2023 Grace Hopper Lecture

Meredith Ringel Morris                                                           

Director for Human-AI Interaction Research at Google DeepMind

“AGI is Coming… Is HCI Ready?”                                                          We are at a transformational junction in computing, in the midst of an explosion in capabilities of foundational AI models that may soon match or exceed typical human abilities for a wide variety of cognitive tasks, a milestone often termed Artificial General Intelligence (AGI). Achieving AGI (or even closely approaching it) will transform computing, with ramifications permeating through all aspects of society. This is a critical moment not only for Machine Learning research, but also for the field of Human-Computer Interaction (HCI).

In this talk, I will define what I mean (and what I do NOT mean) by “AGI” (and related concepts, like superintelligence), and my journey from AGI skeptic to believing we are within five years of reaching this milestone. I will then discuss how this new era of computing necessitates a new sociotechnical research agenda on methods and interfaces for studying and interacting with AGI. For instance, how can we extend status quo design and prototyping methods for envisioning novel experiences at the limits of our current imaginations? What novel interaction modalities might AGI (or superintelligence) enable (e.g., “ESP”)? How do we create interfaces for computing systems that may intentionally or unintentionally deceive an end-user? How do we bridge the “gulf of evaluation” when a system may arrive at an answer through methods that fundamentally differ from human mental models, or that may be too complex for an individual user to grasp? How do we evaluate technologies that may have unanticipated systemic side-effects on society when released into the wild?

I will close by reflecting on the relationship between HCI and AI research. Typically, HCI and other sociotechnical domains are not considered as core to the ML research community as areas like model building. However, I argue that research on Human-AI Interaction and the societal impacts of AI is vital and central to this moment in computing history. HCI must not become a “second class citizen” to AI, but rather be recognized as fundamental to ensuring the path to AGI and beyond is a beneficial one.

2022 Grace Hopper Lecture

Katrina Ligett
Microsoft Visiting Professor, Princeton University Center for Information Technology Policy (CITP) and Professor, School of Computer Science and Engineering, Hebrew University

“Data Privacy is Important, But It’s Not Enough“
Our current data ecosystem leaves individuals, groups, and society vulnerable to a wide range of harms, ranging from privacy violations to subversion of autonomy to discrimination to erosion of trust in institutions. In this talk, I’ll discuss the Data Co-ops Project, a multi-institution, multi-disciplinary effort I co-lead with Kobbi Nissim. The Project seeks to organize our understanding of these harms and to coordinate a set of technical and legal approaches to addressing them. In particular, I’ll mention recent joint work with Ayelet Gordon and Alex Wood, wherein we argue that legal and technical tools aimed at controlling data and addressing privacy concerns are inherently insufficient for addressing the full range of these harms.

2020 Grace Hopper Lecture

Magdalena Balazinska
Professor and Director of the Paul G. Allen School of Computer Science & Engineering, University of Washington

”Video Data Management: From Data Models to Data Storage and Benchmarking”
The proliferation of inexpensive high-quality cameras coupled with recent advances in machine learning and computer vision have enabled new applications on video data. This in turn has renewed interest in video data management systems. In this talk, we explore several challenges related to video data management. We start by discussing data models. How should we expose video data to make it queryable by applications? We look in particular at the case of 360-degree videos. Second, we explore components of video data storage. How can we store videos in a way that makes them efficiently queryable? Finally, we discuss the problem of benchmarking video data management systems.

2019 Grace Hopper Lecture

Rebecca Mercuri
Founder of Notable Software, Inc

“A 2020 Vision of U.S. Election Security”

Despite the provision of a $380M federal grant to enhance technology and improve security in the 2018 midterm elections, machine failures and computer malfunctions again plagued polling places (in GA, PA, NY, IN, TX, and MA), resulting in late openings, long lines, and turned-away voters. Poor ballot layouts resurfaced in Florida, resulting in nearly 25,000 missed votes and the removal of the Broward County Supervisor of Elections, due to “misfeasance, incompetence and neglect of duty.” Many of the unauditable electronic voting machines are now being replaced with paper ballots and scanners, but creative State legislation (including in FL, MI and CA) and new tactics (such as risk-limiting audits) are increasingly being used to thwart and prohibit comprehensive recounts, even when results fall within the range of equipment error. This talk examines some of the shenanigans that we may be looking forward to seeing in 2020, sheds light on the reasons why contrived (and even avoidable) disenfranchisement continues to play a fundamental role in American Democracy, and offers some suggestions for improvement.

Deborah Estrin
Associate Dean and Professor of Computer Science
Cornell Tech

“In Pursuit of Digital Biomarkers”

Tuesday, November 16, 2017
3:00 – 4:15 p.m.
Wu and Chen Auditorium, Levine Hall

Abstract: Social networks, search engines, mobile apps, IoT vendors, online entertainment, and e-commerce sites have lead the way in using an individual’s digital traces to tailor service offerings, improve system performance, and target advertisements. A growing community of researchers are looking to these same data sources to create digital biomarkers for use in personalized health and wellness applications. This talk will discuss motivation and progress.

Bio: Deborah Estrin (PhD, MIT; BS, UCB) is a Professor of Computer Science at Cornell Tech in New York City where she founded the Jacobs Institute’s Health Tech Hub. She is also a co-founder of the non-profit startup, Open mHealth. Her current focus is on mobile health and small data, leveraging the pervasiveness of mobile devices and digital interactions for health and life management (TEDMED http://smalldata.io). Previously, Estrin was the founding director of the NSF-funded Science and Technology Center for Embedded Networked Sensing (CENS) at UCLA (2002-12). Estrin is an elected member of the American Academy of Arts and Sciences (2007) and National Academy of Engineering (2009). She was awarded honorary doctorates from EPFL and Uppsala.

November 8, 2016

Kathleen R. McKeown
Columbia University

“At the Intersection of Data Science and Language”

Abstract: Data science holds the promise to solve many of society’s most pressing challenges, but much of the necessary data is locked within the volumes of unstructured data on the web including language, speech and video. In this talk, I will describe how data science approaches are being used in research projects that draw from language data along a continuum from fact to fiction. I will present research on learning from knowledge of past disasters, as seen through the lens of the media and on the use of data science in understanding subjective, personal narratives of those who have experienced disaster. I will conclude with analysis of both social media and novels.

November 19, 2015

Rosalind W. Picard, Sc.D., FIEEE
Director of Affective Computing Research
Co-director of Advancing Wellbeing Initiative
Professor of Media Arts and Sciences
MIT Media Lab

Abstract: More than fifteen years ago I set out to create computational systems that could recognize and respond intelligently to emotion. My team and I designed and built the world’s first wearable sensors for classifying human emotion: We developed algorithms using pattern analysis, machine learning, and signal processing for extracting affective information from speech, physiology, facial expressions, and more. Today this work has spawned multiple start-up companies, including Affectiva who has collected more than eleven billion facial emotion points from viewers who opted-in online to turn on their cameras, and Empatica who will soon deliver a wrist-worn sensor that can issue potentially life-saving alerts for people having seizures. In this talk I will tell stories to highlight the most surprising findings during this adventure. These include being wrong about the “true smile of happiness,” discovering that we could enable regular cameras (and smartphones, even while in your pocket) to compute heart rate and respiration accurately, finding electrical signals on the wrist that give insight into deep brain activity, discovering connections to memory consolidation during sleep, and learning of surprising implications of wearable sensing for autism, anxiety, depression, epilepsy, and more.

Bio: Rosalind Picard is an MIT professor, founder and director of the Affective Computing Research group in the MIT Media Lab, and faculty chair of MIT’s Mind+Hand+Heart Initiative. Picard earned her BS from Georgia Tech in Electrical Engineering and her MS and ScD from MIT in Electrical Engineering and Computer Science and joined the faculty of MIT after working at AT&T Bell Labs. She has over 200 peer-reviewed publications and is a popular keynote speaker. Picard is best known for her book Affective Computing, which helped to launch a new field by that name. She has co-founded two companies, Affectiva, providing facial expression analytics for emotion communication, and Empatica, creating wearable sensors and analytics to improve health. Picard has received numerous best paper awards with her students, and recognitions such as having two of her inventions named to New York Times Magazine’s Best Ideas of the Year and Popular Science’s “Top Ten Inventions” lists, being named an IEEE Fellow, and being named CNN’s “7 Tech Superheroes to watch in 2015”. For fun, she enjoys spending time with her husband and three sons, where she feels quite outnumbered as the only female in the family.

November 11, 2014

Jennifer Rexford
Gordon Y. S. Wu Professor in Engineering
Department of Computer Science, Princeton University

Affiliated faculty in Center for Information Technology Policy, Electrical Engineering, Program is Applied & Computational Mathematics, Princeton Environmental Institute and Program in Gender and Sexuality Studies

Abstract: The Internet is a “network of networks”, where global connectivity relies on the competitive cooperation of tens of thousands of separately-administered networks.  Over the last several years, the structure of the Internet has changed dramatically with the rapid growth of cloud services and the increasing popularity of video content.  However, the Internet’s underlying routing system has been slow to adapt.  In this talk, we argue that the emerging technology of Software-Defined Networking (SDN) enables a fresh rethinking of how the Internet delivers traffic.  In SDN, a logically-centralized software controller can use a standard interface (e.g., Open Flow) to install packet-processing rules in the underlying network devices.hile initially applied within individual networks, SDN can enable a wide variety of new functionality between administrative domains,including application-specific peering, blocking of unwanted traffic,traffic redirection through so-called “middleboxes”, inbound traffic engineering, and server load balancing.  Internet eXchange Points(IXPs)—major juncture points where many networks meet to exchange traffic—are a compelling place to start.  We present the design and devaluation of a Software-Defined eXchange (SDX), including new programming abstractions that enable participating networks to create and run applications without conflicting with each other or with today’s global routing system.  We also describe a server load-balancing application that we deployed at an operational SDX in New Zealand.

Bio: Jennifer Rexford is the Gordon Y.S. Wu Professor of Engineering in the Computer Science department at Princeton University.  Before joining Princeton in 2005, she worked for eight years at AT&T Labs–Research. Jennifer received her BSE degree in electrical engineering from Princeton University in 1991, and her PhD degree in electrical engineering and computer science from the University of Michigan in1996.  Jennifer was the 2004 winner of ACM’s Grace Murray Hopper Award for outstanding young computer professional for her research on interdomain routing.  She is an ACM Fellow (2008), and a member of the American Academy of Arts and Sciences (2013) and the National Academy of Engineering (2014).

Lydia E. Kavraki
Noah Harding Professor of Computer Science and Bioengineering, Rice University;
Professor, Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine

“From Robots to Biomolecules: Computing for the Physical World”

Abstract: This talk will first describe how sampling-based methods revolutionized motion planning in robotics. The presentation will quickly focus on recent algorithms that are particularly suitable for systems with complex dynamics. The talk will then introduce an integrative framework that allows the synthesis of motion plans from high-level specifications. The framework uses temporal logic and formal methods and establishes a tight link between classical motion planning in robotics and task planning in artificial intelligence. Although research initially began in the realm of robotics, the experience gained has led to algorithmic advances for analyzing the motion and function of proteins, the worker molecules of all cells. This talk will conclude by discussing robotics-inspired methods for computing the flexibility of proteins and large macromolecular complexes with the ultimate goals of deciphering molecular function and aiding the discovery of new therapeutics.

Bio: Lydia E. Kavraki is the Noah Harding Professor of Computer Science and Bioengineering at Rice University. She also holds an appointment at the Department of Structural and Computational Biology and Molecular Biophysics at the Baylor College of Medicine in Houston. Kavraki received her B.A. in Computer Science from the University of Crete in Greece and her Ph.D. in Computer Science from Stanford University. Her research contributions are in physical algorithms and their applications in robotics (robot motion planning, hybrid systems, formal methods in robotics, assembly planning, micromanipulation, and flexible object manipulation), as well as in computational structural biology, translational bioinformatics, and biomedical informatics (modeling of proteins and biomolecular interactions, large-scale functional annotation of proteins, computer-assisted drug design, and systems biology). Kavraki has authored more than 180 peer-reviewed journal and conference publications and a co-author of the popular robotics textbook “Principles of Robot Motion” published by MIT Press. She is heavily involved in the development of The Open Motion Planning Library (OMPL), which is used in industry and in academic research in robotics and biomedicine. Kavraki is currently on the editorial board of the International Journal of Robotics Research, the ACM/IEEE Transactions on Computational Biology and Bioinformatics, the Computer Science Review, and Big Data. She is also a member of the editorial advisory board of the Springer Tracts in Advanced Robotics. Kavraki is a Fellow of the Association of Computing Machinery (ACM), a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), a Fellow of the American Association for the Advancement of Science (AAAS), and a Fellow of the World Technology Network (WTN). Kavraki was elected a member of the Institute of Medicine (IOM) of the National Academies in 2012. She is also a member of the Academy of Medicine, Engineering and Science of Texas (TAMEST) since 2012.

November 19, 2012

Justine Cassell
Director of the Human-Computer Interaction Institute
School of Computer Science, CMU

“Building Rapport between People and Machines: Why and How”

Abstract: In thinking about the interaction between computers and people we often concentrate on the task or cognitive aspects of the collaboration. However,the social aspects of interaction that characterize human-human collaboration also come into play in human-computer collaboration, and understanding the nature of that social interaction can help us to design computational systems that are most effective in real world contexts. To that end, in this talk I report on a series of studies that look at the building of rapport between humans, and between humans and virtual humans. I look at the effects of this rapport building on tasks as concrete as giving directions and educational tutoring. From the results of these studies I draw conclusions about the need for studies of actual human-human interaction to inform the design of robots and graphical agents and, more generally, the need to have models of the interaction between humans and computers to draw on not just technical but also social and cultural phenomena.

Bio: Justine Cassell is the Charles M. Geschke Director of the Human Computer Interaction Institute in the School of Computer Science at CMU. Before coming to CMU, Cassell was faculty at Northwestern University from 2003 to 2010 where she was the founding director of the Center for Technology and Social Behavior. Before that she was a tenured professor at the MIT Media Lab. Cassell received the Edgerton Prize at MIT, is an ACM and CRA Distinguished Lecturer, was honored in 2008 with the “Women of Vision” award from the Anita Borg Institute, and in 2011 was named to the World Economic Forum Global Agenda Council on Robotics and Smart Devices. She spoke at the World Economic Forum in Davos in January 2012 on the promises and perils of online learning.

November 23, 2010

Margo Seltzer
Herchel Smith Professor of Computer Science
School of Engineering and Applied Sciences
Harvard University

“Provenance Everywhere”

Abstract: Digital provenance describes the ancestry or history of a digital object. Computer science research in provenance has addressed issues in provenance capture in operating systems, command shells, languages, work flow systems and applications. However, it’s time to begin thinking seriously about provenance interoperability, what it means, and how we can achieve it. We have undertaken several projects that integrate provenance across multiple platforms. Doing so introduces many challenging research opportunities. In this talk, I’ll present our Provenance-Aware Storage System, focusing on our experiences integrating provenance across different layers of abstraction. I’ll present some of our use cases and discuss important issues for further research.

Bio: Margo I. Seltzer is a Herchel Smith Professor of Computer Science in the Harvard School of Engineering and Applied Sciences. Her research interests include file systems, databases, and transaction processing systems. She is the author of several widely-used software packages including database and transaction libraries and the 4.4BSD log-structured file system. Dr. Seltzer was a founder and CTO of Sleepycat Software, the makers of Berkeley DB, and is now an Architect for Oracle Corporation. She is a Sloan Foundation Fellow in Computer Science, a Bunting Fellow, and was the recipient of the 1996 Radcliffe Junior Faculty Fellowship, the University of California Microelectronics Scholarship. She is recognized as an outstanding teacher and won the Phi Beta Kappa teaching award in 1996 and the Abrahmson Teaching Award in 1999. Dr. Seltzer received an A.B. degree in Applied Mathematics from Harvard/Radcliffe College in 1983 and a Ph. D. in Computer Science from the University of California, Berkeley, in 1992.

November 17, 2009

Elaine J. Weyuker
AT&T Labs–Research
Shannon Laboratory

“Bugs – Find Them Before They Find You”

Abstract: It would obviously be very valuable to know in advance which files in the next release of a large software system are most likely to contain the largest numbers of defects. To accomplish this, we developed a negative binomial regression model and used it to predict the expected number of defects in each file of the next release of a system. The predictions are based on code characteristics and fault and modification history data. We will discuss what we have learned from applying the model to several large industrial systems, each with multiple years of field exposure, and tell you about our success in making accurate predictions and some of the lessons learned and issues that had to be dealt with.

Bio: Elaine Weyuker is an AT&T Fellow doing software engineering research. Prior to moving to AT&T she was a professor of computer science at NYU’s Courant Institute of Mathematical Sciences. Her research interests currently focus on software fault prediction, software testing, and software metrics and measurement. In an earlier life, Elaine did research in Theory of Computation and is the co-author of a book “Computability, Complexity, and Languages” with Martin Davis and Ron Sigal.

Elaine is the recipient of the 2008 Anita Borg Institute Technical Leadership Award and 2007 ACM/SIGSOFT Outstanding Research Award. She is also a member of the US National Academy of Engineering, an IEEE Fellow, and an ACM Fellow and has received IEEE’s Harlan Mills Award for outstanding software engineering research, Rutgers University 50th Anniversary Outstanding Alumni Award, and the AT&T Chairman’s Diversity Award as well has having been named a Woman of Achievement by the YWCA. She is the chair of the ACM Women’s Council (ACM-W) and a member of the Executive Committee of the Coalition to Diversify Computing.

December 2, 2008

Anna R. Karlin
Professor, Computer Science Department
University of Washington

“A Survey of Some Recent Research at the Border of Game Theory, Algorithms and Economics”

Abstract: The emergence of the Internet as one of the most important arenas for resource sharing between parties with diverse and selfish interests has led to a number of fascinating and new algorithmic problems and issues at the intersection of game theory, economics and computer science. In this talk, we survey recent research at this intersection, with a specific focus on keyword auctions, such as those used by Google, Yahoo! and MSN.

Bio: Anna Karlin is a Professor of Computer Science and Engineering at the University of Washington. She received her Ph.D. from Stanford University and then spent 5 years as a researcher at (what was then) Digital Equipment Corporation’s Systems Research Center before coming to the University of Washington. Her professional activities have included serving on the National Research Council’s Computer Science and Telecommunications Board, the editorial board for SIAM Journal on Computing, the committee to award the ACM Paris Kanellakis Theory and Practice Award (including chairing that committee in 2006), and serving as Program Chair for the 1997 IEEE Symposium on Foundations of Computer Science. She has given a number of Distinguished Lectures at universities including MIT and Duke/UNC/NC State.

Her research is primarily in theoretical computer science: the design and analysis of algorithms, particularly probabilistic and online algorithms. Much of her work is also at the interface between theory and other areas, such as economics and game theory, data mining, operating systems, networks, and distributed systems.

Outside of work, her main claim to fame is having formerly been part of “an obscure and very bad rock band of furry Palo Alto geeks” (according to the Rolling Stones) called Severe Tire Damage (or STD for short). STD was the first band to broadcast live over the Internet (back in 1993).

October 30, 2007

Martha E. Pollack
Dean and Professor, School of Information
Professor of Computer Science and Engineering
University of Michigan

“Intelligent Assistive Technology: The Present and the Future”

Abstract: Recent advances in two areas of computer science-wireless sensor networks and AI inference strategies-have made it possible to envision a wide range of technologies that can improve the lives of people with people with physical, cognitive, and/or psycho-social impairments. This talk will focus on assistive technology for people with cognitive impairment, surveying the state-of-the-art and speculating about future design challenges and opportunities. An important theme will be that the usefulness and effectiveness of these systems depends on their being adaptive to the often highly individualized and changing needs of their users, and that to achieve these properties, designers must integrate a variety of strategies for automated reasoning and learning.

Bio: Martha E. Pollack is Dean and Professor in the School of Information at the University of Michigan, where she is also Professor of Computer Science and Engineering. She received her B.A. degree from Dartmouth College and her M.S.E. and Ph.D. degrees from the University of Pennsylvania, and has been a faculty member at the University of Pittsburgh and a research staff member at the AI Center at SRI International. A Fellow of the Association for the Advancement for Artificial Intelligence, for which she is also President-Elect, Dr. Pollack serves on the NSF CISE Advisory Committee and the Board of Directors of the Computing Research Association. She has conducted and published research in a number of subareas of Artificial Intelligence, including natural-language processing, automated plan generation and execution, and adaptive interfaces, and has been a pioneer in the application of AI methods to the design of assistive technology for people with cognitive impairment. Dr. Pollack is the recipient of a number of professional awards, including the Computers and Thought Award, the University of Pittsburgh Distinguished Research Award, and the Sarah Goddard Power Award.

October 26, 2006

Jessica Hodgkin
School of Computer Science
Carnegie Mellon University

“Interfaces for Controlling Human Characters”

Abstract: Computer animations and virtual environments both require a controllable source of motion for their characters. Most of the currently available technologies require significant training and are not useful tools for casual users. Over the past few years, we have explored several different approaches to this problem. Each solution relies on the information about natural human motion inherent in a motion capture database. For example, the user can sketch an approximate path for an animated character which is then refined by searching a graph constructed from a motion database. We can also find a natural looking motion for a particular behavior based on sparse constraints from the user (foot contact locations and timing, for example) by optimizing in a low-dimensional, behavior-specific space found from motion capture. And finally, we have developed performance animation systems that use video input of the user to build a local Model of the user’s motion and reproduce it on an animated character.

Bio: Jessica Hodgins is a Professor in the Robotics Institute and Computer Science Department at Carnegie Mellon University. Prior to moving to CMU in 2000, she was an an Associate Professor and Assistant Dean in the College of Computing at Georgia Institute of Technology. She received her Ph.D. in Computer Science from Carnegie Mellon University in 1989. Her research focuses on computer graphics, animation, and robotics. She has received a NSF Young Investigator Award, a Packard Fellowship, and a Sloan Fellowship. She was editor-in-chief of ACM Transactions on Graphics from 2000-2002 and SIGGRAPH Papers Chair in 2003.

September 14, 2005

Daphne Koller
Stanford University

“Probabilistic Models for Complex Domains: Cells, Bodies, and Webpages”

Abstract: Many domains in the real world are richly structured, containing a diverse set of objects, related to each other in a variety of ways. For example, a living cell contains a rich network of interacting genes, that come together to perform key functions. A robot scan of a physical environment contains diverse objects such as people, vehicles, trees, or buildings, each of which might itself be a structured object. And a website contains a set of interlinked webpages, representing diverse kinds of entities. This talk describes a rich language based on probabilistic graphical models, which allows us to model domains such as these. We show how to learn such models from data generated from the domain, and how to use the learned model both to gain a better understanding of the principles underlying these domains, and to allow us to analyze a new data set from these domains in order to recognize the entities in it and the relationships between them. In particular, I will describe applications of this framework to various tasks, including: recognizing regulatory and protein interactions in a cell from diverse types of genomic data; segmenting and recognizing objects in robot laser range scan data; and identifying the set of entities in a structured website and the relationships between them.

Bio: Daphne Koller received her BSc and MSc degrees from the Hebrew University of Jerusalem, Israel, and her PhD from Stanford University in 1993. After a two-year postdoc at Berkeley, she returned to Stanford, where she is now an Associate Professor in the Computer Science Department. Her main research interest is in creating large-scale systems that reason and act under uncertainty, using techniques from probability theory, decision theory and economics. Daphne Koller is the author of over 100 refereed publications, which have appeared in venues spanning Science, Nature Genetics, the Journal of Games and Economic Behavior, and a variety of conferences and journals in AI and Computer Science. She was the co-chair of the UAI 2001 conference, and has served on numerous program committees and as associate editor of the Journal of Artificial Intelligence Research and of the Machine Learning Journal. She was awarded the Arthur Samuel Thesis Award in 1994, the Sloan Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award in 1998, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the IJCAI Computers and Thought Award in 2001, the Cox Medal for excellence in fostering undergraduate research at Stanford in 2003, and the MacArthur Foundation Fellowship in 2004.