introduction/brief CV
PhD in Computer Science from the University of Manchester working as a data scientist in AI and Bioinformatics. Applying machine learning and AI techniques, developing a mathematical model to medical challenges.
My research interest is focused on developing innovative methods based on explainable and trustable AI, machine learning, causal inference and reinforcement learning to build computational models for addressing the pressing medical need and to understand cellular mechanisms, cell types, pathology, and molecular pathways, resulting from medical data analysis. I have advanced skills and knowledge in Probabilistic Machine Learning, Gaussian Process and sparse inference to model biological systems
areas of expertise
Expert in Artificial Intelligence specialized in machine learning and computational biology
publications
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Motivation: The negative binomial distribution has been shown to be a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides…
The median problem is significantly applied to derive the most reasonable rearrangement phylogenetic tree for many species. More specifically, the problem is concerned with finding a permutation…
BeamGA is a general hybrid heuristic framework that can be used to solve the median problem in comparative genomics, where any distance function can be used. It starts with a heuristic search…
courses
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This course teaches data mining concepts and techniques, and basic machine learning techniques. Topics covered include basic statistical descriptions of data, measuring data similarity and…
This course introduces students to the wide field of Artificial Intelligence (AI) with emphasis on its use to solve real world problems. Students will be trained to get a basic and solid…
This course serves as a broad introduction to artificial intelligence and machine learning. We will cover the fundamentals of supervised and unsupervised learning. We will focus on policy gradient…