Robert Calderbank, Co-PI
Director of the Rhodes Information Initiative at Duke
Charles S. Sydnor Distinguished Professor of Computer Science, Professor of Mathematics and Professor of Electrical and Computer Engineering
Visiting Research Scholars
I am an assistant professor in the Department of Statistics and Operations Research at the University of North Carolina at Chapel Hill, where I am a member of the probability group.
Student and Postdoctoral Research
My research is in analyzing medical image data to improve clinical diagnoses. In particular, I am interested in drawing ideas from machine learning methods to address computational burden and likelihood intractability that pose challenges to applying classical statistical models.
My research is in designing inherently interpretable models for computer vision. By providing interpretable algorithms, I enable scientists to validate their models and ensure that their models are making predictions based on meaningful representations rather than on confounding information.
I work with cancer genomics data, with a particular eye towards using tensor models for datasets with several experimental modalities. More recently, I have been spending time thinking about statistical inference problems using ideas from optimal transport.
Jordan Bryan’s research
My research is focused on imprecise probabilities. I am also interested in more foundational topics, like extended probabilities, and in the theoretical properties of neural networks.
Michele Caprio’s research
I work on theoretical machine learning, with a focus in the areas of representation learning, control theory, and reinforcement learning.
Abraham Frandsen’s research
I work on applied topology, Bayesian statistics and their applications to imaging and biology. I am particularly interested in highly dissimilar data and soft tissue.
Henry Kirveslahti’s research
Anilesh Kollagunta Krishnaswamy
I am primarily interested in questions regarding the design of fair and robust algorithms for machine learning, using ideas from optimization and game theory
I am primarily interested in questions regarding the design of fair and robust algorithms for machine learning, using ideas from optimization and game theory.
Holden Lee’s research
Zining Ma (Past Participant)
In my research, we propose a method to generate a specific form of 3D shapes representation that could be applied in statistical analysis. We use Euler Characteristic curves to create the representation of shapes and utilize scaling function bases from diffusion wavelet to generate the representation. We discuss the details of our method and in the last we apply our method on a shape classification problem to test the performance of the representation.
Ezinne Nwankwo (Past Participant)
My primary research interests lie in improving the trustworthiness and reliability of machine learning models and applying machine learning to address societal challenges. Currently, my research focuses on using causal inference methods to identify and mitigate algorithmic bias/discrimination.
I am interested in the theory of deep learning, especially the optimization of neural networks.
Xiang Wang’s research
Lihan Wang (Past Participant)
I am currently interested in the analytic behavior and numerical performance of Markov Chain Monte Carlo sampling algorithms that arise from stochastic processes.
Lihan Wang’s research