Ed Chen.
computational science & maths
01. About
I am an incoming Mathematics PhD at the Courant Institute of Mathematical Sciences at NYU and current MS student at Stanford University studying Computational and Mathematical Engineering. My research interests surround developing high-order numerical methods and their applications modeling dynamical systems found in natural phenomena and engineering environments.
I studied computational science and applied mathematics with their surrounding uses in various scientific fields from Georgia Tech and UC Berkeley in undergrad. In the past, I have been fortunate to work with NASA Supercomputing, Julia Computing, and various university labs.
I have primarily worked in numerical mathematics, parallel programming, data science, pure mathematics, and web development. Check out the source code for this website!
02. Recent News
04.15.2023
Accepted NYU Courant PhD offer in Mathematics
04.16.2023
Awarded NumFocus grant to develop documentation for CVXPY convex optimization library
04.05.2023
Funded through CA position for CS 246: Graduate Mining Massive Datasets
02.07.2023
Recieved NYU Courant PhD Offer in Mathematics
11.28.2022
Funded through CA position for CS 448B: Graduate Data Visualization
09.30.2022
Funded through CA position for CS 149: Parallel Programming
04.15.2022
Accepted Stanford MS Offer in ICME
03.15.2022
Recieved Rice PhD Offer, Stanford, NYU, UW MS Offers
03. Research Interests
Research-wise, my interests lie in the field of mathematical modeling and simulation, particularly surrounding the development of efficient high-order numerical methods. The simulation of large and complex systems with numerical methods in an accurate and efficient manner presents a lot of challenges I find intriguing.
From a mathematical standpoint, I am interested in studying better preconditioning, discretization, and meshing techniques that can be used for such numerical solvers. From an algorithmic perspective, I aim to study how different predictive methods may be used to accelerate similar simulations and thereby reduce computational cost.
Aside from numerics, I have touched on interesting topics in discrete simulation and data science in the past. Effective data analysis and interactive data visualization in computational journalism is likewise quite interesting.
1. High-Fidelity Numerical Methods
Mathematical formulations and tools towards developing efficient and accurate high-order numerical methods for continuous simulation of partial differential equations
2. Efficient Parallel Algorithms
Fast iterative methods and preconditioners as applied to large matrix computations using new parallel programming toolkits across both CPU and GPU systems
3. Unstructured Mesh Generation
Unstructured methods of developing curvlinear meshes for the use of high-order numerical methods, as well as associated adaptive mesh refinement procedures
4. Network Simulation
Optimizing communication networks with discrete event simulation of distributed edge computing architectures and induced compute load from IOT jobs
04. Education
2023-Now
New York University
PhD Mathematics
2022-Now
Stanford University
MS Computational and Mathematical Engineering
2020-2022
Georgia Tech
BS Mathematics
2018-2020
UC Berkeley
05. Work
2022-Now
Course Assistant, Department of CS
2021-2022
Modeling and Simulation Intern, JuliaSim
2021-2022
Teaching Assistant, Department of CSE
Course Grader, Department of Mathematics
2020-2021
Research Associate, Computational Aerosciences
2019-2021
Course Reader, Department of Mathematics
Research Assistant, Department of EECS
2018-2019
University of Arizona
Research Assistant, Department of Bioengineering
06. Projects
Now
Accelerated High-Order Discontinuous Galerkin Methods
(Stanford) Leveraged Jax to optimize Discontinuous Galerkin numerical method code, to understand methods of parallelizing numerical methods across CPUs/GPUs/TPUs using matrix computations to enable compiler-driven parallelism
Document
Codebase
2022
Task-based Parallel Multigrid and Adaptive Mesh Refinement FEM Solver
(Stanford) Developing a quadratic finite element solver with multigrid and adaptive mesh refinement accelerations using Regent, a Nvidia Legate based research task-based parallel language for proof of concept purposes
Document
Slideset
2022
Space Mission Logistics Discrete Event Simulation Framework
(Georgia Tech) Developed generalized discrete event simulation model of space mission timelines, providing logistical and risk analyses. Testing on a conceptual operation of a future Mars mission
Document
Codebase
2022
Intersectional Demographic Regression Analysis of University Employee Salaries
(Georgia Tech) Regression analyses on individual and intersectional demographic factors affecting faculty salaries to determine best predictors across five public university systems
Document
Codebase
Poster
2021
Portable OpenMP CPU/GPU Parallelization of Iterative Methods
(NASA Supercomputing) Developed CPU and GPU portable iterative method solvers with new OpenMP implementations across different specialized compilers. Performance profiling and optimization across different pragmas and strategies
Document
Notes
2021
Correlative Analysis and Visualization of Transportation Ecosystems
(Georgia Tech) Used confounding factors within historical congestion data to develop predictive autoregressive integrated moving average (ARIMA) analyses, modeling the cyclic nature of transit demand
Document
Codebase
Poster
2021
Buzzbook Course Discovery Platform
(Georgia Tech) Developing a one-stop course planning platform to aggregate a course catalog, a requirements planner, visualized grading distributions, enrollment histories, a schedule optimizer, a ratings system, a course recommender, and an exam/syllabi database.
Website
Codebase
2020
Efficient Computational Methods for Kelvin-Helmholtz Instabilities
(UC Berkeley) Implemented and formulated efficient variations of the Uzawa and GMRES algorithms applied to the matrix computations arising from the numerical solution to the instabilities observed from the 2d Euler Equations for inviscid flows
2020
Natural Convection Boundary-Layer Flow Analysis
(UC Berkeley) Finite-difference method numerical modeling of hydrodynamically and thermally developed convective flow of different fluids in various geometries and analysis of their behaviors, comparison of scheme efficacies with dimensionless quantities
Document
2019
Time-Harmonic Wave Propagation Simulation in Waveguides
(UC Berkeley) Rigorous formulation and implementation of quadratic finite elements and custom meshing algorithm to simulate frequency response and resonance phenomena of a waveguide using the 2D Helmholtz Equation with Sommerfeld radiation conditions
Document
Codebase
2019
Optimal Traffic Signal Timing with a Continuum Flow Congestion Model
(UC Berkeley) Rigorous implementation of finite volume schemes (Godunov, Roe) in evaluating the Lighthill-Whitham-Richards traffic model, a flow-based traffic congestion model based on vehicle density that accounts for disturbance shocks
Document
Codebase
2019
Efficiency of a Domain-based Fog Computing Architecture
(UC Berkeley) Devised and implemented a discrete event simulation of a domain-based fog architecture model and demonstrated different queuing methods (FCFS, EDF, CR) to improve QoS for fog networks, formulating and modeling a discrete-event simulation based on various constraints on job size, deadline, and location.
Document
Codebase
Poster
2018
A Method of Modeling Asteroid Intercept Trajectory
(Independent) Developed a 3D N-body simulation of the solar system and modeled possible intercept trajectories to a celestial object using convex optimization strategies with a time-iterating RK4 approximation method.
Document
07. Teaching
Sp 2023
Course Assistant, Graduate Mining Massive Datasets
Wi 2023
Course Assistant, Graduate Data Visualization
Au 2022
Course Assistant, Parallel Computing
Sp 2022
Math 6321 (Georgia Tech)
Grader, Graduate Complex Analysis
Sp 2022
Head Teaching Assistant, Graduate Data and Visual Analytics
Fa 2021
CSE 6242 (Georgia Tech)
Teaching Assistant, Graduate Data and Visual Analytics
Fa 2021
Math 121A (Berkeley)
Grader, Mathematical Methods for Physical Sciences
Sp 2021
Grader, Mathematical Methods for Physical Sciences
Fa 2020
Grader, Introduction to Complex Analysis
Math
Mathematical Methods Mathematical Programming Introductory Analysis Algebra FEM
Physics
Classical Mechanics Tensor Calculus
CS
Julia Fundamentals Database Systems Webdev
08. Contact
edc.exclass [at] gmail [dot] com
Website
edhschen [dot] info
Last Update
Jan 18, 2023