Select a calendar:
Filter November Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Conferences, Lectures, & Seminars
Events for November
-
CS Distinguished Lecture: Cynthia Dwork (Harvard University) - Skewed or Rescued? The Emerging Theory of Algorithmic Fairness
Thu, Nov 01, 2018 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Cynthia Dwork, Harvard University
Talk Title: Skewed or Rescued? The Emerging Theory of Algorithmic Fairness
Series: Computer Science Distinguished Lecture Series
Abstract: Data, algorithms, and systems have biases embedded within them reflecting designers' explicit and implicit choices, historical biases, and societal priorities. They form, literally and inexorably, a codification of values. 'Unfairness' of algorithms - for tasks ranging from advertising to recidivism prediction - has attracted considerable attention in the popular press. The talk will discuss recent work in the nascent mathematically rigorous study of fairness in classification and scoring.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Cynthia Dwork, the Gordon McKay Professor of Computer Science at the John A. Paulson School of Engineering and Applied Sciences at Harvard, the Radcliffe Alumnae Professor at the Radcliffe Institute for Advanced Study, and an Affiliated Faculty Member at Harvard Law School, is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation. With seminal contributions in cryptography, distributed computing, and ensuring statistical validity, her most recent focus is on fairness in classification algorithms. Dwork is a member of the US National Academy of Sciences, the US National Academy of Engineering, and the American Philosophical Society, and is a Fellow of the American Academy of Arts and Sciences and of the ACM.
Host: Computer Science Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
RASC Seminar: Sanjiv Singh (CMU) - Flying Cars: What's taking so long?
Tue, Nov 06, 2018 @ 10:00 AM - 11:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Sanjiv Singh, CMU
Talk Title: Flying Cars: What's taking so long?
Series: RASC Seminar Series
Abstract: Almost a hundred years ago, before we had foreseen a world where cars drive themselves in traffic, the idea of a vehicle that could be both driven and flown had already taken hold of the public imagination. However compelling the imagery facilitated by the media and science fiction, we are still not close to an aerial analog of the self-driving car. While commonplace flying cars might be some time in coming, we might still ask what would be possible if we could realize "personal aviation". We could ask how such vehicles could operate safely and what steps we need to take to hasten their feasibility.
Because flying cars would almost certainly have to be autonomous to be operable by non-pilots, many of the
building blocks needed have immediate relevance in the agenda for developing autonomous drones, as well as,
safety aids for pilots of the large number of aircraft that must fly at low elevation and land at unprepared sites.
In my talk, I will discuss results from recent work with autonomous aircraft operating in unstructured environments
focused on four technical goals: fly safe, land safe, fly without GPS, and, even when critical systems fail. I will
show how presence of a human onboard an autonomous flying vehicle can improve both performance and reliability.
I also will show results from a new class of methods that simultaneously produce dense reconstruction and
low-drift 6DOF pose estimation in real time, with application to various scales of aircraft.
Biography: Sanjiv Singh is a Research Professor at the Robotics Institute, Carnegie Mellon University and the CEO of
Near Earth Autonomy. He started his career working on the earliest autonomous ground vehicles to operate
outdoors in 1985. Since then, he has led research efforts with applications in aviation, agriculture, mining and
construction. In 2010 he led a team that demonstrated the first autonomous, full-scale helicopter capable of
take off, search for viable landing sites and safe descent. He holds a Ph.D. in Robotics from Carnegie Mellon
University and is the founding editor of the Journal of Field Robotics.
Host: Gaurav Sukhatme
Location: Ronald Tutor Hall of Engineering (RTH) - 406
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
CAIS Seminar: Dr. Adnan Darwiche (UCLA) - Explaining and Verifying AI Systems
Wed, Nov 07, 2018 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Adnan Darwiche, UCLA
Talk Title: Explaining and Verifying AI Systems
Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series
Abstract: Explaining the decisions of AI systems and formally verifying their properties have come into focus recently. In this talk, Dr. Darwiche will discuss an approach for explaining and verifying Bayesian network classifiers, which is based on compiling them into equivalent and symbolic decision graphs. He will also discuss a new class of circuits that are as expressive as neural networks and that can be synthesized from Bayesian network models, allowing one to provide formal guarantees on their behaviors regardless of how they are trained from data.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dr. Adnan Darwiche is a professor and chairman of the computer science department at UCLA. He directs the automated reasoning group which focuses on probabilistic and logical reasoning, and their applications including to machine learning (http://reasoning.cs.ucla.edu/.
Host: Milind Tambe
Location: Mark Taper Hall Of Humanities (THH) - 301
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
CS Colloquium: Paul Rosenbloom (USC) - A Common Model of Cognition (née A Standard Model of the Mind)
Wed, Nov 07, 2018 @ 06:00 PM - 07:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Paul Rosenbloom , USC
Talk Title: A Common Model of Cognition (née A Standard Model of the Mind)
Series: Computer Science Colloquium
Abstract: A common (or standard) model captures a community consensus over a coherent region of science, serving as a cumulative reference point for the field that can provide guidance for both research and applications, while also focusing efforts to extend or revise it. An effort has been initiated recently to build such a model for human-like minds, computational entities -" whether natural or artificial -" whose structures and processes are substantially similar to those found in human cognition. The core hypothesis is that cognitive architectures provide the appropriate computational abstraction for defining such a model, although the model is not itself such an architecture. The model began as a consensus at the 2013 AAAI Fall Symposium on Integrated Cognition but has since been extended via a synthesis across three existing cognitive architectures: ACT-R, Sigma, and Soar. The resulting model spans key aspects of structure and processing, memory and content, learning, and perception and motor; highlighting loci of architectural agreement as well as disagreement with the consensus while identifying potential areas of remaining incompleteness. Work to build this into a community-wide effort is also in progress.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Paul Rosenbloom is Professor of Computer Science in the Viterbi School of Engineering at the University of Southern California and Director for Cognitive Architecture Research at USC's Institute for Creative Technologies. He was a member of USC's Information Sciences Institute for two decades, ending as its Deputy Director, and earlier was on the faculty at Carnegie Mellon University and Stanford University. His research concentrates on cognitive architectures (integrated models of the fixed structures underlying minds), the possibility of a Common Model of Cognition (a community consensus concerning what must be in a cognitive architecture), and on the nature and structure of computing as a scientific domain and its overlap with the other domains of human study. He is a Fellow of the Association for the Advancement of Artificial Intelligence, the Association for the Advancement of Science, and the Cognitive Science Society.
More Info: https://goo.gl/forms/w7WRcpF7xqY3As4T2
Host: AAAI@USC
Location: Mark Taper Hall Of Humanities (THH) - 202
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
MASCLE Machine Learning Seminar: Quanquan Gu (UCLA) - New Variance Reduction Algorithms for Nonconvex Finite-Sum Optimization
Thu, Nov 08, 2018 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Quanquan Gu, UCLA
Talk Title: New Variance Reduction Algorithms for Nonconvex Finite-Sum Optimization
Series: Machine Learning Seminar Series
Abstract: Nonconvex finite-sum optimization problems are ubiquitous in machine learning such as training deep neural networks. To solve this class of problems, various variance reduction based stochastic optimization algorithms have been proposed, which are guaranteed to converge to stationary points and enjoy improved gradient complexity than vanilla stochastic gradient descent. An natural question is whether there is still space for improvement to further speed up the finding of first-order stationary points and even local minimas.
In the first part of this talk, I will introduce our work for finding first-order stationary points in nonconvex finite-sum optimization that further pushes the frontiers of this line of research. In particular, I will introduce a new stochastic nested variance reduced gradient algorithm (SNVRG) that achieves the fastest convergence rate to first-order stationary points in the literature by reducing the variance in stochastic algorithms through multiple referencing points and gradients. It outperforms the folklore variance reduction methods such as stochastic variance reduced gradient (SVRG) and stochastically controlled stochastic gradient (SCSG).
In the second part of the talk, I will talk about methods for finding second-order stationary points (i.e., local minima) in nonconvex finite-sum optimization. Specifically, I will introduce a stochastic variance reduced cubic regularization algorithm that achieves the state-of-the-art second-order oracle complexity for finding local minima in nonconvex optimization.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Host: Yan Liu, USC Machine Learning Center
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
CS Colloquium: Ram D. Sriram (NIST) - Explorations in Artificial Intelligence: A Personal Journey
Mon, Nov 12, 2018 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ram D. Sriram, National Institute of Standards and Technology
Talk Title: Explorations in Artificial Intelligence: A Personal Journey
Series: Computer Science Colloquium
Abstract: My first exposure to Artificial Intelligence (AI) was in the summer of 1981, when Carnegie Mellon University was tasked with the development of a knowledge-based expert system (KBES) to aid in the trouble shooting of the Atlanta People Mover. I was a student member of this team and went on to do my dissertation on AI in Design. Later, I joined MIT as an assistant professor (in 1986) and with my students built one of the most comprehensive computational frameworks for Internet-based collaborative design -“ called DICE. The DICE framework introduced several novel concepts in AI, including an active object-oriented blackboard, constraint satisfaction using asynchronous teams, merging qualitative geometry with traditional modeling, knowledge representation schemes for product and process models, and design rationale. In 1994, I moved to NIST and continued work on knowledge representation for entire product life cycle until 2010, when I took over as the chief of Software and Systems Division. Here, I have provided technical leadership for several AI projects, which include extending deep learning techniques in biomedical image processing, extracting protein-protein interaction sentences from documents, developing a novel natural language term extraction system based on Sanskrit, and applying Category Theory for AI knowledge representation. In this talk, I will describe my journey over nearly four decades with a particular focus on my recent work at NIST on knowledge representation, machine learning, and natural language processing.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Ram D. Sriram is currently the chief of the Software and Systems Division, Information Technology Laboratory, at the National Institute of Standards and Technology (NIST). Before joining the Software and Systems Division, Sriram was the leader of the Design and Process group in the Manufacturing Systems Integration Division, Manufacturing Engineering Laboratory, where he conducted research on standards for interoperability of computer-aided design systems. Prior to joining NIST, he was on the engineering faculty (1986-1994) at the Massachusetts Institute of Technology (MIT) and was instrumental in setting up the Intelligent Engineering Systems Laboratory. Sriram has co-authored or authored more than 250 publications, including several books on artificial intelligence. Sriram was a founding co-editor of the International Journal for AI in Engineering. Sriram received several awards including: an NSF's Presidential Young Investigator Award (1989); ASME Design Automation Award (2011); ASME CIE Distinguished Service Award (2014); the Washington Academy of Sciences' Distinguished Career in Engineering Sciences Award (2015); ASME CIE division's Lifetime Achievement Award (2016); and CMU CEE Lt. Col. Christopher Raible Distinguished Public Service Award (2018). Sriram is a Fellow of ASME, AAAS, IEEE and Washington Academy of Sciences, a Member (life) of ACM and a Senior Member (life) of AAAI. Sriram has a B.Tech. from IIT, Madras, India, and an M.S. and a Ph.D. from Carnegie Mellon University, Pittsburgh, USA.
Host: Computer Science Department
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
CAIS Seminar: Dr. Sanmay Das (Washington University in St. Louis) - Allocating Scarce Societal Resources Based on Predictions of Outcomes
Tue, Nov 13, 2018 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Sanmay Das, Washington University in St. Louis
Talk Title: Allocating Scarce Societal Resources Based on Predictions of Outcomes
Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series
Abstract: Demand for resources that are collectively controlled or regulated by society, like social services or organs for transplantation, typically far outstrips supply. How should these scarce resources be allocated? In this talk, Dr. Das will discuss his work on weighted matching and assignment in two domains, namely living donor kidney transplantation and provision of services to homeless households. His focus will be on how effective prediction of the outcomes of matches has the potential to dramatically improve social welfare both by allowing for richer mechanisms and by improving allocations. He will also discuss implications for equity and justice.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Dr. Sanmay Das is an associate professor in Computer Science and Engineering and the chair of the steering committee of the newly formed Division of Computational and Data Sciences at Washington University in St. Louis. He is vice-chair of the ACM Special Interest Group on Artificial Intelligence and a member of the board of directors of the International Foundation for Autonomous Agents and Multiagent Systems. Dr. Das has served as program co-chair of the AAMAS and AMMA conferences, and has been recognized with awards for research and teaching, including an NSF CAREER Award and the Department Chair Award for Outstanding Teaching at Washington University.
Host: Milind Tambe
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
MASCLE Machine Learning Seminar: Ahmad Beirami (Electronic Arts) - Powering Games with Data & AI
Fri, Nov 16, 2018 @ 02:00 PM - 03:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ahmad Beirami, Electronic Arts
Talk Title: Powering Games with Data & AI
Series: Computer Science Colloquium
Abstract: At EA Digital Platform - Data & AI, we build centralized data-driven and AI-assisted services that power games. In this talk, we begin with an introduction to our data infrastructure and AI platform, that constitute a solid bedrock for solving practical AI problems. We overview several AI applications built on this platform to improve the gameplay experience for hundreds of millions across the globe in addition to contributing scientifically to the research community. We finish with a discussion on the open problems that we are currently tackling.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Ahmad Beirami is a research scientist with Electronic Arts (EA) leading fundamental research and development on training artificial agents in multi-agent systems. His research interests broadly include AI, machine learning, statistics, information theory, and networks. Prior to joining EA in 2018, he held postdoctoral fellow positions at Duke, MIT, and Harvard. He is the recipient of the 2015 Sigma Xi Best PhD Thesis Award from Georgia Tech.
Host: Yan Liu, USC Machine Learning Center
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
MASCLE Machine Learning Seminar: William Wang (UCSB) – Learning to Generate Language and Actions with Structured Agents
Tue, Nov 27, 2018 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: William Wang, University of California, Santa Barbara
Talk Title: Learning to Generate Language and Actions with Structured Agents
Series: Machine Learning Seminar Series
Abstract: A major challenge in Natural Language Processing is to teach machines to generate natural language and actions. However, existing deep learning approaches to language generation do not impose structural constraints in the generation process, often producing low-quality results. In this context, we will introduce our attempt of imposing structural constraints for video captioning via hierarchical reinforcement learning. Moreover, we observe that most of the automated metrics for generation could be gamed, and therefore, we propose an adversarial reward learning method to automatically learn the reward via inverse reinforcement learning. Furthermore, I will discuss our recent attempts in connecting language and vision to actions via a language grounding task for robot navigation, and introduce new algorithms on scheduled policy optimization and combining model-free and model-based reinforcement learning. I will conclude by introducing other exciting research projects at UCSB's NLP Group.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: William Wang is an Assistant Professor in the Department of Computer Science at the University of California, Santa Barbara. He received his PhD from School of Computer Science, Carnegie Mellon University in 2016. He has broad interests in machine learning approaches to data science, including natural language processing, statistical relational learning, information extraction, computational social science, dialogue, and vision. He directs UCSB's NLP Group (nlp.cs.ucsb.edu): in two years, UCSB advanced in the NLP area from an undefined ranking position to top 3 in 2018 according CSRankings.org. He has published more than 60 papers at leading NLP/AI/ML conferences and journals, and received best paper awards (or nominations) at ASRU 2013, CIKM 2013, and EMNLP 2015, a DARPA Young Faculty Award (Class of 2018), two IBM Faculty Awards in 2017 and 2018, a Facebook Research Award in 2018, an Adobe Research Award in 2018, and the Richard King Mellon Presidential Fellowship in 2011. He served as an Area Chair for NAACL, ACL, EMNLP, and AAAI. He is an alumnus of Columbia University, Yahoo! Labs, Microsoft Research Redmond, and University of Southern California. In addition to research, William enjoys writing scientific articles that impact the broader online community: his microblog @çå¨å» has 110,000+ followers and more than 2,000,000 views each month. His work and opinions appear at major international tech media outlets such as Wired, VICE, Fast Company, NASDAQ, The Next Web, Law.com, and Mental Floss.
Host: Yan Liu, USC Machine Learning Center
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
CS Colloquium: Chad Jenkins (University of Michigan) - Semantic Robot Programming... and Making the World a Better Place Too
Tue, Nov 27, 2018 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Chad Jenkins, University of Michigan
Talk Title: Semantic Robot Programming...and Making the World a Better Place Too
Series: CS Colloquium
Abstract: The visions of interconnected heterogeneous autonomous robots in widespread use are a coming reality that will reshape our world. Similar to "app stores" for modern computing, people at varying levels of technical background will contribute to "robot app stores" as designers and developers. However, current paradigms to program robots beyond simple cases remains inaccessible to all but the most sophisticated of developers and researchers. In order for people to fluently program autonomous robots, a robot must be able to interpret user instructions that accord with that user's model of the world. The challenge is that many aspects of such a model are difficult or impossible for the robot to sense directly. We posit a critical missing component is the grounding of semantic symbols in a manner that addresses both uncertainty in low-level robot perception and intentionality in high-level reasoning. Such a grounding will enable robots to fluidly work with human collaborators to perform tasks that require extended goal-directed autonomy.
I will present our efforts towards accessible and general methods of robot programming from the demonstrations of human users. Our recent work has focused on Semantic Robot Programming (SRP), a declarative paradigm for robot programming by demonstration that builds on semantic mapping. In contrast to procedural methods for motion imitation in configuration space, SRP is suited to generalize user demonstrations of goal scenes in workspace, such as for manipulation in cluttered environments. SRP extends our efforts to crowdsource robot learning from demonstration at scale through messaging protocols suited to web/cloud robotics. With such scaling or robotics in mind, prospects for cultivating both equal opportunity and technological excellence will be discussed in the context of broadening and strengthening Title IX.
Biography: Odest Chadwicke Jenkins, Ph.D., is an Associate Professor of Computer Science and Engineering at the University of Michigan. Prof. Jenkins earned his B.S. in Computer Science and Mathematics at Alma College (1996), M.S. in Computer Science at Georgia Tech (1998), and Ph.D. in Computer Science at the University of Southern California (2003). He previously served on the faculty of Brown University in Computer Science (2004-15). His research addresses problems in interactive robotics and human-robot interaction, primarily focused on mobile manipulation, robot perception, and robot learning from demonstration. He is a founder of the Robot Web Tools open-source robotics organization. Prof. Jenkins' work has been recognized by a Sloan Research Fellow, a Presidential Early Career Award for Scientists and Engineers (PECASE), and Young Investigator awards from the Office of Naval Research, the Air Force Office of Scientific Research, and the National Science Foundation. Prof. Jenkins is currently serving as the Editor-in-Chief for the ACM Transactions on Human-Robot Interaction
Host: Maja Mataric
Location: Ronald Tutor Hall of Engineering (RTH) - 105
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
CS Distinguished Lecture: Rodney Allen Brooks (MIT, iRobot, Rethink) - Steps Towards Super Intelligence
Wed, Nov 28, 2018 @ 05:00 PM - 06:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Rodney Allen Brooks, MIT, iRobot, Rethink
Talk Title: Steps Towards Super Intelligence
Series: CS Distinguished Lectures
Abstract: In his 1950 paper "Computing Machinery and Intelligence" Alan Turing estimated that sixty people working for fifty years should be able to program a computer (running at 1950 speed) to have human level intelligence. AI researchers have spent orders of magnitude more effort than that and are still not close. Why has AI been so hard and what are the problems that we might work on in order to make real progress to human level intelligence, or even the super intelligence that many pundits believe is just around the corner? This talk will discuss those steps we can take, what aspects we really still do not have much of a clue about, what we might be currently getting completely wrong, and why it all could be centuries away. Importantly the talk will make distinctions between research questions and barriers to technology adoption from research results, with a little speculation on things that might go wrong (spoiler alert: it is the mundane that will have the big consequences, not the Hollywood scenarios that the press and some academics love to talk about).
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Rodney Brooks earned Bachelors and Masters degrees in pure mathematics from Flinders University in South Australia. In 1977 he joined the Artificial Intelligence Lab at Stanford graduating with a PhD in computer science in 1981. After post-docs at Carnegie Mellow and MIT, and a faculty position back at Stanford he joined the MIT faculty at the Artificial Intelligence Laboratory there in 1984. He worked in computer vision, robotics, and artificial life. He became director of the AI Lab in 1997 and in 2003 he founded the Computer Science and Artificial Intelligence Lab, CSAIL, which is the largest lab at MIT with over 1,000 members. Along the way he started a software company in silicon valley, a boutique robotics venture capital fund, the company iRobot which has delivered tens of millions of home cleaning robots and many thousand ground robots to the US military, and more recently spent 10 years developing collaborative robots for manufacturing at Rethink Robotics. He retired from MIT In 2010, and currently advises companies large and small, including Toyota and their autonomous driving efforts. He is a member of the NAE and a Fellow of the American Academy of Arts and Sciences, and of the IEEE, ACM, AAAS, and AAAI. He writes at rodneybrooks.com/blog.
Host: Maja Mataric
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
CS Tech Talk: DiDi Tech Talk with Dr. Fengmin Gong and Dr. Kevin Knight
Thu, Nov 29, 2018 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Fengmin Gong and Dr. Kevin Knight, Didi
Talk Title: Talk 1: AI for Transportation; Talk 2: The Moment When the Future Fell Asleep
Series: Computer Science Colloquium
Abstract: We are pleased to announce two talks during this colloquium.
Talk 1: Taming Technologies and Transforming Transportation
The world is at the dawn of a Fourth Industrial Revolution fueled by big data, AI, automation, vehicle electrification, and the sharing economy -“ and with this, comes both excitement for the prospect of changing society for the better, and fear of a possible threat to humanity.
Our mission at DiDi is to build a better journey, and we believe we can tame the revolution by designing and building technologies that meet the needs of people, democratize access and benefits, and are aligned with human values.
In this talk, we will share how our R&D teams in the Americas and China work side-by-side to provide rideshare experiences that are safe, convenient and affordable. We will also highlight our continuous research and work on advanced initiatives in safety and security, customer support, smart transportation systems, autonomous vehicles, and AI.
Talk 2: The Moment When the Future Fell Asleep
Recently, recurrent neural networks (RNNs) have been revolutionizing natural language processing and other fields. Among other things, RNNs can assign probabilities to sequences (such as English sentences) and transform one sequence into another (such as English into French). I will describe some of our work over the past couple of years, addressing four questions: What are neural sequence models learning? How can they learn better? Are there theoretical limits? Can they be creative?
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Talk 1: Fengmin is a well-respected technologist in cybersecurity with more than 30 years experience. He is also a serial entrepreneur and angel investor. Fengmin has been a founder and senior executive in several leading security companies. Among them are Palo Alto Networks, FireEye, McAfee and Cyphort. He is a trusted advisor to many entrepreneurs and venture capitalists. He has 18 awarded patents and over 40 technical publications in security and networking.
Talk 2: Kevin Knight is Chief Scientist for Natural Language Processing (NLP) at DiDi Chuxing. He leads a DiDi lab in Marina del Rey devoted to NLP research. He is also Dean's Professor of Computer Science at USC (on leave). He received a PhD in computer science from Carnegie Mellon University and a bachelor's degree from Harvard University. Dr. Knight's research interests include human-machine communication, machine translation, language generation, automata theory, and decipherment. He has co-authored over 150 research paper on natural language processing, including best paper/demo awards at AAAI 2000, ACL 2001, NAACL 2009, ACL 2017, ACL 2018, and NAACL 2018. Dr. Knight also co-authored the widely-adopted textbook "Artificial Intelligence" (McGraw-Hill). In 2001, he co-founded Language Weaver, Inc., a machine translation company acquired by SDL plc in 2010. Dr. Knight was a key researcher in programs run by the Defense Advanced Research Projects Agency (DARPA). He served as President of the Association for Computational Linguistics (ACL) in 2011, as General Chair for the Annual Conference of the ACL in 2005, and as General Chair for the North American ACL conference in 2016. He is a Fellow of the ACL, a Fellow of ISI, and a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI).
Host: Computer Science Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
