[Faculty] Fwd: [CSRC.COLLOQUIUM] "Can We Trust Machine Learning Predictions to Answer Science Questions?"

[Jose Castillo]

*[image: SDSU_CSRC Logo.jpg]*


***** Registration is required in order to get the link to join the seminar
online. Please register at the below web page.*


DATE:
*Friday, March 4, 2022*


TITLE:
*Can We Trust Machine Learning Predictions to Answer Science Questions?*


TIME:
*3:30-5:00PM*



LOCATION:

*Viewing Party at GMCS 314*

*Registration is required in advance.  Please register at the following
site to receive information on how to join the seminar online.   *Meeting
Registration - Zoom
<https://us06web.zoom.us/meeting/register/tZYucu2qqj8oHd0yBSJoy2Uy6j1udHRMHKdt>


SPEAKER/BIO:
*Dr.*  *Diane Oyen, Scientist, Information Sciences, Los Alamos National
Laboratory *


ABSTRACT:

Scientists in fields as diverse as bioscience, geoscience, and cyber
security are successfully applying machine learning models to solve
problems of critical importance to science and security. Machine learning
models generalize patterns from datasets and can result in emergent
behaviors that are poorly understood by their creators and users. Machine
learning is trained and validated on available datasets -- whether from
simulations, experiments or observations -- but must be trusted to deploy
on real data and to answer scientific puzzles. Questions of robustness,
fairness, bias and trustworthiness in machine learning models have arisen
in social contexts (such as the ethics of using machine learning models to
determine prison sentences in criminal court cases). Yet science problems
present a rich testbed for developing trustworthy machine learning methods
and evaluation tools. We are developing methods to evaluate datasets,
machine learning models, and the output predictions of these models to go
beyond only achieving high accuracy on a fixed validation set, but to
ensure that machine learning is answering the science question at hand.

Bio: Diane Oyen is a Scientist in the Information Sciences Group at Los
Alamos National Laboratory. She received her B.S. degree in Electrical
Engineering from Carnegie Mellon University and her Ph.D. in Computer
Science from the University of New Mexico. Diane develops machine learning
methods for scientific analysis; with particular focus in explainable
machine learning, transfer learning, and robust machine learning. She uses
probabilistic graphical models in machine learning to better understand the
dependence among variables in complex systems, and extends the latest
machine learning methods, including deep learning, for use in novel
applications such as pattern recognition and scientific discovery in
ChemCam observations on Mars, accelerating simulations of physics
simulations, malware characterization, and computer vision for technical
images.

Presenter Website <https://public.lanl.gov/doyen/>

Host:
*Rodrigo Navarro Perez, Physics, San Diego State University*


Note: Videos of previous colloquium talks can be seen on the CSRC website
in the colloquium archive section or on the CSRC YouTube page here
<https://www.youtube.com/channel/UCN0ZEztlmyDqG2pm-Rle_Eg/feed>.




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