Anam Nazir hold master’s degree in Computer Science from COMSATS University Islamabad,
Pakistan and bachelor’s degree in Telecommunication
and Networking form COMSATS University Islamabad, wah, Pakistan. She earned her Doctoral Degree in Computer science from Shanghai Jiao Tong University, China. She works in COMSATS institute
of information technology, Pakistan as faculty member and has seven years work experience in the areas of teaching, research, programming, student’s affairs and attending research conferences, seminars, workshops, events.
My research interests span computer vision methods for medical imaging problems. While my recent research uses advances of deep learning in the context of developing hybrid systems to deliver spatial structure-aware hybrid segmentation along with better visualization. My research focuses on machine learning techniques and probabilistic models for healthcare, with an emphasis on the analysis of medical images. As the availability of biomedical data explodes, we have an unprecedented opportunity to accelerate our understanding of fundamental scientific concepts and improve clinical practice. In my research, I seize this opportunity by developing systems that learn to extract knowledge from complex biomedical data, starting with medical images. My goal is to advance techniques in machine learning and computer science that lead to improved understanding of disease and patient care. My main contributions have been published in the top IEEE Transactions journal (TIP, TBME, TII, IET).
I believe successful analytic solutions that impact clinical research and treatment are rooted in close clinical collaborations and draw on technical insights from computer science, biomedical engineering, and medicine. In my research, I developed and published several machine learning methods and probabilistic models that enabled analysis of thousands of patients. In turn, these methods facilitated clinical findings that have been presented in several clinical papers, talks, and posters. I thoroughly enjoy working with students and have supervised several undergraduate students. I draw inspiration from multiple disciplines and enjoy actively collaborating on projects in machine learning, computer vision and computational imaging. Outside the field of medical imaging, my colleagues and I developed a method for natural level processing, network simulator 2 and developed a plugin for ns2. These methods touch a wide range of different fields, yet have direct applicability to medical imaging and healthcare. I will contribute to technical advancements in a breath of fields, with a focus on machine learning for healthcare applications. I am excited to collaborate on natural language processing to connect biomedical data with clinical narratives, computer graphics to improve medical image synthesis and data imputation methods, and video processing to analyze and predict temporal and longitudinal biomedical data.