Big Data Analytics
big data analytics
Dr. D.M. Lyons
Fordham University (New York City) has developed a unique Ph.D. program in Computer Science, tuned to the latest demands and opportunities of the field. Upon completion of the Ph.D. in Computer Science program, students will be able to demonstrate the fundamental, analytical, and computational knowledge and methodology needed to conduct original research and practical experiments in the foundations and theory of computer science, in software and systems, and in informatics and data analytics. They will also be able to apply computing and informatics methods and techniques to understand, analyze, and solve a variety of significant, real-world problems and issues in the cyber, physical, and social domains. Furthermore, they will be able to conduct original, high-quality, ethically informed, scientific research and publish in respected, peer-reviewed, journals and conferences. Lastly, they will be able to effectively instruct others in a variety of topics in Computer Science at the university level, addressing ethics, justice, diversity, and sustainability. This training and education means that graduates can pursue careers at the university level, but also research and leadership positions in industry and government and within the leading technology companies. A hallmark of the program is early involvement in research, within the first two years of the program. Students will have the opportunity to carry out research in machine learning and AI/robotics, big data analytics and informatics, data and information fusion, information and cyber security, and software engineering and systems.
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The KINDI Center for Computing Research at the College of Engineering in Qatar University is seeking high-caliber candidates for a research faculty position at the level of assistant professor in the area of artificial intelligence (AI). The applicant should possess a Ph.D. degree in Computer Science or Computer Engineering or related fields from an internationally recognized university and should demonstrate an outstanding research record in AI and related subareas (e.g., machine/deep learning (ML/DL), computer vision, robotics, natural language processing, etc.) and fields (e.g., data science, big data analytics, etc.). Candidates with good hands-on experience are preferred. The position is available immediately.
How AI is advancing Clinical Neuropsychology and Cognitive Neuroscience
This talk aims to highlight the immense potential of Artificial Intelligence (AI) in advancing the field of psychology and cognitive neuroscience. Through the integration of machine learning algorithms, big data analytics, and neuroimaging techniques, AI has the potential to revolutionize the way we study human cognition and brain characteristics. In this talk, I will highlight our latest scientific advancements in utilizing AI to gain deeper insights into variations in cognitive performance across the lifespan and along the continuum from healthy to pathological functioning. The presentation will showcase cutting-edge examples of AI-driven applications, such as deep learning for automated scoring of neuropsychological tests, natural language processing to characeterize semantic coherence of patients with psychosis, and other application to diagnose and treat psychiatric and neurological disorders. Furthermore, the talk will address the challenges and ethical considerations associated with using AI in psychological research, such as data privacy, bias, and interpretability. Finally, the talk will discuss future directions and opportunities for further advancements in this dynamic field.
Biomedical Image and Genetic Data Analysis with machine learning; applications in neurology and oncology
In this presentation I will show the opportunities and challenges of big data analytics with AI techniques in medical imaging, also in combination with genetic and clinical data. Both conventional machine learning techniques, such as radiomics for tumor characterization, and deep learning techniques for studying brain ageing and prognosis in dementia, will be addressed. Also the concept of deep imaging, a full integration of medical imaging and machine learning, will be discussed. Finally, I will address the challenges of how to successfully integrate these technologies in daily clinical workflow.