Sandy T.

Sandy T. is a Machine Learning Research Engineer who has been involved in the deployment of multi-agent reinforcement learning algorithms and the development of social behaviors in various settings. They have collaborated with industry experts to create a productive research environment and have implemented Hierarchical Framework in MARL as well as explainable algorithms for Reinforcement Learning with human feedback.

Before their current role, Sandy worked as a User Experience Engineer at Refined Interiors. There, they utilized Tokenization and Sentiment Analysis for Natural Language Processing on website user behaviors. They collected and analyzed data to optimize website design for user compatibility, resulting in the acquisition of over 1000 new users. Additionally, they developed a responsive website using React.js and integrated it with WordPress, incorporating Personalization, Predictive Analytics, and A/B Testing to enhance the user experience.

In their previous position as a Graduate Student Instructor at the University of California, Berkeley, Sandy taught courses on Computational Cognitive Neuroscience and Introduction to Statistical Analysis and Research Methods. They provided in-depth tutorials on machine learning concepts, including supervised, unsupervised, and reinforcement learning techniques. Sandy demonstrated a comprehensive understanding of various deep learning algorithms and neural network architectures, such as CNNs, RNNs, LSTMs, MLPs, GANs, and VAEs. They also taught students critical thinking skills for managing messy real-world data using SQL, pandas, and R.

As a Graduate Student Researcher at the University of California, Berkeley, Sandy conducted survey studies and research projects in Multi-agent Reinforcement Learning, Autocurricula Adversarial Learning, and Algorithmic Game Theory. They explored the application of Historical Average methods in machine learning through an Augmented Autocurricula Adversarial Learning approach called "Historical Self-play," demonstrating its effectiveness.

Prior to that, Sandy worked as a Postgraduate Research Engineer at Caltech, where they focused on converting in-lab data collection to online behavioral experiments. They designed and implemented behavioral experiments and established a data collection pipeline using AWS and Heroku. Sandy also developed a web scraping and ETL pipeline to create art and photo databases, automating quality assurance processes.

At Arizona State University, Sandy started their research career as an Undergraduate Research Assistant.

Overall, Sandy T. has extensive experience in machine learning research, user experience engineering, teaching, and conducting research projects in various academic and industry settings.

Sandy T. has completed postgraduate coursework in Computer Science at the University of California, Berkeley. Prior to that, they obtained a Bachelor of Science degree in Mathematics, Symbolic Systems, and Cognitive Science from Arizona State University. 👩‍💻 Sandy also participated in an undergraduate coursework and research program in Computational Cognitive Neuroscience at Caltech. However, specific start and end years for each educational experience are not provided.