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 2023, we will meet on Thursdays, 2:00-3:00pm 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 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: TBA
Schedule Spring 2023
- Jan 19: Kick-off meeting
- Feb 2: Matteo Polimeno (discussion facilitator) - Student discussion on preparing a poster presentation
- Feb 9: Scott West - A Stable Nodal Projection Method on Octree Grids
- Feb 16: Anna Kucherova - Modeling the Opening SARS-CoV-2 Spike: an Investigation of its Dynamic Electro-Geometric Properties
- Feb 23: Jared Stewart - Exponential time integration methods for chemical combustion simulations
- Mar 16: Majerle Reeves - Paper review (Liquid Time-Constant Neural Networks with some background on neural ODEs)
- Mar 23: Govanni Granados - Asymptotic Analysis Applied to Small Volume Inverse Shape Problems
- Apr 6: Hardeep Bassi - Learning to predict electron dynamics via machine learned Hamiltonians
- Apr 13: Matt Blomquist - A stable nodal projection on adaptive grids (the pros and cons of collocated variables for fluid simulations)
- Apr 27: Matteo Polimeno - Breaking up fractal aggregates under stress: a boundary integral approach
- May 4: Tri (Alex) Nguyen - An Introduction to Exponential Fitting
Schedule Fall 2022
- Aug 25: Kick-off meeting
- Sep 1: Harish Bhat - Intro to JAX
- Sep 8: John Butcher (University of Auckland, New Zealand) - B-series and Applications
- Sep 15: Changho Kim - Demystifying Stochastic Integrals
- Sep 22: Changho Kim - Demystifying Stochastic Processes
- Sep 29: Yuanran Zhu (LBNL) - Statistics-informed neural network
- Oct 6: Yue Yu (Office of Information Technology) - HPC info session
- Oct 13: Hannah Love - Intro to NLP and Resume Ranker
- Oct 20: Tanya Tafolla - Intro to weather prediction
- Oct 27: Ali Heydari - No Pairs Left Behind! Improving Metric Learning to Predict Patients'' Health Risk from a Single Lab Visit
- Nov 3: Scott West - A Stable Nodal Projection Method on Octree Grids
- Nov 10: Maia Powell - How to Scrape Data from Twitter
- Nov 17: Alex Ho - Data-Drive Eddy Diffusion Coefficient in Marine Lake
- Dec 1: Zihan Xu - Extension of SINN to Multi-Dimensions
Jack Pham - Linear Algebra in Cryptography - Dec 8: Matteo Polimeno - Computing Stresses in Marine Aggregates: A Boundary Integral Approach
Schedule Spring 2022
- Jan 27: Enrique Mercado
- Feb 3: Harish Bhat
- Feb 10: Jocelyn Ornelas Munoz
- Feb 17: Harish Bhat
- Feb 24: Harish Bhat
- Mar 3: Ali Heydari
- Mar 10: Adam Binswanger
- Mar 17: Majerle Reeves
- Mar 24: Spring Recess
- Mar 31: Tanya Tafolla
- Apr 7: Hardeep Bassi
- Apr 14: Valentin Dallerit
- Apr 21: Kevin Collins
- Apr 28: Matthew Blomquist
