ARTIFICIAL INTELLIGENCE
As advances in machine learning have grown exponentially in recent years, artificial intelligence has become one of the fastest-moving fields in computer science.
From robot vacuum cleaners to machine learning augmented online shopping recommendations and search engine predictions, our work drives innovations that are now part of our everyday lives.
WHAT WE DO
Conduct research in various areas of artificial intelligence theory, improving existing methodology and examining real-world applications.
Develop computer programs and computer-controlled robots that can execute real-world tasks.
SUB AREAS:
Computer Vision
Decision Making & Planning
Knowledge Representation & Reasoning
Learning Theory
Machine Learning
Multi-Agent Systems & Algorithmic Game Theory
Natural Language Processing
Robotics
Trustworthy AI
OUR RESEARCH PROJECTS
Multimodal AI for IoT
This project is partially funded by Microsoft's Accelerate Foundation Models Research Program.
DesCartes WP4: Human-AI Collaboration
OOI Wei Tsang, Brian LIM, ZHAO Shengdong
WP4 focuses on how humans can interact with AI to (i) bring humanity aspects that cannot be computationally modeled into AI systems and algorithms, forming a hybrid AI with human interaction at its core, and (ii) allow hybrid AI to augment human perception and cognition (especially assisting humans in decision-making). Within this WP, we propose to develop interaction and visualization techniques
- Computer Vision, Machine Learning
Recommendation Systems
Recommendations Systems curate our news feeds, and show products for us to buy, shows to watch and music to listen to. Our work examines the use of temporal and prerequisite constraints in improving recommendation systems quality in sparse data application areas, such as module and course recommendation.
- TRL 5
- Knowledge Representation & Reasoning
Task Oriented Dialogue Systems
We now use voice- and text-enabled chatbots and dialogue systems often to accomplish tasks. We examine ways to improve such systems by incorporating everyday knowledge in the form of knowledge graphs and incorporating means to adapt trained systems to new domain application areas.
- TRL 4
- Natural Language Processing
Learning to reason and plan with visual and linguistic inputs
Different subfields of AI, e.g. vision, language, reasoning, learning has been studied separately in depth. However, like the blind men and the elephant, can we truly understand the subject that way? We seek to exploit recent progress, particularly in deep learning, to effectively combine these methods within a single learning architecture. Image credit: Golden Treasury Readers: Primer.
- Computer Vision, Decision Making & Planning, Machine Learning, Natural Language Processing
Algorithmic Inductive Bias
Effective deep learning methods are usually structured, e.g. convolutional neural networks. How do we design a structure for a target task? We study the use of task related algorithms. The process, called neuralizing the algorithm, unrolls the execution of the algorithm into a computation graph and replaces some graph elements with learnable approximators, capturing important information flow.
- Machine Learning
Modern methods for high-dimensional estimation and learning
Extensive research has led to a variety of powerful techniques for high-dimensional learning, with the prevailing approaches assuming low-dimensional structure such as sparsity and low-rankness. This project pursues a paradigm shift towards data-driven techniques, including the replacement of explicit modeling assumptions by implicit generative models based on deep neural networks.
- Learning Theory, Machine Learning
Information-theoretic limits of statistical inference and learning problems
The field of information theory was introduced to understand the fundamental limits of data compression and transmission, and has shaped the design of practical communication systems for decades. This project pursues the emerging perspective that information theory is not only a theory of communication, but a far-reaching theory of data benefiting diverse inference and learning problems.
- Learning Theory, Machine Learning
Robustness considerations in machine learning
Robustness requirements pose many of the most important unsolved challenges in modern machine learning, arising from sources of uncertainty such as mismatched models, corrupted data, and adversaries. This project seeks to better understand some of the most practically pertinent sources of uncertainty and develop new algorithms that are robust in the face of this uncertainty.
- Learning Theory, Machine Learning
Learning with Less Data: Automated Machine Learning with Bayesian Optimization
How can the hyperparameters of a deep learning model be automatically optimized without human intervention? How did the win-rate of AlphaGo improve from 50% to 66.5%? How can the nutrients, lighting and water conditions be optimized to maximize the crop yield? Look no further: Bayesian optimization is what you need.
- TRL 4
- Decision Making & Planning, Machine Learning, Multi-Agent Systems & Algorithmic Game Theory, Robotics
Federated/Collaborative Machine Learning and Data Valuation
LOW Kian Hsiang, NG See Kiong, HE Bingsheng
Federated/Collaborative ML is an appealing paradigm to build improved, high-quality ML models by training on aggregated data from many parties. How then can these parties be incentivized to collaborate & share their data?
- TRL 4
- Decision Making & Planning, Machine Learning, Multi-Agent Systems & Algorithmic Game Theory, Trustworthy AI
OUR RESEARCH GROUPS
Adaptive Computing Laboratory
Our long-term goal is to understand the fundamental computational questions that enable fluid human-robot interaction, collaboration, and ultimately co-existence. Our current research focuses on robust robot decision-making under uncertainty by integrating planning and machine learning.
- Decision Making & Planning, Machine Learning, Robotics
STeAdS Virtual Group
Software Engineering and Technological Advancements for Society. A virtual group that uses Software engineering practices and Technological advancements (Cloud computing, Artificial Intelligence (EdgeAI, ML)) for the benefit of various aspects of society (healthcare, education, art & culture). Looking for students to collaborate on different projects. Look at ganeshniyer.github.io for details.
- Decision Making & Planning, Machine Learning, Multi-Agent Systems & Algorithmic Game Theory
Jonathan Scarlett's Research Group
Our group performs research at the intersection of information theory, machine learning, and high-dimensional statistics, with ongoing areas of interest including information-theoretic limits of learning, adaptive decision-making under uncertainty, scalable algorithms for large-scale inference and learning, and robustness considerations in machine learning.
- Learning Theory, Machine Learning
Multi-Agent Planning, Learning, and Coordination Group (MapleCG)
Our group is multi-disciplinary: CS, math, stats, physics, eng, data science. We believe in theory & practice. Our research cover probabilistic ML (Bayesian deep learning, Gaussian process), learning with less data (autoML, Bayesian optimization, meta-learning, active learning), multi-party ML (federated/collaborative ML, privacy-preserving ML), reinforcement learning & multi-agent/robot systems.
- Decision Making & Planning, Machine Learning, Multi-Agent Systems & Algorithmic Game Theory, Robotics, Trustworthy AI