<div dir="ltr"><br clear="all"><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><br></div></div></div></div></div></div></div></div><div class="gmail_quote"><p><b><img alt="SDSU_CSRC Logo.jpg" width="534px" height="107px" src="cid:3c8e87ea-c414-46f0-9732-e8b8bd720b96"><br></b></p><p><b><br></b></p><p><b><font color="#ff0000" size="4">**** Registration is required in order to get the link to join the seminar online. Please register at the below web page.</font></b></p><p><br></p><p><font size="4">DATE: <br><b>Friday, March 4, 2022</b></font></p><p><br></p><div dir="ltr"><p></p><p></p><p><font size="4">TITLE:<br><b>Can We Trust Machine Learning Predictions to Answer Science Questions?</b> </font> <b> </b> <b> </b> </p><div><br></div><p><font size="4">TIME: <br><b>3:30-5:00PM</b></font></p><p> </p><p><span style="font-size:large">LOCATION:</span></p><p><b style="font-size:large">Viewing Party at GMCS 314</b></p><p><b style="font-size:large">Registration is required in advance. Please register at the following site to receive information on how to join the seminar online. </b><a href="https://us06web.zoom.us/meeting/register/tZYucu2qqj8oHd0yBSJoy2Uy6j1udHRMHKdt" target="_blank" style="font-size:large">Meeting Registration - Zoom</a><br></p><p><br><font size="4">SPEAKER/BIO: <br><b>Dr.</b>
</font><b><font size="4">Diane Oyen, Scientist, Information Sciences, Los Alamos National Laboratory</font> </b> <b> </b> </p><p><br></p><p><font size="4">ABSTRACT:</font></p></div><div dir="ltr"><p><font size="4">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.<br><br>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.<br><br><a href="https://public.lanl.gov/doyen/" target="_blank">Presenter Website</a></font></p></div><div dir="ltr"><font size="4"><br></font></div><font size="4">Host:<br><b>Rodrigo Navarro Perez, Physics, San Diego State University</b><br></font><div><font size="4"><br><br></font></div><div><p><font size="4"><span style="font-weight:bold">Note:</span><span style="font-weight:bold"> </span>Videos of previous colloquium talks can be seen on the CSRC website in the colloquium archive section or on the <a href="https://www.youtube.com/channel/UCN0ZEztlmyDqG2pm-Rle_Eg/feed" target="_blank">CSRC YouTube page here</a>.<br><br><br><br></font></p><p><br></p></div>
<p></p>
-- <br>
You received this message because you are subscribed to the Google Groups "CSRC Colloquium" group.<br>
To unsubscribe from this group and stop receiving emails from it, send an email to <a href="mailto:csrc.colloquium+unsubscribe@sdsu.edu" target="_blank">csrc.colloquium+unsubscribe@sdsu.edu</a>.<br>
To view this discussion on the web visit <a href="https://groups.google.com/a/sdsu.edu/d/msgid/csrc.colloquium/aaa4bcfb-e426-4d3a-97f4-6597ad505d34n%40sdsu.edu?utm_medium=email&utm_source=footer" target="_blank">https://groups.google.com/a/sdsu.edu/d/msgid/csrc.colloquium/aaa4bcfb-e426-4d3a-97f4-6597ad505d34n%40sdsu.edu</a>.<br>
</div></div>