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(1) Energies and residues of manifolds and configuration saces of polygons
(2) Energy of knots and regularization of divergent integrals
(3) Hadamard regularization and regularization via analytic continuation
(4) How to define energies and residues of manifolds (From analysis to geometry)
(5) Mobius invariant metric on the knot space
Accelerating 4D PhotoAcoustic Tomography
A Complexity-Theoretic Perspective on Algorithmic Fairness
Active Community Detection with Maximal Expected Model Change
Active Learning and Optimal Experimental Design
Adapting the Metropolis Algorithm
A Fast Approach to Optimal Transport: The Back-and-forth Method
A Fast Graph-Based Data Classification Method with Applications to 3D Sensory Data in the Form of Point Clouds
A landscape of knots
Algorithmic Questions in High-Dimensional Robust Statistics
An Introduction to Image Compression, Old and New
An Optimal Transport Perspective on Uncertainty Propagation
A PDE Interpretation of Prediction with Expert Advice
A Self-avoiding Approximate Mean Curvature Flow
A stability estimate for the inverse electroseismic problem
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation
Being Smart and Dumb: Building the Sports Analytics Industry
Best Practices A Data Scientist Should Know
Bias-Variance Tradeoffs in Joint Spectral Embeddings
Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization
Challenges and Opportunities in Magnetic Resonance Fingerprinting
Citizen Science and Machine Learning at Zooniverse
Classical knot invariants
Clustering High-dimensional Data with Path Metrics: A Balance of Density and Geometry
Coding and Generative Design for 3D Printing
Coherent Optical Processing with Machine Learning
Community Detection Using Total Variation and Surface Tension
Computational Imaging: Beyond the limits imposed by lenses
Computational Imaging: From structured low-rank methods to model based deep learning.
Computational Imaging with Deep Learning
Computational Methods for Large-scale Inverse Problems: Data-driven VS Physics-driven or Combined?
Computational Microscopy
Computational Radar Imaging
Computational Science for COVID-19 Pandemic Planning and Response
Concluding Remarks
Coordinate Methods for Solving Eigenvalue Problems in High Dimensions
COVID Modeling: Testing Scenarios and Geographical Networks
Creating Value in PE Using Advanced Analytics
Cryo-Electron Microscopy Image Analysis with Multi-Frequency Vector Diffusion Maps
Cyber Security: A New Front for Computational Science and Engineering
Data and Image Domain Deep Learning for Tomographic Computational Imaging
Data Compression in Distributed Learning
Data depths meet Hamilton-Jacobi equations
Data Science at The New York Times
Data Science: What is it? Why is everyone talking about it? Should you be doing it? (You probably are already)
Data Scientists under attack!! Let's help them together