Alemu Sisay

Developed and optimized machine learning algorithms for LBP severity grading, focusing on robust performance across various MRI datasets. Evaluated algorithms on internal and external validation datasets to ensure generalization and clinical applicability. Explored transfer learning and domain adaptation techniques to adapt models to different MRI scanners and patient demographics.

Analyzed MRI data and developed robust Deep Learning models for disc severity grading. Contributed to the design and integration of AI tools within the EMR system. Created a graphical interface for seamless deployment of the DL model to facilitate adoption by medical personnel.

Acquired digital medical images and performed follow-up therapeutic and image data analysis for medical diagnosis activities. Utilized deep learning methods for medical image analysis, including segmentation.

Conducted teaching activities in the Electrical and Computer Engineering department, delivering courses on theoretical and practical aspects. Taught subjects including computer programming, signal and system analysis, fundamental electrical circuits, digital logic design, and communication systems.

Handled customer complaints and troubleshooted technical difficulties related to telecommunication services. Oversaw billing and payments for customers and contributed to developing a new system for SIM card profile data management.

Pursued a Ph.D. focusing on Artificial Intelligence in Medicine and Innovation in Clinical Research and Methodology. This program provided advanced training in applying AI to medical contexts and clinical research.

Participated in a 5-month Erasmus+ mobility study program. Focused on Sensor Network programming, Physical Modeling of Embedded systems, and Business English.

Grade: 3.89/4

Earned a Bachelor's degree in Electrical and Computer Engineering with a specialization in Communication Engineering. The thesis focused on a Fingerprint Attendance System (FAS).

Grade: 110/110 cum laude

Completed a Master of Science in Telecommunications Engineering, specializing in Communication Technologies and Multimedia. The curriculum covered advanced topics in computer programming, machine learning, deep learning, and medical image analysis.

Attended a 6-day PhD School focused on Statistical Methods & Data Analysis in Medical Research. The course provided fundamentals for analyzing clinical data using parametric or non-parametric methodologies.

Participated in the IEEE ComSoc eHealth TC Ph.D. School, designed to empower Ph.D. students with a structured academic curriculum. Engaged with keynotes and lecture series to broaden skill sets and enhance interpersonal abilities, with an emphasis on international networking.

Completed an Applied Data Science Lab short course, demonstrating completion of eight end-to-end, applied data science projects. Demonstrated ability to access, explore, and clean data from various sources, create ETL pipelines, build machine learning models, and create visualizations.

Obtained a Higher Diploma Program Certificate in Teaching Methods. The training focused on classroom management and creating interactive learning activities.

Completed secondary school education.

Completed a 3-month Oxford Machine Learning Summer School, covering MLx Fundamentals, MLx Cases, MLx FINANCE & NLP, and MLx HEALTH. Gained training in linear algebra, optimization, statistical/probabilistic ML, deep learning, and real-world ML implementation.