##
Ernest K. Ryu

Ph.D. Candidate

Institute for Computational and Mathematical Engineering
Stanford University

Advisor:

Professor Stephen P. Boyd

###
Contact

Packard 243, Stanford, CA 94305

### Interests and Current Research

Stochastic Optimization

Convex Analysis

Numerical Analysis

Scientific Computing

### Publications

Adaptive Importance Sampling via Stochastic Convex Programming.
E. Ryu and S. Boyd,
Manuscript.

Stochastic Proximal Iteration: A Non-Asymptotic Improvement Upon Stochastic Gradient Descent.
E. Ryu and S. Boyd,
working draft.

A Primer on Monotone Operator Methods.
E. Ryu and S. Boyd,
*
Applied and Computational Mathematics an International Journal*,
15(1), 2016.

Extensions of Gauss Quadrature via Linear Programming.
E. Ryu and S. Boyd,
*Foundations of Computational Mathematics*, 15(4):953–971, 2015.

Computing
Reaction Rates in Bio-molecular Systems Using Discrete
Macro-states. E. Darve and E. Ryu.
In T. Schlick, editor, *Innovations in Biomolecular Modeling and
Simulations.* Royal Society of Chemistry, 2012.

Structural Characterization of Unsaturated Phosphatidylcholines Using
Traveling Wave Ion Mobility Spectrometry.
H. Kim, H. Kim, E. Pang, E. Ryu, L. Beegle, J. Loo,
W. Goddard, and I. Kanik.
*Analytical Chemistry*, 2009.

### Teaching

Courses taught:

- Convex Optimization I (EE364a), Summer 2014 (sole instructor).
**Course Description:**
Convex sets, functions, and optimization problems. The basics of
convex analysis and theory of convex programming: optimality
conditions, duality theory, theorems of alternative, and
applications. Least-squares, linear and quadratic programs,
semidefinite programming, and geometric programming. Numerical
algorithms for smooth and equality constrained problems;
interior-point methods for inequality constrained
problems. Applications to signal processing, communications, control,
analog and digital circuit design, computational geometry, statistics,
machine learning, and mechanical engineering.

- ICME Refresher Course, Sept. 17–20
2012.
**Course Description:**
The ICME refresher course is a four day long course intended to
provide incoming graduate students
students with an opportunity to review material
relevant to their upcoming coursework.

Teaching assistant at Stanford University for:

- Convex Optimization II (EE364b), Spring 2014.
**Course Description:**
Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Monotone operators and proximal methods; alternating direction method of multipliers. Exploiting problem structure in implementation. Convex relaxations of hard problems. Global optimization via branch and bound. Robust and stochastic optimization. Applications in areas such as control, circuit design, signal processing, and communications. Course requirements include project.

- Numerical Linear Algebra (CME 302), Fall 2013.
**Course Description:**
First in a three quarter graduate sequence. Solution of systems of
linear equations: direct methods, error analysis, structured matrices;
iterative methods and least squares. Parallel techniques.

- Convex Optimization I (EE364a), Winter 2013.
**Course Description:**
Convex sets, functions, and optimization problems. The basics of
convex analysis and theory of convex programming: optimality
conditions, duality theory, theorems of alternative, and
applications. Least-squares, linear and quadratic programs,
semidefinite programming, and geometric programming. Numerical
algorithms for smooth and equality constrained problems;
interior-point methods for inequality constrained
problems. Applications to signal processing, communications, control,
analog and digital circuit design, computational geometry, statistics,
machine learning, and mechanical engineering.

- Computer Programming in C++ for Scientists and Engineers
(CME211), Fall 2011.
**Course Description:**
Computer programming methodology emphasizing modern software
engineering principles: object-oriented design, decomposition,
encapsulation, abstraction, and modularity. Fundamental data
structures. Time and space complexity analysis. The basic facilities
of the programming language C++. Numerical problems from various
science and engineering applications.

Teaching assistant at California Institute of Technology for:

- Introductory Methods of Applied Mathematics
(ACM95abc), 2009–2010.
**Course Description:**
First term: complex analysis: analyticity, Laurent series,
singularities, branch cuts, contour integration, residue
calculus. Second term: ordinary differential equations. Linear initial
value problems: Laplace transforms, series solutions. Linear boundary
value problems: eigenvalue problems, Fourier series, Sturm-Liouville
theory, eigenfunction expansions, the Fredholm alternative, Green's
functions, nonlinear equations, stability theory, Lyapunov functions,
numerical methods. Third term: linear partial differential equations:
heat equation separation of variables, Fourier transforms, special
functions, Green's functions, wave equation, Laplace equation, method of
characteristics, numerical methods.

- Physics Laboratory (Ph7), Spring 2009.
**Course Description:**
Experiments in atomic and nuclear physics, including studies of the
Balmer series of hydrogen and deuterium, the decay of radioactive
nuclei, absorption of X-rays and gamma rays, ratios of abundances of
isotopes, and the Stern-Gerlach experiment.

Other teaching assistant experience:

### Education

PhD, Computational and Mathematical Engineering, Stanford, 2010–Present.

BS with Honor, Electrical Engineering, Caltech, 2010.

BS with Honor, Physics, Caltech, 2010.

### Other Stuff I Have Done

MIT,
Imaging and Computing Group,
visiting student, Summer 2011.

Caltech,
Biophotonics Laboratory, research assistant, Summer 2010.

JPL NASA, internship, Summer
2007, 2008, and 2009.

### Awards

Simons Math+X Graduate Fellowship, 2012–13 and 2014–15.

DOE Office of Science Graduate Fellowship (SCGF), 2010–13.

NASA Tech Brief Award, 2011.

Caltech Upper Class Merit Award, 2008.

### Miscellaneous

Some classical papers that are hard to find on the web.