AI/ML Seminar Series

Standard

Weekly Seminar in AI & Machine Learning
Sponsored by Cylance

Sep 23
No Seminar
Sep 30
4011
Bren Hall
1 pm

Nia Dowell

Assistant Professor
School of Education
University of California, Irvine

Educational environments have become increasingly reliant on computer-mediated communication, relying on video conferencing, synchronous chats, and asynchronous forums, in both small (5-20 learners) and massive (1000+ learner) learning environments. These platforms, which are designed to support or even supplant traditional instruction, have become common-place across all levels of education, and as a result created big data in education. In order to move forward, the learning sciences field is in need of new automated approaches that offer deeper insights into the dynamics of learner interaction and discourse across online learning platforms. This talk will present results from recent work that uses language and discourse to capture social and cognitive dynamics during collaborative interactions. I will introduce group communication analysis (GCA), a novel approach for detecting emergent learner roles from the participants’ contributions and patterns of interaction. This method makes use of automated computational linguistic analysis of the sequential interactions of participants in online group communication to create distinct interaction profiles. We have applied the GCA to several collaborative learning datasets. Cluster analysis, predictive, and hierarchical linear mixed-effects modeling were used to assess the validity of the GCA approach, and practical influence of learner roles on student and overall group performance. The results indicate that learners’ patterns in linguistic coordination and cohesion are representative of the roles that individuals play in collaborative discussions. More broadly, GCA provides a framework for researchers to explore the micro intra- and inter-personal patterns associated with the participants’ roles and the sociocognitive processes related to successful collaboration.

Bio: I am an assistant professor in the School of Education at UCI. My primary interests are in cognitive psychology, discourse processing, group interaction, and learning analytics. In general, my research focuses on using language and discourse to uncover the dynamics of socially significant, cognitive, and affective processes. I am currently applying computational techniques to model discourse and social dynamics in a variety of environments including small group computer-mediated collaborative learning environments, collaborative design networks, and massive open online courses (MOOCs). My research has also extended beyond the educational and learning sciences spaces and highlighted the practical applications of computational discourse science in the clinical, political and social sciences areas.
Oct 7
4011
Bren Hall
1 pm

Shashank Srivastava

Assistant Professor
Computer Science
UNC Chapel Hill

Humans can efficiently learn and communicate new knowledge about the world through natural language (e.g, the concept of important emails may be described through explanations like ‘late night emails from my boss are usually important’). Can machines be similarly taught new tasks and behavior through natural language interactions with their users? In this talk, we’ll explore two approaches towards language-based learning for classifications tasks. First, we’ll consider how language can be leveraged for interactive feature space construction for learning tasks. I’ll present a method that jointly learns to understand language and learn classification models, by using explanations in conjunction with a small number of labeled examples of the concept. Secondly, we’ll examine an approach for using language as a substitute for labeled supervision for training machine learning models, which leverages the semantics of quantifier expressions in everyday language (`definitely’, `sometimes’, etc.) to enable learning in scenarios with limited or no labeled data.

Bio: Shashank Srivastava is an assistant professor in the Computer Science department at the University of North Carolina (UNC) Chapel Hill. Shashank received his PhD from the Machine Learning department at CMU in 2018, and was an AI Resident at Microsoft Research in 2018-19. Shashank’s research interests lie in conversational AI, interactive machine learning and grounded language understanding. Shashank has an undergraduate degree in Computer Science from IIT Kanpur, and a Master’s degree in Language Technologies from CMU. He received the Yahoo InMind Fellowship for 2016-17; his research has been covered by popular media outlets including GeekWire and New Scientist.
Oct 14
4011
Bren Hall
1 pm

Bhuwan Dhingra

PhD Student
Language Technologies Institute
Carnegie Mellon University

Structured Knowledge Bases (KBs) are extremely useful for applications such as question answering and dialog, but are difficult to populate and maintain. People prefer expressing information in natural language, and hence text corpora, such as Wikipedia, contain more detailed up-to-date information. This raises the question — can we directly treat text corpora as knowledge bases for extracting information on demand?

In this talk I will focus on two problems related to this question. First, I will look at augmenting incomplete KBs with textual knowledge for question answering. I will describe a graph neural network model for processing heterogeneous data from the two sources. Next, I will describe a scalable approach for compositional reasoning over the contents of the text corpus, analogous to following a path of relations in a structured KB to answer multi-hop queries. I will conclude by discussing interesting future research directions in this domain.

Bio: Bhuwan Dhingra is a final year PhD student at Carnegie Mellon University, advised by William Cohen and Ruslan Salakhutdinov. His research uses natural language processing and machine learning to build an interface between AI applications and world knowledge (facts about people, places and things). His work is supported by the Siemens FutureMakers PhD fellowship. Prior to joining CMU, Bhuwan completed his undergraduate studies at IIT Kanpur in 2013, and spent two years at Qualcomm Research in the beautiful city of San Diego.

Oct 21
4011
Bren Hall
1 pm

Robert Bamler

Postdoctoral Researcher
Dept. of Computer Science
University of California, Irvine

TBA
Oct 28
4011
Bren Hall
1 pm

Zhou Yu

Assistant Professor
Dept. of Computer Science
University of California, Davis

TBA
Nov 4
Nov 11
Veterans Day
Nov 18
4011
Bren Hall
1 pm

John T. Halloran

Postdoctoral Researcher
Dept. of Biomedical Engineering
University of California, Davis

TBA
Nov 25
4011
Bren Hall
1 pm

Xanda Schofield

Assistant Professor
Dept. of Computer Science
Harvey Mudd College

TBA
Dec 2
4011
Bren Hall
1 pm

Shayan Doroudi

Assistant Professor
School of Education
University of California, Irvine

TBA
Dec 9
Finals week