Tag Archives: University Relations

Emerging practices for Society-Centered AI

Posted by Anoop Sinha, Research Director, Technology & Society, and Yossi Matias, Vice President, Google Research

The first of Google’s AI Principles is to “Be socially beneficial.” As AI practitioners, we’re inspired by the transformative potential of AI technologies to benefit society and our shared environment at a scale and swiftness that wasn’t possible before. From helping address the climate crisis to helping transform healthcare, to making the digital world more accessible, our goal is to apply AI responsibly to be helpful to more people around the globe. Achieving global scale requires researchers and communities to think ahead — and act — collectively across the AI ecosystem.

We call this approach Society-Centered AI. It is both an extension and an expansion of Human-Centered AI, focusing on the aggregate needs of society that are still informed by the needs of individual users, specifically within the context of the larger, shared human experience. Recent AI advances offer unprecedented, societal-level capabilities, and we can now methodically address those needs — if we apply collective, multi-disciplinary AI research to society-level, shared challenges, from forecasting hunger to predicting diseases to improving productivity.

The opportunity for AI to benefit society increases each day. We took a look at our work in these areas and at the research projects we have supported. Recently, Google announced that 70 professors were selected for the 2023 Award for Inclusion Research Program, which supports academic research that addresses the needs of historically marginalized groups globally. Through evaluation of this work, we identified a few emerging practices for Society-Centered AI:

Understand society’s needs
Listening to communities and partners is crucial to understanding major issues deeply and identifying priority challenges to address. As an emerging general purpose technology, AI has the potential to address major global societal issues that can significantly impact people’s lives (e.g., educating workers, improving healthcare, and improving productivity). We have found the key to impact is to be centered on society’s needs. For this, we focus our efforts on goals society has agreed should be prioritized, such as the United Nations’ 17 Sustainable Development Goals, a set of interconnected goals jointly developed by more than 190 countries to address global challenges.
Collective efforts to address those needs
Collective efforts bring stakeholders (e.g., local and academic communities, NGOs, private-public collaborations) into a joint process of design, development, implementation, and evaluation of AI technologies as they are being developed and deployed to address societal needs.
Measuring success by how well the effort addresses society’s needs
It is important and challenging to measure how well AI solutions address society’s needs. In each of our cases, we identified primary and secondary indicators of impact that we optimized through our collaborations with stakeholders.

Why is Society-Centered AI important?

The case examples described below show how the Society-Centered AI approach has led to impact across topics, such as accessibility, health, and climate.

Understanding the needs of individuals with non-standard speech

There are millions of people with non-standard speech (e.g., impaired articulation, dysarthria, dysphonia) in the United States alone. In 2019, Google Research launched Project Euphonia, a methodology that allows individual users with non-standard speech to train personalized speech recognition models. Our success began with the impact we had on each individual who is now able to use voice dictation on their mobile device.

Euphonia started with a Society-Centered AI approach, including collective efforts with the non-profit organizations ALS Therapy Development Institute and ALS Residence Initiative to understand the needs of individuals with amyotrophic lateral sclerosis (ALS) and their ability to use automatic speech recognition systems. Later, we developed the world’s largest corpus of non-standard speech recordings, which enabled us to train a Universal Speech Model to better recognize disordered speech by 37% on real conversation word error rate (WER) measurement. This also led to the 2022 collaboration between the University of Illinois Urbana-Champaign, Alphabet, Apple, Meta, Microsoft, and Amazon to begin the Speech Accessibility Project, an ongoing initiative to create a publicly available dataset of disordered speech samples to improve products and make speech recognition more inclusive of diverse speech patterns. Other technologies that use AI to help remove barriers of modality and languages, include live transcribe, live caption and read aloud.

Focusing on society’s health needs

Access to timely maternal health information can save lives globally: every two minutes a woman dies during pregnancy or childbirth and 1 in 26 children die before reaching age five. In rural India, the education of expectant and new mothers around key health issues pertaining to pregnancy and infancy required scalable, low-cost technology solutions. Together with ARMMAN, Google Research supported a program that uses mobile messaging and machine learning (ML) algorithms to predict when women might benefit from receiving interventions (i.e., targeted preventative care information) and encourages them to engage with the mMitra free voice call program. Within a year, the mMitra program has shown a 17% increase in infants with tripled birth weight and a 36% increase in women understanding the importance of taking iron tablets during pregnancy. Over 175K mothers and growing have been reached through this automated solution, which public health workers use to improve the quality of information delivery.

These efforts have been successful in improving health due to the close collective partnership among the community and those building the AI technology. We have adopted this same approach via collaborations with caregivers to address a variety of medical needs. Some examples include: the use of the Automated Retinal Disease Assessment (ARDA) to help screen for diabetic retinopathy in 250,000 patients in clinics around the world; our partnership with iCAD to bring our mammography AI models to clinical settings to aid in breast cancer detection; and the development of Med-PaLM 2, a medical large language model that is now being tested with Cloud partners to help doctors provide better patient care.

Compounding impact from sustained efforts for crisis response

Google Research’s flood prediction efforts began in 2018 with flood forecasting in India and expanded to Bangladesh to help combat the catastrophic damage from yearly floods. The initial efforts began with partnerships with India’s Central Water Commission, local governments and communities. The implementation of these efforts used SOS Alerts on Search and Maps, and, more recently, broadly expanded access via Flood Hub. Continued collaborations and advancing an AI-based global flood forecasting model allowed us to expand this capability to over 80 countries across Africa, the Asia-Pacific region, Europe, and South, Central, and North America. We also partnered with networks of community volunteers to further amplify flood alerts. By working with governments and communities to measure the impact of these efforts on society, we refined our approach and algorithms each year.

We were able to leverage those methodologies and some of the underlying technology, such as SOS Alerts, from flood forecasting to similar societal needs, such as wildfire forecasting and heat alerts. Our continued engagements with organizations led to the support of additional efforts, such as the World Meteorological Organization's (WMO) Early Warnings For All Initiative. The continued engagement with communities has allowed us to learn about our users' needs on a societal level over time, expand our efforts, and compound the societal reach and impact of our efforts.

Further supporting Society-Centered AI research

We recently funded 18 university research proposals exemplifying a Society-Centered AI approach, a new track within the Google Award for Inclusion Research Program. These researchers are taking the Society-Centered AI methodology and helping create beneficial applications across the world. Examples of some of the projects funded include:

AI-Driven Monitoring of Attitude Polarization in Conflict-Affected Countries for Inclusive Peace Process and Women’s Empowerment: This project’s goal is to create LLM-powered tools that can be used to monitor peace in online conversations in developing nations. The initial target communities are where peace is in flux and the effort will put a particular emphasis on mitigating polarization that impacts women and promoting harmony.
AI-Assisted Distributed Collaborative Indoor Pollution Meters: A Case Study, Requirement Analysis, and Low-Cost Healthy Home Solution for Indian Communities: This project is looking at the usage of low-cost pollution monitors combined with AI-assisted methodology for identifying recommendations for communities to improve air quality and at home health. The initial target communities are highly impacted by pollution, and the joint work with them includes the goal of developing how to measure improvement in outcomes in the local community.
Collaborative Development of AI Solutions for Scaling Up Adolescent Access to Sexual and Reproductive Health Education and Services in Uganda: This project’s goal is to create LLM-powered tools to provide personalized coaching and learning for users' needs on topics of sexual and reproductive health education in low-income settings in Sub-Saharan Africa. The local societal need is significant, with an estimated 25% rate of teenage pregnancy, and the project aims to address the needs with a collective development process for the AI solution.

Future direction

Focusing on society’s needs, working via multidisciplinary collective research, and measuring the impact on society helps lead to AI solutions that are relevant, long-lasting, empowering, and beneficial. See the AI for the Global Goals to learn more about potential Society-Centered AI research problems. Our efforts with non-profits in these areas is complementary to the research that we are doing and encouraging. We believe that further initiatives using Society-Centered AI will help the collective research community solve problems and positively impact society at large.

Acknowledgements

Many thanks to the many individuals who have worked on these projects at Google including Shruti Sheth, Reena Jana, Amy Chung-Yu Chou, Elizabeth Adkison, Sophie Allweis, Dan Altman, Eve Andersson, Ayelet Benjamini, Julie Cattiau, Yuval Carny, Richard Cave, Katherine Chou, Greg Corrado, Carlos De Segovia, Remi Denton, Dotan Emanuel, Ashley Gardner, Oren Gilon, Taylor Goddu, Brigitte Hoyer Gosselink, Jordan Green, Alon Harris, Avinatan Hassidim, Rus Heywood, Sunny Jansen, Pan-Pan Jiang, Anton Kast, Marilyn Ladewig, Ronit Levavi Morad, Bob MacDonald, Alicia Martin, Shakir Mohamed, Philip Nelson, Moriah Royz, Katie Seaver, Joel Shor, Milind Tambe, Aparna Taneja, Divy Thakkar, Jimmy Tobin, Katrin Tomanek, Blake Walsh, Gal Weiss, Kasumi Widner, Lihong Xi, and teams.

Source: Google AI Blog

Google Research, 2022 & beyond: Research community engagement

Posted by Posted by Leslie Yeh, Director, University Relations

(This is Part 9 in our series of posts covering different topical areas of research at Google. You can find other posts in the series here.)

Sharing knowledge is essential to Google’s research philosophy — it accelerates technological progress and expands capabilities community-wide. Solving complex problems requires bringing together diverse minds and resources collaboratively. This can be accomplished through building local and global connections with multidisciplinary experts and impacted communities. In partnership with these stakeholders, we bring our technical leadership, product footprint, and resources to make progress against some of society's greatest opportunities and challenges.

We at Google see it as our responsibility to disseminate our work as contributing members of the scientific community and to help train the next generation of researchers. To do this well, collaborating with experts and researchers outside of Google is essential. In fact, just over half of our scientific publications highlight work done jointly with authors outside of Google. We are grateful to work collaboratively across the globe and have only increased our efforts with the broader research community over the past year. In this post, we will talk about some of the opportunities afforded by such partnerships, including:

· Addressing social challenges together

· Training the next generation of researchers

· Collaborating to advance scientific innovations

· Fueling innovation in products and engineering

· Open-sourcing datasets and tools

Addressing social challenges together

Engaging the wider community helps us progress on seemingly intractable problems. For example, access to timely, accurate health information is a significant challenge among women in rural and densely populated urban areas across India. To solve this challenge, ARMMAN developed mMitra, a free mobile service that sends preventive care information to expectant and new mothers. Adherence to such public health programs is a prevalent challenge, so researchers from Google Research and the Indian Institute of Technology, Madras worked with ARMMAN to design an ML system that alerts healthcare providers about participants at risk of dropping out of the health information program. This early identification helps ARMMAN provide better-targeted support, improving maternal health outcomes.

Google Research worked with ARMMAN to design a system to alert healthcare providers about participants at risk for dropping out of their preventative care information program for expectant mothers. This plot shows the cumulative engagement drops prevented using our restless multi-armed bandit model (RMAB) compared to the control group (Round Robin).

We also support Responsible AI projects directly for other organizations — including our commitment of $3M to fund the new INSAIT research center based in Bulgaria. Further, to help build a foundation of fairness, interpretability, privacy, and security, we are supporting the establishment of a first-of-its-kind multidisciplinary Center for Responsible AI with a grant of $1M to the Indian Institute of Technology, Madras.

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Training the next generation of researchers

Part of our responsibility in guiding how technology affects society is to help train the next generation of researchers. For example, supporting equitable student persistence in computing research through our Computer Science Research Mentorship Program, where Googlers have mentored over one thousand students since 2018 — 86% of whom identify as part of a historically marginalized group.

We work towards inclusive goals and work across the globe to achieve them. In 2022, we expanded our research interactions and programs to faculty and students across Latin America, which included grants to women in computer science in Ecuador. We partnered with ENS, a university in France, to help fund scholarships for students to train through research. Another example is our collaboration with the Computing Alliance of Hispanic-Serving Institutions (CAHSI) to provide $4.8 million to support more than 30 collaborative research projects and over 3,000 Hispanic students and faculty across a network of Hispanic-serving institutions.

Efforts like these foster the research ecosystem and help the community give back. Through exploreCSR, we partner with universities to provide students with introductory experiences in research, such as Rice University’s regional workshop on applications and research in data science (ReWARDS), which was delivered in rural Peru by faculty from Rice. Similarly, one of our Awards for Inclusion Research led to a faculty member helping startups in Africa use AI.

The funding we provide is most often unrestricted and leads to inspiring results. Last year, for example, Kean University was one of 53 institutions to receive an exploreCSR award. It used the funding to create the Research Recruits Program, a two-semester program designed to give undergraduates an introductory opportunity to participate in research with a faculty mentor. A student at Kean with a chronic condition that requires him to take different medications every day, a struggle that affects so many, decided to pursue research on the topic with a peer. Their research, set to be published this year, demonstrates an ML solution, built with Google's TensorFlow, that can identify pills with 99.8% certainty when used correctly. Results like these are why we continue to invest in younger generations, further demonstrated by our long-term commitment to funding PhD Fellows every year across the globe.

Building an inclusive ecosystem is imperative. To this end, we've also partnered with the non-profit Black in Robotics (BiR), formed to address the systemic inequities in the robotics community. Together, we established doctoral student awards that help financially support graduate students and to support BiR’s newly established Bay Area Robotics lab. We also help make global conferences accessible to more researchers around the world, for example, by funding 24 students this year to attend Deep Learning Indaba in Tunisia.

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Collaborating to advance scientific innovations

In 2022 Google sponsored over 150 research conferences and even more workshops, which leads to invaluable engagements with the broader research community. At research conferences, Googlers serve on program committees and organize workshops, tutorials and numerous other activities to collectively advance the field. Additionally, last year, we hosted over 14 dedicated workshops to bring together researchers, such as the 2022 Quantum Symposium, which generates new ideas and directions for the research field, further advancing research initiatives. In 2022, we authored 2400 papers, many of which were presented at leading research conferences, such as NeurIPS, EMNLP, ECCV, Interspeech, ICML, CVPR, ICLR, and many others. More than 50% of these papers were authored in collaboration with researchers beyond Google.

Over the past year, we've expanded our engagement models to facilitate students, faculty, and Google's research scientists coming together across schools to form constructive research triads. One such project, undertaken in partnership with faculty and students from Georgia Tech, aims to develop a robot guide dog with human behavior modeling and safe reinforcement learning. Throughout 2022, we gave over 224 grants to researchers and over $10M in Google Cloud Platform credits for topics ranging from the improvement of algorithms for post-quantum cryptography with collaborators at CNRS in France to fostering cybersecurity research at TU Munich and Fraunhofer AISEC in Germany.

In 2022, we made 22 new multi-year commitments totaling over ~$80M to 65 institutions across nine countries, where each year we will host workshops to select over 100 research projects of mutual interest. For example, in a growing partnership, we are supporting the new Max Planck VIA-Center in Germany to work together on robotics. Another large area of investment is a close partnership with four universities in Taiwan (NTU, NCKU, NYCU, NTHU) to increase innovation in silicon chip design and improve competitiveness in semiconductor design and manufacturing. We aim to collaborate by default and were proud to be recently named one of Australia's top collaborating companies.

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Fueling innovation in products and engineering

The community fuels innovation at Google. For example, by facilitating student researchers to work with us on defined research projects, we've experienced both incremental and more dramatic improvements. Together with visiting researchers, we combine information, compute power, and a great deal of expertise to bring about breakthroughs, such as leveraging our undersea internet cables to detect earthquakes. Visiting Researchers also worked hand-in-hand with us to develop Minerva, a state-of-the-art solution that came about by training a deep learning model on a dataset that contains quantitative reasoning with symbolic expressions.

Minerva incorporates recent prompting and evaluation techniques to better solve mathematical questions. It then employs majority voting, in which it generates multiple solutions to each question and chooses the most common answer as the solution, thus improving performance significantly.

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Open-sourcing datasets and tools

Engaging with the broader research community is a core part of our efforts to build a more collaborative ecosystem. We support the general advancement of ML and related research through the release of open-source code and datasets. We continued to grow open source datasets in 2022, for example, in natural language processing and vision, and expanded our global index of available datasets in Google Dataset Search. We also continued to release sustainability data via Data Commons and invite others to use it for their research. See some of the datasets and tools we released in 2022 listed below.

Dataset	Description

Auto-Arborist	A multiview urban tree classification dataset that consists of ~2.6M trees covering >320 genera, which can aid in the development of models for urban forest monitoring.

Bazel GitHub Metrics	A dataset with GitHub download counts of release artifacts from selected bazelbuild repositories.

BC-Z demonstration	Episodes of a robotic arm performing 100 different manipulation tasks. Data for each episode includes the RGB video, the robot's end-effector positions, and the natural language embedding.

BEGIN V2	A benchmark dataset for evaluating dialog systems and natural language generation metrics.

CLSE: Corpus of Linguistically Significant Entities	A dataset of named entities annotated by linguistic experts. It includes 34 languages and 74 different semantic types to support various applications from airline ticketing to video games.

CocoChorales	A dataset consisting of over 1,400 hours of audio mixtures containing four-part chorales performed by 13 instruments, all synthesized with realistic-sounding generative models.

Crossmodal-3600	A geographically diverse dataset of 3,600 images, each annotated with human-generated reference captions in 36 languages.

CVSS: A Massively Multilingual Speech-to-Speech Translation Corpus	A Common Voice-based Speech-to-Speech translation corpus that includes 2,657 hours of speech-to-speech translation sentence pairs from 21 languages into English.

DSTC11 Challenge Task	This challenge evaluates task-oriented dialog systems end-to-end, from users' spoken utterances to inferred slot values.

EditBench	A comprehensive diagnostic and evaluation dataset for text-guided image editing.

Few-shot Regional Machine Translation	FRMT is a few-shot evaluation dataset containing en-pt and en-zh bitexts translated from Wikipedia, in two regional varieties for each non-English language (pt-BR and pt-PT; zh-CN and zh-TW).

Google Patent Phrase Similarity	A human-rated contextual phrase-to-phrase matching dataset focused on technical terms from patents.

Hinglish-TOP	Hinglish-TOP is the largest code-switched semantic parsing dataset with 10k entries annotated by humans, and 170K generated utterances using the CST5 augmentation technique introduced in the paper.

ImPaKT	A dataset that contains semantic parsing annotations for 2,489 sentences from shopping web pages in the C4 corpus, corresponding to annotations of 3,719 expressed implication relationships and 6,117 typed and summarized attributes.

InFormal	A formality style transfer dataset for four Indic Languages, made up of a pair of sentences and a corresponding gold label identifying the more formal and semantic similarity.

MAVERICS	A suite of test-only visual question answering datasets, created from Visual Question Answering image captions with question answering validation and manual verification.

MetaPose	A dataset with 3D human poses and camera estimates predicted by the MetaPose model for a subset of the public Human36M dataset with input files necessary to reproduce these results from scratch.

MGnify proteins	A 2.4B-sequence protein database with annotations.

MiQA: Metaphorical Inference Questions and Answers	MiQA assesses the capability of language models to reason with conventional metaphors. It combines the previously isolated topics of metaphor detection and commonsense reasoning into a single task that requires a model to make inferences by selecting between the literal and metaphorical register.

MT-Opt	A dataset of task episodes collected across a fleet of real robots, following the RLDS format to represent steps and episodes.

MultiBERTs Predictions on Winogender	Predictions of BERT on Winogender before and after several different interventions.

Natural Language Understanding Uncertainty Evaluation	NaLUE is a relabelled and aggregated version of three large NLU corpuses CLINC150, Banks77 and HWU64. It contains 50k utterances spanning 18 verticals, 77 domains, and ~260 intents.

NewsStories	A collection of url links to publicly available news articles with their associated images and videos.

Open Images V7	Open Images V7 expands the Open Images dataset with new point-level label annotations, which provide localization information for 5.8k classes, and a new all-in-one visualization tool for better data exploration.

Pfam-NUniProt2	A set of 6.8 million new protein sequence annotations.

Re-contextualizing Fairness in NLP for India	A dataset of region and religion-based societal stereotypes in India, with a list of identity terms and templates for reproducing the results from the "Re-contextualizing Fairness in NLP" paper.

Scanned Objects	A dataset with 1,000 common household objects that have been 3D scanned for use in robotic simulation and synthetic perception research.

Specialized Rater Pools	This dataset comes from a study designed to understand whether annotators with different self-described identities interpret toxicity differently. It contains the unaggregated toxicity annotations of 25,500 comments from pools of raters who self-identify as African American, LGBTQ, or neither.

UGIF	A multi-lingual, multi-modal UI grounded dataset for step-by-step task completion on the smartphone.

UniProt Protein Names	Data release of ~49M protein name annotations predicted from their amino acid sequence.

upwelling irradiance from GOES-16	Climate researchers can use the 4 years of outgoing longwave radiation and reflected shortwave radiation data to analyze important climate forcers, such as aircraft condensation trails.

UserLibri	The UserLibri dataset reorganizes the existing popular LibriSpeech dataset into individual “user” datasets consisting of paired audio-transcript examples and domain-matching text-only data for each user. This dataset can be used for research in speech personalization or other language processing fields.

VideoCC	A dataset containing (video-URL, caption) pairs for training video-text machine learning models.

Wiki-conciseness	A manually curated evaluation set in English for concise rewrites of 2,000 Wikipedia sentences.

Wikipedia Translated Clusters	Introductions to English Wikipedia articles and their parallel versions in 10 other languages, with machine translations to English. Also includes synthetic corruptions to the English versions, to be identified with NLI models.

Workload Traces 2022	A dataset with traces that aim to help system designers better understand warehouse-scale computing workloads and develop new solutions for front-end and data-access bottlenecks.

Tool	Description

Differential Privacy Open Source Library	An open-source library to enable developers to use analytic techniques based on DP.

Mood Board Search	The result of collaborative work with artists, photographers, and image researchers to demonstrate how ML can enable people to visually explore subjective concepts in image datasets.

Project Relate	An Android beta app that uses ML to help people with non-standard speech make their voices heard.

TensorStore	TensorStore is an open-source C++ and Python library designed for storage and manipulation of n-dimensional data, which can address key engineering challenges in scientific computing through better management and processing of large datasets.

The Data Cards Playbook	A Toolkit for Transparency in Dataset Documentation.

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Conclusion

Research is an amplifier, an accelerator, and an enabler — and we are grateful to partner with so many incredible people to harness it for the good of humanity. Even when investing in research that advances our products and engineering, we recognize that, ultimately, this fuels what we can offer our users. We welcome more partners to engage with us and maximize the benefits of AI for the world.

Acknowledgements

Thank you to our many research partners across the globe, including academics, universities, NGOs, and research organizations, for continuing to engage and work with Google on exciting research efforts. There are many teams within GoogIe who make this work possible, including Google’s research teams and community, research partnerships, education, and policy teams. Finally, I would especially like to thank those who provided helpful feedback in the development of this post, including Sepi Hejazi Moghadam, Jill Alvidrez, Melanie Saldaña, Ashwani Sharma, Adriana Budura Skobeltsyn, Aimin Zhu, Michelle Hurtado, Salil Banerjee and Esmeralda Cardenas.

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Google Research, 2022 & beyond

This was the ninth and final blog post in the “Google Research, 2022 & Beyond” series. Other posts in this series are listed in the table below:

Language Models	Computer Vision	Multimodal Models
Generative Models	Responsible AI	ML & Computer Systems
Efficient Deep Learning	Algorithmic Advances	Robotics
Natural Sciences	Health	Community Engagement

Source: Google AI Blog

Announcing the 2021 Research Scholar Program Recipients

Posted by Negar Saei, Program Manager, University Relations

In March 2020 we introduced the Research Scholar Program, an effort focused on developing collaborations with new professors and encouraging the formation of long-term relationships with the academic community. In November we opened the inaugural call for proposals for this program, which was received with enthusiastic interest from faculty who are working on cutting edge research across many research areas in computer science, including machine learning, human-computer interaction, health research, systems and more.

Today we are pleased to announce that in this first year of the program we have granted 77 awards, which included 86 principal investigators representing 15+ countries and over 50 universities. Of the 86 award recipients, 43% identify as an historically marginalized group within technology. Please see the full list of 2021 recipients on our web page, as well as in the list below.

We offer our congratulations to this year’s recipients, and look forward to seeing what they achieve!

Algorithms and Optimization
Alexandros Psomas, Purdue University
   Auction Theory Beyond Independent, Quasi-Linear Bidders
Julian Shun, Massachusetts Institute of Technology
   Scalable Parallel Subgraph Finding and Peeling Algorithms
Mary Wootters, Stanford University
   The Role of Redundancy in Algorithm Design
Pravesh K. Kothari, Carnegie Mellon University
   Efficient Algorithms for Robust Machine Learning
Sepehr Assadi, Rutgers University
   Graph Clustering at Scale via Improved Massively Parallel Algorithms

Augmented Reality and Virtual Reality
Srinath Sridhar, Brown University
   Perception and Generation of Interactive Objects

Geo
Miriam E. Marlier, University of California, Los Angeles
   Mapping California’s Compound Climate Hazards in Google Earth Engine
Suining He, University of Connecticut
   Fairness-Aware and Cross-Modality Traffic Learning and Predictive Modeling for Urban Smart Mobility Systems

Human Computer Interaction
Arvind Satyanarayan, Massachusetts Institute of Technology
   Generating Semantically Rich Natural Language Captions for Data Visualizations to Promote Accessibility
Dina El-Zanfaly, Carnegie Mellon University
   In-the-making: An intelligence mediated collaboration system for creative practices
Katharina Reinecke, University of Washington
   Providing Science-Backed Answers to Health-related Questions in Google Search
Misha Sra, University of California, Santa Barbara
   Hands-free Game Controller for Quadriplegic Individuals
Mohsen Mosleh, University of Exeter Business School
   Effective Strategies to Debunk False Claims on Social Media: A large-scale digital field experiments approach
Tanushree Mitra, University of Washington
   Supporting Scalable Value-Sensitive Fact-Checking through Human-AI Intelligence

Health Research
Catarina Barata, Instituto Superior Técnico, Universidade de Lisboa
   DeepMutation – A CNN Model To Predict Genetic Mutations In Melanoma Patients
Emma Pierson, Cornell Tech, the Jacobs Institute, Technion-Israel Institute of Technology, and Cornell University
   Using cell phone mobility data to reduce inequality and improve public health
Jasmine Jones, Berea College
   Reachout: Co-Designing Social Connection Technologies for Isolated Young Adults
Mojtaba Golzan, University of Technology Sydney, Jack Phu, University of New South Wales
   Autonomous Grading of Dynamic Blood Vessel Markers in the Eye using Deep Learning
Serena Yeung, Stanford University
   Artificial Intelligence Analysis of Surgical Technique in the Operating Room

Machine Learning and Data Mining
Aravindan Vijayaraghavan, Northwestern University, Sivaraman Balakrishnan, Carnegie Mellon University
   Principled Approaches for Learning with Test-time Robustness
Cho-Jui Hsieh, University of California, Los Angeles
   Scalability and Tunability for Neural Network Optimizers
Golnoosh Farnadi, University of Montreal, HEC Montreal/MILA
   Addressing Algorithmic Fairness in Decision-focused Deep Learning
Harrie Oosterhuis, Radboud University
   Search and Recommendation Systems that Learn from Diverse User Preferences
Jimmy Ba, University of Toronto
   Model-based Reinforcement Learning with Causal World Models
Nadav Cohen, Tel-Aviv University
   A Dynamical Theory of Deep Learning
Nihar Shah, Carnegie Mellon University
   Addressing Unfairness in Distributed Human Decisions
Nima Fazeli, University of Michigan
   Semi-Implicit Methods for Deformable Object Manipulation
Qingyao Ai, University of Utah
   Metric-agnostic Ranking Optimization
Stefanie Jegelka, Massachusetts Institute of Technology
   Generalization of Graph Neural Networks under Distribution Shifts
Virginia Smith, Carnegie Mellon University
   A Multi-Task Approach for Trustworthy Federated Learning

Mobile
Aruna Balasubramanian, State University of New York – Stony Brook
   AccessWear: Ubiquitous Accessibility using Wearables
Tingjun Chen, Duke University
   Machine Learning- and Optical-enabled Mobile Millimeter-Wave Networks

Machine Perception
Amir Patel, University of Cape Town
   WildPose: 3D Animal Biomechanics in the Field using Multi-Sensor Data Fusion
Angjoo Kanazawa, University of California, Berkeley
   Practical Volumetric Capture of People and Scenes
Emanuele Rodolà, Sapienza University of Rome
   Fair Geometry: Toward Algorithmic Debiasing in Geometric Deep Learning
Minchen Wei, Hong Kong Polytechnic University
   Accurate Capture of Perceived Object Colors for Smart Phone Cameras
Mohsen Ali and Izza Aftab, Information Technology University of the Punjab, Pakistan
   Is Economics From Afar Domain Generalizable?
Vineeth N Balasubramanian, Indian Institute of Technology Hyderabad
   Bridging Perspectives of Explainability and Adversarial Robustness
Xin Yu and Linchao Zhu, University of Technology Sydney
   Sign Language Translation in the Wild

Networking
Aurojit Panda, New York University
   Bertha: Network APIs for the Programmable Network Era
Cristina Klippel Dominicini, Instituto Federal do Espirito Santo
   Polynomial Key-based Architecture for Source Routing in Network Fabrics
Noa Zilberman, University of Oxford
   Exposing Vulnerabilities in Programmable Network Devices
Rachit Agarwal, Cornell University
   Designing Datacenter Transport for Terabit Ethernet

Natural Language Processing
Danqi Chen, Princeton University
   Improving Training and Inference Efficiency of NLP Models
Derry Tanti Wijaya, Boston University, Anietie Andy, University of Pennsylvania
   Exploring the evolution of racial biases over time through framing analysis
Eunsol Choi, University of Texas at Austin
   Answering Information Seeking Questions In The Wild
Kai-Wei Chang, University of California, Los Angeles
   Certified Robustness to against language differences in Cross-Lingual Transfer
Mohohlo Samuel Tsoeu, University of Cape Town
   Corpora collection and complete natural language processing of isiXhosa, Sesotho and South African Sign languages
Natalia Diaz Rodriguez, University of Granada (Spain) + ENSTA, Institut Polytechnique Paris, Inria. Lorenzo Baraldi, University of Modena and Reggio Emilia
   SignNet: Towards democratizing content accessibility for the deaf by aligning multi-modal sign representations

Other Research Areas
John Dickerson, University of Maryland – College Park, Nicholas Mattei, Tulane University
   Fairness and Diversity in Graduate Admissions
Mor Nitzan, Hebrew University
   Learning representations of tissue design principles from single-cell data
Nikolai Matni, University of Pennsylvania
   Robust Learning for Safe Control

Privacy
Foteini Baldimtsi, George Mason University
   Improved Single-Use Anonymous Credentials with Private Metabit
Yu-Xiang Wang, University of California, Santa Barbara
   Stronger, Better and More Accessible Differential Privacy with autodp

Quantum Computing
Ashok Ajoy, University of California, Berkeley
   Accelerating NMR spectroscopy with a Quantum Computer
John Nichol, University of Rochester
   Coherent spin-photon coupling
Jordi Tura i Brugués, Leiden University
   RAGECLIQ - Randomness Generation with Certification via Limited Quantum Devices
Nathan Wiebe, University of Toronto
   New Frameworks for Quantum Simulation and Machine Learning
Philipp Hauke, University of Trento
   ProGauge: Protecting Gauge Symmetry in Quantum Hardware
Shruti Puri, Yale University
   Surface Code Co-Design for Practical Fault-Tolerant Quantum Computing

Structured Data, Extraction, Semantic Graph, and Database Management
Abolfazl Asudeh, University Of Illinois, Chicago
   An end-to-end system for detecting cherry-picked trendlines
Eugene Wu, Columbia University
   Interactive training data debugging for ML analytics
Jingbo Shang, University of California, San Diego
   Structuring Massive Text Corpora via Extremely Weak Supervision

Security
Chitchanok Chuengsatiansup and Markus Wagner, University of Adelaide
   Automatic Post-Quantum Cryptographic Code Generation and Optimization
Elette Boyle, IDC Herzliya, Israel
   Cheaper Private Set Intersection via Advances in "Silent OT"
Joseph Bonneau, New York University
   Zeroizing keys in secure messaging implementations
Yu Feng , University of California, Santa Barbara, Yuan Tian, University of Virginia
   Exploit Generation Using Reinforcement Learning

Software engineering and Programming Languages
Kelly Blincoe, University of Auckland
   Towards more inclusive software engineering practices to retain women in software engineering
Fredrik Kjolstad, Stanford University
   Sparse Tensor Algebra Compilation to Domain-Specific Architectures
Milos Gligoric, University of Texas at Austin
   Adaptive Regression Test Selection
Sarah E. Chasins, University of California, Berkeley
   If you break it, you fix it: Synthesizing program transformations so that library maintainers can make breaking changes

Systems
Adwait Jog, College of William & Mary
   Enabling Efficient Sharing of Emerging GPUs
Heiner Litz, University of California, Santa Cruz
   Software Prefetching Irregular Memory Access Patterns
Malte Schwarzkopf, Brown University
   Privacy-Compliant Web Services by Construction
Mehdi Saligane, University of Michigan
   Autonomous generation of Open Source Analog & Mixed Signal IC
Nathan Beckmann, Carnegie Mellon University
   Making Data Access Faster and Cheaper with Smarter Flash Caches
Yanjing Li, University of Chicago
   Resilient Accelerators for Deep Learning Training Tasks

Source: Google AI Blog

Announcing the Recipients of the 2020 Award for Inclusion Research

Posted by Negar Saei, Program Manager, Google Research

At Google, it is our ongoing goal to support faculty who are conducting innovative research that will have positive societal impact. As part of that goal, earlier this year we launched the Award for Inclusion Research program, a global program that supports academic research in computing and technology addressing the needs of underrepresented populations. The Award for Inclusion Research program allows faculty and Google researchers an opportunity to partner on their research initiatives and build new and constructive long-term relationships.

We received 100+ applications from over 100 universities, globally, and today we are excited to announce the 16 proposals chosen for funding, focused on an array of topics around diversity and inclusion, algorithmic bias, education innovation, health tools, accessibility, gender bias, AI for social good, security, and social justice. The proposals include 25 principal investigators who focus on making the community stronger through their research efforts.

Congratulations to announce this year’s recipients:

"Human Centred Technology Design for Social Justice in Africa"
Anicia Peters (University of Namibia) and Shaimaa Lazem (City for Scientific Research and Technological Applications, Egypt)

"Modern NLP for Regional and Dialectal Language Variants"
Antonios Anastasopoulos (George Mason University)

"Culturally Relevant Collaborative Health Tracking Tools for Motivating Heart-Healthy Behaviors Among African Americans"
Aqueasha Martin-Hammond (Indiana University - Purdue University Indianapolis) and Tanjala S. Purnell (Johns Hopkins University)

"Characterizing Energy Equity in the United States"
Destenie Nock and Constantine Samaras (Carnegie Mellon University)

"Developing a Dialogue System for a Culturally-Responsive Social Programmable Robot"
Erin Walker (University of Pittsburgh) and Leshell Hatley (Coppin State University)

"Eliminating Gender Bias in NLP Beyond English"
Hinrich Schuetze (LMU Munich)

"The Ability-Based Design Mobile Toolkit: Enabling Accessible Mobile Interactions through Advanced Sensing and Modeling"
Jacob O. Wobbrock (University of Washington)

"Mutual aid and community engagement: Community-based mechanisms against algorithmic bias"
Jasmine McNealy (University of Florida)

"Empowering Syrian Girls through Culturally Sensitive Mobile Technology and Media Literacy
Karen Elizabeth Fisher (University of Washington) and Yacine Ghamri-Doudane (University of La Rochelle)

"Broadening participation in data science through examining the health, social, and economic impacts of gentrification"
Latifa Jackson (Howard University) and Hasan Jackson (Howard University)

"Understanding How Peer and Near Peer Mentors co-Facilitating the Active Learning Process of Introductory Data Structures Within an Immersive Summer Experience Effected Rising Sophomore Computer Science Student Persistence and Preparedness for Careers in Silicon Valley"
Legand Burge (Howard University) and Marlon Mejias (University of North Carolina at Charlotte)

"Who is Most Likely to Advocate for this Case? A Machine Learning Approach"
Maria De-Arteaga (University of Texas at Austin)

"Contextual Rendering of Equations for Visually Impaired Persons"
Meenakshi Balakrishnan (Indian Institute of Technology Delhi, India) and Volker Sorge (University of Birmingham)

"Measuring the Cultural Competence of Computing Students and Faculty Nationwide to Improve Diversity, Equity, and Inclusion"
Nicki Washington (Duke University)

"Designing and Building Collaborative Tools for Mixed-Ability Programming Teams"
Steve Oney (University of Michigan)

"Iterative Design of a Black Studies Research Computing Initiative through `Flipped Research’"
Timothy Sherwood and Sharon Tettegah (University of California, Santa Barbara)