Mosharaf Chowdhury

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I am a computer scientist and an associate professor at CSE Michigan, where I lead the SymbioticLab (https://symbioticlab.org/). My research improves application performance and system efficiency of AI/ML and Big Data workloads with a recent focus on optimizing energy consumption and data privacy. I lead the ML Energy initiative (https://ml.energy/), a consortium of researchers focusing on understanding, controlling, and reducing AI/ML energy consumption. Over the course of my career, I have worked on a variety of networked and distributed systems. Recent major projects include Infiniswap, the first scalable memory disaggregation solution; Salus, the first software-only GPU sharing system for deep learning; FedScale, a scalable federated learning and analytics platform; and Zeus, the first GPU energy optimizer for AI. In the past, I invented the coflow abstraction for efficient distributed communication, and I am one of the original creators of Apache Spark. Thanks to my excellent collaborators, I have received many individual awards, fellowships, and paper awards from top venues like NSDI, OSDI, ATC, and MICRO.

Cyrus Omar

Cyrus Omar

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I lead the Future of Programming Lab (FP Lab), where we design modern user interfaces for modern programming languages. Much of how we program today is rooted in tools designed 40+ years ago, e.g. how we enter code (using simple text editing, which leads to profligate parse errors), how we validate code (using tests or impoverished type systems), how we explore code (in a slow, batched, textual manner), how we communicate change (by throwing away the edits we performed and forcing diff algorithms to guess what we did), and so on. My lab develops new programming language and editor mechanisms, starting from theoretical foundations in mathematics and building up to human interfaces.

Integrating live GUIs into programs with holes

Integrating live GUIs into programs with holes

Merve Hickok

Merve Hickok

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Merve will strengthen MIDAS effort to develop best practices and training for the responsible use of data and AI in academic research, strengthen large U-M grant proposals’ responsible data and AI components, and provide insights on AI policy and regulatory priorities to help bridge research with applied work. 

Merve Hickok is the founder of AIethicist.org. She is a globally renowned expert on AI policy, ethics and governance. Her contributions and perspective have featured in the Guardian, CNN, Forbes, Bloomberg, Wired, Scientific American, Politico, Protocol, Vox, The Economist and S&P. Her research, training and consulting work focuses on the impact of AI systems on individuals, society, public and private organizations – with a particular focus on fundamental rights, democratic values, and social justice. She provides consultancy to C-suite leaders, and training services to public and private organizations on Responsible AI development, due diligence and governance. She also teaches data ethics at University of Michigan, and serves as a Board member in multiple organizations.

Merve is the President and Research Director at Center for AI & Digital Policy, deeply engaged in global AI policy and regulatory work. The Center educates AI policy practitioners and advocates across 60+ countries, advises international organizations (such as European Commission, UNESCO, the Council of Europe, OECD).

Merve has provided testimony to the US Congress, State of California Civil Rights Office, New York City Department of Consumer and Worker Protection, Detroit City Council, and many global organizations interested in AI policy and ethics. 

Merve also works with several non-profit organizations globally to advance both the academic and professional research in this field for underrepresented groups. She has been recognized by a number of organizations – most recently as one of the 100 Brilliant Women in AI Ethics™ – 2021, and as Runner-up for Responsible AI Leader of the Year – 2022 (Women in AI).

Previously, Merve held various senior roles in Fortune 100 companies for more than 15 years.

Nishil Talati

Nishil Talati

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I am a research faculty at the Computer Science and Engineering department at University of Michigan. I work with a group of talented PhD students on computer architecture, compiler techniques, and software engineering. My group focuses on developing novel software and hardware solutions to optimize large-scale data intensive worklods (e.g., graph traversals).

I earned my PhD degree in CSE from University of Michigan, Ann Arbor, USA, master’s degree in EE from Technion – Israel Institute of Technology, Haifa, Israel, and an undergraduate degree in EEE from BITS Pilani, Goa Campus, Goa, India.

Max Li

Max Li

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Max’s research interests lies in the design, management, and optimization of large-scale infrastructure systems, focusing on the air transportation system and emerging aerial mobility systems. He is interested in the application of methods applicable to networked systems, especially with resource constraints (e.g., airspace and airport capacity), diverse stakeholders (e.g., passenger-centric, airline-centric), and complex dynamics (e.g., changing temporal behaviors). Max has worked on a variety of data-driven problems related to analyzing flight delays across airport networks, strategic/tactical air traffic management and delay assignments, privacy and routing in drone-based applications, and uncertainty-aware traffic management. He is interested in methods such as graph signal processing and signal processing over non-Euclidean domains, data-driven optimization, mixed-integer/integer/combinatorial programs, resilient network design, and stochastic optimization. Broadly, Max hopes to contribute to a safe, resilient, and efficient air transportation system (inclusive of intra- and inter-city modalities) within the context of a passenger’s (or cargo’s) door-to-door journey.

A flight delay assignment mechanism that does not rely on knowing the exact value of an airline's private per-flight valuation of flight delays. The mechanism requires the system capacities/demand per round, and computes an initial solution. This solution is adjusted by a Coordinating Airline using privatized information from Participating Airlines. The resultant solution is proposed, and any negative public delays incurred is recorded to a ledger. The role of Coordianting/Participating Airlines then rotate depending on the current ledger balance.

Image: A flight delay assignment mechanism that does not rely on knowing the exact value of an airline’s private per-flight valuation of flight delays. The mechanism requires the system capacities/demand per round, and computes an initial solution. This solution is adjusted by a Coordinating Airline using privatized information from Participating Airlines. The resultant solution is proposed, and any negative public delays incurred is recorded to a ledger. The role of Coordinating/Participating Airlines then rotate depending on the current ledger balance.

What is the most significant scientific contribution you would like to make?

Working with air traffic controllers and traffic flow managers to deploy/prototype a congestion management solution/model!

What makes you excited about your data science and AI research?

The potential positive impact on the air transportation system, and helping to make it more efficient and equitable in terms of access.


Accomplishments and Awards

Matthew VanEseltine

Matthew VanEseltine

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Dr. VanEseltine is a sociologist and data scientist working with large-scale administrative data for causal and policy analysis. His interests include studying the effects of scientific infrastructure, training, and initiatives, as well as the development of open, sustainable, and replicable systems for data construction, curation, and dissemination. As part of the Institute for Research on Innovation and Science (IRIS), he contributes to record linkage and data improvements in the research community releases of UMETRICS, a data system built from integrated records on federal award funding and spending from dozens of American universities. Dr. VanEseltine’s recent work includes studying the impacts of COVID-19 on academic research activity.

Jodyn Platt

Jodyn Platt

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Our team leads research on the Ethical, Legal, and Social Implications (ELSI) of learning health systems and related enterprises. Our research uses mixed methods to understand policies and practices that make data science methods (data collection and curation, AI, computable algorithms) trustworthy for patients, providers, and the public. Our work engages multiple stakeholders including providers and health systems, as well as the general public and minoritized communities on issues such as AI-enabled clinical decision support, data sharing and privacy, and consent for data use in precision oncology.

J. Alex Halderman

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My research focuses on computer security and privacy, with an emphasis on problems that broadly impact society and public policy. Topics that interest me include software security, network security, data privacy, anonymity, election cybersecurity, censorship resistance, computer forensics, ethics, and cybercrime. I’m also interested in the interaction of technology with politics and international affairs.


Accomplishments and Awards