PhD Student in the CSEM program in the Oden Institutate for Computational Engineering & Sciences at the University of Texas at Austin. My research interests include machine learning, numerical analysis and computer vision.
August 2021 - Current
PhD Student in Computation Science, Engeneering and Mathematics
GPA: 3.945
Research interests: Scientific Machine Learning from a numerical analysis perspective with applications in computer vision and optimal control.
Methods of Applied Mathematics I
Methods of Applied Mathematics II
Numerical Analysis: Linear Algebra
Introduction to Mathematical Modeling in Science & Engeneering I
Introduction to Mathematical Modeling in Science & Engeneering II
Foundational Techniques in Machine Learning and Data Science
Tools and Techniques in Computational Science
Deep Learning I
Deep Learning II
Computation and Variational Methods for Inverse Problems
Predictive Machine Learning
Linear Systems Analysis
October 2018 - July 2021
MSc in Financial Mathematics. Studied state of the art stochastic models for financial markets.
Thesis: Processes with free Increments
Generalization of classical probablity theory for non-commuting random variables with applications in signal processing and random matrices.
Advances Analysis
Advanced financial Mathematics
Statistical methods in actuarial science
Risk theory and management in actuarial science
Actuarial modeling
Non-life insurance mathematics
Financial management
Mathematical statistics
Advanced probability
Stochastic analysis
Life and health insuracne mathematics
Selected Chapters Analysis (Special functions)
Probability and Analysis on Graphs and Groups
Markov Processes
Project in finance and insurance
Discrete and algebraic structures
Regression analysis
Japanese A2/1
Japanese B1/1
Japanese B1/2
October 2018 - July 2021
BSc in Mathematics. Got a broad eduction covering a wide range of important fields in the subject.
Thesis: The Monte-Carlo Method and Pseudo-Random number generators
Explored the theoretical foundations of random number generation and Monte-Carlo integration methods.
Linear Algebra I
Linear Algebra II
Analysis I
Analysis II
Analysis III
Discrete Mathematics
Computer Mathematics
C++
Fundamentals of Mathematics
Ordinary Differential Equations
Computational Mathematics
Introduction to Functional Analysis
Optimization I
Introduction to Algebra
Parital Differential Equations
Stochastic Processes
Introduction to Complex Analysis
Statistics
Data Structures and Algorithms
Financial and insurance mathematics
Mathematics for Finance and Insurance
Personal Actuarial Science
Optimization problems in mathematics of finance
Bachelor's Thesis
Probability Theory
Advanced Probability
Selected Chapters Analysis (Elliptic Differential Equations)
Advanced actuarial mathematics
Integration and measure theory
Japanese A1/1
Japanese A1/2
We introduce a new objective function for the greedy algorithm to design efficient and robust sensor networks and derive theoretical bounds on the network's optimality. We further introduce a Deep Learning model to accelerate the algorithm for near real-time computations. Correspondingly, we show that understanding the geometric properties of the training data set provides important insights into the performance and training.
September 2023
Preprint
We address the problem of efficient and unobstructed surveillance or communication in complex environments. On one hand, one wishes to use a minimal number of sensors to cover the environment. On the other hand, it is often important to consider solutions that are robust against sensor failure or adversarial attacks. This paper addresses these challenges of designing minimal sensor sets that achieve multi-coverage constraints -- every point in the environment is covered by a prescribed number of sensors. We propose a greedy algorithm to achieve the objective. Further, we explore deep learning techniques to accelerate the evaluation of the objective function formulated in the greedy algorithm. The training of the neural network reveals that the geometric properties of the data significantly impact the network's performance, particularly at the end stage. By taking into account these properties, we discuss the differences in using greedy and ϵ-greedy algorithms to generate data and their impact on the robustness of the network.
May 2023
Presented recent results about efficient and robust sensor placement in complex environments at the Center for Approximation and Mathematical Data Analytics.
August 2019 - July 2021
Risk Management Intern
Programming Python (Pandas, PyTorch, TensorFlow, NumPy, Scikit-learn), R, C/C++, SQL, Matlab,...
Miscellaneous Linux, Shell (Bash/Zsh), Latex, Git, HTML,...
English Professional proficiency
German Native proficiency
Japanese Intermediate proficiency