I was born/grew up in: I was born in Nova Scotia, but grew up in Parry Sound, Ontario. I now live in: I now live in Barrie, Ontario, and work remotely for a company headquartered in Toronto. I work now at a company called Cyclica. We are a biotechnology company that uses Artificial Intelligence (AI) to help make medicines that are more effective for patients. I do research and develop methods for computational drug design.

Supercomputers help researchers study the causes and effects--usually in that order--of complex phenomena. However, scientists occasionally need to deduce the origins of scientific phenomena based on observable results. These so-called inverse problems are notoriously difficult to solve, especially when the amount of data that must be analyzed outgrows traditional machine-learning tools. To better understand inverse problems, a team from the US Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL), NVIDIA, and Uber Technologies developed and demonstrated two new techniques within a widely used communication library called Horovod. Developed by Uber, this platform trains deep neural networks (DNNs) that use algorithms to imitate and harness the decision-making power of the human brain for scientific applications. Because Horovod relies on a single coordinator to provide instructions to many different workers (i.e., GPUs in this case) to complete this process, large-scale deep-learning applications often encounter significant slowdowns during training.

AI techniques have been leveraged that combine known biophysical, machine learning, and reinforcement learning models to effectively predict the constructs' impact on the host and vice versa, but there is much room for improvement.

Computational Research Division (https://crd.lbl.gov/) has an opening for a AI Algorithms Postdoctoral Fellow Postdoc to join the team. In this exciting role, you will join Berkeley Lab's Computer Languages and Systems Software (CLaSS) group, a world-leading team researching and developing programming models and software for parallel and quantum computing. CLaSS research focuses on the design and development of parallel programming languages, compilers, networking middleware, runtime libraries, and quantum synthesis tools. Open-source software produced by CLaSS and its collaborators include the GASNet-EX exascale communication library, the Berkeley UPC compiler, the UPC template library, BQSKit quantum synthesis toolkit, and the OpenCoarrays parallel runtime library. CLaSS researchers collaborate with computational scientists across application domains ranging from large-scale genome assembly to materials modeling and climate simulation.

The establishment of King Abdullah University of Science and Technology (KAUST) in 2009 was the fulfillment of a lifelong dream of its founder, the late King Abdullah of Saudi Arabia. His vision for the university was deeply rooted in the historical and cultural contexts of the Middle East. He intended the university to be seen as a revival of the old "house of wisdom," which was a premier institution of learning in Baghdad from the 9th century until the 13th century. Starting as a private library of the fabled Caliph Harun Al-Rasheed, it developed quickly into the 9th century equivalent of a research laboratory and a university. The house of wisdom was the birthplace of algebra and was a milieu where many developments took place in various fields of science and humanities.

The Large Hadron Collider (LHC) near Geneva, Switzerland became famous around the world in 2012 with the detection of the Higgs boson. The observation marked a crucial confirmation of the Standard Model of particle physics, which organizes the subatomic particles into groups similar to elements in the periodic table from chemistry. The U.S. Department of Energy's (DOE) Argonne National Laboratory has made many pivotal contributions to the construction and operation of the ATLAS experimental detector at the LHC and to the analysis of signals recorded by the detector that uncover the underlying physics of particle collisions. Argonne is now playing a lead role in the high-luminosity upgrade of the ATLAS detector for operations that are planned to begin in 2027. To that end, a team of Argonne physicists and computational scientists has devised a machine learning-based algorithm that approximates how the present detector would respond to the greatly increased data expected with the upgrade.