Departmental Research Areas
Performing research at the foundations of Data Science or in Data Science applications.
Department of Biological Sciences
Research includes topics such as: determination of protein and nucleic acid structures, the structure and mechanism of protein and RNA enzymes (including proteins involved in cancer), structures of macromolecular complexes, study of the structure and mechanism of viruses (including emerging pathogens such as West Nile and Dengue viruses), genomics, transcriptomics, proteomics, systematics, and computational systems biology, molecular dynamics, machine learning and other topics at the interface of experiment and computation.
Department of Computer Science
Faculty in the area of bioinformatics and computational biology apply computational methodologies such as databases, machine learning, discrete, probabailistic, and numerical algorithms, and methods of statistical inference to problems in molecular biology, systems biology, strucutral biology, and molecular biophysics.
The data revolution is having a transformational impact on society and computing technology by making it easier to measure, collect, and store data. Our databases and data mining (big data) research group develops models, algorithms, and systems to facilitate and support data analytics in large-scale, complex domains. Application areas include database privacy and security, web search, spatial data, information retrieval, and natural language processing.
Recent increases in data collection and large-scale computing have facilitated successful application of machine learning and artificial intelligence methods across a wide range of fields, including healthcare, education and industrial systems. The Machine Learning and Information Retrieval group develops statistical methods and algorithms to learn models of the world from observations of past behavior, and evaluates these methods in real world applications in complex domains.
Members of the group work in areas that include analysis of algorithms, parallel computation, computational algebra and geometry, computational complexity theory, digital watermarking, data structures, graph algorithms, network algorithms, distributed computation, information theory, analytic combinatorics, random structures, external memory algorithms, and approximation algorithms.
Department of Statistics
The group is focused on research that includes topics on big data, machine learning, deep/reinforcement learning, semi-nonparametric inferences, and high dimensional statistical inferences.