Conferences, Lectures, & Seminars
Events for June
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NL Seminar- Group Anomaly Detection in Social Media Analysis
Fri, Jun 12, 2015 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Yan Liu , USC Melady
Talk Title: Group Anomaly Detection in Social Media Analysis
Series: Natural Language Seminar
Abstract: Traditional anomaly detection on social media mostly focuses on individual point anomalies while anomalous phenomena usually occur in groups. Therefore it is valuable to study the collective behavior of individuals and detect group anomalies. Existing group anomaly detection approaches rely on the assumption that the groups are known, which can hardly be true in real world social media applications. In this paper, we take a generative approach by proposing a hierarchical Bayes model: Group Latent Anomaly Detection (GLAD) model. GLAD takes both pair-wise and point-wise data as input, automatically infers the groups and detects group anomalies simultaneously. To account for the dynamic properties of the social media data, we further generalize GLAD to its dynamic extension d-GLAD. We conduct extensive experiments to evaluate our models on both synthetic and real world datasets. The empirical results demonstrate that our approach is effective and robust in discovering latent groups and detecting group anomalies.
Biography: Yan Liu is an assistant professor in Computer Science Department at University of Southern California from 2010. Before that, she was a Research Staff Member at IBM Research. She received her M.Sc and Ph.D. degree from Carnegie Mellon University in 2004 and 2007. Her research interest includes developing scalable machine learning and data mining algorithms with applications to social media analysis, computational biology, climate modeling and healthcare analytics. She has received several awards, including NSF CAREER Award, Okawa Foundation Research Award, ACM Dissertation Award Honorable Mention, Best Paper Award in SIAM Data Mining Conference, Yahoo! Faculty Award and the winner of several data mining competitions, such as KDD Cup and INFORMS data mining competition.
Host: Nima Pourdamghani and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 6th Flr Conf Rm # 689, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
AI SEMINAR
Fri, Jun 19, 2015 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Maryam Shanechi, Assistant Professor and Viterbi Early Career Chair
Talk Title: Closed Loop Brain Machine Interface Architecures
Abstract: A brain-machine-interface (BMI) is a system that interacts with the brain either to allow the brain to control an external device or to control the brain's state. While these two BMI types are for different applications, they can both be viewed as closed-loop control systems. In this talk I present our work on developing both these types of BMIs, specifically motor BMIs for restoring movement in paralyzed patients, a BMI for control of the brain state under anesthesia, and finally a new BMI for control of the brains neuropsychiatric state. Motor BMIs have largely used standard signal processing techniques. However, devising novel algorithmic solutions that are tailored to the neural system can significantly improve the performance of these BMIs. Here, I develop a novel BMI paradigm for restoration of motor function that incorporates an optimal feedback-control model of the brain and directly processes the spiking activity using point process modeling. I show that this paradigm significantly outperforms the state-of-the-art in closed-loop primate experiments. In addition to motor BMIs, I construct a new BMI that controls the state of the brain under anesthesia. This is done by designing stochastic controllers that infer the brain's anesthetic state from non-invasive observations of neural activity and control the real-time rate of drug administration to achieve a target brain state. I show the reliable performance of this BMI in rodent experiments. Finally, I show some of our recent results on the development of a BMI for closed-loop electrical stimulation to treat neuropsychiatric disorders in human patients.
Biography: Maryam Shanechi is an assistant professor and the Viterbi Early Career Chair in the Ming Hsieh Department of Electrical Engineering at the University of Southern California (USC). Prior to joining USC, she was an assistant professor in the School of Electrical and Computer Engineering at Cornell University. She received the B.A.Sc. degree in Engineering Science from the University of Toronto in 2004 and the S.M. and Ph.D. degrees in Electrical Engineering and Computer Science from MIT in 2006 and 2011, respectively. She is the recipient of the NSF CAREER Award and has been named by the MIT Technology Review as one of the worlds top 35 innovators under the age of 35 (TR35) for her work on brain-machine interfaces.
Host: Ashish Vaswani
Webcast: http://webcasterms1.isi.edu/mediasite/SilverlightPlayer/Default.aspx?peid=1bcb9ec6f2d24ea58d7a2237816965c01dLocation: Information Science Institute (ISI) - 1135
WebCast Link: http://webcasterms1.isi.edu/mediasite/SilverlightPlayer/Default.aspx?peid=1bcb9ec6f2d24ea58d7a2237816965c01d
Audiences: Everyone Is Invited
Contact: Alma Nava / Information Sciences Institute
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
NL Seminar- Automated Tools For Analyzing Sociophonetic Variation
Tue, Jun 23, 2015 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Sravana Reddy, Dartmouth College
Talk Title: Automated Tools For Analyzing Sociophonetic Variation
Series: Natural Language Seminar
Abstract: The phenomenal amount of text on social media has recently spawned endeavors on computational methods to study language variation and change. However, we also have access to an unprecedented quantity of speech -- from Youtube video blogs to podcasts to recordings of radio and television shows, spanning several different accents and dialects. This data is a boon to sociophoneticians, who have traditionally relied on small-scale interviews to study systematic variation in speech. At the same time, it presents a challenge: the usual manual speech analysis methods do not scale. I will present ongoing work on an application that allows sociophoneticians to identify dialect features from potentially noisy speech data without the need for manual transcription.
Biography: http://www.cs.dartmouth.edu/~sravana/
Host: Nima Pourdamghani and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 6th Flr Conf Rm # 689, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
NL Seminar-Metonymy resolution with multi-faceted knowledge from Wikipedia
Fri, Jun 26, 2015 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Vivi Nastase, USC/ISI
Talk Title: Metonymy resolution with multi-faceted knowledge from Wikipedia
Series: Natural Language Seminar
Abstract: Metonymic words stand-in for concepts closely related to the words' literal interpretation. Resolving metonymies would then require identifying potentially metonymic words, finding closely related concepts, and determining which one fits the local (grammatically-related) and global context best. Each of these tasks can be resolved best by using different types of resources: a network of concepts for finding related concepts; a grammatically analyzed corpus (and, ideally, an ontology) for computing selectional preferences for the local context; a large corpus for computing co-occurrence probabilities, to factor in the global context. Within NLP we do have all these types of resources, but because of their different requirements -- e.g. relational models of meaning rely on differentiating word senses, while distributional representations do not (cannot) make such distinctions -- they are separate from one another. By using Wikipedia and exploiting its various structured/semi-structured sources of information, we can build a resource that combines the three types of meaning representations mentioned above. I will discuss the task of metonymy resolution and show how the combination of representations extracted from Wikipedia makes possible an unsupervised approach to this task.
Biography: Vivi Nastase is a researcher at the Fondazione Bruno Kessler in Trento, working mainly on lexical semantics, semantic relations, knowledge acquisition and language evolution. She holds a Ph.D. from the University of Ottawa, Canada, and has previously worked at the Heidelberg Institute of Theoretical Studies (HITS) and the University of Heidelberg.
Host: Nima Pourdamghani and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
