Tag Archives: University Relations

Announcing the 2021 Research Scholar Program Recipients

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

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)

Source: Google AI Blog


Announcing the Recipients of the 2020 Award for Inclusion 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)

Source: Google AI Blog


Announcing the 2020 Google PhD Fellows

Google created the PhD Fellowship Program in 2009 to recognize and support outstanding graduate students who seek to influence the future of technology by pursuing exceptional research in computer science and related fields. Now in its twelfth year, these Fellowships have helped support approximately 500 graduate students globally in North America and Europe, Africa, Australia, East Asia, and India.

It is our ongoing goal to continue to support the academic community as a whole, and these Fellows as they make their mark on the world. We congratulate all of this year’s awardees!

Algorithms, Optimizations and Markets
Jan van den Brand, KTH Royal Institute of Technology
Mahsa Derakhshan, University of Maryland, College Park
Sidhanth Mohanty, University of California, Berkeley

Computational Neuroscience
Connor Brennan, University of Pennsylvania

Human Computer Interaction
Abdelkareem Bedri, Carnegie Mellon University
Brendan David-John, University of Florida
Hiromu Yakura, University of Tsukuba
Manaswi Saha, University of Washington
Muratcan Cicek, University of California, Santa Cruz
Prashan Madumal, University of Melbourne

Machine Learning
Alon Brutzkus, Tel Aviv University
Chin-Wei Huang, Universite de Montreal
Eli Sherman, Johns Hopkins University
Esther Rolf, University of California, Berkeley
Imke Mayer, Fondation Sciences Mathématique de Paris
Jean Michel Sarr, Cheikh Anta Diop University
Lei Bai, University of New South Wales
Nontawat Charoenphakdee, The University of Tokyo
Preetum Nakkiran, Harvard University
Sravanti Addepalli, Indian Institute of Science
Taesik Gong, Korea Advanced Institute of Science and Technology
Vihari Piratla, Indian Institute of Technology - Bombay
Vishakha Patil, Indian Institute of Science
Wilson Tsakane Mongwe, University of Johannesburg
Xinshi Chen, Georgia Institute of Technology
Yadan Luo, University of Queensland

Machine Perception, Speech Technology and Computer Vision
Benjamin van Niekerk, University of Stellenbosch
Eric Heiden, University of Southern California
Gyeongsik Moon, Seoul National University
Hou-Ning Hu, National Tsing Hua University
Nan Wu, New York University
Shaoshuai Shi, The Chinese University of Hong Kong
Yaman Kumar, Indraprastha Institute of Information Technology - Delhi
Yifan Liu, University of Adelaide
Yu Wu, University of Technology Sydney
Zhengqi Li, Cornell University

Mobile Computing
Xiaofan Zhang, University of Illinois at Urbana-Champaign

Natural Language Processing
Anjalie Field, Carnegie Mellon University
Mingda Chen, Toyota Technological Institute at Chicago
Shang-Yu Su, National Taiwan University
Yanai Elazar, Bar-Ilan

Privacy and Security
Julien Gamba, Universidad Carlos III de Madrid
Shuwen Deng, Yale University
Yunusa Simpa Abdulsalm, Mohammed VI Polytechnic University

Programming Technology and Software Engineering
Adriana Sejfia, University of Southern California
John Cyphert, University of Wisconsin-Madison

Quantum Computing
Amira Abbas, University of KwaZulu-Natal
Mozafari Ghoraba Fereshte, EPFL

Structured Data and Database Management
Yanqing Peng, University of Utah

Systems and Networking
Huynh Nguyen Van, University of Technology Sydney
Michael Sammler, Saarland University, MPI-SWS
Sihang Liu, University of Virginia
Yun-Zhan Cai, National Cheng Kung University

Source: Google AI Blog


Exploring New Ways to Support Faculty Research



For the past 15 years, the Google Faculty Research Award Program has helped support world-class technical research in computer science, engineering, and related fields, funding over 2000 academics at ~400 Universities in 50+ countries since its inception. As Google Research continues to evolve, we continually explore new ways to improve our support of the broader research community, specifically on how to support new faculty while also strengthening our existing collaborations .

To achieve this goal, we are introducing two new programs aimed at diversifying our support across a larger community. Moving forward, these programs will replace the Faculty Research Award program, allowing us to better engage with, and support, up-and-coming researchers:

The Research Scholar Program supports early-career faculty (those who have received their doctorate within the past 7 years) who are doing impactful research in fields relevant to Google, and is intended to help to develop new collaborations and encourage long term relationships. This program will be open for applications in Fall 2020, and we encourage submissions from faculty at universities around the world.

We will also be piloting the Award for Inclusion Research Program, which will recognize and support research that addresses the needs of historically underrepresented populations. This Summer we will invite faculty—both directly and via their institutions—to submit their research proposals for consideration later this year, and we will notify award recipients by year's end.

These programs will complement our existing support of academic research around the world, including the Latin America Research Awards, the PhD Fellowship Program, the Visiting Researcher Program and research grant funding. To explore other ways we are supporting the research community, please visit this page. As always, we encourage faculty to review our publication database for overlapping research interests for collaboration opportunities, and apply to the above programs. We look forward to working with you!

Source: Google AI Blog


Announcing the 2019 Google Faculty Research Award Recipients



In Fall 2019, we opened our annual call for the Google Faculty Research Awards, a program focused on supporting the world-class technical research in Computer Science, Engineering and related fields performed at academic institutions around the world. These awards give Google researchers the opportunity to partner with faculty who are doing impactful research, additionally covering tuition for a student.

This year we received 917 proposals from ~50 countries and over 330 universities, and had the opportunity to increase our investment in several research areas related to Health, Accessibility, AI for Social Good, and ML Fairness. All proposals went through an extensive review process involving 1100 expert reviewers across Google who assessed the proposals on merit, innovation, connection to Google’s products/services and alignment with our overall research philosophy.

As a result of these reviews, Google is funding 150 promising proposals across a wide range of research areas, from Machine Learning, Systems, Human Computer Interaction and many more, with 26% of the funding awarded to universities outside the United States. Additionally, 27% of our recipients this year identified as a historically underrepresented group within technology. This is just the beginning of a larger investment in underrepresented communities and we are looking forward to sharing our 2020 initiatives soon.

Congratulations to the well-deserving recipients of this round's awards. More information on our faculty funding programs can be found on our website.

Source: Google AI Blog


Announcement of the 2019 Fellowship Awardees and Highlights from the Google PhD Fellowship Summit



In 2009, Google created the PhD Fellowship Program to recognize and support outstanding graduate students who are doing exceptional research in Computer Science and related fields who seek to influence the future of technology. Now in its eleventh year, these Fellowships have helped support 450 graduate students globally in North America and Europe, Australia, Asia, Africa and India.

Every year, recipients of the Fellowship are invited to a global summit at our Mountain View campus, where they can learn more about Google’s state-of-the-art research, and network with Google’s research community as well as other PhD Fellows from around the world. Below we share some highlights from our most recent summit, and also announce the latest class of Google PhD Fellows.

Summit Highlights
At this year’s summit event, active Google Fellowship recipients were joined by special guests, FLIP (Diversifying Future Leadership in the Professoriate) Alliance Fellows. Research Director Peter Norvig opened the event with a keynote on the fundamental practice of machine learning, followed by a number of talks by prestigious researchers. Among the list of speakers were Research Scientist Peggy Chi, who spoke about crowdsourcing geographically diverse images for use in training data, Senior Google Fellow and SVP of Google Research and Health Jeff Dean, who discussed using deep learning to solve a variety of challenging research problems at Google, and Research Scientist Vinodkumar Prabhakaran, who presented the ethical implications of machine learning, especially around questions of fairness and accountability. See the complete list of insightful talks delivered by all speakers here.
Google and FLIP Alliance Fellows attending the 2019 PhD Fellowship Summit
Google Fellows had the opportunity to present their work in lightning talks to small groups with common research interests. In addition, Google and FLIP Alliance Fellows came together to share their work with Google researchers and each other during a poster session.
Poster session in full swing
2019 Google PhD Fellows
The Google PhD Fellows represent some of the best and brightest young computer science researchers from around the globe, and it is our ongoing goal to support them as they make their mark on the world. Congratulations to all of this year’s awardees! The complete list of recipients is:

Algorithms, Optimizations and Markets
Aidasadat Mousavifar, EPFL Ecole Polytechnique Fédérale de Lausanne
Peilin Zhong, Columbia University
Siddharth Bhandari, Tata Institute of Fundamental Research
Soheil Behnezhad, University of Maryland at College Park
Zhe Feng, Harvard University

Computational Neuroscience
Caroline Haimerl, New York University
Mai Gamal, Nile University

Human Computer Interaction
Catalin Voss, Stanford University
Hua Hua, Australian National University
Zhanna Sarsenbayeva, University of Melbourne

Machine Learning
Abdulsalam Ometere Latifat, African University of Science and Technology Abuja
Adji Bousso Dieng, Columbia University
Blake Woodworth, Toyota Technological Institute at Chicago
Diana Cai, Princeton University
Francesco Locatello, ETH Zurich
Ihsane Gryech, International University Of Rabat, Morocco
Jaemin Yoo, Seoul National University
Maruan Al-Shedivat, Carnegie Mellon University
Ousseynou Mbaye, Alioune Diop University of Bambey
Redani Mbuvha, University of Johannesburg
Shibani Santurkar, Massachusetts Institute of Technology
Takashi Ishida, University of Tokyo

Machine Perception, Speech Technology and Computer Vision
Anshul Mittal, IIT Delhi
Chenxi Liu, Johns Hopkins University
Kayode Kolawole Olaleye, Stellenbosch University
Ruohan Gao, The University of Texas at Austin
Tiancheng Sun, University of California San Diego
Xuanyi Dong, University of Technology Sydney
Yu Liu, Chinese University of Hong Kong
Zhi Tian, University of Adelaide

Mobile Computing
Naoki Kimura, University of Tokyo

Natural Language Processing
Abigail See, Stanford University
Ananya Sai B, IIT Madras
Byeongchang Kim, Seoul National University
Daniel Patrick Fried, UC Berkeley
Hao Peng, University of Washington
Reinald Kim Amplayo, University of Edinburgh
Sungjoon Park, Korea Advanced Institute of Science and Technology

Privacy and Security
Ajith Suresh, Indian Institute of Science
Itsaka Rakotonirina, Inria Nancy
Milad Nasr, University of Massachusetts Amherst
Sarah Ann Scheffler, Boston University

Programming Technology and Software Engineering
Caroline Lemieux, UC Berkeley
Conrad Watt, University of Cambridge
Umang Mathur, University of Illinois at Urbana-Champaign

Quantum Computing
Amy Greene, Massachusetts Institute of Technology
Leonard Wossnig, University College London
Yuan Su, University of Maryland at College Park

Structured Data and Database Management
Amir Gilad, Tel Aviv University
Nofar Carmeli, Technion
Zhuoyue Zhao, University of Utah

Systems and Networking
Chinmay Kulkarni, University of Utah
Nicolai Oswald, University of Edinburgh
Saksham Agarwal, Cornell University

Source: Google AI Blog


A Summary of the Google Flood Forecasting Meets Machine Learning Workshop



Recently, we hosted the Google Flood Forecasting Meets Machine Learning workshop in our Tel Aviv office, which brought hydrology and machine learning experts from Google and the broader research community to discuss existing efforts in this space, build a common vocabulary between these groups, and catalyze promising collaborations. In line with our belief that machine learning has the potential to significantly improve flood forecasting efforts and help the hundreds of millions of people affected by floods every year, this workshop discussed improving flood forecasting by aggregating and sharing large data sets, automating calibration and modeling processes, and applying modern statistical and machine learning tools to the problem.

Panel on challenges and opportunities in flood forecasting, featuring (from left to right): Prof. Paolo Burlando (ETH Zürich), Dr. Tyler Erickson (Google Earth Engine), Dr. Peter Salamon (Joint Research Centre) and Prof. Dawei Han (University of Bristol).
The event was kicked off by Google's Yossi Matias, who discussed recent machine learning work and its potential relevance for flood forecasting, crisis response and AI for Social Good, followed by two introductory sessions aimed at bridging some of the knowledge gap between the two fields - introduction to hydrology for computer scientists by Prof. Peter Molnar of ETH Zürich, and introduction to machine learning for hydrologists by Prof. Yishay Mansour of Tel Aviv University and Google

Included in the 2-day event was a wide range of fascinating talks and posters across the flood forecasting landscape, from both hydrologic and machine learning points of view.

An overview of research areas in flood forecasting addressed in the workshop.
Presentations from the research community included:
Alongside these talks, we presented the various efforts across Google to try and improve flood forecasting and foster collaborations in the field, including:
Additionally, at this workshop we piloted an experimental "ML Consultation" panel, where Googlers Gal Elidan, Sasha Goldshtein and Doron Kukliansky gave advice on how to best use machine learning in several hydrology-related tasks. Finally, we concluded the workshop with a moderated panel on the greatest challenges and opportunities in flood forecasting, with hydrology experts Prof. Paolo Burlando of ETH Zürich, Prof. Dawei Han of the University of Bristol, Dr. Peter Salamon of the Joint Research Centre and Dr. Tyler Erickson of Google Earth Engine.
Flood forecasting is an incredibly important and challenging task that is one part of our larger AI for Social Good efforts. We believe that effective global-scale solutions can be achieved by combining modern techniques with the domain expertise already existing in the field. The workshop was a great first step towards creating much-needed understanding, communication and collaboration between the flood forecasting community and the machine learning community, and we look forward to our continued engagement with the broad research community to tackle this challenge.

Acknowledgements
We would like to thank Avinatan Hassidim, Carla Bromberg, Doron Kukliansky, Efrat Morin, Gal Elidan, Guy Shalev, Jennifer Ye, Nadav Rabani and Sasha Goldshtein for their contributions to making this workshop happen.

Source: Google AI Blog


Google Faculty Research Awards 2018



We just completed another round of the Google Faculty Research Awards, our annual open call for proposals on computer science and related topics, such as quantum computing, machine learning, algorithms and theory, natural language processing and more. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.

This round we received 910 proposals covering 40 countries and over 320 universities. After expert reviews and committee discussions, we decided to fund 158 projects. The subject areas that received the most support this year were human computer interaction, machine learning, machine perception, and systems.

Congratulations to the well-deserving recipients of this round's awards. More information on how to apply for the next round will be available at the end of the summer on our website. You can find award recipients from previous years here.

Source: Google AI Blog


Google’s Workshop on AI/ML Research and Practice in India



Last month, Google Bangalore hosted the Workshop on Artificial Intelligence and Machine Learning, with the goal of fostering collaboration between the academic and industry research communities in India. This forum was designed to exchange current research and industry projects in AI & ML, and included faculty and researchers from Indian Institutes of Technology (IITs) and other leading universities in India, along with industry practitioners from Amazon, Delhivery, Flipkart, LinkedIn, Myntra, Microsoft, Ola and many more. Participants spoke on the ongoing research and work being undertaken in India in deep learning, computer vision, natural language processing, systems and generative models (you can access all the presentations from the workshop here).

Google’s Jeff Dean and Prabhakar Raghavan kicked off the workshop by sharing Google’s uses of deep learning to solve challenging problems and reinventing productivity using AI. Additional keynotes were delivered by Googlers Rajen Sheth and Roberto Bayardo. We also hosted a panel discussion on the challenges and future of AI/ML ecosystem in India, moderated by Google Bangalore’s Pankaj Gupta. Panel participants included Anirban Dasgupta (IIT Gandhinagar), Chiranjib Bhattacharyya of the Indian Institute of Science (IISc), Ashish Tendulkar and Srinivas Raaghav (Google India) and Shourya Roy (American Express Big Data Labs).
Prabhakar Raghavan’s keynote address
Sessions
The workshop agenda included five broad sessions with presentations by attendees in the following areas:
Pankaj Gupta moderating the panel discussion
Summary and Next Steps
As in many countries around the world, we are seeing increased dialog on various aspects of AI and ML in multiple contexts in India. This workshop hosted 80 attendees representing 9 universities and 36 companies contributing 28 excellent talks, with many opportunities for discussing challenges and opportunities for AI/ML in India. Google will continue to foster this exchange of ideas across a diverse set of folks and applications. As part of this, we also announced the upcoming research awards round (applications due June 4) to support up to seven faculty members in India on their AI/ML research, and new work on an accelerator program for Indian entrepreneurs focused primarily on AI/ML technologies. Please keep an eye out for more information about these programs.