Eigenbytes Logo

EigenBytes decodes data, one byte at a time

About Me

Jeffrey A. Young

Hello! I'm Jeffrey A. Young, a dedicated data science professional with a passion for transforming data into actionable insights. With a strong foundation in statistical analysis and machine learning, I strive to solve complex problems and drive innovation.

Professional Background

I have extensive experience in the field of data science, working on projects that span various industries. My expertise includes data modeling, predictive analytics, and the development of scalable data solutions.

Education

I hold a degree in Computer & Information Science from GSU, where I specialized in predicitve models, feature extraction, IoT security, simulated annealing, stochastic neural networks, stochastic optimization, and genetic algorithms. Also, I am finishing up my PhD from Clemson University in the field of cyber physical system and AI security. My academic journey has equipped me with the skills necessary to excel in the ever-evolving landscape of data science.

📚 Connect with Me

I'm always eager to connect with fellow professionals and enthusiasts. Feel free to reach out via the Contact page or follow one of the following links.

Publications:

Aldeen, M., Young, J., Liao, S., Chang, T.-Y., Cheng, L., Cai, H., Luo, X., & Hu, H. (2024). End-Users Know Best: Identifying Undesired Behavior of Alexa Skills Through User Review Analysis. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 8(3), 1–28.
Cheng, L., Wilson, C., Liao, S., Young, J., Dong, D., & Hu, H. (2020). Dangerous skills got certified: Measuring the trustworthiness of skill certification in voice personal assistant platforms. Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, 1699–1716.
Hashemi, R. R., Ardakani, O., Bahrami, A., Young, J., & Campbell, R. (2018). A Mining Driven Decision Support System for Joining the European Monetary Union. The Eighth International Conference on Advances in Information Mining and Management (IMMM’18), Barcelona, Spain, 39–45.
Hashemi, R. R., Ardakani, O. M., Bahrami, A. A., & Young, J. A. (2017). Extraction of the Essential Constituents of the S&P 500 Index. 2017 International Conference on Computational Science and Computational Intelligence (CSCI), 351–356.
Hashemi, R. R., Ardakani, O. M., Bahrami, A. A., & Young, J. A. (2020). A mediated multi-RNN hybrid system for prediction of stock prices. 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 382–387.
Hashemi, R. R., Ardakani, O. M., Young, J. A., & Baharami, A. G. (2022). An RNN model for exploring the macroeconomic and financial indicators in the context of the COVID-19 pandemic. 2022 International Conference on Computational Science and Computational Intelligence (CSCI), 652–657.
Hashemi, R. R., Ardakani, O. M., Young, J. A., & Tamrakar, C. (2021). Mining the impact of social media on high-frequency financial data. 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 262–267.
Hashemi, R. R., Ardakani, O. M., Young, J., & Bahrami, A. G. (2024). 10 RNN models for evaluating financial indices: Examining volatility and demand-supply shifts in financial markets during COVID-19. Big Data, Data Mining and Data Science: Algorithms, Infrastructures, Management and Security, 2, 165.
Hashemi, R. R., Bahrami, A. A., Young, J. A., Schrey, A., Robbins, T., Ragsdale, A., & Langkilde, T. (2018). Effects of Phenotypical Patterns on Epigenetic Markers. 2018 International Conference on Computational Science and Computational Intelligence (CSCI), 1351–1356.
Hashemi, R. R., Bahrami, A. A., Young, J. A., Tyler, N. R., & Hodgson, J. Y. (2017). Mining Diatom Algae Fossil Data for Discovering Past Lake Salinity. IADIS International Journal on Computer Science & Information Systems, 12(2).
Hashemi, R. R., Bahrami, A., Young, J., & Schrey, A. W. (2017). An Ecological-Based Genetic Investigation Using Association Analysis Approach BIOCOMP’16. Proceedings of the 17th International Conference on Bioinformatics and Computational Biology.
Hashemi, R. R., Rasheed, A., Young, J., & Bahrami, A. A. (2018). A Block Cipher Masking Technique for Single and Multi-Paired-User Environments. International Journal on Advances in Security, 11(1–2), 180.
Hashemi, R. R., Rasheed, A., Young, J., & Bahrami, A. A. (2019). Privacy Preserved Authentication: A Neural Network Approach. 14th System of Systems Engineering Conference (SoSE), Anchorage, Alaska.
Little, A., Hashemi, R. R., & Young, J. A. (2020). Discovery of burglary hotspots and extraction of their features. 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 412–417.
Rasheed, A., Hashemi, R. R., Bagabas, A., Young, J., Badri, C., & Patel, K. (2019). Configurable anonymous authentication schemes for the Internet of Things (IoT). 2019 IEEE International Conference on RFID (RFID), 1–8.
Young, J. A. (2019). IoT Security Through Device Profiling.
Young, J. A., Rasheed, A., Heshemi, R. R., & Bagabas, A. (2020). A Methodological Framework for Validating ZKP Authentication Process. 2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET), 37–43.
Young, J., Liao, S., Cheng, L., Hu, H., & Deng, H. (2022). {SkillDetective}: Automated {Policy-Violation} detection of voice assistant applications in the wild. 31st USENIX Security Symposium (USENIX Security 22).