Research Assistant, Collaborative Machine Learning
Collaborative machine learning is an appealing paradigm to build high-quality machine learning (ML) models by training on the data from many parties. However, concerns over trust and security have hindered the sharing of data between organizations. Much efforts have been focused on breaking the “data silos”, but the silos are inherently difficult to break at the data level, as personal or proprietary data are often involved.
We posit that the ML models can be more amenable for sharing as they are inherently more compact and self-contained with purpose-compiled knowledge from the data. Rather than requiring the learning collaborators to contribute their private data, this project will focus on enabling collaborative machine learning through allowing the collaborators to share heterogeneous black-box models, and to be appropriately incentivized based on their self-interests. Given that most current research are focused on the data level, this project will develop new model-centric collaborative machine learning methods, as well as new notions for trustable model-centric sharing and effective model management techniques for real-world model-centric platforms.
This 4-year project will be led by Prof See-Kiong Ng and be conducted by the leading international and inter-disciplinary AI and data science researchers in the institute’s new Trusted Collaborative Machine Learning (Trusted CollabML) Lab. The CollabML Lab is a collaboration between the Institute of Data Science, the Department of Computer Science, and the Department of Electrical and Computer Engineering at the National University of Singapore (NUS), the School of Law at the Singapore Management University (SMU), as well as the Departments of Electrical Engineering and Computer Science at University of California, Berkeley (UC Berkeley) and Massachusetts Institute of Technology (MIT). The project team will design and develop a trustable, operational and legally-compliant model-centric sharing platform, validated with real-world data and applications provided by industry partners. Such a platform will be instrumental for powering a data-driven AI innovation ecosystem for a vibrant digital economy through trusted self-interest-driven sharing of learned machine intelligence.
The Research Assistant will be responsible for designing and implementing efficient and robust systems, and applications for algorithms and methodologies based on state-of-the-art research in machine learning and big data for collaborative machine learning.
- Design and write robust, readable, and reusable code components and applications to implement state-of-the-art research outcomes in machine learning, artificial intelligence, and big data;
- Perform data cleansing and processing for analysis of real-world datasets;
- Assists with the editing and preparation of manuscripts, reports and presentations.;
- Participate in presentations and demos for exhibiting work at appropriate events
- Bachelors or Masters in Computer Science with a focus in AI/Machine Learning/Big Data;
- Solid programming and application development skills with experience in Python/Perl/R. Mastery of programming languages such as C/C++/Java, and experience with Tensorflow would be a plus;
- Ability to read and understand methodologies in research papers;
- Fluent in English and good team-player
At NUS, the health and safety of our staff and students is one of our utmost priorities and COVID-vaccination supports our commitment to ensure the safety of our community and to make NUS as safe and welcoming as possible. Many of our roles require significant amount of physical interactions with student / staff / public members. Even for job roles that can be performed remotely, there will be instances where on-campus presence is required.
With effect from 15 January 2022, based on Singapore’s legal requirements, unvaccinated workers will not be able work at the NUS premises. As such, we regret to inform that job applicants need to be fully COVID-19 vaccinated for successful employment with NUS.