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Colloquium Series

Once every week while school is in session, EECS invites a distinguished researcher or practitioner in a computer science or electrical and computer engineering-related field to present their ideas and/or work. Talks are generally targeted to electrical engineering and computer science graduate students. This colloquium series is free and open to everyone.

Upcoming Colloquia

Linear-Time Structure Prediction in Language and Biology

Monday, January 23, 2017 - 4:00pm
GILB 124

Speaker Information

Liang Huang
Assistant Prof.
School of EECS
Oregon State University


Why are computers so bad at processing human languages while so good at programming languages? What’s the key difference between English and C++ that makes the former so much harder? In this talk I'll present a linear-time (approximate) dynamic programming algorithm for incremental parsing inspired by both human sentence processing (psycholinguistics) and compiler theory (LR parsing). This algorithm, being linear-time, is much faster than, but also as accurate as, the dominant O(n^3) algorithms. It overcomes the ambiguity explosion problem by local ambiguity packing similar to those found in psycholinguistics. 

More interestingly, there is a striking connection between linguistics and biology: natural language parsing and RNA secondary structure prediction use the same very slow O(n^3) algorithms. While natural language sentences are rarely over 100 words, RNA sequences can be as long as 4,000 nucleotides; so there is a critical need for faster algorithms. We can therefore adapt the same linear-time dynamic programming idea to predict secondary structures for RNA sequences in linear-time, which results in orders of magnitude faster predictions without loss of accuracy. 

Speaker Bio

Liang Huang is currently an Assistant Professor of EECS at Oregon State University, and a part-time Research Scientist with IBM's Watson Group. Before that he was Assistant Professor for three years at the City University of New York (CUNY). He graduated in 2008 from Penn and has worked as a Research Scientist at Google and a Research Assistant Professor at USC/ISI. Most of his work develops fast algorithms and provable theory to speedup large-scale natural language processing, structured machine learning, and computational structural biology. He has received a Best Paper Award at ACL 2008, a Best Paper Honorable Mention at EMNLP 2016, several best paper nominations (ACL 2007, EMNLP 2008, and ACL 2010), two Google Faculty Research Awards (2010 and 2013), a Yahoo! Faculty Research Award (2015), and a University Teaching Prize at Penn (2005). His research has been supported by DARPA, NSF, Google, and Yahoo. He also co-authored a best-selling textbook in China on algorithms for programming contests.

Using Machine Learning to Understand Gene Regulation

Monday, January 30, 2017 -
4:00pm to 4:50pm
GILB 124

Speaker Information

Molly Megraw
Assistant Prof.
Department of Botany and Plant Pathology
Oregon State University


My laboratory is broadly interested in understanding how certain important small RNAs known as “microRNAs” and important protein-coding genes known as “Transcription Factors” work together in living cells.  As a part of these studies, we need to identify (1) which RNAs and genes interact, and (2) which interactions form circuits that play key physiological roles within specific tissues of an organism.  Our recent work in these areas has given rise to two challenges which may interest EECS students, postdocs, or other collaborators.  In the first portion of the talk I will demonstrate how a machine learning model can suggest sets of gene interactions which have the potential to “turn on” a particular gene, and briefly discuss one possible approach for dissecting which of those sets are optimal predictors of gene up-regulation.  In the second portion of the talk I will present a new project that seeks to predict the tissue in which a given gene will express.  At the end of the talk I will briefly present a new course offering for Spring 2017 that is designed to introduce concepts in Genome Biology to students from EECS who would like to explore computational biology as an application area but have never taken a biology class before.

Speaker Bio

Molly Megraw received her doctoral degree in Genomics and Computational Biology from the University of Pennsylvania.  During her post-doctoral work at Duke University, she developed a machine learning model which demonstrates that highly accurate gene and microRNA transcription start site prediction can be achieved using DNA sequence information alone.  Her current work combines computational analysis of gene regulatory network topology with experimental methods for Transcription Start Site Sequencing library generation to identify gene regulatory circuits in multiple tissues of the Arabidopsis thaliana model plant system.  In 2012 she began a faculty position in Systems Biology within the Center for Genome Research and Biocomputing at Oregon State University, the departmental home for her laboratory is Botany & Plant Pathology.

Past Colloquia

Philip J. Guo
Wednesday, March 6, 2013 -
10:00am to 11:15am
Matthew Taylor
Monday, March 4, 2013 -
4:00pm to 4:50pm
Mi Zhang
Thursday, February 28, 2013 -
8:45am to 9:45am
Charalampos Papamanthou
Wednesday, February 27, 2013 -
10:00am to 11:15am
Quanyan Zhu
Monday, February 25, 2013 -
10:00am to 11:00am
Harish Krishnaswamy
Friday, February 22, 2013 -
2:00pm to 2:50pm
Zachary Tatlock
Friday, February 15, 2013 -
10:00am to 11:10am
Eric Walkingshaw
Thursday, February 14, 2013 -
8:30am to 9:40am
Ravi Chugh
Wednesday, February 13, 2013 -
10:00am to 11:10am