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-# personal information
-## identification
-Singapore Permanent Resident|Chinese citizen
-
-## address 
-17 Jalan Masjid, Singapore
-
-## contact 
-[email protected]|+65 91752741|+86 15063250971
-
-# Working Experience
-## Machine Learning Engineer at Huawei Ltd.
-• from Dec 2022 to present
-
-• Built a pipeline to automatically visualize data tables using LSTM network trained on ChatGPT-generated
-data with pairwise loss method, achieving 80% recall@5 on 100+ internal test cases.
-
-• Designed and implemented a novel SISR method that enhanced WIFI-signal simulations for office buildings
-by achieving 10x speedup compared to physics-based simulation with negligible loss in accuracy (1% MAE)
-on over 80 large-scale office layouts.
-
-## Machine Learning Research Engineer at Dyson Ltd.
-• from Sept 2021 to Dec 2022
-
-• Implemented an object localization model in a few -shot context by semi -supervised training. The model
-achieved comparable results to professional  software with improved adaptability and robustness .  
-
-• Designed and implemented  an air quality estimation model, using  LGBM, Bayesian Regression, etc., with
-geographical and meteorological features . Demonstrat ed its advantages over spatial interpolated methods  
-and deployed  the pipeline with Metaflow framework on AWS services.
-
-## ML Research Assistant at NUS -Singtel Cyber  Security Lab
-• from Sept 2020 to July 2021
-
-• Identif ied anomalies from system logs leveraging  DBSCAN  and hierarchical clustering  for model training .
-
-• Developed an information retrieval  method  for web -attack  strategy identification  from system and firewall
-logs. The recall@3 rate achieved 80% on 100+ hand -labelled samples . 
-
-## Data Analyst Intern at GIC Pte. Ltd.  
-• from Dec 2018 to July 2019
-
-• Deployed an R application that forecasts the mid -term returns of portfolio  with  visualization using R shiny .
-
-• Optimized the coefficients of a mean reversion forecasting model using the Genetic Algorithm. 
-
-## Data Analyst Intern at PropertyGuru
-• from May 2018 to Aug 2018
-
-• Developed dashboard s in Tableau to analyze the user behaviors and listings’ performance to better match
-user demand to agents’ recommendations.  
-
-• Implemented a POC to calculate and geographically visualize the liveability  score for properties .  
-
-# Education
-## Master of Computing in Artificial Intelligence at National University of Singapore                                                                        
-• from Aug 2020 to Sept 2021
-• School of Computing : CAP 4.42/5.0   
-• Teaching Assistant : Advanced Analytics and Machine Learning (from Jan 2021 to May 2021)
-
-## Bachelor of Science (Hons) in Business Analytics at National University of Singapore                                                                   
-• from Aug 2016 to June 2020  
-• School of Computing : CAP 4.15/5.0 , Dean’s List in Semester 3 AY 2018/2019  
-• Distinction : Analytics Techniques Knowledge Area (awarded in Dec 2020)
-• Teaching Assistant : Programming Methodology in python (from Aug 2017 to June 2018)
-
-# Relevant  Projects  
-## Distilling ChatGPT for finetuning image captioning models                        
-• from Jan 2023 to Present  
-• Employed Chain -of-Thought with verification prompting technique on ChatGPT to create 10k+ accurate
-capt ions from the xView annotations. Fine -tuned a GIT image captioning model  and significantly improved
-the CIDE r score from 11.59 to 85.93 over 2k RSICD samples.  
-## Dialogue  Response  Generation ( Master Thesis ) at NUS NExT++ Lab          
-• from Nov 2020 to Aug 2021  
-• Built an enriched task -oriented response generation by implementing copy -mechanism on GPT -2 using
-Pytorch. The proposed model is capable of naturally incorporating external tips/user reviews about venues
-into responses. The generated response outperforms m any state -of-the-art models on user satisfaction.  
-## Property Resale Price Prediction                                                                                  
-• from Jan 2021 to May 2021  
-• Fitted CatBoost, LGBM, XGBoost on 43k pieces of property sales data. Selected  features by correlation  and
-information gain. Engineered new features describing properties’ livability. Reduce d data dimensionality  
-with WOE encoding. The f inal ensemble methods’ accuracy achieved 5th/64 place.
-
-# Skills  
-• Python (Pytorch, Tensorflow), R : Machine
-Learning, Deep Learning , Data processing  
-• SQL, Spark:  Data query and  big data   
-• Tableau, PowerBI : Visualization development  
-• Java, Git, Scala, JavaScript, HTML, CSS : Software
-Development