Physics of Information Processing
and Machine Learning
University of Hawaii at Mānoa
We develop scientific foundations
for a new generation of information processing and machine
learning algorithms, as well as for new computing
hardware. Specifically, we study the physics of
information processing, with a focus on how fundamental
physical limits can inform design principles for learning
algorithms, and perhaps even reveal principles underlying
learning and self-organization in living systems.
We also use machine learning to address scientific questions through data analysis, with a focus on problems of relevance to society, such as financial risk, volcano prediction, and large scale scientific collaborations.
The lab is very diverse and
interdisciplinary. Prospective students with aligned
interests are encouraged to apply, even if they are in
other disciplines. We work with students from many diverse
departments, on campus and at other Universities.
Together with other groups,
in the US and abroad, we are pursuing exploratory research
into new technology to make computing more energy
Susanne Still (Director)
MSc Mathematics (2012). Now at Stanford.
Dr. Hunter Hatfield PhD Linguistics (2010). Now faculty at the University of Otago, New Zealand.
lab rotation graduate students:
PhD student, ICS (2017-2019).
Wane, MSc student Mathematics
Victor Miagkikh, PhD Student, ICS
Spencer Long, (2019), Physics & Astronomy.
Josef Bramante, Physics,
UHM (2013), now faculty
at Queens University, Canada.
Sarah Marzen (UC Berkeley, visited 2013) now faculty
at the Keck
science center, Claremont, CA.
Pardis Niknejadi, Physics, UHM
(2016), now at DESY.
Dr. Jonathan Page, Economics, UHM (2016)
Chris Ellison (UC Davis, collaborated 2008-2009)
post doctoral visitor:
Taku Ishikawa, Post-Doctoral Researcher (2015-2017). National Printing Bureau, Japan.
Due to COVID 19 no in-person lab meetings until the situation is under control.
If you are interested to learn more about what is going on in our lab, please send me an email.