Ali Hazem Al Ramini

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.

PUBLICATIONS

• 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

CONFERENCE PRESENTATIONS

• 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.

PROJECTS

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.

AWARDS

• 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