
Featured Speakers

Ford International Professor of the Social Sciences in the Department of Political Science; Director of the Institute for Data, Systems, and Society (IDSS)
Fotini Christia
Moderator
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Bio: Her research had focused on issues of conflict and cooperation in the Muslim world, and she has conducted fieldwork in Afghanistan, Bosnia, Iraq, Iran, the Palestinian Territories, Syria, and Yemen. She is currently working to bridge the social sciences, data science, and computation by bringing researchers from these disciplines together to address systemic racism across housing, healthcare, policing, and social media. She also has a new line of research that examines how to effectively integrate AI tools in public policy.
Fotini is the author of “Alliance Formation in Civil War” (Cambridge University Press, 2012), which was awarded the Luebbert Award for Best Book in Comparative Politics, the Lepgold Prize for Best Book in International Relations, and a Distinguished Book Award from the International Studies Association. She is co-editor with Graeme Blair (UCLA) and Jeremy Weinstein (Stanford) of “Crime, Insecurity, and Community Policing: Experiments on Building Trust”, forthcoming with Cambridge University Press (2024). Her research has also appeared in Science, Nature Human Behavior, Review of Economic Studies, NeurIPs, Communications Medicine, IEEE Transactions on Network Science and Engineering, American Political Science Review, and Annual Review of Political Science among other journals. Her opinion pieces have been published in Foreign Affairs, The New York Times, The Washington Post, and the Boston Globe among other outlets. Fotini graduated magna cum laude from Columbia University in 2001 with a joint BA in Economics–Operations Research and an MA in International Affairs. She joined the MIT faculty in July 2008 after receiving her PhD in Public Policy from Harvard University that year.

Professor in the Electrical Engineering and Computer Science Department at MIT
Vivienne Sze
Keynote Speaker
Talk Title: Efficient Computing for AI and Robotic
Abstract: The compute demands of AI and robotics continue to rise due to the rapidly growing volume of data to be processed; the increasingly complex algorithms for higher quality of results; and the demands for energy efficiency and real-time performance. In this talk, we will discuss the design of efficient tailored hardware accelerators and the co-design of algorithms and hardware that reduce the energy consumption while delivering swift real-time and robust performance for applications including deep neural networks, data analytics with sparse tensor algebra, and autonomous navigation.
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Bio: Vivienne Sze is Professor in the Electrical Engineering and Computer Science Department at MIT. She works on computing systems that enable energy-efficient machine learning, computer vision, and video compression/processing for a wide range of applications, including autonomous navigation, digital health, and the internet of things. She is widely recognized for her leading work in these areas and has received awards, including faculty awards from Google, Facebook, and Qualcomm, the Symposium on VLSI Circuits Best Student Paper Award, the IEEE Custom Integrated Circuits Conference Outstanding Invited Paper Award, and the IEEE Micro Top Picks Award. As a member of the Joint Collaborative Team on Video Coding, she received the Primetime Engineering Emmy Award for the development of the High-Efficiency Video Coding video compression standard. She is a co-editor of High Efficiency Video Coding (HEVC): Algorithms and Architectures (Springer, 2014) and co-author of Efficient Processing of Deep Neural Networks (Synthesis Lectures on Computer Architecture, Morgan Claypool, 2020). For more information about Prof. Sze’s research, please visit http://sze.mit.edu.

Homer A. Burnell Career Development Professor in the MIT Faculty of Artificial Intelligence and Decision-Making
Sara Beery
Morning Panel 1: Sustainable Computing: Climate
Dr. Sara Beery is the Homer A. Burnell Career Development Professor in the MIT Faculty of Artificial Intelligence and Decision-Making. She was previously a visiting researcher at Google, working on large-scale urban forest monitoring as part of the Auto Arborist project. She received her PhD in Computing and Mathematical Sciences at Caltech in 2022, where she was advised by Pietro Perona and awarded the Amori Doctoral Prize for her thesis. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including geospatial and temporal domain shift, learning from imperfect data, fine-grained categories, and long-tailed distributions. She partners with industry, nongovernmental organizations, and government agencies to deploy her methods in the wild worldwide. She works toward increasing the diversity and accessibility of academic research in artificial intelligence through interdisciplinary capacity building and education, and has founded the AI for Conservation slack community, serves as the Biodiversity Community Lead for Climate Change AI, founded and directs the Workshop on Computer Vision Methods for Ecology, and co-leads the NSF Global Climate Center on AI and Biodiversity Change.

Abigail Bonder
Morning Panel 1: Sustainable Computing: Climate
Abigail's research spans climate, physical oceanography, geophysical fluid dynamics, and turbulence. She investigates, quantifies, and parameterizes multi-scale turbulent interactions in the upper ocean, which play an important role in ocean-atmosphere interactions, yet are on scales much smaller than the grid used in climate models, even at the highest possible resolution. She aims towards a comprehensive understanding of these interactions and thus uses a combination of theory, high-resolution idealized simulations, fully complex climate models, and data driven methods to isolate individual processes and understand their subgrid physical effects on the climate system.

Research Associate at BU Center on Forced Displacement
Rana Hussein
Panelist on: Data Science for Humanitarian Work in Conflict Zones
Rana Hussein earned her B.A. in mathematics and computer science at Boston University in 2022. She is currently a research associate at the Center on Forced Displacement at Boston University, an interdisciplinary research center which engages with the topic of forced displacement through research and interventions aiming the improve the lives of those affected by it, including refugees, internally displaced people, and people affected by statelessness. At the Center, she works to apply her background in data analysis to a number of projects aiming to better assess and understand the health of vulnerable and displaced communities as well as contribute to other interdisciplinary collaborations. She has worked on a number of research projects in collaboration with UNICEF where she applied computational modeling techniques to assess and more accurately predict child malnutrition rates in Yemen to guide resource allocation and program planning. Rana also led the planning of a workshop held in collaboration with the National Academies of Sciences, Engineering, and Medicine last spring about incorporating the topic of forced displacement into undergraduate STEM education, which culminated in the development of a biomedical engineering course offered at Boston University (and three other institutions in various STEM fields) which exposes students to the topic and aims to instill in them a desire to engage with it and other similarly complex issues using their engineering education, equips them with the necessary moral and ethical frameworks and encourages them to think in interdisciplinary ways. Currently, she is working on a project in collaboration with a local NGO in Pakistan which aims to assess the health risks of stateless communities in Karachi and the impacts of legal barriers to citizenship on child health and well-being.

Loretta Mickley
Morning Panel 1: Sustainable Computing: Climate
Loretta Mickley co-leads the Atmospheric Chemistry Modeling Group at Harvard. Her research focuses on chemistry-climate interactions in the troposphere. Key topics of her research include:
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Impacts of wildfire smoke on human health and regional climate.
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Effects of climate change on air quality and implications for human health.
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Influence of aerosol trends on regional climate.
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Oxidation capacity and fire activity in preindustrial and paleo atmospheres.

Master's in Data Science Student at Harvard University
Nishtha Sardana
Moderator on: Responsible AI: Democratization of AI tools & AI for sustainability
Nishtha Sardana is currently pursuing a Master's degree in Data Science at Harvard University. Her research interests lie at the intersection of climate change and machine learning, as well as in the impact of exposure to air pollution. She received her Bachelor's degree in Mathematics and Computing Engineering. Before joining Harvard, Nishtha worked as a Product Manager at Microsoft.

Research Fellow at McKinsey & Company
Daphne Joseph
Panelist on: Data Science for Humanitarian Work in Conflict Zones
Daphne Joseph is a highly accomplished Research Fellow at McKinsey & Company, specializing in human rights and humanitarian issues. In her current role, she provides invaluable support to both public and private sector clients, offering insights and expertise on a diverse range of topics. Daphne's work primarily involves collaborating with global foundations, NGOs, non-profit organizations, and government entities to address pressing societal challenges. One of Daphne's key contributions is her provision of crucial philanthropic and economic development data. She meticulously gathers and analyzes data related to various issues, such as poverty reduction for children, access to education for primary students, and broadband and internet access for immigrants and asylum seekers. By leveraging her expertise in these areas, Daphne helps inform decision-making processes and shape impactful strategies. Daphne's academic background further strengthens her ability to tackle complex social issues. She completed her Master of Arts in Human Rights Studies at Columbia University Graduate School of the Arts and Sciences. During her time at Columbia, she conducted comprehensive mixed-methods research on the effects of temporary policies for Haitian immigrants who sought refuge following the political unrest triggered by the devastating 2010 earthquake. This research allowed her to gain a deep understanding of the challenges faced by displaced individuals and the potential solutions that can be implemented to address their needs effectively. Prior to her graduate studies, Daphne earned a Bachelor of Arts in Sociology from Spelman College. Her undergraduate research focused on the experiences of Black Caribbean immigrants who relocated to the United States due to conflicts in their home countries. Through this research, she shed light on the unique struggles and triumphs of this specific migrant population, contributing to a broader understanding of the complexities surrounding migration and displacement. Daphne's career is driven by her unwavering commitment to comprehending the experiences of Black and Brown migrants escaping conflict zones. She employs a holistic approach, utilizing both qualitative and quantitative data and research methods. By combining these approaches, she is able to gain nuanced insights into the challenges faced by marginalized communities and develop sustainable, long-term solutions. In her pursuit of impactful change, Daphne actively collaborates with stakeholders from the public, private, and philanthropic sectors. By leveraging their collective resources and expertise, she strives to create a lasting positive impact on the lives of vulnerable populations.

Assistant Professor and the Silverman (1968) Family Career Development Professor at MIT EECS and LIDS.
Priya L. Donti
Panelist on: Responsible AI: Democratization of AI tools & AI for sustainability
Priya Donti is an Assistant Professor and the Silverman (1968) Family Career Development Professor at MIT EECS and LIDS. Her research focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Methodologically, this entails exploring ways to incorporate relevant physics, hard constraints, and decision-making procedures into deep learning workflows. Priya is also the co-founder and Chair of Climate Change AI, a global nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning. Priya received her Ph.D. in Computer Science and Public Policy from Carnegie Mellon University, and is a recipient of the MIT Technology Review’s 2021 “35 Innovators Under 35” award, the ACM SIGEnergy Doctoral Dissertation Award, the Siebel Scholarship, the U.S. Department of Energy Computational Science Graduate Fellowship, and best paper awards at ICML (honorable mention), ACM e-Energy (runner-up), PECI, the Duke Energy Data Analytics Symposium, and the NeurIPS workshop on AI for Social Good.

Senior Product Manager working on Responsible AI @ Microsoft
Minsoo Thigpen
Panelist on: Responsible AI: Democratization of AI tools & AI for sustainability
Minsoo is a Senior Product Manager working on Responsible AI tools both in the open source and in Microsoft's Azure AI platform to ensure the next generation of AI-based experiences are built with quality and safety in mind, testable and measurable. Coming from an interdisciplinary background with experience in building ML models and applications, analyzing data, and designing UX, she is always looking to work in the intersection of AI/ML, design, and social sciences to empower data and machine learning practitioners to work ethically and responsibly end-to-end.

Associate Partner with QuantumBlack, AI by McKinsey
Devon Chen
Panelist on: Responsible AI: Democratization of AI tools & AI for sustainability
Devon Chen is an Associate Partner with QuantumBlack, AI by McKinsey. She leads AI, data, and digital consulting engagements primarily in the P&C and Life and Annuities insurance space. Devon began her career as a data scientist at a boutique consultancy, Analytics Operations Engineering, which was acquired by McKinsey. At McKinsey, she serves clients launching or scaling analytics transformations, rethinking their digital and analytics strategies, and exploring applications of new technologies and capabilities. Devon graduated from Princeton with a bachelor’s degree in Operations Research & Financial Engineering.