Passionate about building modern data-driven solutions for business and technology, working on research projects with the NSF and NDOT with 4+ years of experience in Data Analytics, machine learning, deep learning, and AI.
- Hasan, M., Al-Ramini, A., Abdel-Rahman, E., Jafari, R. and Alsaleem, F., 2020. Colocalized Sensing and Intelligent Computing in Micro-Sensors.
- Al-Ramini A, Takallou MA, Piatkowski DP, Alsaleem F. Quantifying changes in bicycle volumes using crowdsourced data. Environment and Planning B: Urban Analytics and City Science. January 2022. doi:10.1177/23998083211066103
- Alsaleem, F., Al-Ramini, A., Takallou, M.A. and Piatkowski, D.P., 2020. A Big Data Approach for Improving Nebraska Cycling Routes (No. M095). Nebraska. Department of Transportation.
- Al-Ramini A, Piatkowski DP, Freifeld A, Alsaleem F. How the pandemic changed bicycling: Lessons from The Midwest. 2022 State of Planning Transportation. https://issuu.com/apatransport/docs/2022_sotp
- Al-Ramini, A.; Hassan, M.; Fallahtafti, F.; Takallou, M.A.; Rahman, H.; Qolomany, B.; Pipinos, I.I.; Alsaleem, F.; Myers, S.A. Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data. Sensors 2022, 22, 7432. https://doi.org/ 10.3390/s22197432
- 2021. Al-Ramini*, Ali, Mohammad Takallou, Daniel Piatkowski, & Fadi Alsaleem. Quantifying the Effect of Signage on Bicycle Ridership. Presentation at the 100th Annual Meeting of the Transportation Research Board; Washington D.C; January 25-29.
Big Data Approach to Analyze Nebraska Cycling Routes (2019 –2020)
Research Project Funded by the Nebraska Department of Transportation.
- Created statistical analysis and visualization of cyclist data.
- Built machine learning models to predict the effect of weather on cycling activities.
- Performed statistical Correlation study between Strava cycling application and stationary counters data.
- Used GIS software to properly analyze and visualize the data.
- Quantified the effect of newly added infrastructure on cycling activities using machine learning methods.
Colocalized Sensing and Intelligent Computing in Micro-Sensors (2019 – 2020)
Research Project Funded by the NSF.
- Demonstrated a reservoir computing scheme using a single MEMS sensor to perform colocalized sensing and computing to reduce the cost of reservoir computing implementation.
- Studied the effect of continuous and shock signals on MEMS using a mechanical shaker and laser Doppler Vibrometer.
COVID-19 Rapid Response (2020 – 2022)
Research Project Funded by the University of Nebraska
- Used several data sources to predict COVID-19 hotspots, including smart thermometer data, demographics, and mobility.
- The prediction model ranked as one of the Top 10 predictions in the XPRIZE Pandemic Response Global Challenge.
- Showed the effect of COVID-19 on the cycling activities, and published the results in the 2022 Transportation Planning
Peripheral Artery Disease (PAD) Identification and quantifying treatment effectiveness using Deep Learning and Artificial Intelligence (2021 – Present)
Research Project Funded by the NSF
- Built a machine learning neural network model that identifies Patients with PAD.
- Estimating PAD treatment effectiveness using machine learning probabilistic models.
- 2021 TRB MATC/NTC Scholarship
- Ranked Top 10 XPRIZE Pandemic Response Global Challenge.
Ph.D. COURSE WORK
Data Visualization · Data Science · Linear Models · MEMS and Machine Learning · Machine Learning · Deep Learning · Advanced Dynamics and Vibrations · Engineering Advanced Mathematics · Lab View (sensors and data acquisition) · Building Control
CITI Programs Training
• Group 1: Biomedical Research • Group 2: Good Clinical Practice (GCP) • VA ORD Biosecurity Training
• VA Human Subjects Protection