photoI am an Assistant Professor in the University of Toronto working on quantum-enhanced machine learning and applications of high-performance learning algorithms in quantum physics. I am also the Academic Director of the Quantum Machine Learning Program in the Creative Destruction Lab, a Data Scientist in Residence in the TD Management Data and Analytics Lab, a Faculty Affiliate in the Vector Institute for Artificial Intelligence, and an Affiliate in the Perimeter Institute for Theoretical Physics.

Outside my academic work, I cofounded the Quantum Open Source Foundation, I am the Chair-Elect of the Special Interest Group on Quantum Computing in the American Statistical Association. I serve in an advisory role for various startups, and I am a member of the NUS Overseas Colleges Alumni and an assessor in the Toronto branch of NOC.

Trained as a mathematician and computer scientist, I received my PhD from the National University of Singapore. Previously I worked in the Quantum Information Theory group in ICFO-The Institute of Photonic Sciences and in the University of BorĂ¥s. I did longer research stints at several institutions, including the School of Information Systems in the Queensland University of Technology, the Quantum Information Group in the University of Tokyo, Centre for Quantum Technologies in the National University of Singapore, Tsinghua University, the Barcelona Supercomputing Center, and the Indian Institute of Science.

Research Interests

Quantum-enhanced machine learning Quantum-enhanced machine learning: Current and near-future quantum technologies have a potential of improving learning algorithms. Of particular interest are algorithms that have a high computational complexity or that require sampling. The latter type includes many probabilistic graphical models in which not only the training phase, but also the inference phase has been infeasible at scale, prompting a need for quantum-enhanced sampling. This in turn will enable deep architectures for probabilistic models, as well as scalable implementations of statistical relational learning, both of which go beyond the black-box model of neural networks and shift the focus towards explainable artificial intelligence. While speedup is the primary consideration, we also investigate the fundamental limits of statistical learning theory in the framework of quantum physics.

Quantum many-body systems, optimization, and machine learning: Identifying the ground state of a many-particle system whose interactions are described by a Hamiltonian is an important problem in quantum physics. During the last decade, different relaxations of the previous Hamiltonian minimization problem have been proposed. These algorithms include the lower levels of a general hierarchy of semidefinite programming (SDP) relaxations for non-commutative polynomial optimization, which provide a lower bound on the ground-state energy, complementing the upper bounds that are obtainable using variational methods. The latest developments step away from optimization, and introduce machine learning as an ansatz for ground-state energy problems and for the study of quantum phase transitions. In fact, strong links between quantum many-body physics (tensor networks in particular) and deep learning are being established. We are developing a set of theoretical and numerical tools to pursue these synergies.