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Department of Computer Science

Dr. Shamsuddin's Lab

iLead


Biography

Dr. Rittika Shamsuddin aims to improve communication between researchers in fields of computer science, biology, and medicine via knowledge-sharing and by developing algorithms and experiments, which will increase the interpretability and generalizing ability of various machine learning and artificial intelligence models. Such libraries and developments are necessary for extending the technological success of computational fields to solve problems in healthcare/biology with a higher degree of trustworthiness and reliability than exists at present. She has multiple internal grants, published papers and is active in service.

Dr. Shamsuddin is also very passionate about computer science and programming and has been an active advocate for CS, especially among women, since her Ph.D. years. She believes that programming is a way of thinking and can be taught and learned through the use of the newer technology-based curriculum.

She obtained her Ph.D. from the University of Texas at Dallas on Analyzing and Synthesizing Healthcare Time Series Data for Decision-Support, where she worked in the Multimedia Systems Laboratory led by Prof. Balakrishnan. Before that, she graduated from Mount Holyoke College, with a double major in Biology and Computer Science, where her honor thesis included working on Using Rigidity Analysis To Identify Hinge Motion in Proteins under the supervision of Professor Audrey St. John


Research Interest

  • Machine learning

  • Health informatics and information systems

  • Artificial Intelligence

  • Data mining and knowledge discovery

  • Data engineering and data science

  • Pattern recognition


iLead Lab Values

Interdisciplinary Research

Applying machine learning and other computational tools to datasets without domain knowledge is dangerous! A big no, no!

Collaboration, Collaboration!

Learning to work in teams to create a brighter future and to dream bigger!

Healthcare Innovations

Using technology and computer science to improve healthcare systems and services.

Machine Learning, Artificial Intelligence, Statistics, Data Science

Develop concepts and algorithmic structures to make intelligent decisions

Ethics

We have a responsibility to provide a broader perspective on the implications of technology including where and how it might be used unethically or cause harm.

Privacy and Security

Attacks, unfortunately, are possible in each of the three phases (data collection, training, and inference) of the machine learning pipeline.

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