Rami Abielmona

Dr. Rami Abielmona

is the Vice President of Research & Engineering at Larus Technologies Corporation. He is responsible for all research and development of AI/ML software, hardware and products, as well as the management and direction of the research team.

Rami joined Larus in 2007 to lead the integration of software engineering corporate knowledge with research in artificial intelligence, machine learning, wireless sensor and robotic networks, multi-sensor data fusion and computationally intelligent processing architectures, resulting in the patented Larus Total::Insight™ Decision Support System (DSS) as well the Larus Total::Perception™ Systems Simulation Engine (SSE). Rami also directs, manages and oversees the internal technology pipelines and innovation cycles as well as the technical demonstrations given to Larus’ strategic partners.

Rami received his B.A.Sc., M.A.Sc. and Ph.D. degrees in Computer and Electrical Engineering, from the School of Information Technology and Engineering at the University of Ottawa, where he currently serves as an Assistant Professor. He was a CMC Success Story in 2005 for his Ph.D. research. He received the NSERC Industrial Research and Development Fellowship (IR&DF) in 2007 and the IEEE MGA Achievement Award in 2008. He was one of the recipients of the Ottawa Business Journal (OBJ) Top 40 Under 40 Award in 2011. He was also named as the Part-Time Professor of the Year at both the Faculty of Engineering and the University of Ottawa in 2012. He was awarded the IEEE Ottawa Section Outstanding Volunteer Award in 2014. He was a recipient of the NSERC Synergy Award for Innovation (for Small and Medium-Sized Companies) in 2016 and most recently, received the IEEE Ottawa Section Outstanding Engineer Award in 2020. He also became a licensed Professional Engineer in Ontario in 2008, as well as a Senior Member of the IEEE in 2011.

Larus Technologies (Vice-President) and University of Ottawa (Assistant Professor)

Email: rabielmo@ieee.org

DVP term expires December 2023

Presentations

All-Domain Awareness According to AI/ML-Driven Big Data Analytics

Decision support systems (DSSs) are playing an increasingly important role in the characterization of suspicious activities in an area of interest given their proven ability to turn vast amounts of raw data into actionable intelligence that is easy to understand and act upon by human operators. In this talk, we present real-world AI/ML Big Data solutions involving unique learning algorithms that allow one to process vast amounts of critical information combined with knowledge acquired from specific domains. These unique models and architectures continually deliver the most accurate information possible in order to constantly optimize the decision maker’s domain awareness. Attendees will learn innovative concepts such as anomaly detection, sensor cross-cueing and tasking, automated data collection scheduling and planning as well as response generation to provide focused surveillance and account for behavioural intents while ensuring that no event of interest is missed because of human fatigue or because of data overload while only relevant alarms are investigated.

5 Questions Every Decision Maker Should Ask (and answer with the help of AI/ML)

In this talk, I will present real-world AI/ML Big Data solutions involving unique learning algorithms that allow one to process vast amounts of critical information combined with knowledge acquired from specific domains. Attendees will learn innovative concepts such as anomaly detection, trajectory prediction, sensor cross-cueing and tasking, automated data collection scheduling and planning as well as response generation to provide focused surveillance and account for behavioral intents while ensuring that events of interest are not missed because of human fatigue or because of data overload while only relevant situations are investigated.

Distilling AI: The Hitchhiker's Guide

In this talk, I will present real-world AI/ML Big Data solutions involving unique learning algorithms that allow one to process vast amounts of critical information combined with knowledge acquired from specific domains. These unique models and architectures continually deliver the most accurate information possible in order to constantly optimize the decision maker’s domain awareness. Attendees will learn innovative concepts such as the five levels of Big Data Analytics, the various variations of AI including Machine Learning, Deep Learning and Machine Intelligence, the transformational changes that AI can bring about in the near future as well as the challenges and opportunities to deploy AI-ready applications in mission-critical tactical environments.