Chi-Hsien (Salima) Chang⋄ Salima’s website ⋄ +886-905-615-769 ⋄ salimachchang@gmail.com
Room No. 1021 No.30, Hsin Hai Road, Sec. 3, Taipei, 106, Taiwan, ROC

RESEARCH INTERESTSMy research focuses on developing theoretical support for model-based genetic algorithms, aiming tobridge the gap between theoretical understanding and practical applications of algorithms in solving complex
optimization problems. Additionally, my research focuses on developing improved fully interpretable
simulators for pre-mRNA splicing, aiming to enhance our understanding of splicing processes, thereby
improving the interpretation of genetic variants and facilitating therapeutic interventions in gene expression.

EDUCATION
National Taiwan University (NTU)
September 2018 – June 2025 (Expected)
Doctor of Science in Electrical Engineering, Computer Science Division; GPA: 4.00/4.30
Topic: Theoretical Analysis for Module-building Genetic Algorithms
Advisor: Tian-Li Yu
National Taiwan University (NTU)
September 2016 – June 2018
Master of Science in Statistics; GPA: 3.71/4.30
Topic: Statistical Analysis of the Application of Augmented Reality in the Salah Learning System
Advisors: Cheng-Ying Chou, Yi-Ping Hung
National Chengchi University (NCCU)
Bachelor of Science in Mathematics; GPA: 3.22/4.30

September 2013 – June 2016

PUBLICATION• Chi-Hsien Chang, Szu-Ping Chen, Christopher Childers, and Monica Poelchau. From Genetic Variation toVisual Representation: Image Predictions in Insect Databases Using SNP. In the Joint Conference of theInternational Association for the Study of Traditional Asian Medicine (IASTAM) and the Asian Society for
the History of Medicine (ASHM), 2024.
• Chi-Hsien Chang and Ting-Shuo Chuang. Phased Integration of the BOPPPS Model and Large Language
Model for Enhanced English Learning. In the International Conference on Applied Linguistics and Language
Teaching (ALLT), 2024.
• Jung-Chun Liu, Chi-Hsien Chang, Shao-Hua Sun, and Tian-Li Yu. Integrating Planning and DeepReinforcement Learning via Automatic Induction of Task Substructures. In the International Conference onLearning Representation (ICLR), 2024.
• Tzu-Hao Hsu, Chi-Hsien Chang, and Tian-Li Yu. Program Synthesis on Single-Layer Loop Behavior inPure Functional Programming. In the IEEE World Congress on Computational Intelligence (WCCI), 2024.
• Tu-Chin Chiang, Chi-Hsien Chang, and Tian-Li Yu. A Novel Symbolic Regressor Enhancer Using GeneticProgramming. In the IEEE World Congress on Computational Intelligence (WCCI), 2024.
• Chi-Hsien Chang, Szu-Ping Chen, Monica Poelchau, and Christopher Childers. Exploring Genetic
Information with Ease: The Linkout Plugin for JBrowse 2. In the microPublication Biology Journal, 2023.
• Chi-Hsien Chang, Tu-Chin Chiang, Tzu-Hao Hsu, Ting-Shuo Chuang, Wen-Zhong Fang, and Tian-Li Yu.
Taylor Polynomial Enhancer using Genetic Programming for Symbolic Regression. In the proceedings of theCompanion Conference on Genetic and Evolutionary Computation (GECCO), 2023.
• Wen-Zhong Fang, Chi-Hsien Chang, Jung-Chun Liu, and Tian-Li Yu. GP with Ranging-Binding Techniquefor Symbolic Regression. In the proceedings of the Companion Conference on Genetic and Evolutionary
Computation (GECCO), 2023.

• Po Ying Law, Chia-Cheng Tsai, Tsz Wun Fok, Ching-Ting Wang, Chi-Hsien Chang, Tsung-Yu Chin,
Yi-Chen Liao, Jen-Kuang Lee, and Chung-Wei Lin. Secure Medical Data Management Based on
Homomorphic Encryption and Secret Sharing. In the IEEE International Conference on Smart Cloud(SmartCloud), 2023.

RESEARCH EXPERIENCE
Massachusetts Institute of Technology (MIT) Visiting StudentJune 2024 (Expected) – May 2025

• Developing fully-interpretable simulators for pre-mRNA splicing to better understand splicing
processes for improving the interpretation of genetic variants and facilitating therapeutic interventions in geneexpression. Focusing on applying algorithms to identify suboptimal parses and establishing a quantitative
metric for the robustness of predictions for exon and intron.
National Institute of Informatics (NII) Research Intern

February. 2024 – April 2024

• Developing molecular generation algorithms with model-based genetic algorithms, linkage learning, andclustering techniques. This approach aims to enhance the representation and analysis of molecular structures,
facilitating advancements in computational chemistry and drug discovery.
United States Department of Agriculture (USDA) Research Intern

February 2023 – August 2023

• Developed Linkout plugin for JBrowse 2, enhancing the accessibility and utility of genomic data.
Additionally, developed a machine learning model capable of predicting images in insect databases
based on single nucleotide polymorphisms (SNPs), which holds potential for improving pest
identification and management strategies.
National Taiwan University Research AssistantDuring the doctoral program

September 2016 – March 2024

• Research on Model-based Genetic Programming Applied to Symbolic Regression: Guided
master’s students through in-depth research discussions on genetic programming, specifically applied tosymbolic regression, and conducted comprehensive code reviews to enhance project outcomes.
• Development of Model-based Real Number Optimization Techniques - Subspace Exploration
Combining Characteristics of Discrete and Real Number Domains: Led the experimental design
discussions and co-authored the research report for a pioneering study on model-based real number
optimization techniques.
• Learning with Limited Data : Successfully improved the accuracy of a semi-supervised learning modelfrom 77.4% to 81.2% on the MIT-BIH Arrhythmia Database. This enhancement was achieved by integrating
data augmentation techniques, including both feature space and instance space augmentation, demonstrating
advancements in learning with limited datasets.
• Research on the Social, Ethical, and Legal Aspects of Artificial Intelligence: Conducted acomprehensive survey and synthesis of existing research related to the social, ethical, and legal implications ofartificial intelligence, aiming to highlight key challenges within the field.
During the master’s program• Comprehensive Improvement of Interdisciplinary Teaching/Research Facilities: Provided guidancein statistical analysis, encompassing experimental design, non-parametric analysis, and post-hoc testing, tosignificantly enhance the capabilities of interdisciplinary teaching and research infrastructures.
• Development of Phytosanitary Treatment and Risk Assessment Techniques - Research ofAutomated Detection Technologies for Significant Quarantine Pests: Leveraged statistical analysis
using Python to advance the research and development of automated detection technologies for identifying
significant quarantine pests.

• Digital Spectral Mammography Tomography Reconstruction Combining Scatter and Angular
Correction: Employed statistical analysis using R to refine digital spectral mammography tomographyreconstruction techniques.
• Development of Phytosanitary Pest System Management and Quarantine Treatment
Techniques - Development of Plant Quarantine Treatment Technologies: Utilized statisticalanalysis using SAS to facilitate the optimization of plant quarantine treatment technologies.

TEACHING EXPERIENCE
National Taiwan University Teaching Assistant

September 2019 – Current• Python: Mentored 100+ students in their team projects and contributed to designing assignments. Earned4.35/5.0 score from students’ feedback on the teaching assistant’s course evaluation.
• C#: Received the National Summer College Excellent Teaching Assistant Award. Earned 4.89/5.0 score fromstudents’ feedback on the teaching assistant’s course evaluation.
• Genetic Algorithms: Actively engaged in note-taking during lectures and made these notes available forstudent review post-class, facilitating deeper understanding and retention of course material. Earned 4.96/5.0
score from students’ feedback on the teaching assistant’s course evaluation.
• Artificial Intelligence and Machine Learning: Taught in English, this course required a nuanced
understanding of both subject matter and language to effectively communicate complex concepts. Earned
4.0/5.0 score from students’ feedback on the teaching assistant’s course evaluation.
• Calculus: Played a crucial role in calculus tutorial sessions by addressing students’ questions, thereby
enhancing their comprehension and problem-solving skills in this fundamental mathematical discipline.

HONORS & AWARDS• Outstanding University Youth Award at NTU

2023• Student Altruism Award at College of Electrical Engineering and Computer Science, NTU

2023• Excellent Teaching Assistant Award at National Summer College, NTU

2022• Commencement Speaker for the Master’s Degree in Statistics at NTU

2018• Awarded U-stat Plan at National Taiwan University of Science and Technology (NTUST)

2018• Awarded 1st Place in 2nd Yunus Prize on Social Innovation/Entrepreneurship Competition2017• Club Excellence Award during my tenure as President of Islamic Cultural Club at NCCU

2016