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.

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.

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.

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.

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.

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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