Adrian Kieback

Welcome to My Portfolio

My name is Adrian Kieback. I specialize in Machine Learning and Big Data Analytics.

View My Work

About Me

I'm a passionate Machine Learning Researcher currently focused on audio voice processing for advanced depression detection. I enjoy working on projects that combine cutting-edge neural networks with practical data solutions. My background includes data analytics, Python, R, and a strong interest in bridging research with real-world applications.

Skills

Programming Languages

Python, R, JavaScript, Java, C++, ABAP

Frameworks & Libraries

PyTorch, scikit-learn, TensorFlow, Transformers, NumPy, Pandas, p5.js

Web & Big Data Tools

HTML, CSS, PHP, Docker, Spark, SQL, AWS EC2, Tableau, Power BI, PowerApps

Fundamentals

CI/CD pipelines, Linux CLI, time-series modeling, model evaluation, data cleaning, stakeholder collaboration

Languages & Certifications

English (Fluent), German (Native), French (Basic)
Certified Scrum Master, iOS App Development

Work Experience

Machine Learning Researcher I 09/2023 – Present

James Silberrad Brown Center for Artificial Intelligence, San Diego, USA

  • Automated and enhanced audio voice processing by implementing advanced neural network models, improving depression detection accuracy.
  • Increased macro F1 score by 21% compared to state-of-the-art systems.
  • Utilized a server with two A100 to optimize models to be 72% more parameter-efficient than industry standards.
  • Optimized and fine-tuned stable diffusion models (LAION 5B dataset), improving performance and efficiency.
  • Published research findings on arxiv.org/abs/2409.00391.

Data Science Intern 02/2024 – 05/2024

SANDAG, San Diego, USA

  • Visualized geospatial data for San Diego County to track UN Sustainable Development Goals.
  • Conducted extensive data cleaning to ensure accuracy of published statistics.
  • Automated predictive analytics workflows for toll operations on 90,000+ customer accounts.

Data Analyst Research / Graduate Assistant 08/2023 – 05/2024

San Diego State University, San Diego, USA

  • Proctored and graded exams for two professors' classes, ensuring timely feedback.
  • Automated a web scraping process to collect data using Python.
  • Utilized Python and R to enhance data collection and processing efficiency.

Business Scientist Intern / Bachelor Thesis Author 02/2022 – 08/2022

Carl Zeiss AG, Aalen, Germany

  • Developed BI reports for 1,200+ machines globally, integrating live data from multiple sources.
  • Performed stakeholder interviews and built a forecasting dashboard, improving data-driven decisions.
  • Influenced software choice, achieving annual savings of \$30,000.

Projects

Baseball Prediction

  • Developed a Dockerized Python tool for statistical analysis and predictive modeling, generating HTML reports.
  • Engineered 20+ time series features (SQL, PySpark, MariaDB) and implemented an Elo-ranking system.
  • Evaluated six ML models using ROC curves and cross-validation, selecting Logistic Regression for best performance.

Interactive Web-Based Visualization of Public Health Data

  • Created interactive web maps with p5.js and Leaflet.js to show surgical site infection data across California.
  • Processed 26,000+ rows of public health data, enabling dynamic, user-friendly visualization.

Education

San Diego State University | MS Big Data Analytics (Summa cum laude)

GPA: 3.9

  • Master Thesis: “Enhancing Mental Health Diagnosis with Representation Learning: A Transformer-Based Approach”

Albstadt-Sigmaringen University | BS Business Informatics

GPA: 3.5

  • Bachelor Thesis: “Conceptual design of a central tool for the globally standardized presentation and analysis of process and machine data”

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