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All graduate seminars are held on Mondays and Wednesdays from 1:20 p.m. to 2:35 p.m. via Zoom. Registering students for the seminar: See the requirements to receive credit. Join us for this open seminar! 


Spring 2021

Houston, We Have a Narrative, a book by Randy Olson

Monday, Jan. 25

Dr. Ricardo Gutierrez-Osuna
Professor
Texas A&M University

Abstract
This talk describes the important role that storytelling plays in science communication. The talk is based on Randy Olson’s book titled “Houston, We Have a Narrative: Why Science Needs Story.” The talk will describe the basic structure of research articles and how that structure relates to narrative, going back to classical Greece and mythology. The talk will also present three simple techniques (the word, sentence and paragraph templates) that can be used to inject a narrative component into science writing.  

Biography
Ricardo Gutierrez-Osuna received a Bachelor of Science degree in electrical engineering from the Polytechnic University of Madrid (Spain) in 1992, and Master of Science and doctorate degrees in computer engineering from North Carolina State University in 1995 and 1998, respectively. He is currently a professor in the Department of Computer Science and Engineering at Texas A&M University. He has broad research interests in speech processing, machine learning and models of human perception.

Faculty Contact:

Ricardo Gutierrez-Osuna


Writing Science, a book by Joshua Schimel

Wednesday, Jan. 27

Dr. Ricardo Gutierrez-Osuna
Professor
Texas A&M University

Abstract
This talk describes the basic structure of a scientific paper. The talk is based on Dr. Joshua Schimel’s book titled “Writing Science, How to Write Papers That Get Cited and Proposals That Get Funded.” We will describe four core story structures (Opening, Challenge, Action, and Resolution Structure (OCAR), ABDCE, LDR and LR) and when to use them in storytelling. We will then focus on how to map the OCAR structure, with its hourglass shape, into the traditional sections of a research article: Introduction, methods, results and discussion. We will also discuss the basic structure of paragraphs and sentences, and how to use them effectively to improve flow. 

Biography
Ricardo Gutierrez-Osuna received a Bachelor of Science degree in electrical engineering from the Polytechnic University of Madrid (Spain) in 1992, and Master of Science and doctorate degrees in computer engineering from North Carolina State University in 1995 and 1998, respectively. He is currently a professor in the Department of Computer Science and Engineering at Texas A&M University. He has broad research interests in speech processing, machine learning and models of human perception.

Faculty Contact:

Ricardo Gutierrez-Osuna


The Seeing-Eye Robot: Developing a Human-Aware Artificial Collaborator

Monday, Feb. 1

Dr. Reuth Mirsky
Postdoctoral Fellow, Department of Computer Science
The University of Texas at Austin

Abstract
In this talk, I will present the seeing-eye robot grand challenge and discuss the components required to design and build a service robot that can replace most, if not all, functionalities of a Seeing-Eye dog. This challenge encompasses a variety of research problems that can benefit from human-inspired artificial intelligence (AI): reasoning about other agents, human-robot interactions, explainability, teaching teammates and more. For each of these problems, I will present an example novel contribution that leverages the bilateral investigation of human and AI. Finally, I will discuss the many remaining challenges towards achieving a Seeing-Eye robot and how I plan to tackle these challenges.

Biography
Reuth Mirsky is a postdoctoral fellow in the computer science department at The University of Texas at Austin. She received her doctorate on plan recognition in real world environments in the Department of Software and Information Systems Engineering at Ben Gurion University. Mirsky is interested in the similarities and the differences between AI and natural intelligence, and how these can be used to extend AI. In her research, she seeks algorithms, behaviors and frameworks that can improve existing AI with human-inspired design. In her research, she introduces fundamental theoretical concepts and algorithms by applying them to real problems, putting new spins on them in ways inspired by human intelligence under realistic constraints. Her work has granted her several awards including two awards from the Israeli Ministry of Science (award for Leading Applied Research and scholarship for Excelling Women in STEM) and the Eric and Wendy Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences.

Faculty Contact:

Guni Sharon


The Sense of Structure

Wednesday, Feb. 3

Dr. Ricardo Gutierrez-Osuna
Professor
Texas A&M University

Abstract
This talk will discuss how readers of English tend to make decisions concerning what a given document means. It is based on the existence of recognizable patterns in the interpretative process of most readers. Readers take the greatest percentage of their clues not from word choice but rather from the location of words within the structure of a sentence or a paragraph. The talk is based on the book “The Sense of Structure: Writing from the Reader’s Perspective” by George D. Gopen. It will describe and provide examples on reader’s expectations at the sentence and paragraph levels. It will also challenge pieces of advice that writers often receive on how to improve their writing.

Biography
Ricardo Gutierrez-Osuna received a Bachelor of Science degree in electrical engineering from the Polytechnic University of Madrid (Spain) in 1992, and Master of Science and doctorate degrees in computer engineering from North Carolina State University in 1995 and 1998, respectively. He is currently a professor in the Department of Computer Science and Engineering at Texas A&M University. He has broad research interests in speech processing, machine learning and models of human perception.

Faculty Contact:

Ricardo Gutierrez-Osuna


Tools and Applications for Multi-Agent Coordination

Monday, Feb. 8

Dr. Harel Yedidsion
Postdoctoral Researcher, Department of Computer Science
The University of Texas at Austin

Abstract
As multi-agent systems become more abundant and increase in scale, there is a growing need for efficient mechanisms that determine how the agents share knowledge, coordinate and collaborate. Practical applications of multi-agent systems include autonomous vehicles, drones, service robots, sensor networks, wearable sensors, Internet of Things devices, and, last but not least, humans. Ideally, these multi-agent systems would quickly reach optimal solutions, produce strategy-proof and fair outcomes, relying on distributed decision-making, using minimum communication and computation overhead. In reality however, obtaining these objectives in large scale systems is challenging and requires creative integration of knowledge and tools from different research areas. In this talk, I will present a number of multi-agent and robotic applications, discuss the challenges they present and the diverse set of tools used to design efficient solution methods, including distributed constraint optimization, computational geometry, game theory, planning, human-robot interaction and reinforcement learning.

Biography
Harel Yedidsion is a postdoctoral researcher in the computer science department at The University of Texas at Austin, hosted by Dr. Peter Stone. His main research focus is how to design intelligent multi-agent systems that can efficiently cooperate to solve tasks in dynamic environments. Yedidsion received his Ph.D. from Ben Gurion University in Israel and in his dissertation he developed a framework to represent and solve distributed multi-agent coordination problems. He currently works on developing autonomous service robots and smart transportation applications. These projects encompass many aspects of artificial intelligence development including: perception, navigation, planning, human-robot interaction, natural language processing, reinforcement learning, mechanism design and fair division.

Faculty Contact:

Guni Sharon


PCI Express® 6.0: a low latency, high bandwidth, high reliability and cost-effective interconnect with 64.0 GT/s PAM-4 signaling

Monday, Feb. 22

Dr. Debendra Das Sharma
Fellow and director of the I/O Technology and Standards Group
Intel Corporation

Abstract
PCI Express® (PCIe®¬) specification has been doubling the data rate every generation in a backwards compatible manner every two to three years. PCIe 6.0 specification will adopt PAM-4 signaling at 64.0 GT/s for maintaining the same channel reach of prior generations. A forward error correction (FEC) mechanism will offset the high BER of PAM-4. We propose a new flit-based approach with a light-weight, low-latency FEC coupled with a strong CRC and a low-latency link level retry mechanism to meet the stringent low-latency, high bandwidth and high reliability goals. Shared credit pooling across multiple virtual channels will be deployed to reduce the area and power overhead while providing the necessary quality of service guarantees. We also present a new low-power state that ensures power consumption is proportional to bandwidth usage without impacting the traffic flow.

Biography
Debendra Das Sharma is an Intel fellow in the Data Platforms Group and director of the I/O Technology and Standards Group at Intel Corporation. He is a leading expert on I/O subsystem and interface architecture. Das Sharma’s team delivers Intel-wide critical interconnect technologies in peripheral component interconnect express (PCIe), compute express link (CXL), Intel’s coherency interconnect and multichip package interconnect. He is a key driver of external standards for PCIe and CXL, and internal proprietary interfaces, as well as implementation. 

Das Sharma joined Intel in 2001 as a technical lead in the Advanced Components Division, designing server chipsets. He previously worked with Hewlett-Packard, where he led development of their server chipsets. He holds 123 U.S. patents and is a frequent speaker and panelist at the PCI-SIG Developers Conference, CXL consortium events, Open Server Summit, Open Fabrics Alliance, Flash Memory Summit and Intel Developer Forum.

Das Sharma is a member of the Board of Directors for the PCI Special Interest Group (PCI-SIG) and a lead contributor to PCIe specifications since its inception. He is a co-inventor and founding member of the CXL consortium and co-leads the CXL Technical Task Force.

Das Sharma has a bachelor’s in technology (with honors) degree in computer science and engineering from the Indian Institute of Technology, Kharagpur, and a Ph.D. in computer engineering from the University of Massachusetts Amherst. He has also  been awarded the Distinguished Alumnus Award from Indian Institute of Technology, Kharagpur.

Faculty Contact:

Rabi Mahapatra


Computer-Generated Animation of Faces

Monday, March 1

Dr. Frederic Parke
Professor Emeritus, Department of Visualization
Texas A&M University

Abstract
This talk will be on early work in computer facial animation. It will focus on the context and developments presented in the two related seminal papers listed below.

Parke, F. I., “Computer Generated Animation of Faces,” Proceedings of the ACM ’72 National Conference, August 1972.  Reprinted in Seminal Graphics: Pioneering Efforts that Shaped the Field, R. Wolfe (ed.), ACM Siggraph, 1998 and in Interactive Computer Graphics, H. Freeman (ed.), IEEE Computer Society, 1980. 

Parke, F. I., “A Model for Human Faces that Allows Speech Synchronized Animation,” Computers and Graphics, 1(1), 1975. Presented at the first Siggraph conference, July 1974.

Biography
Frederic Parke is professor emeritus in the Department of Visualization at Texas A&M University.  While at Texas A&M Parke served as associate department head and director of the Visualization Laboratory. Prior to Texas A&M, he was at IBM in Austin as part of the Future Systems, Human Centered Technologies, RS/6000 Visual Systems and PowerPC development groups. Earlier, Parke was a professor of computer science and director of the Computer Graphics Laboratory at the New York Institute of Technology and was a member of the computer science and engineering faculty at Case Western Reserve University. He did his graduate work at the University of Utah. Parke is best known for his seminal work in facial modeling and animation. More recently, his research focuses on immersive visualization.

Faculty Contact:

Ricardo Gutierrez


Rethinking the Role of Language Runtimes for Cloud 3.0

Wednesday, March 3

Dr. Chenxi Wang 
Postdoctoral Researcher, Department of Computer Science
University of California Los Angeles

Abstract
The public cloud’s deployment model is shifting from machine VMs in an infrastructure-as-a-service setting to a platform-as-a-service or function-as-a-service model where the cloud operator provides services such as databases, machine learning frameworks, or speech engines, and customers access these services through APIs. At the application level, developers increasingly focus on logical functionality written in high-level languages, while performance-critical components such as machine learning infrastructure and data stores are managed by the cloud provider. At the architecture level, new hardware models such as resource disaggregation are making their way into data centers and redefining how cloud services should be provided.

A language runtime is in the middle of the compute stack, and hence a good fit to coordinate and manage the rapidly changing software and hardware. It understands application semantics more than the kernel and hardware details more than applications. By acquiring information from both upper and lower layers, a language runtime can make holistic decisions, allowing cloud applications to enjoy the benefits of new hardware features without significant modifications to applications. In this talk, I will discuss the problems with resource disaggregation in modern clouds as well as our recent works on the design of disaggregation-aware runtime systems that enable cloud applications to run efficiently on the emerging resource-disaggregated architecture.

Biography
Chenxi Wang is currently a postdoctoral researcher at University of California Los Angeles, working with Dr. Harry Xu. He got his Ph.D. degree from the Institute of Computing Technology, Chinese Academy of Science in 2018 under the supervision of Dr. Xiaobing Feng. Wang’s research interest is to build hardcore systems, managed runtime and big data systems for emerging hardware, such as non-volatile memory and disaggregated cluster.

Faculty Contact:

Khanh Nguyen


Reimagining Online Discussion Systems

Monday, March 8

Dr. Amy X. Zhang
Assistant Professor, Allen School of Computer Science and Engineering
University of Washington

Abstract
The internet was supposed to democratize discussion, allowing people from all walks of life to communicate with each other at scale. However, this vision has not been fully realized — instead online discourse seems to be getting worse, as people are increasingly drowning in discussion, with much of it unwanted, unpleasant or downright harmful. In this talk, I argue that to tackle these problems, we first must break out of the assumptions that have been baked into the design of discussion software for years, and instead allow for more diverse, community-led configurations. I present new systems that empower discussion participants to take control of their social space with new capabilities for curation, including: 1) condensing forum threads using Wikum; 2) annotation of group chat using Tilda; 3) friend-sourced moderation to combat harassment using Squadbox; and 4) community self-governance using PolicyKit.

In a world of abundant discussion and mass capabilities for amplification, the curation of a social space becomes as equally essential as content creation in defining the nature of that space. By putting more powerful techniques for curation in the hands of everyday people, I envision a future where end users are empowered to actively co-curate every aspect of their online discussion environments, bringing in their nuanced and contextual insights to solve social issues.

Biography
Amy X. Zhang is an assistant professor at University of Washington's Allen School of Computer Science and Engineering, where she leads the Social Futures Lab. Previously, she was a 2019-20 postdoctoral researcher at Stanford Computer Science after completing her Ph.D. at the Massachusetts Institute of Technology’s (MIT) Computer Science and Artificial Intelligence Lab in 2019, where she received the George Sprowls Best Ph.D. Thesis Award at MIT in computer science. During her Ph.D., she was an affiliate and 2018-19 fellow at the Berkman Klein Center at Harvard University, a Google Ph.D. Fellow and a National Science Foundation Graduate Research Fellow. Her work has received multiple awards at top computing venues such as the Association for Computing Machinery (ACM), ACM Conference on Computer-Supported Cooperative Work and Social Computing, and ACM Conference on Human Factors in Computing Systems, and has been profiled on BBC's Click television program, CBC radio, and featured in articles by ABC News, The Verge, New Scientist, and Poynter. She is a founding member of the Credibility Coalition, a group dedicated to research and standards for information credibility online. She received a Master of Philisophy in computer science at the University of Cambridge on a Gates Fellowship and a B.S. in computer science at Rutgers University, where she was captain of the Division I women's tennis team.

Faculty Contact:

James Caverlee


Establishing the Essential Hardware Primitives for Quantum-Proof Secure Computer Systems

Wednesday, March 10

Dr. Michel A. Kinsey
Associate Professor, Department of Electrical and Computer Engineering
Texas A&M University

Abstract
In the last five years, we have witnessed a raft of breakthroughs and several key milestones towards the development of general quantum computers. These advances do bring with them critical challenges to classical cryptosystems like Rivest-Shamir-Adleman, Elliptic Curve Cryptography and ElGamal. The strength of these algorithms rests on the hardness of integer factorization and discrete logarithm problems under the classic computing paradigm, but not under quantum computing approaches. Thus, researchers have been actively investigating new algorithms and designs for cryptosystems for the post-quantum era. Among these techniques, designs based on Ring-learning with errors (Ring-LWE) thus far have proven to be the most promising approach. In this talk, I will introduce a set of highly-optimized, parameterizable hardware modules to serve as post-quantum primitives for faster design space exploration of post-quantum cryptosystems, especially, cryptosystems using Ring-LWE algorithms. This post-quantum primitive set consist of the four frequently-used security components: the public key cryptosystem (PKC), key exchange (KEX), oblivious transfer (OT) and zero-knowledge proof (ZKP). The PKC and KEX form the basis of most modern cryptographic systems. The OT is used in many privacy-preserving applications, e.g., DNA database and machine learning. Similarly, ZKP is used in a number of applications, for example, it has been proposed as an ideal candidate for next generation blockchain algorithms. These primitives will serve as the fundamental building blocks and aid hardware designers in constructing quantum-proof secure systems in the post-quantum era.

Biography
Michel A. Kinsy is an associate professor in the Department of Electrical and Computer Engineering at Texas A&M University, where he directs the Adaptive and Secure Computing Systems Laboratory. Kinsy is also the associate director of the Texas A&M Cybersecurity Center. He focuses his research on computer architecture, hardware-level security, and efficient hardware design and implementation of post-quantum cryptography systems. Kinsy is a Massachusetts Institute of Technology Presidential Fellow and an Inaugural Skip Ellis Career Award recipient. He earned his Ph.D. in electrical engineering and computer science in 2013 from MIT. Before joining the Texas A&M faculty, Kinsy was an assistant professor in the Department of Electrical and Computer Engineering at Boston University (BU). Prior to BU, he was an assistant professor in the Department of Computer and Information Systems at the University of Oregon. From 2013 to 2014, he was a member of the technical staff at the MIT Lincoln Laboratory, where he led the Advanced Computer Architecture Concepts sub-group tasked with exploring future secure computing architectures in critical Department of Defense systems. 

Faculty Contact:

Dilma Da Silva


Deep Learning Algorithms and Hardware Co-Design for Edge Computing 

Monday, March 15

Dr. Deming Chen
Professor, Department of Electrical and Computer Engineering 
University of Illinois at Urbana-Champaign 

Abstract
In a conventional top-down design flow for smart-domain applications, deep neural network (DNN) algorithms are first designed concentrating on the model accuracy, and then optimized, implemented, and accelerated through hardware accelerators, trying to meet various system design targets on power, energy, speed, and cost. However, this approach often does not work well, especially for edge computing targeting various emerging Internet of Things (IoT) applications mainly because this design flow ignores the stringent physical constraints that the edge or IoT devices themselves would have towards the DNN algorithm design, optimization and deployment. An ideal scenario would be that algorithms and their hardware accelerators are developed simultaneously. In this talk, we will present our DNN/Accelerator co-design and co-search methods. Our results have shown great promises for delivering high-performance hardware-tailored DNNs and DNN-tailored accelerators naturally, simultaneously and elegantly. One of the DNN models coming out of this co-design method, called SkyNet, won a double championship in the competitive ACM/IEEE DAC System Design Contest for both the embedded GPU and the FPGA tracks for low-power object detection with real drones in 2019.

Biography
Deming Chen obtained his Bachelor of Science in computer science from the University of Pittsburgh, Pennsylvania, in 1995, and his Master of Science and Ph.D. in computer science from University of California at Los Angeles in 2001 and 2005, respectively. He joined the electrical and computer engineering department at the University of Illinois at Urbana-Champaign (UIUC) in 2005 and has been an affiliate professor of the computer science department as well. His current research interests include reconfigurable computing, machine learning and cognitive computing, system-level and high-level synthesis, and hardware security. He received nine best paper awards and has given more than 120 invited talks based on research results in these areas. Chen is the Donald Willett Faculty Scholar and the Abel Bliss Professor of the Grainger College of Engineering of UIUC, an IEEE Fellow, an ACM Distinguished Speaker, and the editor-in-chief of ACM Transactions on Reconfigurable Technology and Systems. 

Faculty Contact:

Jiang Hu


Symmetry, Growth and Jigsaw Puzzles: Exploring Interlocking as a Principle for Computational Design

Monday, March 29

Dr. Vinayak Krishnamurthy 
Assistant Professor, J. Mike Walker ’66 Department of Mechanical Engineering  
Texas A&M University

Abstract
Interlocking shapes are all around us, be it a piece of cloth, a simple assembly of a nut and a bolt, or an impact-resistant material. In fact, interlocking manifests itself in nature in various forms. Interlocking shapes can have a profound impact on a whole host of applications in engineering, architecture, manufacturing and product design. In this talk, we will explore the notion of interlocking as a principle for computer-aided geometric design. Starting with an example from the biological world, we will see how interlocking manifests in the growth of animal skin cells and discuss computational schemes to capture this growth for designing interlocking shapes. We will then discuss topological and geometric interlocking as two canonical forms of interlocking. Subsequently, we will take a detailed look at how to use growth-based principles along with spatial symmetries to generate specific classes of topologically and geometrically interlocking shapes. Finally, we will end our discussion with specific examples of how these shapes can be used to design new types of mechanical structures, new types of structural (or architected) materials and new manufacturing methodologies.

Biography
Vinayak Krishnamurthy is an assistant professor in the J. Mike Walker’66 Department of Mechanical Engineering and Department of Computer Science and Engineering (by affiliation) at Texas A&M University. His work is at the intersection of four fields of research, namely, geometric modeling, human computer interactions, product design, and artificial intelligence. Krishnamurthy directs the Mixed-initiative Design Lab to create and investigate advanced tools, methodologies, and theories for engineering, industrial, and architectural design. He studies the role of spatial user interfaces in creative design ideation, new workflows for humans-computer collaboration for information-based ideation and new geometric modeling techniques for generative design of shapes. Krishnamurthy’s dissertation research led to the commercial deployment of zPots, a virtual pottery app using Leap Motion controller. Through the National Science Foundation-AIR program, he collaborated with zeroUI, a startup located in California. The technology was showcased at TechCrunch Disrupt, San Fransisco (2012) and MakerFaire - Bay Area (2013).

Faculty Contact:

Ricardo Gutierrez-Osuna


Quantifying Civility in Broadcast News: A Challenge

Wednesday, March 31

Dr. Ani Nenkova
Associate Professor, Department of Computer and Information Science
University of Pennsylvania

Abstract
Political scientists studying online incivility agree that incivility leads to polarization, erodes trust in political institutions and increases support for limiting expression online. Automating the estimation of incivility can help track the problem and study its nuanced effects.  Systems for detecting incivility in online comments have already been developed and have been fairly successful in supporting content moderation efforts. Incivility in video, including news, however, cannot be detected computationally.

In this talk I will overview our work on analyzing American news shows across the political spectrum. We find that systems developed for content moderation fail in this domain. Analyzing news shows also call for a multi-modal approach, including voice and video features, which have not yet been developed.

Overall, the talk aims to introduce a compelling need in computational social science, a technical discussion of the shortcomings of text systems in predicting incivility and an invitation to researchers interested in multi-modal processing.

Biography
Ani Nenkova is an associate professor of computer and information science at the University of Pennsylvania. Her main areas of research are computational linguistics and artificial intelligence, with emphasis on developing computational methods for analysis of text quality and style, discourse, affect recognition and summarization. She obtained her Ph.D. degree in computer science from Columbia University.

Nenkova and her collaborators are recipients of the best student paper award at SIGDial in 2010 and best paper award at EMNLP-CoNLL in 2012. The Penn team, co-led by Nenkova, won the audio-visual emotion recognition challenge for word-level prediction in 2012.

She is a co-editor-in-chief of the Transactions of the Association for Computational Linguistics. Nenkova was a member of the editorial board of Computational Linguistics (2009-2011) and an associate editor for the IEEE/ACM Transactions on Audio, Speech and Language Processing (2015-2018). She regularly serves as an area chair/senior program committee member for ACL, NAACL and AAAI. Nenkova was a program co-chair for SIGDial 2014 and NAACL-HLT in 2016.

Faculty Contact:

Theodora Chaspari


Machine Learning Fairness through Social Choice: (Something like) a Research Agenda

Monday, April 12

Dr. Robin Burke 
Professor and Chair, Department of Computer and Information Science
University of Colorado Boulder

Abstract
Research in machine learning fairness makes two key simplifying assumptions that have proven challenging to move beyond. One assumption is that we can productively concentrate on a uni-dimensional version of the problem: achieving fairness for a single protected group defined by a single sensitive feature. The second assumption is that technical solutions need not engage with the essentially political nature of claims surrounding fairness. I argue that relaxing these assumptions is necessary for machine learning fairness to achieve practical utility. While some recent research in rich subgroup fairness has considered ways to relax the first assumption, these approaches require that fairness be defined in the same way for all groups, which amounts to a hardening of the second assumption. In this talk, I argue for a formulation of machine learning fairness based on social choice. Social choice is inherently multi-agent, escaping the single group assumption and, in its classic formulation, places no constraints on agents' preferences. In addition, social choice was developed to formalize political decision-making mechanisms, such as elections, and therefore offers some hope of directly addressing the inherent politics of fairness. Social choice has complexities of its own, however, and the talk will outline the beginnings of a research agenda aimed at understanding the challenges and opportunities afforded by this approach to machine learning fairness.

Biography
Robin Burke is professor and chair of the Department of Information Science at the University of Colorado Boulder. He conducts research in personalized recommender systems, a field he helped found and develop. His most recent projects explore fairness, accountability and transparency in recommendation through the integration of objectives from diverse stakeholders. He joined the Department of Information Science in 2019 from the School of Computing at DePaul University. Burke obtained his Ph.D. in computer science from Northwestern University in 1993 and a Bachelor of Science in computer science from Harvey Mudd College in 1986. He is the author of more than 100 peer-reviewed articles in various areas of artificial intelligence including recommender systems, machine learning and information retrieval. Burke’s work has received support from the National Science Foundation, the National Endowment for the Humanities, the Fulbright Commission and the MacArthur Foundation, among others.

Faculty Contact:

James Caverlee