In 2016, the Department of Computer Science recognized Masson’s visionary leadership with a series of special events marking 30 years of computing at Johns Hopkins. That celebration included the establishment of the Gerald M. Masson Distinguished Lecture Series, which each year brings computer science researchers to the Homewood campus to talk about important topics in the field.
To learn more about Gerald Masson, click here.
Internet of Things
Title: Building the Smartest and Open Virtual Assistant to Protect Privacy
Date: October 8, 2019
Virtual assistants, providing a voice interface to web services and IoTs, can potentially develop into monopolistic platforms that threaten consumer privacy and open competition. This talk presents Almond as an open-source alternative. Unlike existing commercial assistants, Almond can be programmed in natural language to perform new tasks. It lets users control who, what, when, where, and how their data are to be shared, all without disclosure to a third party. Almond has a Write-Once-Run-Anywhere (WORA) skill platform: skills need to be written only once and can run automatically on other assistants. This helps level the playing field for new assistants.
ML + Privacy
Title:The Unreasonable Effectiveness of Structure
Date: October 10, 2019
Abstract: Our ability to collect, manipulate, analyze, and act on vast amounts of data is having a profound impact on all aspects of society. Much of this data is heterogeneous in nature and interlinked in a myriad of complex ways. From information integration to scientific discovery to computational social science, we need machine learning methods that are able to exploit both the inherent uncertainty and the innate structure in a domain. Statistical relational learning (SRL) is a subfield that builds on principles from probability theory and statistics to address uncertainty while incorporating tools from knowledge representation and logic to represent structure. In this talk, I will give a brief introduction to SRL, present templates for common structured prediction problems, and describe modeling approaches that mix logic, probabilistic inference and latent variables. I’ll overview our recent work on probabilistic soft logic (PSL), a SRL framework for large-scale collective, probabilistic reasoning in relational domains. I’ll close by highlighting emerging opportunities (and challenges!!) in realizing the effectiveness of data and structure for knowledge discovery.
Human Computer Interactions
Talk Title: TBD
Date: November 7, 2019
University of Pennsylvania
Title: Opportunities and Challenges in Autonomy for Small Aerial Vehicles
Date: September 25, 2018
Abstract:The last decade has seen significant advances in robotics and artificial intelligence leading to an irrational exuberance in technology. I will discuss the challenges of realizing autonomous flight in environments with obstacles in the absence of GPS. I will describe our approaches to state estimation and designing perception-action loops for high speed navigation with applications to precision agriculture and first response. Finally, I will discuss some of limitations with current approaches in the field and challenges in robotics and autonomy.
Title: BFT in the lens of Blockchains and Blockchains in the lens of BFT
Date: October 9, 2018
Abstract: Blockchain is a Byzantine Fault Tolerant (BFT) replicated state machine, in which each state-update is by itself a Turing machine with bounded resources. The core algorithm for achieving BFT in a Blockchain appears completely different from classical BFT algorithms:Recent advances in blockchain technology blur these boundaries. Namely, hybrid solutions such as Byzcoin, Bitcoin-NG, Hybrid Consensus, Casper and Solida, anchor off-chain BFT decisions inside a PoW chain or the other way around. This talk keynote will describe Blockchain in the lens of BFT and BFT in the lens of Blockchain, and provide common algorithmic foundations for both.
University of British Columbia
Talk Title: Automatically Scalable Computation
Date: November 6, 2018
Abstract:As our computational infrastructure races gracefully forward into increasingly parallel multi-core and clustered systems, our ability to easily produce software that can successfully exploit such systems continues to stumble. For years, we’ve fantasized about the world in which we’d write simple, sequential programs, add magic sauce, and suddenly have scalable, parallel executions. We’re not there. We’re not even close. I’ll present a radical, potentially crazy approach to automatic scalability, combining learning, prediction, and speculation. To date, we’ve achieved shockingly good scalability and reasonable speedup in limited domains, but the potential is tantalizingly enormous.
Title: Learning and Efficiency of Outcomes in Games
Date: April 2, 2019
Abstract: Selfish behavior can often lead to suboptimal outcome for all participants, a phenomenon illustrated by many classical examples in game theory. Over the last decade we have studied Nash equilibria of games, and developed good understanding how to quantify the impact of strategic user behavior on overall performance in many games (including traffic routing as well as online auctions). In this talk we will focus on games where players use a form of learning that helps them adapt to the environment. We ask if the quantitative guarantees obtained for Nash equilibria extend to such out of equilibrium game play, or even more broadly, when the game or the population of players is dynamically changing and where participants have to adapt to the dynamic environment.
Professor and Chief Academic Officer
Toyota Technological Institute at Chicago
Talk Title: Algorithmic fairness in Online Decision-making
Date: April 16, 2019
Abstract: There is growing concern about fairness in algorithmic decision making: Are algorithmic decisions treating different groups fairly? How can we make them fairer? What do we even mean by fair? In this talk I will discuss some of our work on this topic, focusing on the setting of online decision making. For instance, a classic result states that given a collection of predictors, one can adaptively combine them to perform nearly as well as the best of those predictors in hindsight (achieve “no regret”) without any stochastic assumptions. Can one extend this guarantee so that if the predictors are themselves fair (according to a given definition), then the overall combination is fair as well (according to the same definition)? I will discuss this and other issues. This is joint work with Suriya Gunasekar, Thodoris Lykouris, and Nati Srebro.
University of Pennsylvania
Title: Why Data Citation is a Computational Problem
Date: October 5, 2017
Abstract: While principles and standards have been developed for data citation, they are unlikely to be used unless we can couple the process of extracting information with that of providing a citation for it. I will discuss the problem of automatically generating citations for data in a database given how the data was obtained (the query) as well as the content (the data), and show how the problem of generating a citation is related to two well-studied problems in databases: query rewriting using views and provenance.To download Dr. Davidson’s lecture poster, click here. To view the seminar video, click here.
Massachusetts Institute of Technology
Title: Pseudo Deterministic Algorithms and Proofs
Date: Thursday, October 12, 2017
Abstract: Pseudo-deterministic algorithms are a class of randomized search algorithms, which output a unique answer with high probability. Intuitively, they are indistinguishable from deterministic algorithms by an polynomial time observer of their input/output behavior. In this talk I will describe what is known about pseudo-deterministic algorithms in the sequential, sub-linear and parallel setting. To download Dr. Goldwasser’s lecture series poster, click here.
University of Chicago
Title: Computing Just What You Need: Online Data Analysis and Reduction of Extreme Scales
Date: Tuesday, December 5, 2017
Abstract: A growing disparity between supercomputer computation speeds and I/O rates makes it increasingly infeasible for applications to save all results for offline analysis. Instead, applications must analyze and reduce data online so as to output only those results needed to answer target scientific question(s). To download Dr. Foster’s lecture series poster, click here. To view the seminar video, click here.
University of California, Berkeley
Title: Human-AI Interaction: Symbiotic Autonomy, Learning, and Transparency in Service Robots
Date: Thursday, April 12, 2018
Abstract: In this talk, I will first introduce our CoBot service robots and their novel localization and symbiotic autonomy, which enable them to consistently move in our buildings, and learn from asking humans or the web for help to overcome their limitations. I frame the research as human-AI interaction also including an interpretability approach by language generation to alert and respond to human explanation requests. I will conclude with a brief discussion of AI, machine learning, and robotics, and their potential social impact.
To download Dr. Veloso’s lecture series poster, click here.
Title: The Deep Learning Revolution in Building Intelligent Computer Systems
Date: Thursday, April 26, 2018
Abstract: In this talk, I’ll highlight some of the design decisions we made in building TensorFlow, discuss research results produced within our group in areas such as computer vision, language understanding, translation, healthcare, and robotics, and describe ways in which these ideas have been applied to a variety of problems in Google’s products, usually in close collaboration with other teams. I will also touch on some exciting areas of research that we are currently pursuing within our group.
To download Dean’s lecture series poster, click here. To view the seminar video, click here.
|Mark D. Hill
University of Wisconsin-Madison
Title: Computer Architecture 1975-2025
Date: Thursday, November 8
Abstract: This talk will explain how computer architects contribute to information technology that is transforming the world. It will present computer architecture basics and trends since the first microprocessor in the mid-1970s. It will then discuss how present challenges to Moore’s Law will open up new directions for computer systems, including architecture as infrastructure, energy first, impact of emerging technologies, and cross-layer opportunities. Reference: CCC “21st Century Computer Architecture.”To download Dr. Hill’s lecture poster, click here. To view the seminar video, click here.
National Science Foundation
Directorate of Computer & Information Science & Engineering (CISE)
Title: An Expanding and Expansive View of Computing
Date: Thursday, November 17
Abstract: Advances in computer and information science and engineering are providing unprecedented opportunities for research and education. My talk will begin with an overview of CISE activities and programs at the National Science Foundation and include a discussion of current trends that are shaping the future of our discipline. I will also discuss the opportunities as well as the challenges that lay ahead for our community and for CISE.To download Dr. Kurose’s lecture series poster, click here. To view the seminar video, click here.
University of Maryland, College Park
Title: Interactive Visual Discovery in Event Analytics Electronic Health Records and Other Applications
Date: Tuesday, March 7, 2017
Abstract: Advances in computer and information science and engineering are providing unprecedented opportunities for research and education. My talk will begin with an overview of CISE activities and programs at the National Science Foundation and include a discussion of current trends that are shaping the future of our discipline. I will also discuss the opportunities as well as the challenges that lay ahead for our community and for CISE. To download Dr. Shneiderman’s lecture series poster, click here.
University of California, Berkeley
Title: Development of Kinematic and Dynamic Models For Individual Using System Estimation and Identification Techniques
Date: Tuesday, April 11, 2017
Abstract: This talk will introduce a kinematic and dynamic framework for creating a representative model of an individual. Building on results from geometric robotics, a method for formulating a geometric dynamic identification model is derived.
To download Dr. Bajcsy’s lecture series poster, click here. To view the seminar video, click here.