Machine Learning Department Team

 

 

Issam El Naqa, PhD - Chair/Senior Member

Issam El Naqa, PhDDr. El Naqa is a renowned leader in the field of data science with formal training in electrical engineering, computer science, biology and medical physics. Dr. El Naqa’s research focuses on developing large-scale data mining methods to identify biomarkers of response to chemoradiotherapy, multimodality image-guided targeted and adaptive radiotherapy, and radiobiology-based treatment outcome modeling. 

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Meet our Staff Members


Yoga Balagurunathan, PhDYoga Balagurunathan, PhD - Assistant Member

Dr. Balagurunathan’s research is focused on understanding the physiology of the tumor and its relationship to the underlying genome. His interests include data integration from various modalities (radiology, pathology, genome) to improve clinical decision support. His disease focus includes prostate cancer, lung cancer and B-cell lymphomas.

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Aleksandra Karolak, PhD

Aleksandra Karolak, PhD - Assistant Member

Dr. Karolak’s background includes applications and development of tools from the fields of computational and biophysical chemistry, structural biology, mathematical oncology, machine learning and information theory. Her interests focus on understanding cancer development, progression, and variability in the response to treatment using models that can be translated into the clinic.

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Yi LuoYi Luo, PhD - Assistant Member

Dr. Luo’s research focuses on machine learning, systems informatics and their application to health outcomes, decision support, interpretable and credible models at both the individual and community levels for precision medicine, health equity and healthcare quality.

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Thanh Thieu, PhD - Assistant Member

Thanh ThiuDr. Thieu has been pursuing research in natural language processing, machine learning, and artificial intelligence with application in healthcare, education, and bioinformatics. His work involved standardization and identification of whole-person mobility terminology from clinical notes, high throughput text mining from scientific literature, lexical complexity and language generation, computer-assisted coding for medical billing, and compositional and evolutionary learning. Having worked in academia, government, and industry, Dr. Thieu has developed the capability to collaborate across educational backgrounds, ethnicities, genders, and origins.

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Ghulam Rasool

Ghulam Rasool, PhD - Assistant Member

Dr. Rasool has a background in machine learning, biomedical image analysis, and signal processing. His prior work has focused on using Bayesian techniques for trustworthiness, robustness, uncertainty estimation, and continual learning in deep neural networks. He currently focuses on developing robust and explainable machine learning models for various medical imaging and signal processing applications. He is also interested in exploring machine learning paradigms that can tackle datasets from multiple scales and learn to answer clinically relevant questions. Such models will be robust to day-to-day changes in the input data and must explain their decisions to users, thus building trustworthiness.