Name Jet New
Education B.Comp Computer Science at NUS
Work Data Scientist at Indeed (Offer Rescinded Due to Layoffs Impacting Entire SG Office)


  • 2023
    Bachelor of Computing, Computer Science
    National University of Singapore
    • Special Programmes - Turing Research Programme, University Scholars Programme (Honour Roll)
    • Relevant Coursework - Machine Learning (Top Student, Best Project), Computer Vision (Best Project), AI Planning & Decision Making (Best Project), Natural Language Processing, Data Structures & Algorithms, Software Engineering, Research Methodology


  • May 2022 - Jul 2022
    Data Scientist Intern
    Indeed - Match Recommendation Platform
    • Feature Experimentation - Improved Indeed's core recommendation system to match jobseekers and employers, by experimenting on a new "no. of hires needed" feature, improving job outcomes for >300M jobseekers.
    • Data Engineering - Added new features of >300M jobseekers of >100 days to training database, backfilling historical data over aggregated windows using Spark's reduce(). Orchestrated data dumping to AWS servers job using Airflow.
    • Feature Analysis - Performed EDA and feature engineering on new features. Trained and evaluated 4 new models against original models using internal ML platform, showing improved AUROC and justifying a full A/B experiment.
    • A/B Experimentation - Performed power analysis to determine required traffic size and ran A/B experiments using Proctor with orthogonal design to account for interaction effects. Deployed improved models to production.
  • Nov 2020 - Apr 2023
    Student AI Researcher
    NUS - Collaborative, Learning, and Adaptive Robots Lab
    • Transformer Models - Improved generalization of world models during distribution shift in the multi-agent context, by exploiting permutation invariance of transformers, exceeding performance of state-of-the-art Trajectory Transformer.
    • Representation Learning - Improved robustness of reinforcement learning to noise in visual observations, by learning decorrelated latent variables using Barlow Twins, exceeding performance of state-of-the-art algorithm Dreamer.
    • Survey Paper - Reviewed representation learning for model-based reinforcement learning, challenges and directions."
  • May 2020 - Jul 2020
    Machine Learning Engineer Intern
    Grab - Marketplace
    • Probabilistic Models - Built a probabilistic modelling framework used by Grab's core dynamic pricing algorithm, implementing 5 models using Tensorflow Probability (Gaussian mixture density network, Bayesian neural network, etc)
    • Utility Tools - Built tools for evaluation on industry datasets (KL & JS divergence), visualization of model probability densities (3D and violin plots), and hyperparameter tuning using Ax Bayesian optimization framework.
    • Internship Sharing - Presented to the data science community at Google Developer Space Singapore.
  • Nov 2018 - Jun 2019
    Executive (Machine Learning)
    IMDA - Digital Services Lab
    • Anomaly Detection - Designed an anomaly detection algorithm, achieving 0.92 F1 score and deployed as client's main solution, securing a $500K project deal, after evaluating >10 algorithms (e.g. LSTM, Holt-Winters, SARIMA).
    • Natural Language Processing - (Confidential) Built hierarchical attention network with GloVe vectors using Keras and Spacy, scraped 40K webpages using Selenium, and delivered insights to Director of Tech & Infra Group.

Research Projects

  • 2021
    Structured Multi-Agent World Models
    • Researched graph neural networks for multi-agent reinforcement learning, improving performance and planning accuracy. Awarded CS4246 Class Project Competition Winner out of 142 students.
  • 2021
    Barlow Twins for Model-Based Reinforcement Learning
    • Researched Barlow Twins and contrastive self-supervised learning for model-based reinforcement learning, improving robustness and training stability.
  • 2021
    Bayesian Multi-Agent Reinforcement Learning
    • Researched flipout for competitive multi-agent reinforcement learning, improving performance, training stability and generalization. Awarded 1st Place at NUS Project Showcase out of 78 teams.
  • 2021
    NLP Question Answering Transfer Learning
    • Researched transfer learning of NLP question answering (QA) capabilities, benchmarking 6 BERT-based models over 14 QA datasets on zero-shot and fine-tuned performance.


  • 2022
    • Honourable Mention Project - for CS4243 Computer Vision
  • 2021
    • Top Project - out of 142 students for CS4246 AI Planning & Decision Making
    • Silver (Achievement) - NUS School of Computing Student Awards
    • 1st Runner Up - Optigram Data Visualization Competition
    • Honour Roll - University Scholars Programme
  • 2020
    • Top Student & Top Project - out of 272 students & 54 teams for CS3244 Machine Learning
    • Top 10 Finalist - out of 54 teams at HackAsia Global Hackathon
  • 2019
    • Best AI Hack - out of 130 participants at NTU iNTUition Hackathon


  • Programming Languages: Python, Java, SQL, C++
  • Data Science/Machine Learning: Jax, PyTorch, TensorFlow, Apache (Spark, Hive, Airflow), Numpy, Pandas, Matplotlib
  • Software Engineering: Docker, Git, AWS (S3, Athena, Presto, Glue), Jenkins, Linux, GCP


  • 2021
    President at NUS Statistics and Data Science Society
    • Led 40 students to organize data science workshops and an annual competition with 850 participants.
  • 2020
    Technology Associate (Data Analytics) at Google Developer Student Club
    • Organized 5 data science workshops at Google Developer Space Singapore, reaching 120 students.