Featured in 2018
Rushil Anirudh | Postdoctoral Researcher
With a PhD in computer vision and machine learning, Anirudh joined LLNL’s Center for Applied Scientific Computing in 2016. He enjoys the challenges of an exponentially growing field, noting, “Something on a whiteboard today is likely to end up being used by someone within a few months.” Anirudh develops convolutional neural networks that can complete computed tomography (CT) images when the scanned object is only partially visible. His team’s paper, “Lose the Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion,” is one of only 7% selected for a spotlight presentation at the 2018 Computer Vision and Pattern Recognition conference. Anirudh’s related work with generative adversarial networks was recently featured in NVIDIA’s developer blog. “I am very glad the Lab has the DSI,” says Anirudh. “A central institute that brings together everyone working on similar ideas is a great step toward becoming a leader in artificial intelligence and machine learning.”
T. Nathan Mundhenk | Computer Scientist
Mundhenk enjoys “nerding around” in LLNL’s Computational Engineering Division, especially when it comes to research aimed at improving people’s lives. With a PhD in computer science from the University of Southern California, he works on projects that use LLNL’s powerful computing capabilities to advance neural network technologies. Mundhenk recently co-authored a paper, “Improvements to Context Based Self-Supervised Learning,” which was accepted to the 2018 Computer Vision and Pattern Recognition conference. His team is developing a state-of-the-art technique for refining unsupervised deep learning. In their method of self-supervision, a deep neural network can be pre-trained on a large generic dataset before training on a small labeled dataset, resulting in better accuracy (e.g., of image recognition) in the latter. “The entire field of artificial intelligence is bursting with new innovation,” says Mundhenk. “It’s challenging to keep up with the extraordinary pace of research, but also very exciting to be part of it.”
Marisa Torres | Senior Bioinformatics Software Developer
Since joining LLNL in 2002, Torres has combined her love of biology with coding. She serves as lead bioinformatics software developer on biosecurity projects supporting the Global Security Program. Her team is building the Gene Surprise Toolkit, which determines biothreat severity and detects potential genetic engineering of pathogens. In addition, Torres contributes to the Accelerating Therapeutics for Opportunities in Medicine consortium. The project aims to accelerate the drug discovery pipeline by building predictive, data-driven pharmaceutical models. In March 2018, Torres organized a regional symposium in conjunction with Stanford University’s Women in Data Science conference.