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Stony Brook University

University Libraries STEM Speaker Series: Spring 2020

First Lecture

Guest Speaker: Dr. Steven Skiena, Distinguished Teaching Professor of Computer Science and Director of the Institute for AI-Driven Discovery and Innovation at Stony Brook University

Title: "Representing Knowledge through Word and Graph embeddings"

Date: Tuesday, February 11, 2020

Time: 1pm-2pm

LocationSpecial Collections Seminar Room, E-2340, second floor of the Melville Library

Please register here.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Second Lecture (Canceled due to COVID-19)

Guest Speaker: Dr. Melanie Chiu, Department of Chemistry

Title: "Photoregulation of Polymerization Processes"

Date: Tuesday, March 24, 2020

Time: 1pm-2pm

LocationSpecial Collections Seminar Room, E-2340, second floor of the Melville Library

Please register here.

First Lecture: Dr. Steven Skiena, Distinguished Teaching Professor of Computer Science and Director of the Institute for AI-Driven Discovery and Innovation at Stony Brook University

Title: "Representing Knowledge through Word and Graph Embeddings"

Libraries are about representing large quantities of knowledge to make them broadly useful and available.  Similarly, word and graph embeddings (e.g. word2vec) provide powerful ways to reduce large text corpora to concise features readily applicable to a variety of problems in NLP and data science.  I will introduce word embeddings, and apply them in variety of new and interesting directions, including:
(1) Multilingual NLP -- The Polyglot project (www.polyglot-NLP.com) employs deep learning and other techniques to build a basic NLP pipeline (including entity recognition, POS tagging, and sentiment analysis) for over 100 different languages.  We train our systems over each language's Wikipedia edition, providing unified data resources in the absence of explicitly annotated data, but substantial challenges in interpretation and evaluation.
(2) Detecting Historical Shifts in Word Meaning -- Words like "gay" and "mouse" have substantially shifted their meanings over time in response to societal and technological changes.  We use word embeddings trained over texts drawn from different time periods to detect changes in word meanings.  This is part of our efforts in historical trends analysis.
(3) Feature Extraction from Graphs --  We present DeepWalk, our approach for learning latent representations of vertices in a network, which has become extremely popular.  DeepWalk uses local information on truncated random walks to learn embeddings, by treating walks as the equivalent of sentences in a language.  It is suitable for a broad class of applications such as network classification and anomaly detection.  We also introduce new graph embedding techniques based on random projections, which produce DeepWalk-quality embeddings thousands of times faster than previous algorithms.

 

Biosketch: 

 

Dr. Steven Skiena is Distinguished Teaching Professor of Computer Science and Director of the Institute for AI-Driven Discovery and Innovation at Stony Brook University.  His research interests include data science, bioinformatics, and algorithms. He is the author of six books, including "The Algorithm Design Manual", "The Data Science Design Manual", and "Who's Bigger: Where Historical Figures Really Rank". 

 

Dr. Skiena received his Ph.D. in Computer Science from the University of Illinois in 1988.  He is the author of over 150 technical papers. He is a Fellow of the American Association for the Advancement of Science (AAAS), a former Fulbright scholar, and recipient of the ONR Young Investigator Award and the IEEE Computer Science and Engineer Teaching Award.  More info is available at http://www.cs.stonybrook.edu/~skiena/.

Second Lecture: Dr. Melanie Chiu, Department of Chemistry (Canceled due to COVID-19)

Title: "Photoregulation of Polymerization Processes"

This seminar will explore fundamental developments in polymer synthesis, including methods for controlling polymer dispersity and copolymer sequence. Growing evidence indicates that these parameters, dispersity and sequence, profoundly impact polymer material properties, such thermal stability and degradation profiles. Yet, methods for high-resolution control over these parameters are rare, preventing systematic correlation of polymer structure with material functions. We have developed new classes of photoswitchable initiators and catalyst systems that enable dynamic manipulation of dispersity and copolymer sequence, respectively. This work is the first demonstration of using light to deterministically control the dispersity of poly(vinyl ethers) and the sequence of poly(lactides). These results serve as a foundation for further exploring external control of polymer structure, and for accessing new polymer structures with tunable properties.

 

Biosketch:

 

Dr. Melanie Chiu is a native of San Diego, California, and graduated summa cum laude from Dartmouth College in 2004 with an A.B. in Chemistry. She earned her Ph.D. in 2009 as a National Science Foundation Pre-Doctoral Fellow in the Department of Chemistry at the University of California, Berkeley. She was a postdoctoral researcher at ETH Zürich (2009–2011, ETH Research Council Postdoctoral Fellowship) and Stanford University (2012–2014) before joining the faculty in the Department of Chemistry at Stony Brook University in 2014. Dr. Chiu especially values the privilege of pursuing some of science’s biggest mysteries as a member of the Stony Brook community because of its tremendous diversity, collaborative environment, and commitment to education as an engine of social mobility. When she’s not in the chemistry lab, Melanie enjoys training for triathlons, climbing, and playing violin.

Event Organizer: Clara Tran, Head of Science and Engineering