Biology is filled with noisy signals. The statistical algorithms I am building in the context of biology will allow a leap forward in the areas of psychiatric genetics, bioinformatics, fundamental genomic sequencing chemistry, healthcare diagnosis, brain and medical imaging, human-multi-robot wet lab automation and collaboration, and scientific interpretation of noisy, low-signal biological data.
Most recently, I worked at Bionano Genomics on optical imaging of genomes in San Diego. I am currently exploring new opportunities for 2020. Below you can find a summary of previous work.
In 2015, I defended my Robotics PhD thesis on Bayesian Aggregation, a sensor processing framework applied to scientific domains such as radiation spectroscopy (supported by DoD), focusing on:
- Low Signal Detection: Detection and property characterization of weak signals with limited sensors.
- Online Sensor Reliability: Self-calibrating sensors that can adapt to new noise conditions.
- Retrospective Learning: Allow robotic agents to automate reinterpretation of past beliefs about noise variation.
My postdoctoral work (UCSD) applied many of these ideas to genomics, especially for modeling background mutation rates in neurodevelopmental disorders. I also explored Deep Learning GPU approaches to modeling fMRI data as a consultant (UCSF).
I have an M.S. in Robotics with emphasis on AI/ML/Statistics and a B.S. in Computer Engineering/Computer Science with emphasis on software and hardware as well as a Thematic Option liberal arts certificate.
In my spare time, I lead the Quantum Robotics Group and have coauthored a book on Quantum Robotics (quantumrobotics.org), setting a vision for the development of quantum computing for more intelligent robotics. I am also foraying into helping develop critical climate tech to address the threat of global warming and leading climate tech strategy discussions. Finally, I have many years of experience with nonprofit and education programs, helping teach computer science globally and building software for persons with disabilities.
Here are some of my current research topic areas and interests related to Genomics/Robotics/ML/AI (in no particular order):
- Working on improving models of structural variation mutation rate in the human genome as a postdoc in the UCSD Sebat Lab. Poster published at American Society for Human Genetics (ASHG) 2018.
- Worked on improving approaches to pathogenicity prediction of structural variation using machine learning. Poster published at Society for Neuroscience (SfN) 2018.
- Organizer for NIPS Machine Learning for Health (ML4H) Workshop in December 2017. Workshop successfully aggregated clinicians and machine learning experts to discuss healthcare domain applications!
- The Quantum Robotics Group I helped spearhead published a book in January 2017, Quantum Robotics: A Primer on Current Science and Future Perspectives. Book available on Amazon via Morgan Claypool Publishers!
- Deep Learning consultant for UCSF Strigo lab exploring Deep Learning architectures for fMRI of pain representation task. Presented to Siemens Research Group, New Jersey in 2017!
- Explored EMG and EEG signal processing as part of CyborgDistro plans to control multiple robot arms! CyborgDistro was featured on RaspPiPod, Adafruit, as a quarterfinalist in the Hackaday 2015 Prize Contest, and a winner of the Element 14 Halloween Build-A-Thon!
- I defended my PhD Dissertation on Bayesian Aggregation at Robotics Institute, Carnegie Mellon University in July 2015. PhD Awarded!
- Developed general machine learning algorithms for neural data and applications to experimental physics. Posters presented at Big Neuro Data and Aleph workshops at NIPS 2015.
- Developed algorithms for Fault-Tolerant Radiological Threat Detection using Bayesian Reliability Models and Anomaly-Match Bayesian Aggregation (AM-BA) for Efficient Radiation Source Search. Posters presented at IEEE Nuclear Science Symposium 2015.
- Lead developer of Lunar Tabs, an Intelligent, Accessible Guitar Tab Reader. Developed personalized difficulty classifiers for guitar learners using large amounts of guitar tab data online, enabling swarms of humans and robots to autonomously learn guitar. Project poster featured at AAAI 2015.
- Building Biologically-Inspired Deep Learning algorithms that are more pragmatic, realistic, and believable by being more biologically plausible than backpropagation for tasks such as neural hammering. They can also have better optimization properties! Senior thesis on population-coded neural network controlled robots still pending publication after 6 years. When will the world be ready for Biologically-Plausible Deep Learning?