- Jobs
ResumeCreate your job-winning resume using our free resume builder.
PortfolioShowcase your skills and projects with a professional portfolio.ResumeCreate your job-winning resume using our free resume builder.Resume BuilderMake a resume for free.Resume TemplatesAccess our extensive library of professional & ready-to-use templates.Resume ExamplesGet inspired by real resume examples to create your own.Occupation GuideAccess resume writing guides tailored for different professions.Resume HelpGet expert advice on all things resume from our team of recruitment specialists.- ResourcesSuccess StoriesBusiness ExcellenceAbout CakeResumeFeatured Reads
- Hire
- Download our App
CakeResume Talent Search
Advanced filters
On
4-6 years
6-10 years
10-15 years
More than 15 years
資料分析師 Data Analyst @Portto 門戶科技| Blocto2022 ~ 2024
・
Data Analyst、Data Engineer、Data Scientist、Customer Experience Analyst
Within one month
Employed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
臺灣大學
・
流行病學與預防醫學所 生物統計組智慧製造全端開發工程師 @聯華電子股份有限公司2022 ~ Present
・
AI工程師、機器學習工程師、深度學習工程師、影像演算法工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Within one month
Employed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
元智大學
・
工業工程與管理學系所Past
博士後研究員 @洛桑大學神經發育疾病實驗室2023 ~ 2023
・
Data Scientist, Data Analyst, Machine Learning Engineer
Within one month
Unemployed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
洛桑聯邦理工學院(EPFL)
・
神經科學
Past
Service Provider @Taron Solutions Limited2023 ~ 2023
・
AI工程師、機器學習工程師、電腦視覺工程師、資料科學家、Machine Learning Engineer、Computer Vision Engineer、Data Scientist
Within one month
李慕全(MuChuan Li) 畢業於國立臺北科技大學資工所，研究領域為深度學習、電腦視覺、及影像處理。在學期間致力於應用電腦視覺技術解決交通問題，擁有多項產學合作的專案開發經驗，亦在電腦視覺領域中發表過多篇學術論文，主要研究主題包含物
Unemployed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
國立臺北科技大學
・
資訊工程
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Within one month
宋浩茹 Hao-Ru Sung| [email protected] | LinkedIn ｜ GitHub A s a Research Assistant at Academia Sinica , specializing in Generative AI research and application. With 3 + years of experience in NLP a nd Machine Learning , along with 4+ years in Backend Development . Proficient at translating complex theories into practical applications. Skills Languages: Python, R, SQL, MATLAB, C, C#, JavaScript, Node.js Software & Tools: PyTorch, PyTorch Lightning, Tensorflow, Scikit-Learn, NLTK , GCP, Linux, SQL / NoSQ , Pandas, Hugging Face, Gradio, LangChain, Tensorflow, Keras, FastAPI, OpenCV, Airflow
Employed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
國立政治大學（National Chengchi University）
・
資訊科學系
Past
Data Engineer @Rooit Inc. (XO App)2023 ~ 2023
・
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Within one month
邱義塵 於獨角獸多媒體設計有限公司擔任 遊戲測試工程師一職 建立公司測試團隊的測試流程和撰寫自動化測試程式 SDET、AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist 城市，TW [email protected] 工作經歷 獨角獸多媒體
Unemployed
・
Ready to interview
Full-time / Interested in working remotely
6-10 years
中國醫藥大學(China Medical University)
・
臨床醫學研究所
OPC Chief Engineer @TSMC2020 ~ Present
・
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Within one month
Chun-Jung Huang [email protected] Chiao-Tung University, Ph.D. - Photonics,2015 ~ 2020 Member of The Phi Tau Phi Scholastic Honor Society of the Republic of China. Work Experience TSMC, OPC Chief Engineer (MarPresent) ◆Introduced image anomaly detection techniques to identify and address defects in photomask manufacturing, significantly improving product quality and reducing turnaround time. ◆Managed large-scale data processing tasks, demonstrating expertise in analyzing and handling datasets of hundreds of millions, to bolster model development and optimization. ◆Excelled in distributed computing, optimizing code execution across thousands of systems to
Employed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
National Chiao-Tung University
・
Ph.D. - Clinical Engineering
Product Manager @東元電機股份有限公司 (TECO Electric & Machinery Co. Ltd.)2023 ~ 2023
・
Data Scientist, Data Analyst, Machine Learning Engineer
Within one month
梁賦康 （Foo-Hong, Leong） Taoyuan City, Taiwan Email: [email protected] Tel:Skills • Languages: Python • DataBases: MySQL, SQLite • Infrastructure tools: Github • Machine learning libraries: TensorFlow, Keras, and Scikit-learn • Data visualization tools: Power BI, Seaborn and Matplotlib • Deployment: Streamlit Summary I have been working in Motor Manufacturing Industry for 8 years. My first programming was going to my Bachelor's degree, C++ was the first program I learned. Then I started to learn Python in 2018 at TEDU and my first project was the Stock Trend Prediction by CNN. I kept
Employed
・
Ready to interview
Full-time / Interested in working remotely
6-10 years
國立成功大學 National Cheng Kung University
・
Mechanical Engineering
Past
Senior Data Analyst @趨勢科技2022 ~ Present
・
Data Scientist, Data Analyst, Machine Learning Engineer
Within one month
customers by using statistical methods and machine learning methods. Developing automation regular reports, maintaining SQL store procedures, Tableau dashboards and Power BI dashboards. Cooperated with cross-functional team (Product, Marketing, Platform, PM, IT, Sales) to provide timely and accuracy business insight analysis. Developing automated web crawler on MMA website to collect ETF, fund, bond information. Skill : Microsoft SQL Server · Microsoft Power BI · Data Cubes · R · Python · Tableau · Web Crawling · machine learning · IMPALA · HIVE · Git · Docker Data Analyst • Catchplay AprOct 2020 Indonesia OTT customer profile analysis - Collecting, analyzing and evaluating data and campaign performa...
Unemployed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
輔仁大學 Fu Jen Catholic University
・
統計資訊學系
Senior engineer @Chicony Electronics Co, Ltd.2018 ~ Present
・
全端工程師、後端工程師、前端工程師、軟體專案主管、AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Within one month
Nelson Chen Senior engineer Dedicated Software Engineer with 6+ Years of Experience Senior software engineer specializing in web page development and deep learning. Proficient with machine learning technologies, such as TensorFlow, Numpy, etc. Experience Senior engineer • Chicony Electronics Co, Ltd. ．Build an Auto-Encoder AI model for defective detection. ．Build an object detection model for detecting car types. ．Developed a Front-End and Back-End website for data analysis. ．Manage the production process and make it automated production. NovPresent Software engineer • Teco image systems co. ltd ．Developed and maintained MFP driver
Employed
・
Ready to interview
Full-time / Interested in working remotely
6-10 years
National Taiwan Ocean University
・
Computer science and engineeringThe Most Lightweight and Effective Recruiting Plan
Search resumes and take the initiative to contact job applicants for higher recruiting efficiency. The Choice of Hundreds of Companies.
- Browse all search results
- Unlimited access to start new conversations
- Resumes accessible for only paid companies
- View users’ email address & phone numbers
Search Tips
1
Search a precise keyword combination
senior backend php
2
Use quotes to search for an exact phrase
"business development"
3
Use the minus sign to eliminate results containing certain words
UI designer -UX
Only public resumes are available with the free plan.
Upgrade to an advanced plan to view all search results including tens of thousands of resumes exclusive on CakeResume.
Definition of Reputation Credits
Technical Skills
Specialized knowledge and expertise within the profession (e.g. familiar with SEO and use of related tools).
Problem-Solving
Ability to identify, analyze, and prepare solutions to problems.
Adaptability
Ability to navigate unexpected situations; and keep up with shifting priorities, projects, clients, and technology.
Communication
Ability to convey information effectively and is willing to give and receive feedback.
Time Management
Ability to prioritize tasks based on importance; and have them completed within the assigned timeline.
Teamwork
Ability to work cooperatively, communicate effectively, and anticipate each other's demands, resulting in coordinated collective action.
Leadership
Ability to coach, guide, and inspire a team to achieve a shared goal or outcome effectively.
Professional Background
Current status
Employed
Job Search Progress
Open to opportunities
Professions
Data Scientist, Machine Learning Engineer
Fields of Employment
Banking, Artificial Intelligence / Machine Learning, AdTech / MarTech
Work experience
4-6 years
Management
None
Skills
Languages
English
・
Fluent
Job search preferences
Positions
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Job types
Full-time
Locations
台灣台北, 台灣新北市
Remote
Interested in working remotely
Freelance
Yes, I freelance in my spare time
Educations
School
政治大學
Major
統計
Education
National Chenchi University, MS, Statistics, 2015 – 2017
- GPA : 3.84 / 4.0
- Master Thesis: Entropy Based Feature Selection, Professor Pei-Ting, Chou
- Objective: Build a similarity matrix based on Mutual Entropy under Hierarchical Clustering. Afterwards, select clustered features as the final selection.
- Compare the model with other feature selection methods like RF, Lasso, F-score.
National Chen-Kung University, BS, Mathematics, 2011 – 2015
Skills
Programing
- Python
- Scala
- R
- MSSQL
Data-related Tools
- Tensorflow (Keras)
- PyTorch
- Spark
- Docker
- Scikit-Learn
- Pandas
Cloud Platform
- AWS
- GCP
Language
- English: TOEFL 98 / 120
Work Experience
CTBC Bank, Model Development Department, Data Scientist
2021.12 – present
- About the department:
- Responsible for developing models related to bank recommendations and risks, including projects such as coupon recommendations, account opening marketing lists, and fraud detection.
- Job responsibilities:
- Throughout the entire project lifecycle, my primary responsibilities included model design, model training, end-to-end process development, feature design, performance tracking, and method research.
Fraud Alert Project
- Objective:
- Predicting potential fraudulent accounts based on transaction data, restricting transactions in advance to prevent harm.
- Responsibilities/Achievements:
- Development and deployment of credit card and financial features.
- Managing the data flow process from receiving variables to model predictions, identifying risk factors, and updating alert lists.
- Implemented Autoencoder + contrastive learning to achieve a 1.81% improvement in model effectiveness.
Coupon Recommendation
- Objective:
- Personalized coupon recommendations for mobile banking users to increase click-through rates and redemption rates.
- Responsibilities/Achievements:
- Utilized multi-task learning to simultaneously predict click-through behavior and coupon redemptions, resulting in a 14% increase in click-through rate and a 74% increase in redemption rate.
- Created performance tracking reports to monitor online model performance and provide insights to Business Units.
Financial Product Recommendations
- Objective:
- Tailored financial product recommendations for mobile banking users to enhance click-through rates without compromising conversion rates.
- Responsibilities/Achievements:
- Applied multi-task learning to jointly learn click-through and conversion behaviors, fine-tuned model architecture, achieving a 90% outperformance against competitor models in online testing.
Marketing List for Digital Savings Accounts
- Objective:
- Optimized conversion rates for marketing lists related to digital savings accounts
- Responsibilities/Achievements:
- successfully raising conversion rates from 0.23% to 1.16%
Work Experience
CLICKFORCE, Data Engineer Supervisor, 2020.1 – 2021.11
- About the company:
- As a top domestic digital advertisement company, CLICKFORCE cooperates with over 900 web media and over 400 mobile media to build a huge advertising environment. CLICKFORCE considers data-driven solution as the core concept of the company, and dedicates to help advertisers to achieve their commercial goals.
- At 2020, CLICKFORCE won 2 awards at Agency & Advertiser of the Year.
- Successfully acquire the exclusive advertising agency qualification for Tokyo 2020 Olympics in Taiwan.
- Job responsibilities:
- Optimize ad performance from all aspects, including the system, target audience tags, etc.
- Do researches for new ML model (recommender model, NLP model) or architecture which is suitable for our system.
- Develop data-related products or projects.
- Analyze data to help improve our system or inspect whether the demands from business side is doable.
Real-time AD Recommender System
- Objective:
- Building a real-time ad recommender system to upgrade our ad server and get better performance.
- Responsibilities:
- Figure out what kind of recommender system components that is suitable for our ad system.
- Build a tower-like and feature-cross model refer to other famous recommender system model.
- Responsible for system engineering, which includes data preprocessing, embedding generates, memory cache, cold start, model API, etc.
Interest Tags
- Objective:
- Build interest tags for ads to help ad optimizers choose their target audience.
- Responsibilities:
- Create the features from what articles they saw, what website they viewed, and what ads they interacted.
- Deal with 20 million rows data and 120 million inference samples.
- Build ML model to predict each user's behavior on certain ads.
- Using Spark through AWS EMR to accelerate the speed of producing tags.
- Achievements:
- Raise CTR performance up to 200-300% of the original tags depends on different tags, and gain more impression while maintain better performance.
- After accomplishing this project, we terminated the cost on purchasing interest tags from other company, and successfully turned the original cost into revenue by providing profitable data.
First Party Cookie Mapping
- Objective:
- Deal with the Google 3rd party Cookie issue, figure out a method to map numerous 1st party Cookies to a user.
- Responsibility:
- Transform this problem into a ML mission. Design the label of the data, figure out what feature we can get or produce and whether the feature is useful for the goal.
- Apply XGboost on this mission.
- Build a small test to prove this method works.
- Achievement:
- 70% of precision.
- One of the solution of our company while the cancelation of 3rd party Cookie happen.
Invoice Data Application
- Objective:
- Develop invoice data application.
- Responsibility:
- Responsible for fine-tuning BERT to predict category for each product.
- Produce invoice data report to brands or business unit. It demonstrates the sales volume across different channel, what kind of products are frequently bought together, and also shows comparison of target brand to the other brands.
- Achievements:
- Produce an invoice data report product.
- Produce invoice tags for ad system.
Other Experience
E.Sun AI 2020 Summer Competition, 2020.7 – 2020.8
- Objective:
- Extract names of money laundering suspects from an article.
- Responsibilities:
- Crawl the articles from different media, and parse them by using Selenium, Requests, and Beautiful Soup.
- Construct 2-step model: First, identify whether the article is related to money laundering. Second, extract the suspects' names.
- Build model serving API by Tensorflow Serving.
- Build REST API for preprocessing request data and return the prediction.
- Achievement:
- 23rd place among 409 teams.
Youtube Data-Driven Marketing System, Institute for Information Industry, 2019.8 – 2019.11
- Objectives:
- Use the title and the description of videos to automatically classify videos.
- Use the title and the description of videos to identify whether a video is sponsored.
- Give suggestions for Youtubers or companies who desire to sponsor in a video based on data analysis.
- Responsibilities:
- Apply Google API and write Python functions to get structured raw data.
- Train word vectors using Gensim based on Wiki's open data.
- Use the frequency of each sentence as a criteria to eliminate useless words.
- Tune LSTM, Conv1D, BERT on the NLP mission.
- Use EDA methods to see the insights of the data under different classes and different sponsored status.
- Achievement:
- 71% accuracy in classifying video’s type.
- 89% accuracy in detecting sponsored content.
E.Sun Real Estate Price Prediction Competition, 2019.7 – 2019.8
- Objective:
- Use the real estate training data to build a model and predict the real estate price within 10% residual.
- Responsibilities:
- Apply XGBoost, LGBM and other ML models to train the model.
- Collect the outputs as new features from each ML model and add them into the original data set to enhance the performance of the final model.
- Achievement:
- 150th place out of 1200 teams.
KKTV Data Game，2017.5 – 2017.6
- Objective:
- Predict the next video a user watch in the next time interval.
- Responsibilities:
- Extract different features from raw data, such as the latest video, the video which got the longest viewing time, the video which got the largest number of viewing.
- Use the user viewing data to construct a similarity matrix of each video as additional features.
- Achievement:
- 10th place out of 50 teams.
MRT Open Data Competition, 2017.4 – 2017.5
- Objective:
- Study the changes of passenger volume of MRT by surrounding geometric data.
- Responsibilities:
- Apply bisection method to build the edges between MRT stations.
- Combine other geometric data based on these borders.
- Use Lasso feature selection method to explore the importance of each feature.
- Add noises into features to check the features are not randomly selected.
- Achievement:
- Certificate of Honorable Mention.
Education
National Chenchi University, MS, Statistics, 2015 – 2017
- GPA : 3.84 / 4.0
- Master Thesis: Entropy Based Feature Selection, Professor Pei-Ting, Chou
- Objective: Build a similarity matrix based on Mutual Entropy under Hierarchical Clustering. Afterwards, select clustered features as the final selection.
- Compare the model with other feature selection methods like RF, Lasso, F-score.
National Chen-Kung University, BS, Mathematics, 2011 – 2015
Skills
Programing
- Python
- Scala
- R
- MSSQL
Data-related Tools
- Tensorflow (Keras)
- PyTorch
- Spark
- Docker
- Scikit-Learn
- Pandas
Cloud Platform
- AWS
- GCP
Language
- English: TOEFL 98 / 120
Work Experience
CTBC Bank, Model Development Department, Data Scientist
2021.12 – present
- About the department:
- Responsible for developing models related to bank recommendations and risks, including projects such as coupon recommendations, account opening marketing lists, and fraud detection.
- Job responsibilities:
- Throughout the entire project lifecycle, my primary responsibilities included model design, model training, end-to-end process development, feature design, performance tracking, and method research.
Fraud Alert Project
- Objective:
- Predicting potential fraudulent accounts based on transaction data, restricting transactions in advance to prevent harm.
- Responsibilities/Achievements:
- Development and deployment of credit card and financial features.
- Managing the data flow process from receiving variables to model predictions, identifying risk factors, and updating alert lists.
- Implemented Autoencoder + contrastive learning to achieve a 1.81% improvement in model effectiveness.
Coupon Recommendation
- Objective:
- Personalized coupon recommendations for mobile banking users to increase click-through rates and redemption rates.
- Responsibilities/Achievements:
- Utilized multi-task learning to simultaneously predict click-through behavior and coupon redemptions, resulting in a 14% increase in click-through rate and a 74% increase in redemption rate.
- Created performance tracking reports to monitor online model performance and provide insights to Business Units.
Financial Product Recommendations
- Objective:
- Tailored financial product recommendations for mobile banking users to enhance click-through rates without compromising conversion rates.
- Responsibilities/Achievements:
- Applied multi-task learning to jointly learn click-through and conversion behaviors, fine-tuned model architecture, achieving a 90% outperformance against competitor models in online testing.
Marketing List for Digital Savings Accounts
- Objective:
- Optimized conversion rates for marketing lists related to digital savings accounts
- Responsibilities/Achievements:
- successfully raising conversion rates from 0.23% to 1.16%
Work Experience
CLICKFORCE, Data Engineer Supervisor, 2020.1 – 2021.11
- About the company:
- As a top domestic digital advertisement company, CLICKFORCE cooperates with over 900 web media and over 400 mobile media to build a huge advertising environment. CLICKFORCE considers data-driven solution as the core concept of the company, and dedicates to help advertisers to achieve their commercial goals.
- At 2020, CLICKFORCE won 2 awards at Agency & Advertiser of the Year.
- Successfully acquire the exclusive advertising agency qualification for Tokyo 2020 Olympics in Taiwan.
- Job responsibilities:
- Optimize ad performance from all aspects, including the system, target audience tags, etc.
- Do researches for new ML model (recommender model, NLP model) or architecture which is suitable for our system.
- Develop data-related products or projects.
- Analyze data to help improve our system or inspect whether the demands from business side is doable.
Real-time AD Recommender System
- Objective:
- Building a real-time ad recommender system to upgrade our ad server and get better performance.
- Responsibilities:
- Figure out what kind of recommender system components that is suitable for our ad system.
- Build a tower-like and feature-cross model refer to other famous recommender system model.
- Responsible for system engineering, which includes data preprocessing, embedding generates, memory cache, cold start, model API, etc.
Interest Tags
- Objective:
- Build interest tags for ads to help ad optimizers choose their target audience.
- Responsibilities:
- Create the features from what articles they saw, what website they viewed, and what ads they interacted.
- Deal with 20 million rows data and 120 million inference samples.
- Build ML model to predict each user's behavior on certain ads.
- Using Spark through AWS EMR to accelerate the speed of producing tags.
- Achievements:
- Raise CTR performance up to 200-300% of the original tags depends on different tags, and gain more impression while maintain better performance.
- After accomplishing this project, we terminated the cost on purchasing interest tags from other company, and successfully turned the original cost into revenue by providing profitable data.
First Party Cookie Mapping
- Objective:
- Deal with the Google 3rd party Cookie issue, figure out a method to map numerous 1st party Cookies to a user.
- Responsibility:
- Transform this problem into a ML mission. Design the label of the data, figure out what feature we can get or produce and whether the feature is useful for the goal.
- Apply XGboost on this mission.
- Build a small test to prove this method works.
- Achievement:
- 70% of precision.
- One of the solution of our company while the cancelation of 3rd party Cookie happen.
Invoice Data Application
- Objective:
- Develop invoice data application.
- Responsibility:
- Responsible for fine-tuning BERT to predict category for each product.
- Produce invoice data report to brands or business unit. It demonstrates the sales volume across different channel, what kind of products are frequently bought together, and also shows comparison of target brand to the other brands.
- Achievements:
- Produce an invoice data report product.
- Produce invoice tags for ad system.
Other Experience
E.Sun AI 2020 Summer Competition, 2020.7 – 2020.8
- Objective:
- Extract names of money laundering suspects from an article.
- Responsibilities:
- Crawl the articles from different media, and parse them by using Selenium, Requests, and Beautiful Soup.
- Construct 2-step model: First, identify whether the article is related to money laundering. Second, extract the suspects' names.
- Build model serving API by Tensorflow Serving.
- Build REST API for preprocessing request data and return the prediction.
- Achievement:
- 23rd place among 409 teams.
Youtube Data-Driven Marketing System, Institute for Information Industry, 2019.8 – 2019.11
- Objectives:
- Use the title and the description of videos to automatically classify videos.
- Use the title and the description of videos to identify whether a video is sponsored.
- Give suggestions for Youtubers or companies who desire to sponsor in a video based on data analysis.
- Responsibilities:
- Apply Google API and write Python functions to get structured raw data.
- Train word vectors using Gensim based on Wiki's open data.
- Use the frequency of each sentence as a criteria to eliminate useless words.
- Tune LSTM, Conv1D, BERT on the NLP mission.
- Use EDA methods to see the insights of the data under different classes and different sponsored status.
- Achievement:
- 71% accuracy in classifying video’s type.
- 89% accuracy in detecting sponsored content.
E.Sun Real Estate Price Prediction Competition, 2019.7 – 2019.8
- Objective:
- Use the real estate training data to build a model and predict the real estate price within 10% residual.
- Responsibilities:
- Apply XGBoost, LGBM and other ML models to train the model.
- Collect the outputs as new features from each ML model and add them into the original data set to enhance the performance of the final model.
- Achievement:
- 150th place out of 1200 teams.
KKTV Data Game，2017.5 – 2017.6
- Objective:
- Predict the next video a user watch in the next time interval.
- Responsibilities:
- Extract different features from raw data, such as the latest video, the video which got the longest viewing time, the video which got the largest number of viewing.
- Use the user viewing data to construct a similarity matrix of each video as additional features.
- Achievement:
- 10th place out of 50 teams.
MRT Open Data Competition, 2017.4 – 2017.5
- Objective:
- Study the changes of passenger volume of MRT by surrounding geometric data.
- Responsibilities:
- Apply bisection method to build the edges between MRT stations.
- Combine other geometric data based on these borders.
- Use Lasso feature selection method to explore the importance of each feature.
- Add noises into features to check the features are not randomly selected.
- Achievement:
- Certificate of Honorable Mention.