Alexa Hudnut studied Biomedical Engineering at the University of Southern California under the advisement of Professor Andrea Armani. She worked to create tools to improve how physicians diagnose diseases such as cancer. She worked on developing a wide array of platforms that vary drastically in their applications, yet all use fundamentals of optics as their scientific foundation. In a field where overworked doctors are often disconnected from their patients, Alexa hopes to act as a bridge to refocus the conversation onto areas critical to patient well-being.
Dave Kale as a PhD student in Computer Science he worked on learning latent space representations of health and illness from multimodal clinical data, including sensor time series, lab tests, text, and codes. His advisor was Professor Greg Ver Steeg of the Information Sciences Institute.
Most recently, Dave was among a small number of researchers investigating whether deep neural networks, which represent the state of the art in a variety of applications, can be successfully applied to clinical data and problems.
Dave was affiliated with the Laura P. and Leland K. Whittier Virtual Pediatric Intensive Care Unit (VPICU) at Children’s Hospital Los Angeles and the USC Center for Body Computing. He is a co-founder of Podimetrics and was a judge in the Qualcomm Tricorder XPRIZE Competition.
Dave holds a Bachelor of Science degree in Symbolic Systems and a Master of Science degree in Computer Science from Stanford University.
Daeun Kim studied Electrical Engineering, under the guidance of Professor Justin Haldar in the Biomedical Imaging Group (BIG) at the University of Southern California. Her research interests included multidimensional signal processing, constrained signal processing, inverse problems, Magnetic Resonance Imaging (MRI), and biomedical data processing.
Her research was focused on developing an advanced MRI technique that could provide more accurate characterization of microstructure in biological tissues.
She is a recipient of the USC Women in Science and Engineering Merit Award and the 1st Place Award for Best Abstract Presentation at the Quantitative MR study group in the International Society for Magnetic Resonance in Medicine from her recent work.
Samantha McBirney is a PhD candidate at USC, graduating in May 2018. She was a student in Professor Andrea Armani’s research group. Her research interests included developing novel optical sensors to study blast-induced neurotrauma and to detect catheter-related bloodstream infections before a patient becomes symptomatic, among other things. See Samantha’s latest discovery to detect staph infections earlier in this article.
Samantha was also very passionate about empowering young women to pursue STEM, as she was involved in several different outreach groups that focus on mentoring young women interested in STEM-related fields.
Daniel Bone received his PhD in Electrical Engineering at the USC Viterbi School of Engineering. His research concerned human-centered signal processing and machine learning with an emphasis on developing engineering techniques and systems to characterize and eventually treat developmental disorders such as Autism (which has a high prevalence of 1 in 68). He worked with Dr. Shrikanth Narayanan and was a Postdoctoral Research Scholar in the interdisciplinary Signal Analysis and Interpretation Lab (SAIL) at USC.
He received Bachelor of Science degrees in Electrical Engineering and Computer Engineering from the University of Missouri-Columbia in 2009, and a Master of Science degree in Electrical Engineering from USC in 2011. He was an NSF GK-12 Body Engineering Fellow during 2012-2013.
Danny has first-authored five journal papers, nine conference papers, and one book chapter. He has led and been a part of two teams that won international engineering competitions (Interspeech 2011, 2015). His work has been featured on KPCC off-ramp, SFARI.org, and USC media.
Dr. Bone is a Senior Scientist at yomdle, inc. in Los Angeles, where he researches and develops novel products that incorporate state-of-the-art speech processing and computer vision methodologies.