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I'd like to make computers do more with less help from us, learn from experience, adapt effortlessly, and discover new knowledge.
My main research interests are in the fields of Applied Machine Learning, such as AI for Cyberdefense, memory/storage optimization, NLP, Predictive treat intelligence, and big data analytics. I'd like to make computers do more with less help from us, learn from experience, adapt effortlessly, and discover new knowledge. This poses many deep and fascinating scientific problems:
My main research interests are in the fields of Applied Machine Learning, such as AI for Cyberdefense, memory/storage optimization, NLP, Predictive treat intelligence, and big data analytics. I'd like to make computers do more with less help from us, learn from experience, adapt effortlessly, and discover new knowledge. This poses many deep and fascinating scientific problems:
- How can a computer decide autonomously which representation is best for target knowledge? How can it tell genuine regularities from chance occurrences?
- How can pre-existing knowledge be exploited?
- How can a computer learn with limited computational resources? How can the learned results be made understandable by us (i.e.., explainable AI)?
Overall, my research addresses these and related questions.
Overall, my research addresses these and related questions.
AI in Cyberdefense
AI in Cyberdefense
Threat detection is undoubtedly the main focus of today's digital business and government.
Threat detection is undoubtedly the main focus of today's digital business and government.
In the last few years, a lot of AI development is being spent in the cybersecurity space, as well it should come with the advent of ransomware, sophisticated malware, and the like.
In the last few years, a lot of AI development is being spent in the cybersecurity space, as well it should come with the advent of ransomware, sophisticated malware, and the like.
So far, our team has conducted three pieces of research that enable AI utilization for cyberattack detections and mitigations.
So far, our team has conducted three pieces of research that enable AI utilization for cyberattack detections and mitigations.
Enhancing Data Access Performance
Enhancing Data Access Performance
Since the first computer's invention, the clear division between the processing and storage units constrains the computer system's performance. Multicore processors dominate today's computing landscape, and the CPU execution keeps increasing, widening the gap with the storage devices.
Since the first computer's invention, the clear division between the processing and storage units constrains the computer system's performance. Multicore processors dominate today's computing landscape, and the CPU execution keeps increasing, widening the gap with the storage devices.
We devise a mechanism to speculate to be requested data with the AI model and move it closer to the processing unit to address this challenge. In doing so, we speedup the execution time up to 17% compared to the baseline designs.
We devise a mechanism to speculate to be requested data with the AI model and move it closer to the processing unit to address this challenge. In doing so, we speedup the execution time up to 17% compared to the baseline designs.
Natural Language Processing
Natural Language Processing
The global Natural Language Processing (NLP) market size is projected to reach USD 27.6 Billion by 2026. The major growth factors of the NLP market include the increase in smart device usage, growth in the adoption of cloud-based solutions and NLP-based applications to improve customer service, and the increase in technological investments in the healthcare industry.
The global Natural Language Processing (NLP) market size is projected to reach USD 27.6 Billion by 2026. The major growth factors of the NLP market include the increase in smart device usage, growth in the adoption of cloud-based solutions and NLP-based applications to improve customer service, and the increase in technological investments in the healthcare industry.
However, existing AI-based NLP solutions are not sufficient for resource-scarce languages. Hence, one of my research directions is to enable NLP models for resource-scarce languages such as Amharic, Oromiffa, Somali, Tigrinya, and other Ethiopian Languages.
However, existing AI-based NLP solutions are not sufficient for resource-scarce languages. Hence, one of my research directions is to enable NLP models for resource-scarce languages such as Amharic, Oromiffa, Somali, Tigrinya, and other Ethiopian Languages.
Big Data Analytics
Big Data Analytics
Driven by data growth and the availability of computing power, big data analytics is utilized by organizations to analyze raw data to make to find trends or conclusions about that information, which is vital to drive business solutions. Many of the techniques and processes of data analytics have been automated into mechanical methods and algorithms that work over raw data for human consumption.
Driven by data growth and the availability of computing power, big data analytics is utilized by organizations to analyze raw data to make to find trends or conclusions about that information, which is vital to drive business solutions. Many of the techniques and processes of data analytics have been automated into mechanical methods and algorithms that work over raw data for human consumption.
I'm also interested in applying a data analytics model on medical data, weather data, financial data, and IoT/sensor data.
I'm also interested in applying a data analytics model on medical data, weather data, financial data, and IoT/sensor data.
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