The optimization research group meets on a weekly basis to share and discuss current optimization research in the Applied Mathematics Department here at UC Merced. In particular we have bi-weekly seminar talks, where students and faculty present aspects of their research.

Our group meetings and seminar are led by Professors Roummel Marcia and Noemi Petra and consist of graduate students, postdocs and faculty in the Applied Mathematics Department.

During the Fall 2019 semester, the optimization seminar/meetings will take place on

**Thursdays in ACS 362B from 10am to 11am.****Upcoming Talk/Event**

**Fall 2019 **

- September 19th:

**Alex Ho**(grad student, Applied Mathematics, University of California, Merced)

**Title Talk:**Image Disambiguation with Deep Neural Networks**Ki-Tae Kim**(postdoc, Applied Mathematics, University of California, Merced)

**Title Talk:**Some advances in computational methods for fracture*mechanics, linear wave propagations and eigenvalue*

*problems*

- October 3rd:

**Noemi Petra**(Assistant Professor, Applied Mathematics, University of California, Merced)

**Title Talk**: Introduction to hIPPYlib- October 17th:

**Omar DeGuchy**(grad student, Applied Mathematics, University of California, Merced)

**Title Talk:**Deep Learning with Pytorch (Part I)- October 31st:

**Amalia Kokkinaki**(PhD, Assistant Professor, Environmental Science, Environmental Engineering, University of San Francisco)

**Title Talk:**Large-scale inverse problems and data assimilation in hydrogeology: developments and challenges**Abstract:**In hydrogeology, inverse problems and data assimilation are used to estimate the properties of the subsurface using noisy, indirect measurements, as well as to track fluid movement through the soil or rock matrix. Applications include characterization for improved site cleanup, water resources management, and identification of contaminant sources. The corresponding inverse problems range from weakly nonlinear, such as pressure dissipation in mildly heterogeneous formations, to strongly nonlinear, such as multiphase flow in heterogeneous formations. A variety of techniques have been used over the last two decades to tackle such inverse problems, including deterministic/regularization based techniques to stochastic Bayesian estimation techniques. In this talk, a review of these methods will be presented, focusing on methods that are applicable for large scale systems with thousands to millions of unknowns, and specifically addressing the tradeoff between computational efficiency and estimation accuracy. The challenges associated with strongly non-linear problems, and Kalman Filtering variants that can address such problems will be discussed. The talk will close with an overview of current research needs for inverse modeling methods in the field of hydrogeology.

- November 14th:

**Omar DeGuchy**(grad student, Applied Mathematics, University of California, Merced)

**Title Talk:**Deep Learning with Pytorch (Part II)- December 5th:

**Tucker Hartland**, (grad student, Applied Mathematics, University of California, Merced)

*Title Talk: Hierarchical Off-Diagonal Low-Rank Approximation forHessians in Inverse Problems*

**Radoslav****Vuchkov**(grad student, Applied Mathematics, University of California, Merced)

*Title Talk: Quasi-Newton Methods for Infinite-Dimensional Inverse Problems Governed by PDEs*

**Spring 2019 **

- February 14th:

Omar DeGuchy (grad student, Applied Mathematics, University of California, Merced)

*Title Talk: The Fundamentals of Deep Learning*

- February 21st:

Tucker Hartland (grad student, Applied Mathematics, University of California, Merced)

*Title Talk:*

*Hierarchical Off-diagonal Low-rank Approximation for Hessians in Inverse Problems*

- March 7th:

Roummel Marcia (Professor, Applied Mathematics, University of California, Merced)

*Title Talk: Quasi-Newton Methods for Off-the-Shelf Machine Learning*

- March 21st:

Ekkehard Sachs (Professor, Trier University)

*Title Talk: Second-order adjoints in optimization with application*

*to machine*

*learning for the training of neural networks*

- April 4th:

Radoslav Vuchkov (grad student, Applied Mathematics, University of California, Merced)

*Title Talk: On mesh-independent secant quasi-Newton formulas*

- April 18th:

Lekan Babaniyi (postdoc, Applied Mathematics, University of California, Merced)

*Title Talk: Bayesian inversion for the basal sliding parameter field in a nonlinear Stokes ice sheet model with a nuisance rheology parameter in hIPPYlib*

- May 2nd:

Derek Hollenbeck

*Title Talk: Odor Plume Problems and Introduction to POSIM*