Hey, my name is Amber Chang. I am studying in MSc in Scientific Computing and Data Analysis at Durham University and am actively pursuing a career in the field of Computational Data Science and AI.
A passion for problem solving and analytical thinking led me to combine High Performace Computing and Data Science.
This gave me a thorough knowledge of supervised and unsupervised learning as well as some deep learning, I have listed several projects related to machine learning in Data Sceince section.
As a computer science major, I found my interest in both Uncertainty Quantification and Parallel Computing. Under the supervision of Anne Reinarz, I have been devoted myself in improving the prediction of an existing software development– OpenGo Simulation
Having completed my masters, I am looking to secure a job in Data in which I look forward to applying cutting edge machine learning models to real world problems.
Key Skills:
Statistical Machine Learning, Neural Networks, AI, Uncertainty Quantification, Deep Learning, Data Visualization, Probability Theory, Probabilistic Computing, Parallel Computing, Statistcial Modeling
Amber Chang
Education
- MSc Scientific Computing & Data Analytics, Durham University (UK), 2023 – 2024
- Applied Data Science (Online), Massachusetts Institute of Technology (USA), 2022 – 2023
- BA in Foreign Languages and Literature, Minor in Art and Design, National Taiwan University, 2015 – 2019
Research Interests
- Computer Graphics: Data-Driven Methods, Physics-Based Simulation, Digital Fabrication, HCI
- Computational Data Science: Statistical Machine Learning, Neural Networks, AI, Uncertainty Quantification, Deep Learning, Data Visualization, Probability Theory, Probabilistic Computing, Parallel Computing
Research Experience
Uncertainty Quantification for Reservoir Engineering, Durham University, 2024
- Explored computational efficiency of Quasi-Monte Carlo methods for Reservoir Engineering
- Applied UQ framework on HPC systems using large-scale synthetic data
- Achieved 10x faster performance for 1000+ physics-based simulations over 200 years
Facial Emotion Detection, MIT IDSS Professional Education, 2023
- Developed data-driven computer vision model for facial emotion classification
- Utilized CNN, VGG-net, and other pretrained models
- Achieved 80.02% accuracy through dropout, data augmentation, and early stopping
Professional Experience
Durham University & OPENGO SIM,R&D intern, Durham, UK, 2024
- Created Docker environment on Ubuntu and resolved UMBridge server connection issues
- Implemented advanced sampling methods (Quasi & Multilevel Monte Carlo)
- Achieved 10x faster processing for 1000+ simulations on HPC system
Ministry of Foreign Affairs, Data Analyst, Taipei, Taiwan, 2021
- Analyzed international relations trends and produced parliamentary reports
- Liaised with team colleagues and external departments
Skills
- Programming: Python, R, C, C++, CUDA, OpenMP, MPI, Tensorflow, PyTorch
- Tools: Git, Docker, Singularity, Virtual Machine
- Systems: Linux, Shell Script, Bash
Professional Outreach
- IoT Theory and Practice – IEEE Seasonal School (UK) (2024)
- Member: ACM, WiGraph (since Aug. 2024)
- Women in High Performance Computing (WHPC), Global, Aug. 2024 – Dec. 2024
- UM-Bridge, Open Source Software, Europe, Feb. 2024 – Dec. 2024
Relevant Coursework
- HPC: Scientific and High Performance Computing, Parallel Computing, Performance Modeling (A), Vectorization and GPU Programming (A)
- AI/ML: Intro to Machine Learning (A+), Advanced Statistics and Machine Learning I(A) & II (A)
- Math: Calculus-Based Probability(A), Discrete Mathematics(A), Logic, Numerical Algorithm