Neurogenomics Research
• Decoding Emotions: Can AI Understand and Simulate Human Emotional Processing?
• Understanding Consciousness: Can AI Models Teach Us About the Mind?
• The Intersection of Synaptic Plasticity and AI: Hebbian Learning in Modern Algorithms
• Deep Learning Architectures for Medical Image Segmentation
• Beyni Anlamak için Neden Matematiğe İhtiyacımız var? (Why Do We Need Mathematics to Understand the Brain?)
• Niçin Tıp Alanında Yapay Zeka'ya İhtiyaç Var? (Why Is There a Need for Artificial Intelligence in Medicine?)
• Data Science Life Cycle
• Sağlık Alanında Veri Bilimi (Data Science in Healthcare)
• MOBILISE Projesi (MOBILISE Project)
• STOpFrac Projesi (STOpFrac Project)

I am always open to discussing collaborative opportunities, research initiatives, and advancements in AI and neuroscience. Please feel free to connect.

Linkedin | Kaggle | Personal Website | Mail