Topic: AI Seminar: Owen Rambow
Time: Mar 17, 2021 10:00 AM Eastern Time (US and Canada)
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Natural Language Understanding and Semantic Parsing

(Partly joint work with former colleagues at Elemental Cognition)

Semantic parsing refers to the task of determining the propositional content of language: who did what to whom.  It is part of the larger task of natural language understanding (NLU).  I will start out by discussing what full NLU means, and argue that we are still far away, as a field, from solving full NLU, or even from knowing how to evaluate it.

In the second part of the talk, I will situate semantic parsing in the context of several other NLU subtasks.  Typically, the target representation of semantic parsing uses an ontology (such as PropBank or FrameNet).  Semantic parsing includes the subtasks of word sense disambiguation, argument detection, and argument role labeling.  I will discuss choices among possible target ontologies.  I will justify why we created a new ontology, Hector, based on FrameNet and the lexical resource NOAD, and explain some of its characteristics.

In the third part of the talk, I will present experiments we performed using transformer models.  We obtain best results using a two-phase model, in which we first choose the frame, and then, given the frame, choose the arguments.  We encode the problem for both tasks using indices in the sentence.  While we develop the parser for our new ontology Hector, this approach also beats the state of the art for FrameNet and PropBank parsing.Biography:  I am a professor in the Department of Linguistics at Stony Brook University with a joint appointment in IACS.

Until recently, I was a research scientist at Elemental Cognition. Elemental Cognition is working on deep natural language understanding.

I got my PhD with Aravind Joshi at the University of Pennsylvania in 1994. I have worked at CoGenTex, and at AT&T Labs -- Research, and for many years I was a research scientist at Columbia University in the Center for Computational Learning Systems.

This workshop synthesizes the latest research on the impact of AI usage in education so that you could make informed decisions on whether and how to use AI to facilitate your learning. You might have seen conflicting reports on whether the use of AI is good for learning. In this workshop, we are going to tease out, drawing on the latest research, which types of AI usage are beneficial or harmful for different kinds of learning. At the end of the workshop, you should walk away with more clarity on when and how to use AI for your own learning. Join PRODIG+ fellow on critical AI, Zheng Fu, in this informative workshop.

Register for this Zoom workshop.

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Abstract: As we enter the AI era, domain scientists face a critical question: What can we do to harness AI effectively for scientific discovery? AI has demonstrated remarkable capabilities, from accelerating simulations to uncovering hidden patterns in complex datasets. While these advancements offer unprecedented opportunities, they also raise concerns--AI models often function as black boxes, making it difficult to connect their outputs to established scientific principles. This lack of interpretability can undermine trust and limit adoption, particularly in fields like meteorology where physical understanding is critical.
In this talk, I will explore how interpretable AI can bridge this gap, highlighting its potential to generate explicit, physically meaningful equations rather than opaque neural networks. Through four case studies from my lab, I will showcase how interpretable AI can enhance scientific understanding:
  1. Satellite Precipitation Retrieval: Using AI-based approaches to interpret precipitation retrieval algorithms from AMSU data, we identified critical microwave channels (89 and 150 GHz) that directly link to physical processes in the atmosphere.
  2. Quantitative Precipitation Estimation (QPE): By applying symbolic regression models to polarimetric radar data, we derived mathematical expressions that outperform traditional Z-R relationships and existing QPE algorithms, offering new insights into rainfall microphysics.
  3. Tornado Probability Prediction: Leveraging reinforcement learning-based symbolic deep learning models, we developed interpretable equations that outperform the traditional Significant Tornado Parameter (STP) index, providing a clearer understanding of the relationships between key atmospheric variables and tornado risk.
  4. Domain-Aware Symbolic Regression for Scientific Equations: In our latest work, we introduced a symbolic regression framework that incorporates domain-specific symbol priors extracted from thousands of scientific publications. By encoding common mathematical structures--such as the prevalence of trigonometric functions in physics or logarithmic forms in biology--into a tree-structured reinforcement learning model, we improved both the accuracy and interpretability of discovered equations. This approach accelerates convergence, enforces physical plausibility, and reveals new governing relationships in climate and geophysical data.
Through these examples, I hope to spark discussion on the evolving role of domain scientists in the AI era and inspire new ways to integrate AI with physical understanding in atmospheric research.

IACS Seminar Speaker: Yixin Wen, University of Florida

Location: IACS Seminar Room or Zoom

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Speaker Petar Djuric Refreshments will be provided Deep Gaussian processes: Theory and applications Petar M. Djurić Department of Electrical and Computer Engineering Stony Brook University Abstract: Gaussian processes are an infinite-dimensional generalization of multivariate normal distributions. They provide a principled approach to learning with kernel machines and they have found wide applications in many fields. More recently, with the advance of deep learning, the concept of deep Gaussian processes has emerged. Deep Gaussian processes can be viewed as multilayer hierarchical organizations of Gaussian processes that are equivalent to infinitely wide multiple layer neural networks. Deep Gaussian processes have improved capacity for prediction and classification over standard Gaussian processes, while models based on them continue to allow for full Bayesian treatment and for applications when the amount of available data is limited. The theory of recent progress in deep Gaussian processes will be presented and some applications will be provided. Biosketch: Petar M. Djurić received the B.S. and M.S. degrees in electrical engineering from the University of Belgrade, Belgrade, Yugoslavia, respectively, and the Ph.D. degree in electrical engineering from the University of Rhode Island, Kingston, RI, USA. He is a SUNY Distinguished Professor and currently, he is a Chair of the Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA. Djurić was a recipient of the IEEE Signal Processing Magazine Best Paper Award in 2007 and the EURASIP Technical Achievement Award in 2012. From 2008 to 2009, he was a Distinguished Lecturer of the IEEE Signal Processing Society. He was the Editor-in-Chief of the IEEE Transactions on Signal and Information Processing over Networks (2015-2018). Djurić is a Fellow of IEEE and EURASIP
Communication-Efficient Heterogeneity-Aware Machine Learning System and Architecture by Xuehai Qian

ABSTRACT: The key success of deep learning is the increasing size of models that can achieve high accuracy. At the same time, it is difficult to train the complex models with large data sets. Therefore, it is crucial to accelerate training with distributed systems and architectures, where communication and heterogeneity are two key challenges. In this talk, I will present two heterogeneity-aware decentralized training protocols without communication bottleneck. Specifically, Hop supports arbitrary iteration gap between workers by novel queue-based synchronization which can tolerate heterogeneity with system techniques. Prague uses randomized communication to tolerate heterogeneity with a new training algorithm based on partial reduce -- an efficient communication primitive. If time permits, I will present the systematic tensor partitioning for training on heterogeneous accelerator arrays (e.g., GPU/TPU). We believe that our principled approaches are crucial for achieving high-performance and efficient distributed training.

BIO: Xuehai Qian is an assistant professor at University of Southern California. His research interests include domain-specific systems and architectures, performance tuning and resource management of cloud systems and parallel computer architectures. He received his PhD from the University of Illinois Urbana Champaign and was a postdoc at UC Berkeley. He is the recipient of W.J Poppelbaum Memorial Award at UIUC, NSF CRII and CAREER Award, and the inaugural ACSIC (American Chinese Scholar In Computing) Rising Star Award.

Abstract:

In recent years, the landscape of artificial intelligence (AI) has been reshaped by the rapid emergence of Foundation Models (FMs). These versatile models have garnered widespread attention for their remarkable ability to transcend the boundaries of traditional, bespoke AI solutions and to generalize to a large set of downstream tasks. In this presentation we will describe the development of geospatial FMs with earth observation and weather data and discuss initial results of such models. We will also show how such foundation models can be a new and exciting tool for assisting with and accelerating scientific discovery.

Speaker:

Hendrik Hamann
Distinguished Researcher
IBM T.J. Watson Research Center