2019 keynote

CORPORATE TALK SERIES


 

Terry MandinTerry Mandin

(IoT Technical Solution Professional at Microsoft, Canada)

Bio: Terry Mandin is IoT technical solution provider for Microsoft based in Calgary, Alberta.  Working closely with Microsoft’s engineering teams, he provides in-depth technical guidance to Microsoft’s largest IoT customers.  Although he works across all industries, he specializes in industrial IoT (IIoT) and fleet management.

Title of the Talk: Anatomy of a Modern Industrial IoT Solution

Abstract: IoT technologies are transforming the industry by increasing efficiency and safety while reducing cost.  Scale, security and high availability raise unique challenges for IIoT solutions such as administrating and maintaining millions of devices in the field, hybrid on-premise / cloud architectures, and new IoT devices secured from the silicon up.  New consumers of industrial data such as data scientists require open standards and secure, access to data.  Join this session to hear the end to end story of implementing a modern, cloud-based IIOT solution.


 

Jeffrey de Leeuw

(IoT Specialist, Rogers Communications Canada)

Bio: Jeffry is  an IoT Specialist at Rogers Communications and he began his career with Alcatel-Lucent, gaining unparalleled experience in telephony and embedded systems in the early days of VOIP and mobile applications, while also establishing himself as a respected wireless expert working with companies like Boeing, Samsung and Google. Currently working for Rogers Communications, Jeffrey is widely known for his creative thinking and innovative approach to solve complex problems in the enterprise space. For the past 17 years he has worked with companies in IoT space in verticals such as Healthcare, Transportation, Manufacturing, Energy, and Public sectors leveraging AI, BigData, AR, VR, BI, microservices and next generation wireless technologies to build innovative solutions.

Title of the Talk: A Wireless Carriers Technology Stack - its not about connectivity anymore

Abstract:  Highest form of human interaction is not to talk about where we have been but how we can shape the future. Please join Jeffrey de Leeuw from Rogers Communications where he will share thoughts and interactions Rogers is having with customers and R&D they are doing to make it a reality. Learn how 5G, private licensed wireless networks and multi-purpose edge compute all need to be orchestrated to enable the promise of a fully connected world. Every element of these networks need to be virtualised, on-demand and dynamic in order to meet society's insatiable demands of more data, lower latency, security to enable the Internet of Things from consumer electronics to industrial automation and robotics.


 

Wayne Madhlangobe

(Director, Artificial Intelligence at Air Canada)

Bio:  Wayne Madhlangobe is an experienced advanced analytics leader with an extensive background in developing and implementing Data Science practices in large organizations. He has a PhD in Information Systems on Big Data Analytics. He started in career in aviation as an Air Traffic Controller and holds a private pilot license. Wayne is passionate about data and analytics. He sees an amazing future of AI due to increased rate of advances computational capabilities and advances in Machine Learning and Deeping Learning.

Title of the Talk: Data-Driven Economy: Data as an Organizational Asset

Abstract: Many economic activities of any nature, data and information are key ingredients and products. Data and information act as a glue that connects people, processes and events. It’s the lifeblood of many things that keeps the economy moving. We are in a data-driven economy and its no longer simply about getting access to data but about ensuring the right information at the right time for better decisions. It is important for organizations to be ready for a data-driven economy and understand the value Artificial
Intelligence to its operations. As a principle, data is an asset that has value. Data is a complemental product to prediction therefore as the cost of predictions goes down, the value of data is increasing.Data is a valuable resource and real with a measurable value. As an aid to decision-making, data needs to be accurate and timely. Better decisions are a result of good judgment and that is based on accurate predictions. Data is the foundation for accurate predictions.


 

Yonathan Dattner

(Co-founder & CEO, Luxmux Technology Corporation, Canada)

Bio: Yonathan Dattner is an award-winning entrepreneur who has a track record of successfully commercializing innovative new technologies and products around the globe. Yonathan has
taken products through the whole life cycle from conceptual to commercial ready products, being sold worldwide across multiple industries and markets. As founder and President of Luxmux Technology Corporation, Yonathan has negotiated and secured capital from private, industrial and multiple government sources. In 2015, Luxmux received the ASTECH award for outstanding Science and Technology Start-Up. In 2017 the BeST-SLED® product was a finalist in the Photonics West PRISM Awards in San Francisco. PRISM Awards have been called “the global Oscars of photonics”.
Prior to founding Luxmux, Yonathan worked at Freescale Semiconductors and helped develop its I.MX application processors. In 2012, Freescale sold more than $2 billion worth of I.MX products, seen today in leading end products such as the Amazon Kindle 3, Sony PRS-650 and Kobo.
Among his awards, Yonathan has received the Queen Elizabeth II Award and the Alexander Graham Bell Canada Award through the National Science and Engineering Council of Canada.
Yonathan earned his B.Sc in Electrical Engineering from the Technion - Institute of Technology in Israel, his M.Sc at the University of Calgary, and was an external student at the University of British Colombia’s Microsystems and Nanotechnology Group. Yonathan is a professional member in the Association of Professional Engineers and Geoscientists of Alberta.

Title of the Talk: Greenhouse Gas Emmision (GHG) Monitoring System, data transmission, data collection and data conversion to large area flux emission rates.


Abstract: 
The ability to quantify air for fugitive methane (CH4) and carbon dioxide (CO2) emissions from large industrial area sources has been a prerequisite for industry and government to develop Greenhouse Gas mitigation strategies. The techniques currently used to measure and model air emissions from these sites are widely considered inadequate. Luxmux Technology Corporation has developed an Accurate Remote Monitoring System (ARMS) which measures, transmits and quantifies GHG emissions from large industrial area sources. The problem of accurately measuring and modelling air emissions over large industrial areas is required by industries such as the oil sands, natural gas production & processing, oil production, landfills, wastewater, coal mines, agriculture and other large area sources. This talk will include an overview of the ARMS system from measurement, to transferring the data over radio to a central server and from the central server over mobile to a cloud platform for user access and interface.

 

Steve Liang

(Founder and CTO, SensorUp Inc., Canada)

Bio: Dr. Steve Liang is a global influencer and change maker of Internet of Things. Steve is the founder and CTO of SensorUp, and an associate professor who holds an IoT research chair at the University of Calgary. Dr. Liang is chairing and the editor of several key IoT enabling standards in Open Geospatial Consortium and UN's ITU-T. Dr. Steve Liang's leading edge research and product development efforts are designed to unlock the full potential of the IoT by transforming heterogeneous "silo-ed" IoT systems into a homogeneous IoT system of systems. For example, Steve is the editor of the OGC SensorThings API, an IoT cloud API standard that has been widely implemented around the world for applications ranging from homeland security, logistics, smart cities to energy.

Steve is recognized as a thought leader, especially in IoT and geospatial technologies. Dr. Liang has published more than 60 research publications, and delivered more than 100 invited talks in more than ten countries around the world. Dr. Liang also received numerous pretisgeous awards, include Killam Emerging Research Leader Award, Calgary's Top 40 under 40, the SSE Early Research Excellence Award, the SSE Research Excellence Award, University of Calgary's Peak Scholar Award, and more. He served on the prestigious Council of Canadian Academies' expert panel on Enabling Sustainability in an Interconnected World, Competing in a Global Innovation Economy: The Current State of R&D in Canada, and US Transportation Research Board's report on Connected Airport and the Internet of Things.

Title of the Talk: Aggregate Multiple IoT Silos for Actionable Insights

Abstract:  In the near future, millions to billions of small sensors and actuators will be embedded in real-world objects and connected to the Internet forming the Internet of Things (IoT). The basic premise of the IoT is that everyday objects or devices
can sense their environment, collect information, and communicate and interact with each other. The changing nature of smart, connected "things" is disrupting value chains and will force organizations to rethink and retool nearly everything they do internally
in order to stay competitive. For example, progressive cities and businesses around the world are using IoT to transform themselves into smart cities or smart products, and changing how we live, work, and play. In this talk, Dr. Liang will present
the opportunities and challenges of building an interconnected and interoperable Internet of Things system of systems. In
particular, Dr. Liang will present the real-world use cases of how IoT system of systems transforming existing organizations
and make them more competitive. 

Mark Scantlebury

(President and CEO, Extreme Telematics Corp., Canada)

Bio: Mark Scantlebury has a Bachelor’s of Applied Science in Electronic Systems Engineering with a minor in Computer Science and has over 18 years of experience in electronic product design and manufacturing in both telecommunications and industrial control. He is currently the CEO of Extreme Telematics Corp., a technology company that designs electronic controls and sensors for the oilfield. Mark is also the Chairperson for Alberta IoT, a not-for- profit committed to positioning Alberta as the worldwide centre of excellence for IoT Technology. He has worked to improve the reliability, safety, and optimization of electronic systems which has resulted in 3 granted and 2 pending patents.

Title of the Talk: Using IoT to Democratize Industrial Automation

Abstract: In the past, control and monitoring technology has been developed by individual companies in silos without being open and standardized preventing collaboration between competitors. This constant drive to maximize profit over advancing the industry has led to stagnant growth of digital technology, a lack of open and integrated solutions, and a high cost model. With the emergence of cloud and edge computing, there is the opportunity to use IoT to not only revolutionize industrial automation, but change business models at the same time using a collaborative framework.
We have seen 3 major technology phase shifts over the past 4 decades that has led to the democratization of personal computing. This has increased the capability and pace of technology, provided opportunities to empower everyday people, and dramatically lowered the cost of computing. The PC (Personal Computer) used an open hardware platform and common operating system, the Smartphone added an unparalleled level of integration, cloud connectivity, and the app store, and Raspberry Pi standardized a core computing platform that unlocked the ability for any software developer to build cheap, complete systems.
By aligning the goals of industrial users, cloud service companies, app developers, controls and sensor companies, equipment manufacturers, and process experts we can create a standardize hardware and software framework to transform industry in the same way. We have made substantial progress already on:
 A common edge computing operating system
 Acquiring and transporting data in a common format
 Standardizing how cloud services interact
 Enabling edge apps from multiple developers to work together
 Achieving an unparalleled level of hardware integration to drive down system cost
 Mass producing a computing core while proving a common hardware expansion interface.


Yang Gao

(Co-Founder and CTO of Profound Positioning Inc., Canada)

Bio: Dr. Yang Gao is Professor in the Department of Geomatics Engineering at The University of Calgary. His research expertise includes both theoretical aspects and practical applications of satellite-based positioning and navigation systems with a focus on high-precision and low-cost systems, including real-time kinematic (RTK), wide-area augmentation, precise point positioning (PPP) and multi-sensor fusion. He is also the co-founder and CTO of Profound Positioning Inc.

Dr. Gao has published over 300 technical papers in academic journals and conference proceedings and his research results have been applied by companies in Canada as well as international customers for commercial product development. Dr. Gao has received many national and international awards in recognition of his significant contributions and leadership in the development and application of high-precision GNSS technology, including the ION Thurlow Award, APEGA Research Excellence Award and CPGPS Distinguished Leadership Award. He also received awards for excellence in research, teaching and graduate supervision from Schulich School of Engineering at The University of Calgary. He is active in scientific and professional associations who has served on the editorial boards of five international journals including IEEE Transactions on Vehicular Technology and Journal of Geodesy, and led the technical commissions on high-precision GNSS at the International Association of Geodesy (IAG) for many years. Dr. Gao earned a Ph.D. from The University of Calgary and is a registered professional engineer.

Title of the Talk: Precision GNSS to Mass-Market Applications

Abstract: High-precision location information from Global Navigation Satellite Systems (GNSS) is widely obtainable using high-end GNSS receivers which has enabled many demanding applications over a broad range of disciplines from aerospace, marine, agriculture, survey, mining, construction to disaster detection and monitoring. Lately there is a strong market demand to
bring high-precision GNSS to mass-market applications such as the ever-expanding uses of smartphones and the emerging self-driving cars and drones. For example, high precision location information (position, velocity and orientation) are essential for connected Unmanned Aerial Vehicles (UAVs) in confined environments, self-driving cars in intelligent transportation
systems, and handheld mobile devices in emergency responses.
Precision GNSS to mass-market applications however faces some significant challenges since existing solutions are mainly designed for professional applications with the use of high-end expensive GNSS receivers and systems. A major challenge is that higher accuracy must be affordable, robust, versatile and globally obtainable for mass market applications. Since GNSS
signal is vulnerable to various sources of effects including radio frequency interference, atmospheric disturbance, and signal loss or attenuation, how to ensure precise location information to be continuously available in all environments presents further challenge for mass market applications. Recent technology advances in multi-frequency and multi- constellation GNSS and integration with other enabling sensors such as micro-electro- mechanical system (MEMS) based inertial navigation systems (INS) open up new solutions to help bring precision GNSS to mass market applications.
This presentation will discuss opportunities in bringing precision GNSS to mass-market applications, major barriers that must overcome, and progresses in technology and product development including demonstration of low-cost high-precision GNSS systems and their performance in operational environments.


Hamid Alemohammad

(Co-Founder & CEO at AOMS Technologies Inc., Canada)

Bio: Dr. Hamid Alemohammad is the co-founder and CEO of AOMS Technologies, a company specialized in the development of industrial IoT systems for harsh environments. Hamid has over 15 years of industrial and entrepreneurship experience in industrial sensing systems and instrumentation and the Internet of Things (IoT). Dr. Alemohammad is specialized in technology commercialization, leadership for technology ventures and startups, and technology capital funding and investment. He is an advocate for commercializing transformative scientific research to address global challenges in energy and environment industries.

Title of the Talk: Industrial IoT Solutions with Fiber Optic Sensors

Abstract: Over the past decades, optical fiber, which was originally developed for the telecommunication industry, has been recognized as a promising platform for industrial sensing. In the early days, the fiber optic sensor technology was only adopted by the oil & gas and defense industries. Thanks to the new advancements in the development of low-cost opto-electronic systems, the technology is finding niche markets in other industry sectors including environmental monitoring, process industry, transportation, structural health monitoring, and biomedical. The adoption of fiber optic sensors, especially in harsh and heavy industrial applications, stems from unique features and technical capabilities unmatched by electronic sensors which enables high-fidelity and reliable measurement of critical parameters (such as temperature, pressure, strain, chemicals, etc.); these features include low-loss remote sensing, the ability to work in harsh environments, immunity to electromagnetic interference, small size, and capability of integrated and distributed sensing.
This presentation will focus on existing applications of fiber optic sensors for industrial sensing and monitoring applications and new opportunities to expand the technology for the Industrial Internet of Things.


RESEARCH KEYNOTE SERIES


Prof. Eric Paulos

Prof. Eric Paulos 

(University of California, Berkeley)

Bio: Dr. Eric Paulos is Director of the Hybrid Ecologies Lab, an Associate Professor in Electrical Engineering Computer Sciences at UC Berkeley, Director of the CITRIS Invention Lab, Chief Learning Officer for the Jacobs Institute for Design Innovation, Co-Director of the Swarm Lab, and faculty within the Berkeley Center for New Media (BCNM). Previously, Eric held the Cooper-Siegel Associate Professor Chair in the School of Computer Science at Carnegie Mellon University in the Human-Computer Interaction Institute and earlier was a Senior Research Scientist at Intel Research. His research interests include cosmetic computing, critical making, citizen science, urban computing, telerobotics, and new media. Eric received his PhD in Electrical Engineering and Computer Science from UC Berkeley but his real apprenticeship was earned through three decades of explosive, excruciatingly loud, and quasi-legal activities with a band of misfits at Survival Research Laboratories.

Title of the talk: Plastic Dynamism:  From Disobedient Objects to Poetic Wearables

Abstract: This talk will present and critique a new body of evolving collaborative work at the intersection of art, computer science, and design research. It will present an argument for hybrid materials, methods, and artifacts as strategic tools for insight and innovation within computing culture. The narrative will explore work across two primary themes – Emancipation Fabrication and Cosmetic Computing. 
Cosmetic Computing is a new vision for wearable technologies that is a catalyst towards an open, playful, and creative expression of individuality. It's a liberation call across gender, race, and body types. Leveraging the term "cosmetics", originally meaning "technique of dress", we envision how intentionally designed new-wearables, specifically those that integrate with fashionable materials and overlays applied directly atop the skin or body, can (and should) empower individuals towards novel explorations of body and self expression. Unlike many modern traditional cosmetics that are culturally laden with prescriptive social norms of required usage that are restrictive, sexually binary, and oppressive, we desire a new attitude and creative engagement with wearable technologies that can empower individuals with a more personal, playful, performative, and meaningful "technique of dress" — Cosmetic Computing.


Prof. Kevin Leyton-Brown

Prof. Kevin Leyton-Brown

(University of British Columbia, Canada)

Bio: Dr. Brown is a professor of Computer Science at the University of British Columbia and an associate member of the Vancouver School of Economics. He holds a PhD and M.Sc. from Stanford University (2003; 2001) and a B.Sc. from McMaster University (1998). He studies the intersection of computer science and microeconomics, addressing computational problems in economic contexts and incentive issues in multiagent systems. He also applies machine learning to various problems in artificial intelligence, notably the automated design and analysis of algorithms for solving hard computational problems.

Title of the talk:  Economics and Computer Science of a Radio Spectrum Reallocation

Abstract:  Over 13 months in 2016--17 the US Federal Communications Commission conducted an "incentive auction" to repurpose radio spectrum from broadcast television to wireless internet. In the end, the auction yielded $19.8 billion USD, $10.05 billion USD of which was paid to 175 broadcasters for voluntarily relinquishing their licenses across 14 UHF channels. Stations that continued broadcasting were assigned potentially new channels to fit as densely as possible into the channels that remained. The government netted more than $7 billion USD (used to pay down the national debt) after covering costs (including retuning). A crucial element of the auction design was the construction of a solver, dubbed SATFC, that determined whether sets of stations could be "repacked" in this way; it needed to run every time a station was given a price quote.

This talk describes the design of both the auction and of SATFC. Compared to typical market design settings, the auction design was particularly unconstrained, with flexibility in the definitions of participants' property rights, the goods to be traded, their quantities, and the outcomes the market should seek to achieve. Computational tractability was also a first-order concern. The design of SATFC was achieved via a data-driven, highly parametric, and computationally intensive approach we dub "deep optimization". More specifically, to build SATFC we designed software that could pair both complete and local-search SAT-encoded feasibility checking with a wide range of domain-specific techniques, such as constraint graph decomposition and novel caching mechanisms that allow for reuse of partial solutions from related, solved problems. We then used automatic algorithm configuration techniques to construct a portfolio of eight complementary algorithms to be run in parallel, aiming to achieve good performance on instances that arose in proprietary auction simulations.

Experiments on realistic problems showed that within the short time budget required in practice, SATFC solved more than 96% of the problems it encountered. Furthermore, simulations showed that the incentive auction paired with SATFC produced nearly optimal allocations in a restricted setting and achieved substantially better economic outcomes than other alternatives at national scale.


 

Prof. Cristina Conati

Prof. Cristina Conati

(University of British Columbia, Canada)

Bio: Dr. Conati is a Professor of Computer Science at the University of British Columbia, Vancouver, Canada. She received a M.Sc. in Computer Science at the University of Milan, as well as a M.Sc. and Ph.D. in Intelligent Systems at the University of Pittsburgh. Conati’s research is at the intersection of Artificial Intelligence (AI), Human Computer Interaction (HCI) and Cognitive Science, with the goal to create intelligent interactive systems that can capture relevant user’s properties (states, skills, needs) and personalize the interaction accordingly. Her areas of interest include User Modeling, Affective Computing, Intelligent Virtual Agents, and Intelligent Tutoring Systems. Conati has over 100 peer-reviewed publications in these fields, and her research has received awards from a variety of venues, including UMUAI, the Journal of User Modeling and User Adapted Interaction (2002), the ACM International Conference on Intelligent User Interfaces (IUI 2007), the International Conference of User Modeling, Adaptation and Personalization (UMAP 2013, 2014), TiiS, ACM Transactions on Intelligent Interactive Systems (2014), and the International Conference on Intelligent Virtual Agents (IVA 2016).
Dr. Conati is an associate editor for UMUAI, ACM TiiS, IEEE Transactions on Affective Computing, and the Journal of Artificial Intelligence in Education. She served as President of AAAC, (Association for the Advancement of Affective Computing), as well as Program or Conference Chair for several international conferences including UMAP, ACM IUI, and AI in Education. She is a member of the Executive Committee of AAAI (Association for the Advancement of Artificial Intelligence).

Title of the talk:  Toward User-Adaptive Visualizations

Abstract: User-adaptive interaction (UAI), a field at the intersection of artificial intelligence (AI) and human-computer interaction (HCI), aims to create intelligent interactive systems that provide users with a personalized interaction experience by modeling and adapting in real-time to relevant users' needs and abilities. The benefits of UAI have been shown for a variety of tasks and applications. In this talk I will describe a new research thread in UAI: user-adaptive visualizations.
   Infovis is becoming increasingly important given the continuous growth of applications that allow users to view and manipulate complex data, not only in professional settings, but also for personal usage. To date, visualizations are typically designed based on the type of tasks and data to be handled, without taking into account user differences. However, there is mounting evidence that visualization effectiveness depends on a user’s specific preferences, abilities, states, and even personality.
   These findings have triggered research on  user-adaptive visualizations, i.e.,,visualizations that can track and adapt to relevant user characteristics and specific needs. In this talk, I will present results on which user differences can impact visualization processing and on  how these differences can be captured using predictive machine learning models based on eye-tracking data. I will also discuss how  to leverage these models to provide personalized support that can improve the user's experience with a visualization.


Prof. Jenq-Neng Hwang

Prof. Jenq-Neng Hwang

(University of Washington, USA)

Bio:  Dr. Jenq-Neng Hwang received the BS and MS degrees, both in electrical engineering from the National Taiwan University, Taipei, Taiwan, in 1981 and 1983 separately. He then received his Ph.D. degree from the University of Southern California. In the summer of 1989, Dr. Hwang joined the Department of Electrical and Computer Engineering (ECE) of the University of Washington in Seattle, where he has been promoted to Full Professor since 1999. He served as the Associate Chair for Research from 2003 to 2005, and from 2011-2015. He is currently the Associate Chair for Global Affairs and International Development in the ECE Department. He is the founder and co-director of the Information Processing Lab., which has won several AI City Challenges awards in the past years. He has written more than 350 journals, conference papers and book chapters in the areas of machine learning, multimedia signal processing, and multimedia system integration and networking, including an authored textbook on " Multimedia Networking: from Theory to Practice" published by Cambridge University Press. Dr. Hwang has close working relationship with the industry on multimedia signal processing and multimedia networking.
Dr. Hwang received the 1995 IEEE Signal Processing Society's Best Journal Paper Award. He is a founding member of Multimedia Signal Processing Technical Committee of IEEE Signal Processing Society and was the Society's representative to IEEE Neural Network Council from 1996 to 2000. He is currently a member of Multimedia Technical Committee (MMTC) of IEEE Communication Society and also a member of Multimedia Signal Processing Technical Committee (MMSP TC) of IEEE Signal Processing Society. He served as associate editors for IEEE T-SP, T-NN and T-CSVT, T-IP and Signal Processing Magazine (SPM). He is currently on the editorial board of ZTE Communications, ETRI, IJDMB and JSPS journals. He served as the Program Co-Chair of IEEE ICME 2016 and was the Program Co-Chairs of ICASSP 1998 and ISCAS 2009. Dr. Hwang is a fellow of IEEE since 2001.

Title of the talk: Electronic Visual Monitoring for the Smart Ocean

Abstract: With the increasing incorporation of cameras for fishery applications, such as underwater fish survey based on bottom /midwater trawls and/or ROVs, as well as electronic monitoring (EM) for catch accounting and/or compliance with catch retention requirements. Moreover, they can also enable a non-extractive and non- lethal approach to fisheries surveys and abundance estimation. The camera-based monitoring and sampling approaches not only can conserve depleted fish stocks but also provides an effective way to analyze a greater diversity of marine animals and environmental assessment. This approach, however, generates vast amounts of image/video data very rapidly, effective machine learning techniques to handle these big visual data are thus critically required to make such monitoring and sampling approaches practical. Thanks to many advanced deep learning and computer vision techniques, along with the help of powerful computing resources, many of these tasks can be reliably and real-time performed, a big step toward the smart ocean once these monitoring systems are deployed on every fishing vessel and real-time collecting/analyzing data anytime and anywhere on the ocean. In this talk, I will report some progresses jointly made with NOAA to develop a live fish counting, catch event detection, length measurement and species recognition system, based on the data collected using the Camtrawl, chute or rail camera systems.


Prof. Ryan C.N. D'Arcy

(Simon Fraser University, Canada)

Bio: Dr. Ryan C.N. D'Arcy is the co-founder and senior scientist/entrepreneur for Health Tech Connex Inc. Trained in neuroscience and medical imaging, Dr. D’Arcy holds a BC Leadership Chair in Medical Technology and is full Professor at Simon Fraser University.
He also serves as Head of Health Sciences and Innovation at Fraser Health’s Surrey Memorial Hospital and is widely recognized for founding Innovation Boulevard. Dr. D’Arcy received a B.Sc. (with distinction) from the University of Victoria along with both M.Sc. and Ph.D. degrees in neuroscience from Dalhousie University.
He did post-doctoral training in medical imaging at the National Research Council’s Institute for Biodiagnostics and spent over a decade leading the development of Atlantic Canada’s biomedical imaging cluster. He has extensive experience in translational neuro-imaging, has been the driving force in taking several biotechnology products to market.

Title of the talk: Do you know how your brain is doing? We didn't so we undertook technological development of the world's first brain vital sign framework

Abstract: Vital signs have been critical to improving health care across the globe. In brain care, no such concept existed. Given the basic axiom - You can't treat what you can't measure - the development of brain vital signs addresses a critical gap. This talk will provide an overview of the technological delivery of the world's first brain vital signs along with initial clinical applications in brain injury, aging, and dementia. It will showcase the nearly 25 years of studies to identify key electroencephalography (EEG) responses and the rapid push to translate this science into a point-of-care technology. The technology is a medical grade, deployable, automated, rapid, and easy to use brain vital sign monitor currently rolling out for clinical and research use across North America.  


Prof. Joseph G. Peters

Prof. Joseph G. Peters

(Simon Fraser University, Canada)

Bio: Dr. Joseph Peters is a Professor of Computing Science at Simon Fraser University, Burnaby, Canada. He received a B.Math. from the University of Waterloo, and M.Sc. and Ph.D. degrees in Computer Science from the University of Toronto. His main research interests are in the areas of communication networks, and multimedia networking with an emphasis on the use of algorithmic techniques to enhance performance. He has published more than 70 peer-reviewed articles on these topics with funding from INRIA and CNRS in France, NATO, and Strategic Grants from NSERC and the B.C. Innovation Council.

Title of the talk: Enhancing Multimedia Performance with Algorithmic Techniques

Abstract: It is well known that advances in battery technology for mobile devices have not kept pace with advances in memory, graphics, and processing power. At the same time, increasingly complex video codecs provide better compression but also increase the complexity of decoding which translates into increased power consumption in mobile devices. One of the main challenges in mobile computing is to develop video systems that can play for longer times given the battery limitations of mobile devices.

 In this presentation, I will describe a project to develop encoding methods that maximize video quality while respecting the physical limitations of different mobile receivers. The resulting system computes the encodings in near-real time (a fraction of a second per frame in software on a commodity PC). The decodings on the mobile devices are also real-time. Almost all of the efficiency of the system is achieved by adapting, specializing, and combining standard algorithmic techniques. Using algorithmic techniques to solve systems-level engineering problems is the main focus of this presentation.


Prof. Raouf Boutaba

Prof. Raouf Boutaba

(University of Waterloo, Canada)

Bio: Dr. Raouf Boutaba is a University Chair Professor of Computer Science at the University of Waterloo. He also holds an INRIA International Chair in France. He is the founding Editor in Chief of the IEEE Transactions on Network and Service Management (2007-2010), and the current Editor-in-Chief of the IEEE Journal on Selected Areas in Communications (JSAC). He served as the general or technical program chair for a number of international conferences including IM, NOMS and CNSM. His research interests are in the areas of network and service management. He has published extensively in these areas and received several journal and conference Best Paper Awards such as the IEEE 2008 Fred W. Ellersick Prize Paper Award. He also received other recognitions, including the Premier's Research Excellence Award, Industry research excellence Awards, fellowships of the Faculty of Mathematics, of the David R. Cheriton School of Computer Science and several outstanding performance awards at the University of Waterloo. He has also received the IEEE Communications Society Hal Sobol Award and the IFIP Silver Core in 2007, the IEEE Communications Society Joe LociCero and the Dan Stokesbury awards in 2009, the Salah Aidarous award in 2012, the IEEE Canada McNaugthon Gold Medal in 2014, the Technical Achievement Award of the IEEE Technical Committee on Information Infrastructure and Networking as well as the Donald W. McLellan Meritorious Service Award in 2016. He served as a distinguished lecturer for the IEEE Computer and Communications Societies. He is fellow of the IEEE, a fellow of the Engineering Institute of Canada and a fellow of the Canadian Academy of Engineering.

Title of the talk: The “Cloud” to “Things” Continuum

Abstract: Few years ago, we introduced the concept of a multi-tier cloud as part of the “Smart Applications on Virtualized Infrastructure (SAVI)” NSERC Strategic Network Project. SAVI extends the traditional cloud computing environment into a two-tier cloud including smart edges – small to moderate size data centers located close to the end-users (e.g., service provider premises), and massive scale data centers with abundant high-performance computing resources typically located in remote areas. We designed the smart edge as a converged infrastructure that uses virtualization, cloud computing and network softwarization principles to support multiple network protocols, customizable network services, and high- bandwidth low latency applications. Since then the concept of a multi-tier cloud has been widely adopted by telecom operators and in initiatives such as the Mobile Edge Computing (MEC). In the meantime, the advent of the Internet of Things (IoT) has seen an explosive growth in the number of connected devices generating a large variety of data in high volumes at high velocities. The unique set of requirements posed by the IoT data demands innovation in the information infrastructure with the objective of minimizing latency and conserving bandwidth resources. The multi-tier cloud computing model proposed in SAVI falls short in addressing the needs of the IoT applications, since, most voluminous, heterogeneous and short-lived data will have to be processed and analyzed closer to IoT devices generating the data. Therefore, it is imperative that the future information infrastructure should incorporate more tiers (e.g., IoT gateways, customer premise equipments) into the multi-tier cloud to enable true at-scale end-to-end application orchestration. In this keynote, we will discuss the research challenges in realizing the future information infrastructure that should be massively distributed to achieve scalability; highly interoperable for seamless interaction between different enabling technologies; highly flexible for collecting, fusing, mining, and processing IoT data; and easily programmable for service orchestration and application-enablement.


Prof. Shai Ben-David

Prof. Shai Ben-David 

(University of Waterloo, Canada)

Bio: Dr. Shai Ben-David earned his PhD in mathematics from the Hebrew University in Jerusalem and has been a professor of computer science at the Technion (Israel Institute of Technology).  Over the years, he has held visiting faculty positions at the Australian National University, Cornell University, ETH Zurich, TTI Chicago and the Simons institute at Berkeley. Since 2004 Shai is a professor at the David Cheriton school of computer science at University of Waterloo. He has also been a program committee chair for the major machine learning theory conferences (COLT and ALT) and an area chair in all major ML conferences (NIPS, ICML and  AISTATS). 

Shai’s research interests span a range of topics in computer science theory including logic, theory of distributed computation and complexity theory. In recent years his focus turned to machine learning theory. Among his notable contribution in that field are pioneering steps in the analysis of domain adaptation, learnability of real valued functions, and change detection in streaming data.

In the domain of unsupervised learning Shai has made fundamental contributions to the theory of clustering and developing tools for guiding users in picking algorithms to match their domain needs. He has also published seminal works on average case complexity, competitive analysis and alternatives to worst-case complexity.

Title of the talk: Unsupervised learning; what can, what cannot and what should not be done.

Abstract:  Unsupervised learning refers to the process of finding patterns and drawing conclusions from raw data (in contrast to supervised learning, where the training data is labeled, or scored, and the learner is expected to figure out a labeling/scoring  rule for use in yet-unseen examples). Unlabeled data is, naturally, more readily available than supervised examples, and there is therefore much to gain from being able to utilize such data. However, our understanding on unsupervised learning is much less satisfactory than the established theory of supervised learning.

 

Important Deadlines

Full Paper Submission:20th September 2021
Acceptance Notification: 7th October 2021
Final Paper Submission:15th October 2021
Early Bird Registration: 15th October 2021
Presentation Submission: 19th October 2021
Conference: 27 - 30 October 2021

Previous Conference

IEEE IEMCON 2020

Sister Conferences

IEEE UEMCON 2020

IEEE AIIOT 2021

IEEE CCWC 2021

IEEE CCWC 2020

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Announcements

• Conference Proceedings will be submitted for publication at IEEE Xplore® digital library .

• Best Paper Award will be given for each track.

• Conference Record No 53756