Featured in 2018
Bhavya Kailkhura | Computer Scientist
Kailkhura thrives on solving challenging problems in data science, focusing on improving the reliability and the safety of machine learning systems. “Reliability and safety in AI should not be an option but a design principle,” he states. “The better we can address these challenges, the more successful we will be in developing useful, relevant, and important ML systems.” Kailkhura also pursues mathematical solutions to open optimization problems, including a novel sphere-packing theory. He is building provably safe, explainable deep neural networks to enable reliable learning in applications for materials science, autonomous drones, and inertial confinement fusion. Thanks to his efforts with gradient-free algorithms and experiment designs, LLNL is the only national lab with research accepted at two high-profile venues—NIPS and JMLR—in 2018. Prior to joining LLNL’s Center for Applied Scientific Computing, Kailkhura attended Syracuse University where his PhD dissertation won an all-university prize. Recently, he co-authored the book Secure Networked Inference with Unreliable Data Sources.
Kassie Fronczyk | Applied Statistics Group Leader
Fronczyk is a “total nerd” whose multifaceted job makes her an ideal panelist for the Women in Statistics and Data Science conference, where she recently discussed research opportunities at national labs. Fronczyk leads LLNL’s Applied Statistics Group while providing statistical analysis and uncertainty quantification for several projects, including a warhead life-extension program and the U.S. Nuclear Detection System. “I love learning new things and tackling interesting problems,” states Fronczyk. “Standard approaches rarely work on real-world data, so finding the right tool for the job often means exploring new methods and combining or modifying others.” She brings this creative mentality to on- and offsite collaborations, such as with the Innovations and Partnerships Office and the Institute of Makers of Explosives Science Panel. She also sits on LLNL’s Engineering Science & Technology Council, manages two seminar series (including DSI’s), and co-organized DSI’s inaugural workshop. Fronczyk holds a PhD in statistics and stochastic modeling from UC Santa Cruz.
Jose Cadena Pico | Postdoctoral Researcher
Cadena Pico enjoys the discovery process when analyzing new data sets, despite the difficulties in preparing data before building machine learning models. “Often a data set is incomplete or contains errors from different sources. Sometimes its size makes it difficult to extract knowledge,” he says. “Solving these challenges and knowing that I’m helping other researchers advance their work is very gratifying.” Once a PhD student at Virginia Tech, Cadena Pico now contributes to LLNL’s brain-on-a-chip project by studying complex networks among brain cells. He also investigates ways to detect anomalous activity in networks, and his recent work—developing a method for finding clusters of under-vaccinated populations to inform public health resources—was presented at the 24th KDD Conference. Formerly a three-time LLNL summer intern, Cadena Pico values ongoing education: “I like to keep learning about different research domains while developing a data science skill set applicable to many problems of global importance.”
DSSI Students | Summer Interns
A select group of undergraduate and graduate science and engineering students with backgrounds in machine learning, applied mathematics, computer science, and statistics joined our team at LLNL to work with some of the best minds in data science to tackle some of the world's largest problems during the summer of 2018. Find out more about these rising stars of Data Science on our Class of 2018 page.
Rushil Anirudh | Research Scientist
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. She also encourages local middle school students to explore computer science through the Girls Who Code program and mentors student interns for LLNL’s Data Science Summer Institute (DSSI). “I’m interested in collaborating across domains with similar data analysis needs,” says Torres. “I look forward to strengthening networking and educational opportunities through DSI, especially for the DSSI.”