Research in an academic environment provides a unique opportunity to solve real-life problems and vision the technology of the future. My current research interests are concerned with the development of methods and algorithms for decentralized artificial intelligence, machine learning, and data mining. Computer-generated data is becoming ubiquitous in all real-life applications ranging from business to more serious applications like medical and scientific data visualization. In the past two decades, there have been many advances in the field of machine learning and AI. Despite numerous technological advances in both graphics hardware as well as algorithms, many challenging problems still limit the realism and effectiveness of computer-generated data.

My main research goal is to create tools and algorithms for efficient machine learning and AI with private data. Nowadays, data regulated under GDPR and HIPPA are becoming more and more complex to analyze, and as the Web continues to grow, it is important to understand these huge amounts of data for two reasons, first, to better serve customers. For example, giant companies use the data to suggest products, and movies to their users; Google and similar companies are providing personalized search results, targeted advertising, and many other services. Second, an in-depth understanding of the data allows us to leverage it for a variety of purposes. By preserving data privacy, I am particularly interested in developing new methods and algorithms for knowledge discovery, machine learning, AI applications and present them as a valid element for decision-makers.

While pursuing my Ph.D., I implemented state-of-the-art knowledge discovery techniques, big data, machine learning, and multiple criteria decision analysis to find solutions for problems around us such as road safety. I also proposed methodologies to evaluate association rules issued from the KDD process, which has been used in real-life challenges, for instance, road safety, text mining, and social networks. Consequently, I had proposed many approaches to process and analyze large datasets. Besides, I have a substantial amount of industry experience in data mining-related research and applied machine learning. From my previous experiences and current interests, I see myself doing research in data mining, social science, transportation, and business, with a special focus on data mining, Artificial Intelligence, and machine learning.

In the upcoming years, I would like to explore a broad range of topics both within machine learning and federated learning areas that improve efficiency, quality, and effectiveness of depiction while exploiting fields ranging from computer science to medical and business. My research agenda decomposes into many conceptual problems, which will often require technical innovations along three dimensions: models, algorithms, and experimental evaluation. Indeed, I see myself doing research in federated learning with researchers from computer science and other disciplines such as healthcare and business. Following are a few suggested projects but not limited to: