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