Featured Member: Gerard Shu Fuhnwi on HBCU CONNECT!

Connect with Gerard

• Email Me or Poke Me
• Interview Me
• Send Group Invite
• Add To Favorites
• Block This Member
• Send Friend Request

Uploads

Upload Content | Show More

Connections

more-->

Recent Visitors

more-->

More Members

more-->

Q&A With Gerard Shu Fuhnwi

(interview me)

Gerard Shu Fuhnwi
PhD Candidate @CS @Machine Learning @Data Science@Fair ML, Montana State University
Location: Bozeman, MT Guam - USA
Joined: Mar 17th, 2020

About (request update)

An interdisciplinary scientist with skills and experience in Artificial Intelligence, NLP, Empirical Anomaly detection, malware
detection, Intrusion detection, Data science, Deep learning, Machine learning (supervised: regression and classification and unsupervised: Clustering and dimensionality reduction), Machine learning for cybersecurity, statistical inference, A/B testing, Predictive modeling, data mining, malware exploits/analysis, platform security, time series forecasting, Bias and Fairness issues in AI and Statistical modeling. I have collaborated with projects, resulting in 12 peer-reviewed publications at IEEE, Springer, etc.

Current Whereabouts:

Student at Montana State University

Website:

https://scholar.google.com/citations?user=dkGRyIMAAAAJ&hl=en

Education (request update)

Montana St Univ-Bozeman class of 2025

Undergrad Major: Computer and Information Science

Montana St Univ-Bozeman class of 2025

Grad Major: Computer and Information Science

Experience

I currently work with Montana State University as PhD Candidate @CS @Machine Learning @Data Science@Fair ML
I have 3 years of experience working in the Computers, Software industry.

Machine Learning Science Intern | Expedia Group
From May 2023 to July 2023 • 0 year(s)
* Developed a mean directional accuracy model using Xgboost for flight prices movement achieving high accuracy and savings when compared to price forecasting models used by the flight interaction team at EG and Hopper advising model. • The developed MDA model is currently being used by EG as a message relevancy model to help advise travelers when to book a flight or wait for better prices in the days ahead, best day of the week to fly and how much they can save. • The flight interaction team at EG benefited from my productivity, where I shared novel approaches on model training at scale by cutting the time spent on training existing models from days to minutes.

Machine Learning Science Intern | Expedia Group
From May 2022 to July 2022 • 0 year(s)
• Played a pivotal role in the development of a media mix marketing(MMM) model for Vrbo France, a significant step in our marketing strategy, utilizing Ridge regression. • Parameter optimization using simulated annealing for hyperparameter tuning that specifically optimized parameters (k & s) for the shape effect function to effectively mimic the laws of diminishing returns. • The resultant model equips EG with a strategic advantage, enabling them to precisely determine the timing and focus on various advertising channels for Vrbo. This enhances marketing efficacy and maximizes ROI

Applied AI and Machine Learning Intern | JP Morgan Chase & Co.
From June 2021 to September 2021 • 0 year(s)
• Development of a Probability of Default (PD) score model using Random Forest to predict customer delinquency, which is crucial for credit risk management strategies at JP Morgan. • Conducted hyperparameter tuning optimization employing Grid Search and Bayesian methods like Random Search, Annealing, and Tree-Structured Parzen Estimator (TPE). • Utilized SHAP (SHapley Additive exPlanations) for model interpretation, enabling clear insights into the factors influencing PD predictions and facilitating informed decision-making. • Conducted AI research in financial services, collaborating closely natural language processing team to tackle complex and challenging research problems in the finance.

My Groups

Invite Me To A Group

Guestbook Comments