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ACM CHI 2019 Conference Serves as Showcase for Ideas Shaping the Future

New York, NY, 
April 23, 2019 – The Association for Computing Machinery’s Special Interest Group on Computer Human Interaction (SIGCHI) will hold its annual flagship conference, the ACM Conference on Human Factors in Computing Systems (CHI 2019), in Glasgow, UK, on May 4-9, 2019. CHI is widely recognized as the most important global showcase for human-computer interaction.

Held annually since 1982, the CHI conference has consistently grown in prominence and attendance. Over the years, dozens of widely used products and technologies have debuted at CHI prior to market deployment, including the Internet of Things (IoT), smart homes, wearable devices, fitness tracking, social networking, instant text messaging, human-robot interaction, multi-touch and 3D interaction, and tangible interfaces, among others. As the premier worldwide forum for the exchange of information on all aspects of HCI, the CHI conference is often the first public demonstration of such advanced technologies.

“CHI offers a unique look into the inspiring visions of future technologies,” said Stephen Brewster, CHI 2019 Co-chair. “This year, we are proud to host the conference in the United Kingdom for the first time ever. We are also excited to continue the commitment to make CHI, and CHI content, more widely accessible, including livestreaming most paper sessions. As the premier international conference celebrating HCI innovation and exploring its tremendous potential, CHI 2019 promises to reflect the myriad ways these technologies are changing the ways in which we live and work.”

The theme of this year’s conference is “Weaving the Threads of CHI.” “In organizing this year’s event, we sought to celebrate the different threads of CHI: individuals from multiple disciplines, computing professionals and academics alike, reflecting different cultures, sectors, communities, and backgrounds, weaving into one cohesive community, with the shared end of developing technologies to work for people and for society,” added CHI 2019 Co-chair Geraldine Fitzpatrick. “We reflected this celebration of our differences and our commonalities in our Celtic knot logo, a symbol of strength and friendship.”

Additionally, a special UX practitioner event will be held to create better links between UX practitioners and researchers.


(Below is a partial list of events. For a complete calendar of accepted papers, workshops, and courses, visit the CHI 2019 Full Schedule of Events).

Keynote Speakers

Aleks Krotoski

“Touchies and Feelies: Everything I Know about Human Interfaces”
An award-winning broadcaster, journalist and academic who has been studying and writing about technology and interactivity since 1999, Krotoski is the host of The Guardian’s Tech Weekly podcast, and the BBC series ”Digital Human”; she also holds fellowships at the University of Oxford and at the London School of Economics. Krotoski will share what baking, beach volleyball, parenting and radio storytelling have taught her about how we interact with each other, ourselves and the world around us--and what this tells us about the possibilities for future social, digital humans. 

Ivan Poupyrev

Poupyrev is an award-winning technology leader, scientist, and designer working at the cutting edge of interactive technologies. He is currently Director of Engineering in Google’s Advanced Technology and Projects division, where he leads a team focused on inventing and developing interaction technologies and products for future digital lifestyle. Fast Company recognized him as one of the World’s 100 Most Creative People. Poupyrev will deliver the closing keynote of CHI2019, discussing his explorations of the present and the future where technology, connectivity and intelligence are woven into the very fabric of our lives.

CHI 2019 Research Paper Highlights


“Online Grocery Delivery Services: An Opportunity to Address Food Disparities in Transportation-scarce Areas”

Tawanna R. Dillahunt, Sylvia Simioni, Xuecong Xu, University of Michigan 
Online grocery delivery services present new opportunities to address food disparities, especially in underserved areas. This study evaluates such services’ potential to provide healthy-food access and influence healthy-food purchase among individuals living in transportation-scarce and low-resource areas, and also contributes policy recommendations to bolster affordability of healthy-food access and design opportunities to promote healthy foods to support the adoption and use of these services among low-resource and transportation-scarce groups.

“What Makes a Good Conversation? Challenges in Designing Truly Conversational Agents”
Leigh Clark, University College Dublin; Nadia Pantidi, University College Cork; Orla Cooney, University College Dublin; Philip Doyle, Sage Ireland, Diego Garaialde,  Justin Edwards, University College Dublin; Brendan Spillane, Trinity College Dublin; Christine Murad,  Cosmin Munteanu, University of Toronto; Vincent Wade, Trinity College Dublin; Benjamin R. Cowan, University College Dublin
Conversational agents promise conversational interaction but fail to deliver. The authors aim to understand what people value in conversation and how this should manifest in agents. Findings from a series of semi-structured interviews show people make a clear dichotomy between social and functional roles of conversation, emphasizing the long-term dynamics of bond and trust along with the importance of context and relationship stage in the types of conversations they have. Drawing on these findings the authors discuss key challenges for conversational agent design, most notably the need to redefine the design parameters for conversational agent interaction.

“Engagement with Mental Health Screening on Mobile Devices: Results from an Antenatal Feasibility Study”

Kevin Doherty, Trinity College Dublin; José Marcano-Belisario, Martin Cohn, Nikolaos Mastellos, Imperial College London; Cecily Morrison, Microsoft Research Cambridge; Josip Car, Imperial College London; Gavin Doherty, Trinity College Dublin

This paper presents the results of the first feasibility study to examine the potential of mobile devices

to engage women in antenatal (pre-birth) mental health screenings given that it is estimated that at least 50 percent of perinatal (the period immediately before and after birth) depression (PND) cases go undiagnosed.  PDN affects up to 15 percent of women within the United Kingdom and has a lasting impact on a woman’s quality of life, birth outcomes and her child’s development. Using a mobile application, 254 women attending 14 National Health Service midwifery clinics provided 2,280 momentary and retrospective reports of their wellbeing over a 9-month period, engaging with this technology regardless of age, education, wellbeing, number of children, marital or employment status, or past diagnosis of depression.

“‘I Feel It Is My Responsibility to Stream’: Streaming and Engaging with Intangible Cultural Heritage through Livestreaming”

Zhicong Lu, University of Toronto; Michelle Annett, University of Alberta; Mingming Fan, University of Toronto ; Nikolaos Mastellos, Imperial College London; Cecily Morrison, Microsoft Research Cambridge; Josip Car, Imperial College London; Gavin Doherty, Trinity College Dublin

To better understand the practices, opportunities, and challenges inherent in sharing and safeguarding Intangible Cultural Heritage through livestreaming, the authors conducted a qualitative investigation, finding that ICH streamers had altruistic motivations and engaged with viewers using multiple modalities beyond livestreams. The authors also found that livestreaming encouraged real-time interaction and sociality, while non-live, curated videos attracted attention from a broader audience and assisted in the archiving of knowledge, many practices are still in danger of being lost or forgotten forever. With the increased popularity of livestreaming in China, some streamers have begun to use livestreaming to showcase and promote ICH activities.

CHI 2019 Workshop Highlights

“Exploring Participatory Design Methods to Engage with Arab Communities”
Ebtisam Alabdulqader, Newcastle University; Shaimaa Lazem, City for Scientific Research and Technology Applications; Mohamed Khamis, LMU Munich; Susan Dray, Dray & Associates 
This workshop invites Arab and non-Arab researchers to discuss the extent to which participatory approaches are culturally and methodologically challenged in the Arab context. The workshop’s aim is to share experiences on designing with Arab communities, discuss and develop the facets of an Arab participatory design research agenda.

“Interacting with Autonomous Vehicles: Learning from other Domains”

Alexander Meschtscherjakov, University of Salzburg; Manfred Tscheligi, AIT Austrian Institute of Technology GmbH; Bastian Pfleging, LMU Munich; Shadan Sadeghian, OFFIS Institute for Information Technology; Wendy Ju, Cornell Tech; Philippe Palanque, University of Toulouse; Andreas Riener, Technische Hochschule Ingolstadt; Bilge Mutlu, University of Wisconsin; Andrew Kun, University of New Hampshire

This workshop aims at research and designs that have been done in areas where humans interact with autonomous systems such as automated vehicles, Human-Robot-Interaction (HRI), aeronautics and space, conversational agents, and smart devices. We discuss what can be learnt from other domains for the design and evaluation of autonomous vehicles.

A video highlighting demonstrations at the conference is here.

About the ACM CHI Conference

Originally a small conference for psychologists interested in user interface design, the annual CHI conference has grown to include a diverse group of interaction designers, computer scientists, engineering psychologists, developers, and performing artists. CHI also addresses the organizational integration of technology, and the use of technology in all areas of life.


SIGCHI, the ACM Special Interest Group on Computer-Human Interaction, is the premier international society for professionals, academics and students who are interested in human-technology and human-computer interaction (HCI). SIGCHI serves as a forum for ideas on how people communicate and interact with computer systems. This interdisciplinary group of computer scientists, software engineers, psychologists, interaction designers, graphic designers, sociologists, and anthropologists is committed to designing useful, usable technology which has the potential to transform individual lives.

About ACM

ACM, the Association for Computing Machinery, is the world’s largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the field’s challenges. ACM strengthens the computing profession’s collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence.  ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.

Bengio, Hinton and LeCun Ushered in Major Breakthroughs in Artificial Intelligence


New York, NY, March 27, 2019 – ACM, the Association for Computing Machinery, today named Yoshua Bengio, Geoffrey Hinton, and Yann LeCun recipients of the 2018 ACM A.M. Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing. Bengio is Professor at the University of Montreal and Scientific Director at Mila, Quebec’s Artificial Intelligence Institute; Hinton is VP and Engineering Fellow of Google, Chief Scientific Adviser of the Vector Institute, and University Professor Emeritus at the University of Toronto; and LeCun is Professor at New York University and VP and Chief AI Scientist at Facebook.

Working independently and together, Hinton, LeCun and Bengio developed conceptual foundations for the field, identified surprising phenomena through experiments, and contributed engineering advances that demonstrated the practical advantages of deep neural networks. In recent years, deep learning methods have been responsible for astonishing breakthroughs in computer vision, speech recognition, natural language processing, and robotics—among other applications.

While the use of artificial neural networks as a tool to help computers recognize patterns and simulate human intelligence had been introduced in the 1980s, by the early 2000s, LeCun, Hinton and Bengio were among a small group who remained committed to this approach. Though their efforts to rekindle the AI community’s interest in neural networks were initially met with skepticism, their ideas recently resulted in major technological advances, and their methodology is now the dominant paradigm in the field.

The ACM A.M. Turing Award, often referred to as the “Nobel Prize of Computing,” carries a $1 million prize, with financial support provided by Google, Inc. It is named for Alan M. Turing, the British mathematician who articulated the mathematical foundation and limits of computing. Bengio, Hinton and LeCun will formally receive the 2018 ACM A.M. Turing Award at ACM’s annual awards banquet on Saturday, June 15, 2019 in San Francisco, California.

“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society,” said ACM President Cherri M. Pancake. “The growth of and interest in AI is due, in no small part, to the recent advances in deep learning for which Bengio, Hinton and LeCun laid the foundation. These technologies are used by billions of people. Anyone who has a smartphone in their pocket can tangibly experience advances in natural language processing and computer vision that were not possible just 10 years ago. In addition to the products we use every day, new advances in deep learning have given scientists powerful new tools—in areas ranging from medicine, to astronomy, to materials science.”

"Deep neural networks are responsible for some of the greatest advances in modern computer science, helping make substantial progress on long-standing problems in computer vision, speech recognition, and natural language understanding,” said Jeff Dean, Google Senior Fellow and SVP, Google AI.  “At the heart of this progress are fundamental techniques developed starting more than 30 years ago by this year's Turing Award winners, Yoshua Bengio, Geoff Hinton, and Yann LeCun. By dramatically improving the ability of computers to make sense of the world, deep neural networks are changing not just the field of computing, but nearly every field of science and human endeavor."

Machine Learning, Neural Networks and Deep Learning

In traditional computing, a computer program directs the computer with explicit step-by-step instructions. In deep learning, a subfield of AI research, the computer is not explicitly told how to solve a particular task such as object classification. Instead, it uses a learning algorithm to extract patterns in the data that relate the input data, such as the pixels of an image, to the desired output such as the label “cat.” The challenge for researchers has been to develop effective learning algorithms that can modify the weights on the connections in an artificial neural network so that these weights capture the relevant patterns in the data.

Geoffrey Hinton, who has been advocating for a machine learning approach to artificial intelligence since the early 1980s, looked to how the human brain functions to suggest ways in which machine learning systems might be developed. Inspired by the brain, he and others proposed “artificial neural networks” as a cornerstone of their machine learning investigations. 

In computer science, the term “neural networks” refers to systems composed of layers of relatively simple computing elements called “neurons” that are simulated in a computer.  These “neurons,” which only loosely resemble the neurons in the human brain, influence one another via weighted connections. By changing the weights on the connections, it is possible to change the computation performed by the neural network. Hinton, LeCun and Bengio recognized the importance of building deep networks using many layers—hence the term “deep learning.” 

The conceptual foundations and engineering advances laid by LeCun, Bengio and Hinton over a 30-year period were significantly advanced by the prevalence of powerful graphics processing unit (GPU) computers, as well as access to massive datasets. In recent years, these and other factors led to leap-frog advances in technologies such as computer vision, speech recognition and machine translation.

Hinton, LeCun and Bengio have worked together and independently. For example, LeCun performed postdoctoral work under Hinton’s supervision, and LeCun and Bengio worked together at Bell Labs beginning in the early 1990s. Even while not working together, there is a synergy and interconnectedness in their work, and they have greatly influenced each other.

Bengio, Hinton and LeCun continue to explore the intersection of machine learning with neuroscience and cognitive science, most notably through their joint participation in the Learning in Machines and Brains program, an initiative of CIFAR, formerly known as the Canadian Institute for Advanced Research.  

Select Technical Accomplishments

The technical achievements of this year’s Turing Laureates, which have led to significant breakthroughs in AI technologies include, but are not limited to, the following:

Geoffrey Hinton

Backpropagation: In a 1986 paper, “Learning Internal Representations by Error Propagation,” co-authored with David Rumelhart and Ronald Williams, Hinton demonstrated that the backpropagation algorithm allowed neural nets to discover their own internal representations of data, making it possible to use neural nets to solve problems that had previously been thought to be beyond their reach. The backpropagation algorithm is standard in most neural networks today. 

Boltzmann Machines: In 1983, with Terrence Sejnowski, Hinton invented Boltzmann Machines, one of the first neural networks capable of learning internal representations in neurons that were not part of the input or output.

Improvements to convolutional neural networks: In 2012, with his students, Alex Krizhevsky and Ilya Sutskever, Hinton improved convolutional neural networks using rectified linear neurons and dropout regularization. In the prominent ImageNet competition, Hinton and his students almost halved the error rate for object recognition and reshaped the computer vision field. 

Yoshua Bengio

Probabilistic models of sequences: In the 1990s, Bengio combined neural networks with probabilistic models of sequences, such as hidden Markov models. These ideas were incorporated into a system used by AT&T/NCR for reading handwritten checks, were considered a pinnacle of neural network research in the 1990s, and modern deep learning speech recognition systems are extending these concepts.

High-dimensional word embeddings and attention: In 2000, Bengio authored the landmark paper, “A Neural Probabilistic Language Model,” that introduced high-dimension word embeddings as a representation of word meaning. Bengio’s insights had a huge and lasting impact on natural language processing tasks including language translation, question answering, and visual question answering.  His group also introduced a form of attention mechanism which led to breakthroughs in machine translation and form a key component of sequential processing with deep learning.

Generative adversarial networks: Since 2010, Bengio’s papers on generative deep learning, in particular the Generative Adversarial Networks (GANs) developed with Ian Goodfellow, have spawned a revolution in computer vision and computer graphics. In one fascinating application of this work, computers can actually create original images, reminiscent of the creativity that is considered a hallmark of human intelligence.  

Yann LeCun

Convolutional neural networks: In the 1980s, LeCun developed convolutional neural networks, a foundational principle in the field, which, among other advantages, have been an essential in making deep learning more efficient. In the late 1980s, while working at the University of Toronto and Bell Labs, LeCun was the first to train a convolutional neural network system on images of handwritten digits. Today, convolutional neural networks are an industry standard in computer vision, as well as in speech recognition, speech synthesis, image synthesis, and natural language processing. They are used in a wide variety of applications, including autonomous driving, medical image analysis, voice-activated assistants, and information filtering.

Improving backpropagation algorithms: LeCun proposed an early version of the backpropagation algorithm (backprop), and gave a clean derivation of it based on variational principles. His work to speed up backpropagation algorithms included describing two simple methods to accelerate learning time.

Broadening the vision of neural networks: LeCun is also credited with developing a broader vision for neural networks as a computational model for a wide range of tasks, introducing in early work a number of concepts now fundamental in AI. For example, in the context of recognizing images, he studied how hierarchical feature representation can be learned in neural networks—a concept that is now routinely used in many recognition tasks. Together with Léon Bottou, he proposed the idea, used in every modern deep learning software, that learning systems can be built as complex networks of modules where backpropagation is performed through automatic differentiation. They also proposed deep learning architectures that can manipulate structured data, such as graphs.

Biographical Background

Geoffrey Hinton  

Geoffrey Hinton is VP and Engineering Fellow at Google, Chief Scientific Adviser at The Vector Institute and an University Professor Emeritus at the University of Toronto. Hinton received a Bachelor’s degree in experimental psychology from Cambridge University and a Doctoral degree in artificial intelligence from the University of Edinburgh. He was the founding Director of the Neural Computation and Adaptive Perception (later Learning in Machines and Brains) program at CIFAR.

Hinton’s honors include Companion of the Order of Canada (Canada’s highest honor), Fellow of the Royal Society (UK), foreign member of the National Academy of Engineering (US), the International Joint Conference on Artificial Intelligence (IJCAI) Award for Research Excellence, the NSERC Herzberg Gold medal, and the IEEE James Clerk Maxwell Gold medal. He was also selected by Wired magazine for “The Wired 100 —2016’s Most Influential People” and by Bloomberg for the 50 people who changed the landscape of global business in 2017.

Yoshua Bengio 

Yoshua Bengio is a Professor at the University of Montreal, holds a Canada CIFAR AI Chair, and is the Scientific Director of both Mila (Quebec’s Artificial Intelligence Institute) and IVADO (the Institute for Data Valorization). He is Co-director (with Yann LeCun) of CIFAR’s Learning in Machines and Brains program. Bengio received a Bachelor’s degree in electrical engineering, a Master’s degree in computer science and a Doctoral degree in computer science from McGill University.

Bengio’s honors include being named an Officer of the Order of Canada, Fellow of the Royal Society of Canada and the Marie-Victorin Prize. His work in founding and serving as Scientific Director of the Quebec Artificial Intelligence Institute (Mila) is also recognized as a major contribution to the field. Mila, an independent nonprofit organization, now counts 300 researchers and 35 faculty members among its ranks. It is the largest academic center for deep learning research in the world, and has helped put Montreal on the map as a vibrant AI ecosystem, with research labs from major companies as well as AI startups.

Yann LeCun 

Yann LeCun is Silver Professor of the Courant Institute of Mathematical Sciences at New York University, and VP and Chief AI Scientist at Facebook.  He received a Diplôme d'Ingénieur from the Ecole Superieure d'Ingénieur en Electrotechnique et Electronique (ESIEE), and a PhD in computer science from Université Pierre et Marie Curie.

His honors include being a member of the US National Academy of Engineering; Doctorates Honoris Causa, from IPN Mexico and École Polytechnique Fédérale de Lausanne (EPFL); the Pender Award, University of Pennsylvania; the Holst Medal, Technical University of Eindhoven & Philips Labs; the Nokia-Bell Labs Shannon Luminary Award; the IEEE PAMI Distinguished Researcher Award; and the IEEE Neural Network Pioneer Award. He was also selected by Wired magazine for “The Wired 100—2016’s Most Influential People” and its “25 Geniuses Who are Creating the Future of Business.” LeCun was the founding director of the NYU Center of Data Science, and is a Co-director (with Yoshua Bengio) of CIFAR’s Learning in Machines and Brains program. LeCun is also a co-founder and former Member of the Board of the Partnership on AI, a group of companies and nonprofits studying the societal consequences of AI.


About the ACM A.M. Turing Award

The A.M. Turing Award was named for Alan M. Turing, the British mathematician who articulated the mathematical foundation and limits of computing, and who was a key contributor to the Allied cryptanalysis of the Enigma cipher during World War II. Since its inception in 1966, the Turing Award has honored the computer scientists and engineers who created the systems and underlying theoretical foundations that have propelled the information technology industry.


About ACM

ACM, the Association for Computing Machinery, is the world’s largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the field’s challenges. ACM strengthens the computing profession’s collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.

Contact:         Jim Ormond


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