October 27th – 30th, 2020

AI for Social Good

Applying AI to help solve social, climate, and humanitarian challenges
Register

Data Science for Good Track

As data proliferates and becomes more freely available, the power of driving impact in social sector increases. 

  • See the many ways organizations are applying their data science infrastructure in the name of making the world a better place.
  • Learn through stories of success and failures, and core practices that are implemented by change makers in the social sector that can differ from industry and academia.
  • Get exposed to data science & Machine learning workflows and models being utilized steered towards causes like climate change, agriculture, socio-economic impacts, disaster management etc.

data-for-good-conference

You Will Meet

  • Principal Researchers 

  • AI for Good Activists

  • Program Designers

  • Nonprofit Professionals

  • Social Service Professionals

  • Government Agencies

Why Attend?

Network and connect with like minded attendees to discover non profits and volunteer opportunities.

Apply your data science skills to improve the lives of others.

Discover how you can more effectively harness and gain value from your data by solving real world problems.

Learn how to use the skills and tools of corporations & governments, to make a lasting impact on future generations.

Current DS for Social Good Speakers

DirectorIBM Research

Lisa Amini, PhD

Dr. Lisa Amini is the Director of IBM Research Cambridge, which is also home to the MIT-IBM Watson AI Lab, and of IBM’s AI Horizons Network. Lisa was previously Director of Knowledge & Reasoning Research in the Cognitive Computing group at IBM’s TJ Watson Research Center in New York, and she is also an IBM Distinguished Engineer. Lisa was the founding Director of IBM Research Ireland, and the first woman Lab Director for an IBM Research Global (i.e., non-US) Lab (2010-2013). In this role she developed the strategy and led researchers in advancing science and technology for intelligent urban and environmental systems (Smarter Cities), with a focus on creating analytics, optimizations, and systems for sustainable energy, constrained resources (e.g., urban water management), transportation, and the linked open data systems that assimilate and share data and models for these domains. She earned her PhD degree in Computer Science from Columbia University.

Advances and Frontiers in Auto AI & Machine Learning(Track Keynote)

Chief Strategy Officer, Professor of Computer ScienceTrifacta, Berkeley

Joe Hellerstein, PhD

Joseph M. Hellerstein is the Jim Gray Professor of Computer Science at the University of California, Berkeley, whose work focuses on data-centric systems and the way they drive computing. He is an ACM Fellow, an Alfred P. Sloan Research Fellow and the recipient of three ACM-SIGMOD “Test of Time” awards for his research. Fortune Magazine has included him in their list of 50 smartest people in technology , and MIT’s Technology Review magazine included his work on their TR10 list of the 10 technologies “most likely to change our world”. Hellerstein is the co-founder and Chief Strategy Officer of Trifacta, a software vendor providing intelligent interactive solutions to the messy problem of wrangling data. He has served on the technical advisory boards of a number of computing and Internet companies including Dell EMC, SurveyMonkey, Captricity, and Datometry, and previously served as the Director of Intel Research, Berkeley.

The State of Serverless and Applications to AI(Talk)

Advanced Analytics Solutions LeadSingleStone

Vida Williams

Vida Williams is a revered leader in the tech space. She currently serves as the Head of Data at SingleStone, and was recently named the company’s Chief Diversity Officer as an intentional approach towards mitigating bias in the Data and Technology industries. Vida is also a professor and Innovator in Resident at Virginia Commonwealth University, a gubernatorial appointee and co-chair of the Virginia Biotechnology Research Partnership Authority, and an impassioned activist for data privacy. She has contributed to TedX, TomTom Festival, The Frontier Partners, and countless client presentations. Vida also volunteers her data expertise to non-profits, offering free workshops and consulting. She leads organizations through exercises that help them to identify areas of improvement, new and better ways of showcasing their impact on the community, and how to use their data to foster growth.

Creating Equality and Inclusivity with Feature Engineering(Talk)

Executive DirectorHuman Rights Data Analysis Group

Megan Price, PhD

As the Executive Director of the Human Rights Data Analysis Group, Megan Price designs strategies and methods for statistical analysis of human rights data for projects in a variety of locations including Guatemala, Colombia, and Syria. Her work in Guatemala includes serving as the lead statistician on a project in which she analyzed documents from the National Police Archive; she has also contributed analyses submitted as evidence in two court cases in Guatemala. Her work in Syria includes serving as the lead statistician and author on three reports, commissioned by the Office of the United Nations High Commissioner of Human Rights (OHCHR), on documented deaths in that country. Megan is a member of the Technical Advisory Board for the Office of the Prosecutor at the International Criminal Court and a Research Fellow at the Carnegie Mellon University Center for Human Rights Science. She is the Human Rights Editor for the Statistical Journal of the International Association for Official Statistics (IAOS) and on the editorial board of Significance Magazine. She earned her doctorate in biostatistics and a Certificate in Human Rights from the Rollins School of Public Health at Emory University. She also holds a master of science degree and bachelor of science degree in Statistics from Case Western Reserve University.

Data Science: How Do We Achieve the Most Good and Least Harm?(Talk)

Machine Learning Researcher and Vanier Doctoral ScholarMila and McGill University

Charles Onu, PhD

Charles is a PhD candidate at McGill University and Mila – the Québec AI Institute. He is interested in the design and development of improved diagnostic and decision-making tools in healthcare. He leads the Ubenwa Health, a collaboration between researchers in Canada and Nigeria, developing low-cost, AI-powered mobile app for the diagnosis of perinatal asphyxia from the infant cry. At the Montreal Children’s Hospital, Charles is involved in the APEX project, where he has been developing machine learning algorithms for analyzing cardiorespiratory behaviour of preterm newborns in order to determine their readiness for extubation. He has worked with Health Experiences Research Canada contributing to the design and leading the development of the HERS mobile app – a personalized recommendation tool for breast cancer patients. Charles is a Jeanne Sauvé Fellow and a Vanier Doctoral Scholar.

Using Artificial Intelligence to Save Lives at Birth(Talk)

CEOAllen Institute for AI

Oren Etzioni, PhD

Dr. Oren Etzioni has served as the Chief Executive Officer of the Allen Institute for AI (AI2) since its inception in 2014. He has been a Professor at the University of Washington’s Computer Science department since 1991, and a Venture Partner at the Madrona Venture Group since 2000. He has garnered several awards including Seattle’s Geek of the Year (2013), the Robert Engelmore Memorial Award (2007), the IJCAI Distinguished Paper Award (2005), AAAI Fellow (2003), and a National Young Investigator Award (1993). He has been the founder or co-founder of several companies, including Farecast (sold to Microsoft in 2008) and Decide (sold to eBay in 2013). He has written commentary on AI for The New York Times, Nature, Wired, and the MIT Technology Review. He helped to pioneer meta-search (1994), online comparison shopping (1996), machine reading (2006), and Open Information Extraction (2007). He has authored over 100 technical papers that have garnered over 2,000 highly influential citations on Semantic Scholar. He received his Ph.D. from Carnegie Mellon in 1991 and his B.A. from Harvard in 1986.

Semantic Scholar and the Fight Against COVID-19(Talk)

Boeing Assistant Professor of Aeronautics and AstronauticsMIT

Julie A. Shah, PhD

Julie Shah is an Associate Professor in the Department of Aeronautics and Astronautics at MIT and leads the Interactive Robotics Group of the Computer Science and Artificial Intelligence Laboratory. Shah received her SB (2004) and SM (2006) from the Department of Aeronautics and Astronautics at MIT, and her PhD (2010) in Autonomous Systems from MIT. Before joining the faculty, she worked at Boeing Research and Technology on robotics applications for aerospace manufacturing. She has developed innovative methods for enabling fluid human-robot teamwork in time-critical, safety-critical domains, ranging from manufacturing to surgery to space exploration. Her group draws on expertise in artificial intelligence, human factors, and systems engineering to develop interactive robots that emulate the qualities of effective human team members to improve the efficiency of human-robot teamwork. In 2014, Shah was recognized with an NSF CAREER award for her work on “Human-aware Autonomy for Team-oriented Environments,” and by the MIT Technology Review TR35 list as one of the world’s top innovators under the age of 35. Her work on industrial human-robot collaboration was also recognized by the Technology Review as one of the 10 Breakthrough Technologies of 2013, and she has received international recognition in the form of best paper awards and nominations from the International Conference on Automated Planning and Scheduling, the American Institute of Aeronautics and Astronautics, the IEEE/ACM International Conference on Human-Robot Interaction, the International Symposium on Robotics, and the Human Factors and Ergonomics Society.

What to Expect When You Are Expecting Robots – The Future of Human-Robot Collaboration(Talk)

Director of Applied Statistics CenterColumbia University

Andrew Gelman, PhD

Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), and A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina). Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.

Bayesian Workflow as Demonstrated with a Coronavirus Example(Talk)

Chief Data Evangelist, Director of Data ScienceCisco Systems Inc.

Jennifer Redmon

Jennifer Redmon joined Cisco in 2009 and serves as its, and industry’s 1st, Chief Data & Analytics Evangelist as well as sits on Forbes’ Technology Council. Jennifer is a leader in developing data-driven cultures and organizational analytical maturity through qualitative and quantitative methods. Her approach to fostering a data-driven workforce is taught at multiple higher ed. institutions around the world. Jennifer is passionate about giving back and consequently, founded Cisco’s Data Science and AI for Good initiative. She leads the company’s award-winning Data Science and AI for Suicide Prevention Team, which, inspired by and in collaboration with WHO researchers, focus on de-stigmatizing mental illness through cultural change. Her authorship focuses on data- and analytics-driven cultures, mental health in the workplace, suicide prevention, as well as innovative applications of data science and AI including social good. Jennifer holds an international MBA from Duke University with a concentration in Strategy, Bachelor’s Degrees in Economics and Art History from UC Davis, is in Georgia Tech’s Master of Science in Analytics Spring 2021 class, and is a certified Suicide First Responder.

Data Science for Suicide Prevention(Talk)

Data Science ManagerCisco Data Science Lab

Dr. Annie Ying

Annie is a leader in data science with over a decade of industry experience spanning corporate research and startups. Currently, she is the head of the Cisco Data Science Lab in Vancouver. Coming from a research background with a PhD in Computer Science from McGill, Annie is a former Research Scientist at IBM T. J. Watson Research Center in New York and a two-time winner of an ACM Distinguished Paper Award (both in applying data science to software engineering) with five patents (granted and applied). Annie is active in the data science community as a Meetup organizer (Data Science for Social Good), speaker, and mentor.

Data Science for Suicide Prevention(Talk)

 

Previous DS for Social Good Speakers

ML Researcher, Adjunct ProfessorMicrosoft Research New England, Stanford University

Lester Mackey, PhD

Lester Mackey is a machine learning researcher at Microsoft Research, where he develops new tools, models, and theory for large-scale learning tasks driven by applications from healthcare, climate, recommender systems, and the social good. Lester moved to Microsoft from Stanford University, where he was an assistant professor of Statistics and (by courtesy) of Computer Science. He earned his PhD in Computer Science and MA in Statistics from UC Berkeley and his BSE in Computer Science from Princeton University. He co-organized the second-place team in the $1M Netflix Prize competition for collaborative filtering, won the $50K Prize4Life ALS disease progression prediction challenge, won prizes for temperature and precipitation forecasting in the yearlong real-time $800K Subseasonal Climate Forecast Rodeo, and received a best student paper award at the International Conference on Machine Learning.

Improving Subseasonal Forecasting in the Western U.S. with Machine Learning(Track Keynote)

AI ResearcherGoogle Research and Machine Intelligence Group

Margaret Mitchell, PhD

Margaret is a Senior Research Scientist in Google’s Research & Machine Intelligence group, working on artificial intelligence.

Her research generally involves vision-language and grounded language generation, focusing on how to evolve artificial intelligence towards positive goals. This includes research on helping computers to communicate based on what they can process, as well as projects to create assistive and clinical technology from the state of the art in AI.

Her work combines computer vision, natural language processing, social media, many statistical methods, and insights from cognitive science.

Best Selling Author"Weapons of Math Destruction"

Cathy O’Neil

Cathy O’Neil is an American mathematician and the author of the blog mathbabe.org and several books on data science, including Weapons of Math Destruction She was the former Director of the Lede Program in Data Practices at Columbia University Graduate School of Journalism‘s Tow Center and was employed as Data Science Consultant at Johnson Research Labs.

Professor, Best Selling AuthorUniversity at Albany, SUNY, "Automating Inequality"

Virginia Eubanks

Virginia Eubanks is an Associate Professor of Political Science at the University at Albany, SUNY. She is the author of Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor; Digital Dead End: Fighting for Social Justice in the Information Age; and co-editor, with Alethia Jones, of Ain’t Gonna Let Nobody Turn Me Around: Forty Years of Movement Building with Barbara Smith. Her writing about technology and social justice has appeared in Scientific American, The Nation, Harper’s, and Wired. For two decades, Eubanks has worked in community technology and economic justice movements. She was a founding member of the Our Data Bodies Project and a 2016-2017 Fellow at New America. She lives in Troy, NY.

Director, Co-founderUniversity of San Francisco Center for Applied Data Ethics, Fast.ai

Rachel Thomas

Rachel Thomas is director of the USF Center for Applied Data Ethics and co-founder of fast.ai, which has been featured in The Economist, MIT Tech Review, and Forbes. She was selected by Forbes as one of 20 Incredible Women in AI, earned her math PhD at Duke, and was an early engineer at Uber. Rachel is a popular writer and keynote speaker.

Former U.S. Chief Data Scientist, Head of TechnologyDevoted Health

DJ Patil

DJ Patil is perhaps the most influential data scientist in the world. Having been appointed by President Obama as the very first U.S. Chief Data Scientist, he was tasked with making the largest organization in history—the U.S. Federal Government—a data driven enterprise.

Working directly with the highest ranking officials in government, DJ’s efforts led to the establishment of nearly 40 Chief Data Officer roles across a vast array of departments and programs. Patil’s experience in national security initiatives is extensive, and for his efforts was awarded by Secretary Carter the Department of Defense Medal for Distinguished Public Service which the highest honor the department bestows on a civilian.

Professor, National Center Chair, Founding DirectorWarren Center for Network and Data Sciences, UPenn

Michael Kearns, PhD

Michael Kearns is a professor in the Computer and Information Science department at the University of Pennsylvania, where he holds the National Center Chair and has joint appointments in the Wharton School.He is founder of Penn’s Networked and Social Systems Engineering (NETS) program, and director of Penn’s Warren Center for Network and Data Sciences. Michael is also the co-author of the book The Ethical Algorithm that talks about the science of designing algorithms that embed social values like privacy and fairness. His research interests include topics in machine learning, algorithmic game theory, social networks, and computational finance. He has worked and consulted extensively in the technology and finance industries. He is a fellow of the American Academy of Arts and Sciences, the Association for Computing Machinery, and the Association for the Advancement of Artificial Intelligence.

Michael has worked extensively in quantitative and algorithmic trading on Wall Street (including at Lehman Brothers, Bank of America, and SAC Capital; see further details below). He often serve as an advisor to technology companies and venture capital firms. He is also involved in the seed-stage fund Founder Collective and occasionally invest in early-stage technology startups. Michael is also a member of the Scientific Advisory Board of the Alan Turing Institute, and of the Market Surveillance Advisory Group of FINRA.

Data ScientistFenix International

Brianna Schuyler, PhD

Brianna leads the data science team at Fenix International. Their work spans multiple countries, including the US, Uganda, Zambia, and Ivory Coast. She and the data team at Fenix work on a wide range of problems to help provide clean, safe, and sustainable energy to people living off the grid in Sub-Saharan Africa. She has a bachelor’s degree in Physics from Johns Hopkins University, a master’s degree in Physics from the University of Wisconsin – Madison, and a Ph.D. in Neuroscience from the University of Wisconsin – Madison. After years of particle physics and functional MRI analyses, she took a break from academia and served as a Peace Corps volunteer in Northern Uganda. She’s delighted to use her background in big data at the perfect crossroads of sustainable energy and energy access for underserved populations.

Postdoctoral Research FellowUniversity of Pennsylvania

David Rolnick, PhD

David Rolnick is an NSF Mathematical Sciences Postdoctoral Research Fellow at the University of Pennsylvania. His research focuses on the mathematical foundations of deep learning. David is co-founder of Climate Change AI, an organization dedicated to furthering applications of machine learning that meaningfully address the climate crisis.

Founder & Executive DirectorDataKind

Jake Porway, PhD

A pioneer of the Data for Good movement, Jake Porway is an expert in the field of data and technology. As Founder and Executive Director of DataKind, a global nonprofit dedicated to using data science and AI in the service of humanity, he’s worked alongside the nonprofit community to drive social change with the power of data science since 2011. Jake’s career spans more than a decade in the data science sector as a statistician and computer scientist. He’s worked as a data scientist for The New York Times R&D Lab and was a research scientist studying machine learning and probabilistic modeling tasks with NASA, the Office of Naval Research, and other government agencies. A PopTech Social Innovation Fellow and a National Geographic Emerging Explorer, Jake was also noted as one of LinkedIn’s Next Wave Top Professionals 35 & Under, and his efforts have led to DataKind being named one of Fast Company’s Top 10 Most Innovative Nonprofits. Jake holds a B.S. in Computer Science from Columbia University and an M.S. and Ph.D. in Statistics from UCLA.


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