Current areas of focus for the Artificial Intelligence for Design Challenge program

 

AI-assisted design of photonic components

A multitude of parameters determine the performance of a photonic device, encompassing the optical properties of the constituent materials, structural geometry and dimensions. The availability of simulators to generate data opens the door for the introduction of data-driven modelling, analysis and optimization techniques developed in the field of machine learning and AI in general. Although the use of AI techniques to facilitate photonic design process has been increasing over the last several years, it is still in its infancy. The AI-assisted design of photonics components master project explores AI-based design optimization along a number of directions including design structure, simulation acceleration and accuracy, intelligent search of design space and design fabricability. Appropriate use of AI methods in these areas has significant impact on the outcome of the design process and the time it takes to reach the target design in high-dimensional problems. In addition, it helps domain experts to identify the possibilities and limitations of the design space, opening doors for new research directions.

Master Project Lead

Dr. Yuri Grinberg

Yuri is an associate research officer with the National Research Council and is an expert in applied and theoretical machine learning and reinforcement learning. His primary research interests are in advancing the machine learning state of the art to address problems in physics and engineering. In particular, he is interested in development of appropriate AI methodologies for the design of efficient, small footprint, easy to fabricate photonic components with significantly reduced human effort.

Yuri is leading the AI for Design photonics master project and is a principal investigator on the AI-assisted miniaturization of integrated photonic components collaborator project with McGill University and the AI-assisted inverse design project.

Find out more about Yuri's research interests and his publications.

Contact information

Dr. Yuri Grinberg
613-993-0665

Projects and teams

AI for design of biological systems

AI for design of biological systems master project focusses on AI-based biological therapy design for increased safety, efficacy and specificity with reduced cost and side effects. AI and particularly machine learning is becoming an integral part of modern biological research and is expected to enable more accurate, faster and less expensive innovations in life sciences while at the same time providing prediction of outcomes under different situations. This development relies on implementation of appropriate algorithms, availability of appropriate data and metadata as well as expert input for data labelling, model development and result interpretation. In the long term this master project aims to develop, test and make accessible to non-experts AI-driven simulations combined into digital twins of biological systems and platforms including cellular systems, biodevices and gene editing modalities. These provide methods for unbiased selection and development of optimized cell therapies for different applications.

Master Project Lead

Dr. Miroslava Cuperlovic-Culf

Mira is leading the AI for Design for biological systems master project and is a principal investigator on the AI for simulation of biological systems project, a collaborative project with Queen's University, University of Ottawa and Carleton University. She is also a principal investigator on the AI-enabled design of aptamer sensors and co-principal investigator for the digital twin for bioreactors project.

Find out more about Dr. Cuperlovic-Culf.

Contact information

Dr. Miroslava Cuperlovic-Culf
613-993-0116

Projects and teams

  • AI for drug design
  • AI for precision discovery on associations in biological systems
  • AI for design of multi-targeted therapeutics
  • Artificial intelligence protein design for drugs and gene therapies
  • AI for simulation of biological systems
  • Digital-twin of bioreactor for accelerated design and optimal operations in production of complex biologics
  • AI-enabled design of aptamer sensors

Deep material science

Improving the material pipeline is important for many societal and technological problems, including energy, high-speed networking and health. AI shows great promise for the design of new materials, design of new processes for materials synthesis, and design of methodologies for the discovery process itself. The deep material science master project leverages a collection of projects to develop AI algorithms and methods that enable material scientists to find better materials. Increased exposure and understanding within the AI community results in more AI practitioners focussing on the many impactful and outstanding problems which exist in the materials domain.

Master Project Lead

Dr. Isaac Tamblyn

Isaac is a research officer at the National Research Council and is a Vector Institute faculty affiliate. He is also an adjunct professor of physics at the University of Ottawa. Isaac's current research interests are focussed on the application of AI and deep learning to problems in nanoscience, in particular materials and processes related to renewable energy.

Isaac is leading the AI for materials master project and is a principal investigator on several AI for Design collaborator projects.

Find out more about Dr. Tamblyn.

Contact information

Dr. Isaac Tamblyn
613-796-5148

Projects and teams

  • Automated material synthesis using deep reinforcement learning
  • AI for simulation and design of materials with targeted properties
  • Use of artificial intelligence to speed up numerical simulations: application to the design of superconducting tapes for energy and medicine domains
  • Fast spectroscopic signatures accelerated by deep learning

Core AI for Design

AI has the power to accelerate scientific discovery through a combination of big data and intelligent algorithms. Widespread adoption of AI in design however requires advancements in theory, methods and algorithms to address challenges in simulation and modelling and to enable generalizability and adoption by non-AI experts. This master project's research efforts focus on AI methods for increasing the speed of simulations, improving the search of a design space and making explainable models that better align with real-world observation. A key outcome will be AI algorithms that are useful across a very broad set of domains and useable by non-AI experts.

Master Project Lead

Dr. Chris Drummond

Chris is a senior research officer with the National Research Council of Canada and an adjunct professor in the School of Electrical Engineering and Computer Science at the University of Ottawa. Chris's contributions to the AI community are in advancing machine learning research with applications to the monitoring and control of complex systems. The work for which he is best known (with over 1000 citations) is focussed on experimental evaluation, particularly when classes are highly imbalanced, an area of both theoretical and practical significance. Chris participates in the AI community through editorships, chairing conferences and reviewing scientific papers. He is the scientific lead for the core master project within the AI for Design program.

Contact information

Dr. Chris Drummond
613-993-0252

Projects and teams