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Events for March 24, 2022
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CS Colloquium: Tesca Fitzgerald (Carnegie Mellon University) - Learning to address novel situations through human-robot collaboration
Thu, Mar 24, 2022 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Tesca Fitzgerald, Carnegie Mellon University
Talk Title: Learning to address novel situations through human-robot collaboration
Series: CS Colloquium
Abstract: As our expectations for robots' adaptive capacities grow, it will be increasingly important for them to reason about the novel objects, tasks, and interactions inherent to everyday life. Rather than attempt to pre-train a robot for all potential task variations it may encounter, we can develop more capable and robust robots by assuming they will inevitably encounter situations that they are initially unprepared to address. My work enables a robot to address these novel situations by learning from a human teacher's domain knowledge of the task, such as the contextual use of an object or tool. Meeting this challenge requires robots to be flexible not only to novelty, but to different forms of novelty and their varying effects on the robot's task completion. In this talk, I will focus on (1) the implications of novelty, and its various causes, on the robot's learning goals, (2) methods for structuring its interaction with the human teacher in order to meet those learning goals, and (3) modeling and learning from interaction-derived training data to address novelty.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dr. Tesca Fitzgerald is a Postdoctoral Fellow in the Robotics Institute at Carnegie Mellon University. Her research is centered around interactive robot learning, with the aim of developing robots that are adaptive, robust, and collaborative when faced with novel situations. Before joining Carnegie Mellon, Dr. Fitzgerald received her PhD in Computer Science at Georgia Tech and completed her B.Sc at Portland State University. She is an NSF Graduate Research Fellow (2014), Microsoft Graduate Women Scholar (2014), and IBM Ph.D. Fellow (2017).
www.tescafitzgerald.com
Host: Heather Culbertson
Location: Olin Hall of Engineering (OHE) - 132
Audiences: By invitation only.
Contact: Assistant to CS chair
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CS Colloquium: Seo Jin Park (MIT CSAIL) - Towards Interactive Big Data Processing Through Flash Burst Parallel Systems
Thu, Mar 24, 2022 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Seo Jin Park , MIT CSAIL
Talk Title: Towards Interactive Big Data Processing Through Flash Burst Parallel Systems
Series: CS Colloquium
Abstract: Today, many organizations store big data on the cloud and lease relatively small clusters of instances to run analytics queries, train machine learning models, and more. However, the exponential data growth, combined with the slowdown of Moore's law, makes it challenging (if not impossible) to run such big data processing tasks in real-time. Most applications run big data workloads on timescales of several minutes or hours and resort to complex, application-specific optimizations to reduce the amount of data processing required for interactive queries. This design pattern hurts developer productivity and restricts the scope of applications that can use big data. However, as we have many servers in a cloud datacenter, a natural question is "can we borrow thousands of servers briefly to accelerate big data processing enough to be interactive?"
In this talk, I'll share my vision to enable massively parallel data processing even for very short-duration (1-10 ms), which I call "flash bursts." This will empower interactive, real-time applications (e.g., cyber security attack defense, self-driving cars or drones, etc) to utilize much larger data than before. For this moonshot, I take a two-pronged approach. First, I restructure important big data applications (analytics and DNN training) so that they can run efficiently in a flash burst fashion. On this prong, the talk will focus on how I efficiently scaled distributed sorting to 100+ servers even for a 1-millisecond time budget. Second, I rebuild various layers in distributed systems to reduce overheads of flash burst scaling. On this prong, I will focus on how I removed the overheads of consistent replication.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Seo Jin Park is a postdoctoral researcher at MIT CSAIL. He received a Ph.D. in Computer Science from Stanford University in 2019, advised by John Ousterhout. He is broadly interested in distributed systems, focusing on low-latency systems: scaling low-latency data processing, optimizing consensus protocols (both standard and byzantine), suppressing tail-latencies, and building efficient performance debugging tools. His Ph.D. study was supported by Samsung Scholarship.
Host: Barath Raghavan
Location: Michelson Center for Convergent Bioscience (MCB) - 101
Audiences: By invitation only.
Contact: Assistant to CS chair
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CS Colloquium: Feras Saad (MIT) - Scalable Structure Learning and Inference via Probabilistic Programming
Thu, Mar 24, 2022 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Feras Saad, MIT
Talk Title: Scalable Structure Learning and Inference via Probabilistic Programming
Series: CS Colloquium
Abstract: Probabilistic programming supports probabilistic modeling, learning, and inference by representing sophisticated probabilistic models as computer programs in new programming languages. This talk presents efficient probabilistic programming-based techniques that address two fundamental challenges in scaling and automating structure learning and inference over complex data.
First, I will describe scalable structure learning methods that make it possible to automatically synthesize probabilistic programs in an online setting by performing Bayesian inference over hierarchies of flexibly structured symbolic program representations, for discovering models of time series data, tabular data, and relational data. Second, I will present fast compilers and symbolic analyses that compute exact answers to a broad range of inference queries about these learned programs, which lets us extract interpretable patterns and make accurate predictions in real time.
I will demonstrate how these techniques deliver state-of-the-art performance in terms of runtime, accuracy, robustness, and programmability by drawing on several examples from real-world applications, which include adapting to extreme novelty in economic time series, online forecasting of flu rates given sparse multivariate observations, discovering stochastic motion models of zebrafish hunting, and verifying the fairness of machine learning classifiers.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Feras Saad is a PhD candidate in Computer Science at MIT working at the intersection of programming languages, probabilistic machine learning, and computational statistics. His research is accompanied with a collection of popular open-source probabilistic programming systems used by collaborators at Intel, Takeda, Liberty Mutual, IBM, and the Bill & Melinda Gates Foundation for practical applications of structure learning and probabilistic inference. Feras' MEng thesis on probabilistic programming and data science has been recognized with the 1st Place Computer Science Thesis Award at MIT.
Host: Mukund Raghothaman
Location: Ronald Tutor Hall of Engineering (RTH) - 105
Audiences: By invitation only.
Contact: Assistant to CS chair
