About Me
I’m an Mid-Senior Data Scientist and AI professional with over 5+ years of experience across both corporate industry and academic environments. My background includes hands-on experience in AI research and practical applications, such as developing real-time AI predictive services, building object detection systems, creating generative AI models, optimizing product designs, and enhancing quality performance through machine learning and deep learning techniques. Currently, pursing MSE in Data Science at Johns Hopkins University Whiting School of Engineering to specialize and advance my expertise. I’m a lifelong learner, passionate about applying advanced AI techniques to solve complex and real-world problems.
Education
MSE. in Data Science ; Jan 2025 – Apr 2026
Johns Hopkins University, Whiting School of Engineering ; Baltimore, USA
Cumulative GPA: 3.86/4.0
Relevant Coursework: Computing for Applied Math., Statistical Methods&Data Analysis, Cloud Computing, Machine Translation, Data Mining, Non-linear Optimization, AI&BioInformatics.
Mechatronics Engineering ; – Aug 2021
Hanyang University, Graduate School of Hanyang University ; Seoul, South Korea
Cumulative GPA: 4.0/4.0
Relevant Coursework: Machine Learning, Artificial Intelligence & Problem solving, Reinforcement Learning, Internet of Things(IoT)
SKILLS
Programming Lang. and Tools: Python, R, SQL, Java, Tableau, Tensorflow, Pytorch, Keras, Git, GitHub, FastAPI, Streamlit, Azure, AWS, MATLAB, LATEX, Linux, Excel.
Industry Knowledge: Machine/Deep Learning, Natural Language Processing(NLP), LLM, Feature Enginering, Data Analysis, DevOps, MLOPs, Model Deploymen, Retrieval Augmented Generation(RAG), Agentic AI.
Interpersonal Skills: Excellent Communication, Cross functional Collaboration, Multitasking, Strong leadership, Problem-Solving, goal-oriented.
PUBLICATION
Thomas Kidu, Y. Song, K.-W. Seo, S. Lee, T. Park. An Intelligent Real-Time Driver Activity Recognition System Using Spatio-Temporal Features. Applied Sciences (MDPI), Sep 2024.
Thomas Kidu, Y. Legese, Y. Abraha, H. Gebrekidan, T. Tesfaye, A. Bhuvaneswari. Towards an Optimization-Driven Deep Learning Framework for Multi-Class Brain Tumor Detection in MRI Imaging. Proc. IEEE 6th International Conference on Computers and Artificial Intelligence Technology (CAIT), 2025.
Thomas Kidu, H. Kethar, H. Gebrekidan, H. Farman, A. Sedik, W. El-Shafai, J. Khan. Predicting Immunotherapy Outcomes in Colorectal Cancer Using Machine Learning and Multi-Omic Biomarkers: Development of a Real-Time Predictive Web Application. CMES (Under review).
Thomas Kidu, Y. Abraha, Y. Berhane, H. Gebrekidan, T. Tesfaye. Automated Weld Quality and Defect Detection for Industrial Applications: A Deep Learning Framework for Real-Time Visual. IEEE ISDFS, 2026 (Accepted).
K. Gleason, Thomas Kidu, V. Babu, B. Hasselfeld. A Secure User Interface for Pre-Clinical Evaluation of Artificial Intelligence in Patient Portal Message Management. JMIR (Submitted).
H. Gebrekidan, Thomas Kidu. Point-of-Care Segmentation: A Pragmatic Benchmark of Lightweight CNN, Transformer, and MLP-Hybrid Models with a Composite Accuracy–Efficiency Metric. Conference on Health, Inference, and Learning (CHIL) (Submitted).
H. Gebrekidan, Thomas Kidu. Data-Driven Model Predictive Voltage Control for an IEEE 9-Bus System with High Renewable Penetration. Proc. IEEE ICPEE, 2025.
Thomas Kidu, V. R. Babu, A. Zhu. Cloud Reliability Analysis via Log-Based Anomaly Detection: An End-to-End Evaluation on HDFS. IEEE ISDFS, 2026 (Submitted).
A. Pandita, G. Rios, Thomas Kidu. Sentiment Analysis of STEM Students’ Academic Experiences: A Comparative Study Across Top U.S. Universities. IEEE STEM EDU (Accepted).
Contact Me
Feel free to reach out with questions, collaboration ideas, or just to connect.
Get in touch with me