Keynote Speakers





Jeffrey D. Ullman
(Professor, Stanford University)

Bio: Jeff Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford and CEO of Gradiance Corp. He received the B.S. degree from Columbia University in 1963 and the PhD from Princeton in 1966. Prior to his appointment at Stanford in 1979, he was a member of the technical staff of Bell Laboratories from 1966-1969, and on the faculty of Princeton University between 1969 and 1979. From 1990-1994, he was chair of the Stanford Computer Science Department. Ullman was elected to the National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, the National Academy of Sciences in 2020, and has held Guggenheim and Einstein Fellowships. He has received the Sigmod Contributions Award (1996), the ACM Karl V. Karlstrom Outstanding Educator Award (1998), the Knuth Prize (2000), the Sigmod E. F. Codd Innovations award (2006), the IEEE von Neumann medal (2010), the NEC C&C Foundation Prize (2017), and the ACM A.M. Turing Award (2020). He is the author of 16 books, including books on database systems, data mining, compilers, automata theory, and algorithms.

Title of the Talk:  Computational Complexity Theory for MapReduce

Abstract:  MapReduce, as embodied in systems such as Hadoop and Spark, has proven to be an important tool for developing reliable, resilient, parallel software.  But the mechanics of MapReduce require that we think differently about the cost of different algorithms to accomplish a given task.  We shall give the elements of a useful algorithm-design theory that involves tradeoffs between the amount of data permitted at any one reducer ("reducer size") and the amount of communication from mappers to reducers ("replication rate").  We shall give the exact form of this tradeoff for several interesting problems, including "all-pairs" comparisons, and detecting strings that differ by one bit.

Danijela Cabric

(Professor, University of California, Los Angeles)

Bio: Danijela Cabric is a Professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles. She received M.S. from the University of California, Los Angeles in 2001 and Ph.D. from University of California, Berkeley in 2007, both in Electrical Engineering. In 2008, she joined UCLA as an Assistant Professor, where she heads Cognitive Reconfigurable Embedded Systems lab. Her current research projects include novel radio architectures, signal processing, communications, machine learning and networking techniques for spectrum sharing, 5G millimeter-wave, massive MIMO and IoT systems. 
Prof. Cabric was a recipient of the Samueli Fellowship in 2008, the Okawa Foundation Research Grant in 2009, Hellman Fellowship in 2012, the National Science Foundation Faculty Early Career Development (CAREER) Award in 2012, and the Qualcomm Faculty Award in 2020 and 2021. She is serving as an Associate editor for several IEEE journals and on the IEEE Signal Processing for Communications and Networking Technical Committee. She was the General Chair of IEEE Vehicular Networking Conference (VNC) in 2019 and IEEE Dynamic Spectrum Access (DySPAN) in 2021, and a Distinguished Lecturer for IEEE Communications Society from 2018 to 2019. Prof. Cabric is an IEEE Fellow.

Title of the Talk: Ultra-Low-Latency Millimeter Wave Networking using True-Time-Delay Array Architecture

Abstract: Future generations of millimeter wave networks (mmW-nets) will operate in the upper mmW frequency band where ≥ 10 GHz bandwidth can be used to meet the ever increasing demands. Their realization will demand addressing a completely new set of challenges including wider bandwidths, larger antenna array sizes, and higher cell density.  These new system requirements demand fundamental rethinking of radio architectures, signal processing, and networking protocols. Major breakthroughs are required in radio front-end architectures to enable wideband mmW-nets, as most commonly adopted phased antenna array (PAA) based radios face many challenges in achieving fast beam acquisition, interference suppression and wideband data communication. This talk will present the potential of true-time-delay (TTD) based array architecture for wideband mmW-nets for low latency initial beam training and data communication.

Stephen Boyd

(Professor, Stanford University)

Bio: Stephen Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering at Stanford University, with courtesy appointments in Computer Science and Management Science and Engineering. He received the A.B. degree in Mathematics from Harvard University in 1980, and the Ph.D. in Electrical Engineering and Computer Science from the University of California, Berkeley, in 1985, before joining the faculty at Stanford. His current research focus is on convex optimization applications in control, signal processing, machine learning, finance, and circuit design. He is a member of the US National Academy of Engineering, a foreign member of the Chinese Academy of Engineering, and a foreign member of the National Academy of Korea.

Title of the Talk: Convex Optimization

Abstract: Convex optimization has emerged as a useful tool for applications that include data analysis and model fitting, resource allocation, engineering design, network design and optimization, finance, and control and signal processing. After an overview of the mathematics, algorithms, and software frameworks for convex optimization, we turn to common themes that arise across applications, such as sparsity and relaxation. We describe recent work on real-time embedded convex optimization, in which small problems are solved repeatedly in millisecond or microsecond time frames.

Edith Elkind

(Professor, Oxford University)

Bio: Dr. Elkind joined the Oxford Computer Science Department in 2013. Prior to coming to Oxford she was an Assistant Professor at Nanyang Technological University (Singapore), where her research was supported by the National Research foundation (NRF) Fellowship. Dr. Elkind obtained her PhD from Princeton University in 2005, and was a postdoctoral research fellow at the University of Warwick, University of Liverpool and Hebrew University of Jerusalem, as well as a lecturer (Roberts Fellow) at University of Southampton.

Title of the Talk: United for Change: Deliberative Coalition Formation to Change the Status Quo

Abstract:  We study a setting in which a community wishes to identify a strongly supported proposal from a space of alternatives, in order to change the status quo. We describe a deliberation process in which agents dynamically form coalitions around proposals that they prefer over the status quo. We formulate conditions on the space of proposals and on the ways in which coalitions are formed that guarantee deliberation to succeed, that is, to terminate by identifying a proposal with the largest possible support. Our results provide theoretical foundations for the analysis of deliberative processes, in particular in systems for democratic deliberation support, such as, for instance, LiquidFeedback or Polis.

Based on joint work with Davide Grossi, Nimrod Talmon, Udi Shapiro and Abheek Ghosh.

Geoffrey Fox

(Professor, University of Virginia)

Bio: Dr. Fox received a Ph.D. in Theoretical Physics from Cambridge University, where he was Senior Wrangler. He is now a Professor in the Biocomplexity Institute & Initiative and Computer Science Department at the University of Virginia.  He previously held positions at Caltech, Syracuse University, Florida State University, and Indiana University. after being a postdoc at the Institute for Advanced Study at Princeton, Lawrence Berkeley Laboratory, and Peterhouse College Cambridge. He has supervised the Ph.D. of 75 students. He has an hindex of 86 with over 41,000 citations. He received the High-Performance Parallel and Distributed Computing (HPDC) Achievement Award and the ACM - IEEE CS Ken Kennedy Award for Foundational contributions to parallel computing in 2019. He is a Fellow of APS (Physics) and ACM (Computing) and works on the interdisciplinary interface between computing and applications. He is currently active in the Industry consortium MLCommons/MLPerf.

Title of the Talk: AI for Science illustrated by Deep Learning for Geospatial Time Series

Abstract: AI is expected to transform both science and the approach to science. As an example, we take the use of deep learning to describe geospatial time series. We present a general approach building on previous work on recurrent neural networks and transformers. We give three examples of so-called spatial bags from earthquake nowcasting, medical time series, and particle dynamics and focus on the earthquake case. The latter is presented as an MLCommons benchmark challenge with three different implementations: a pure recurrent network, a Spatio-temporal science transformer, and a version of the Google Temporal Fusion Transformer. We discuss how deep learning is used to both clean up the inputs and describe hidden dynamics. We show that both data engineering (wrangling data into desired input format) and data science (the deep learning training/inference) are needed and comment on achieving high performance in both. We briefly speculate how such particular examples can drive broad progress in AI for science.


Ken Birman

(Professor, Cornell University)

Bio: Ken Birman joined Cornell after receiving his Ph.D. degree from U.C. Berkeley in Computer Science. He currently holds the N. Rama Rao Chair in Computer Science. A researcher in distributed systems, Professor Birman focuses on high assurance applications. His past work was used in settings that include the New York Stock Exchange, French Air Traffic Control System and US Navy AEGIS. More recent systems transitioned to companies like IBM, Microsoft, Cisco, and Amazon. Professor Birman has been the Editor in Chief of the ACM Transactions on Computer Systems and has chaired or participated in program committees for numerous conferences. He has also run a number of studies on behalf of the Air Force, NSF, DARPA and DOE, aimed at understanding how best to exploit cloud computing in sensitive settings. At present Professor Birman is working on a new software platform for reliable cloud computing to support use of machine-learning in cloud computing environments that track sensor data and need to take actions under tight time pressure.  One concrete example involves managing the smart power grid, a topic he is exploring in collaboration with the New England ISO, the New York Power Authority, and the New York ISO.     He has several recent publications on this work, and one of the main components, a system he calls Derecho, is available on for open-source download.  Beyond the smart power grid, Derecho has applications to other kinds of smart infrastructures (such as highways, homes, cities), and can be used to create cloud storage infrastructures that use machine intelligence to decide what to store, how to preprocess it, and what forms of indexing to run now in anticipation of future requests.

Professor Birman is a member of the Computer Science graduate field, and plays an active role in advising Cornell NYC Tech post-docs through the Jacobs' Institute's Runway program.

Title of the Talk: Cascade: Ultra-fast Edge Computing for Intelligent IoT

Abstract: Cascade is a new open-source computing platform designed to host AI or ML software close to cameras, other sensors and actuators, in settings where it is important to obtain ultra-low latencies and very high data rates.  While preserving a standard programming model, Cascade maps data movement and computing to accelerators such as RDMA or 5G networking, NMVe memory and leverages GPU when available.    Cascade is dramatically faster than widely popular platforms such as Spark and Apache Flink, and yet just as easy to use – indeed, code from those platforms can often be ported to run on Cascade with little or no change.   

Alyssa B. Apsel

(Professor, Cornell University)

Bio: Alyssa Apsel received the B.S. from Swarthmore College, Swarthmore, PA, in 1995 and the Ph.D. from Johns Hopkins University, Baltimore, MD, in 2002. She joined Cornell University in 2002, where she is currently a Professor of Electrical and Computer Engineering. She is also a Visiting Professor at Imperial College in London working on RF interfaces for implantable electronics. Apsel became the Director of Electrical and Computer Engineering at Cornell in July 2018.

She has authored or coauthored over 100 refereed publications in related fields of RF mixed-signal circuit design, ultra-low power radio, photonic integration with VLSI, and circuit design techniques in the presence of variation resulting in five patents and several pending patent applications. Apsel is also a Distinguished Lecturer for IEEE CAS Society for 2018-2019.

Brian A. Barsky

(Professor, University of California, Berkeley)

Bio: Brian A. Barsky is Professor of the Graduate School at the University of California, Berkeley where he is a Warren and Marjorie Minner Faculty Fellow in Engineering Ethics and Professional/ Social Responsibility.  Prof. Barsky has faculty affiliations in Electrical Engineering and Computer Sciences (EECS), Optometry, Vision Science, Bioengineering, the Berkeley Institute of Design (BID), the Berkeley Center for New Media (BCNM), the Arts Research Center (ARC), and the Berkeley Canadian Studies Program.  He attended McGill University in Montréal, where he received a D.C.S. in engineering and a B.Sc. in mathematics and computer science.  He studied computer graphics and computer science at Cornell University in Ithaca, where he earned an M.S. degree.  His Ph.D. degree is in computer science from the University of Utah in Salt Lake City.  His research interests include computational photography, contact lens design, computer methods for optometry and ophthalmology, image synthesis, computer aided geometric design and modeling, CAD/CAM/CIM, interactive and realistic three-dimensional computer graphics, visualization in scientific computing, computer aided cornea modeling and visualization, medical imaging, vision correcting displays, and virtual environments for surgical simulation.

Title of the Talk:

How Prioritizing Profits over Safety Created the Deadly Boeing 737 MAX and its Ill-Conceived Automated Software

Abstract: The Boeing 737 MAX airplane crashed twice with no survivors within two years of its first commercial flight.  It was grounded worldwide for 19 months in the U.S. and remains prohibited from flying in the airspace in some countries. Examination of the many factors that led to these disastrous consequences illuminates disquieting ethical issues of corporate behavior and lack of government oversight. There is a complex web of concerns involved. At the heart of the tragedy is an ill-conceived automated computer software approach to a flawed aerodynamic design. Prof. Barsky became involved in this topic when his friend’s granddaughter was killed in the second crash. He met with the head of the Aviation Accident Investigation Sub-Committee of the National Transportation Safety Committee of Indonesia in Jakarta to obtain first-hand the details of the first crash. He was featured prominently in a recent Smithsonian documentary shown in the U.S. and U.K. His full-page op-ed in the Globe and Mail was discussed in the Parliament of Canada. In this talk, Prof. Barsky will elucidate how these tragedies were the consequence of a corporation prioritizing profits over safety as well as of regulatory capture of the government agency which was derelict in its duty to protect the flying public.

Pavel Dmitriev

(Vice President of Data Science, Outreach)

Bio: Pavel Dmitriev is a Vice President of Data Science at Outreach - the leading engagement and intelligence platform, where he works on enabling data driven decision making in Sales through experimentation and machine learning. He was previously a Principal Data Scientist with Microsoft's Analysis and Experimentation team, where he worked on scaling experimentation in Bing, Skype, and Windows OS. His work was presented at a number of international conferences such as KDD, ICSE, WWW, CIKM, BigData, and SEAA. Pavel received a Ph.D. degree in Computer Science from Cornell University and a B.S. degree in Applied Mathematics from Moscow State University.

Title for the Talk: A/B Testing A.I.

Abstract: A.I. systems are complex. They are trained on massive datasets and adapt their behavior on the fly. Their real impact on users is often unclear until the system is deployed in the real world. In this sense, the field of A.I. is similar to medicine, where the impact of a drug is not known until it is tested on real people. Yet, while in medicine FDA demands that every drug undergoes a rigorous evaluation via a randomized clinical trial, most A.I. practitioners employ very weak evaluation procedures that rely on offline metrics such as precision, recall, AUC, etc.

In this talk, I will discuss issues resulting from this lack of rigor, and show how A/B testing can help detect bias, catch performance issues, obtain deeper insights, and ensure better user experience when developing and deploying A.I. systems. I will share examples, pitfalls and lessons learned from over a decade of my experience of applying A/B testing to evaluate A.I. systems at Outreach and Microsoft. These lessons will help anyone who wants to apply A/B testing to A.I. scenarios do it correctly and maximize their learnings.

Djordje Gligorijevic

(Senior Applied Researcher, eBay)

Bio: Djordje Gligorijevic is a senior applied researcher at eBay working on a range of problems including large-scale machine learning and data mining, structured regression, extreme multi-Label classification, natural language processing, analysis of big data from heterogeneous sources, and integration of qualitative knowledge. Applications of Djordje's work span domains such as computational healthcare and medicine, computational advertising, sponsored search, and bidding landscape prediction. He received B.Sc. degree in information systems from University of Belgrade, Serbia, in 2013, and the Ph.D. degree in computer and information sciences from Temple University, Philadelphia, PA, in 2018. His professional experience also includes working as academic researcher for IQVIA and as research scientist at Yahoo! Research.

Title of Talk: Deep Learning Methods for Modeling User Behavior in Display Advertising

Abstract: Digital media companies typically collect rich data in the form of sequences of online user activities. Such data is used in various applications, involving tasks ranging from click or conversion prediction to recommendation or user segmentation. Nonetheless, each application depends upon specialized feature engineering that requires a lot of effort and typically disregards the time-varying nature of the online user behavior and heterogeneity of data collected from diverse sources. In this talk, we will discuss advances in modeling deep representations of users’ online activities through time-preserving and noise annealing techniques. These techniques, readily accessible for many problem formulations and seamlessly applicable to desired tasks, sidestep the burden of task-driven feature engineering. Conducted experiments both on public and private data show their effectiveness on a range of tasks from conversion prediction to targeting efforts within a major advertising company.

Adel Ahmadyan

(Senior Research Engineer, Google Core ML team)

Bio: Adel Ahmadyan is a senior research engineer at the Google Core ML team, where he is working on the research and development of leading on-device machine learning solutions. Adel specializes in real-time computer vision and has previously worked across the industry in computer vision teams at Google, Zillow, and Snap. He has published papers in several international conferences and journals such as CVPR, CDC, DAC, DATE, TCAD. Adel received a Ph.D. degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign and a B.S. and M.S. degree in Computer Engineering from Sharif University of Technology, Tehran. 


Important Deadlines

Full Paper Submission:5th January 2022
Acceptance Notification: 14th January 2022
Final Paper Submission:23rd January 2022
Early Bird Registration: 22nd January 2022
Presentation Submission: 24th January 2022
Conference: 26th - 29th January 2022

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•    Conference Proceedings will be submitted for publication at IEEE Xplore Digital Library.
•    Best Paper Award will be given for each track
•    Conference Record No- 54503