Eda AYDIN

AI Research Engineer | Cognitive AI & Human-Machine Interaction (HMI)

About Me

I am an AI Research Engineer and Computational Neuroscientist pivoting into the intersection of advanced Human-Machine Interaction(HMI) and high-performance motorsports. My expertise spans deep learning, real-time biomarker analysis, NLP (LLMs), and cloud-native AI architecture (AWS). While my foundational background lies in clinical neuroscience, my current focus is on dynamic cognitive mapping—developing predictive AI systems that analyze decision-making under extreme time pressure and physical stress. My goal is to engineer low-latency, life-saving predictive algorithms that transform multi-modal telemetry and neural data into actionable safety protocols for elite racing, autonomous systems, and high-stakes environments.

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Technical Focus

• Machine Learning, Deep Learning & Edge AI
• NLP, Large Language Models (LLMs) & Prompt Engineering
• AI Agents & Responsible AI
• Cloud-Native AI Architecture (AWS) & Real-Time Data Pipelines
• Sensor Fusion & Multimodal Data Analysis

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Research Focus

• Predictive Neural Mapping & Cognitive Safety Systems
• Decision-Making Under Extreme Time Pressure & Stress
• High-Performance Motorsports Telemetry & Human-in-the-Loop AI
• Cognitive Load Optimization & Advanced HMI

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Research Projects

• Generative AI Knowledge Base with AWS Bedrock, Aurora & Terraform (Udacity - AWS)
  • ✅ Completed – Industry | 👉 GitHub
  • A cloud-native RAG system was built to enable natural language querying over technical documents using AWS infrastructure.
• Smart Budget Buddy: A Responsible AI Agent (Udacity - AWS)
  • ✅ Completed – Industry | 👉 GitHub
  • A safety-focused conversational AI agent was developed to teach financial literacy using responsible AI guardrails.
• Distinguishing Cognitive Reading States using NLP and Transformers (NeuroQuantix)
  • ✅ Completed – Research | 👉 GitHub
  • Transformer-based NLP models were applied to classify cognitive reading states using linguistic features from the ZuCo dataset.

<!-- **Optimization of Human Sensory Neuron Differentiation for Pain Research (LifeArc)** * ✅ Completed – Research) | 👉 [GitHub](https://github.com/edaaydinea/LifeArc_BiologyResearch) * _Multimodal data analysis was used to optimize fibroblast-to-sensory neuron differentiation protocols for pain research._ * **Low-Grade Glioma Segmentation** * ✅ Completed – Research | 👉 [GitHub](https://github.com/edaaydinea/Low-Grade-Glioma-Segmentation) * _Deep learning–based segmentation models were developed to identify low-grade gliomas from brain MRI scans._ * **Alzheimer’s Disease Progression Prediction with MRI** * ✅ Completed – Research | 👉 [GitHub](https://github.com/edaaydinea/OP2-Prediction-of-the-Different-Progressive-Levels-of-Alzheimer-s-Disease-with-MRI-data) * _MRI-based features were used with machine learning models to predict the progression stages of Alzheimer’s disease._ * **Personalized Medicine: Redefining Cancer Treatment** * ✅ Completed – Research | 👉 [GitHub](https://github.com/edaaydinea/personalized-medicine-redefining-cancer-treatment) * _Personalized therapeutic strategies based on patient-specific data._ * **Estimating COVID-19 ICU Admission Probability** * ✅ Completed – Research | 👉 [GitHub](https://github.com/edaaydinea/Estimating-the-Probability-of-Confirmed-COVID-19-Cases-Taking-into-the-Intensive-Care-Unit-ICU-) * _Predictive models were developed to estimate the likelihood of confirmed COVID-19 patients requiring ICU admission._ * **Pneumonia Detection on Chest X-ray Images (Keras)** * ✅ Completed – Research | 👉 [GitHub](https://github.com/edaaydinea/Pneumonia-Detection-on-Chest-Xray-Images-with-Deep-Leaning) * _Deep learning models implemented with Keras were used to classify pneumonia from chest X-ray images._ -->

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Education

• MicroMasters in Artificial Intelligence | Columbia University (Sep 2020 - Aug 2021)
• Bachelor of Engineering in Computer Engineering | Altınbaş University (Sep 2014 - Aug 2019)

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Professional Experience

• Founder & AI Research Engineer in Human-Machine Interaction (HMI) and high-performance motorsports | NeuroQuantix (Jan 2025 - Present)
• AI Coding Specialist | NLP Engineer | Outlier (Nov 2024 - Aug 2025)
• Prompt Engineer | Outlier (Jul 2024 - Nov 2024)
• NLP Engineer | hevi.ai (May 2023 - Jun 2024)
• ML & Deep Learning Engineer | UpSchool & Google Developers (Jul 2022 - Jan 2023)
• Machine Learning Project Team Lead | Kodluyoruz (Sep 2021 - Nov 2021)
• Artificial Intelligence Engineer (Internship) | Conff R&D (Jan 2019 - Sep 2019)

Volunteer Experience

• Data Mining Analyst | AYA: Açık Yazılım Ağı (Feb 2023 - May 2023) - Kahramanmaraş Earthquake
  • Data-driven analysis to support community-led disaster response and recovery efforts.
• Research Student | Arterys (Oct 2020 - Sep 2021)
  • Participated in research activities focused on medical imaging and AI applications in diagnostics.

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Achievements

• 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.

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Certifications

• Future AWS AI Engineer | AWS / Udacity (October 2025) - Certificate
• 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
• Fundamentals of Neuroscience XSeries Program | Harvard University / edX (May 2022) - Certificate

Skills

• Programming and Language Technologies: Python, R, SQL (PostgreSQL), MATLAB, Bash
• Data Science & Machine Learning: PyTorch, TensorFlow, Keras, Scikit-Learn, Pandas, NumPy, Hugging Face, LangChain, Retrieval-Augmented Generation (RAG), Prompt Engineering
• Computational Neuroscience: Neuroimaging Analysis (MNE-Python, Nilearn, NiBabel, FSL), Medical Image Analysis, Statistical Modeling
• Cloud Technologies: AWS (Bedrock, S3, Aurora, SageMaker, Guardrails), Google Cloud Platform (GCP), Azure
• DevOps & Version Control: Docker, Kubernetes, Git, MLflow , Terraform (Infrastructure as Code)
• Web & API Development: FastAPI, Gradio, Ollama
• Research Skills: Responsible AI, AI Agent Design, Experimental Design, Statistical Analysis, Data Synthesis, Benchmarking, Model Validation, Scientific Communication

Publications & Scientific Writing

Curated scientific and research-oriented writing on AI, neuroscience, and motorsports:

👉 Scientific Writings – AI & Neuroscience
👉 NeuroQuantix Research (Neuroscience & Motosports) Blog 👉 Synaptic Library Blog

Connect with Me

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