We intend the terms scientific computing and data science to be broadly defined and inclusive. Topics of interest include but are not limited to:
- Novel numerical methods, numerical analysis, high-performance computing, parallel algorithms, and application problems that involve numerical challenges.
- Machine learning algorithms, deep learning and neural networks, applied/predictive modeling with real-world data, data-enabled science, dimensionality reduction, Bayesian methods, natural language processing, and computational statistics.
During Fall 2024, we will meet on Thursdays, 3:00-4:00pm in ACS 362B.
This seminar is part of the RTG theme “Scientific Computing and Data Science”. If you have questions, please contact Prof. Changho Kim (ckim103@ucmerced.edu).
Schedule Fall 2024
- Aug 29: Organizational meeting
- Sep 12: Andy Wan - Bayesian Inference for dynamical systems - Part I
- Sep 19: Scott West - Mesh free interpolation of initial conditions on octree grids
- Oct 3: Adam Binswanger - Numerical simulations of incompressible multi-phase fluid flows
- Oct 17: Alex Villa - Better tasks for task Based parallelism in multiphysics framework
- Oct 31: Hardeep Bassi - Ground state energy estimation from noisy quantum observables
- Nov 7: Alex Ho
- Nov 14: Tanya Tafolla
- Nov 21: Andy Wan
- Dec 5: Matthew Blomquist - Characteristic bending
- Dec 12: Cole Cooper
Schedule Spring 2024
- Jan 24: Organizational meeting
- Feb 7: [Discussion] How to do machine-learning research
- Feb 14: Harish Bhat - How to Write a Research Proposal
- Feb 21: Changho Kim - Brief overview of different Monte Carlo approaches
- Feb 28: Maia Powell - Utilizing satellite data for groundwater analysis
- Mar 6: Hardeep Bassi - A time-delay scheme for the propagation of reduced 1 electron density matrices and the effect of memory
- Mar 13: Joseph Simpson - 2D synthetic aperture radar imaging of extended targets
- Mar 20: [Discussion] How to use the Pinnacles cluster
- Apr 3: Adam Binswanger - Collocated numerical simulations of incompressible fluid flows
- Apr 10: Alex Nguyen - Nyström type exponential integrators for strongly magnetized particle pushing problems
- Apr 17: Matt Blomquist - Gaussian process regression for the estimation of two-dimensional interface curvature
- Apr 24: Zihan Xu - Developing statistics-informed neural network as a robust and computationally efficient surrogate modeling tool
- May 1: [Discussion] Planning for the next semester
- May 2 THR, 11:30am - 12:30pm: Dr. Andy Nonaka (LBNL)
Schedule Fall 2023
- Aug 24: Kick-off meeting
- Aug 31: Zihan Xu - Extension of Statistics-informed Neural Network to Multi-dimensions with Self-Adaptive Loss Balancing for Enhanced Performance
- Sep 7: Adam Binswanger - Stable nodal projection method on quadtree grids for incompressible, multi-phase fluid flows
- Sep 14: Alex Nguyen - A Runge-Kutta-Nyström type exponential integrator with an application to numerically simulate strongly magnetized charged particle dynamics
- Sep 22 (Friday, 2pm, ACS 362B): Hong Zhang (ANL) - PETSc Library and its Application to the Multiphysics Simulation over Networks
- Sep 28: Discussion (programming skills)
- Oct 5: Harish Bhat - Machine learning for the time-dependent Hartree-Fock equation
- Oct 19: Scott West - Nodal Projection Methods for Incompressible Fluid Flows
- Nov 9: Andy Wan - Bayesian inference using Hamiltonian Monte Carlo
- Nov 30: Kevin Collins - Vortices and the shape of flow
