{"id":55455,"date":"2026-01-21T12:16:01","date_gmt":"2026-01-21T17:16:01","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&p=55455"},"modified":"2026-02-02T15:21:40","modified_gmt":"2026-02-02T20:21:40","slug":"ams-special-seminar-series-ana-anddreea-stoica","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-special-seminar-series-ana-anddreea-stoica\/","title":{"rendered":"AMS Special Seminar Series | Ana-Andreea Stoica"},"content":{"rendered":"

Location: <\/strong>Shaffer 3<\/p>\n

When:<\/strong> February 5th at 1:30 p.m.<\/p>\n

Title: <\/strong>Designing for Society: AI in Networks, Markets, and Platforms\u00a0<\/span><\/p>\n

Abstract: <\/strong>AI systems increasingly reshape our networks, markets, and platforms. When deployed in social environments\u2014online platforms, labor markets, and information ecosystems\u2014AI interacts with complex human behavior, strategic incentives, and structural inequality. This talk focuses on foundational challenges and opportunities for AI systems: how to design and evaluate algorithmic interventions in complex social environments.\u00a0I will present recent work on causal inference under competing treatments, which formalizes how competition for user attention and strategic behavior among experimenters distort experimental data and invalidate na\u00efve estimates of algorithmic impact. By modeling experimentation as a strategic data acquisition problem, we show how evaluation itself becomes an optimization problem, and we derive mechanisms that recover meaningful estimates despite interference and competition. I connect this problem to deriving foundational properties of systems that enable reliable experimentation.\u00a0Beyond this case study, the talk highlights broader implications for the design and evaluation of AI systems in networks, markets, and platforms. I argue that responsible deployment requires rethinking evaluation methodologies to account for incentives, feedback loops, and system-level effects, and I outline directions for how algorithmic and statistical tools can tackle these challenges.<\/span><\/p>\n

Bio:<\/strong> Ana-Andreea Stoica<\/a><\/span>\u00a0is a Research Group Leader in the Social Foundations of Computation Department at the Max Planck Institute for Intelligent Systems in T\u00fcbingen, Germany. Her work\u00a0centers\u00a0algorithms and evaluation methods for multi-objective\u00a0optimization problems with applications in society. Ana\u2019s research combines efficiency and societal\u00a0objectives\u00a0using unsupervised learning techniques, game theory, and behavioral and sociological modeling. From algorithmic design with fairness considerations to evaluating resource-allocation systems with equity constraints and incentive alignment, Ana is particularly interested in designing algorithms to improve people’s access to information and opportunities. Ana holds a Ph.D. in Computer Science from Columbia University and a B.A. in Mathematics from Princeton University.\u00a0Since 2019, Ana has been co-organizing the <\/span>EAAMO\u00a0Bridges<\/a>\u00a0<\/span>initiative. Ana co-founded the <\/span>ACM conference series on Equity and Access in Algorithms, Mechanisms, and Optimization<\/a><\/span>\u00a0and served as a Program Co-Chair for the inaugural edition.<\/span><\/p>\n

Zoom link:<\/strong> https:\/\/wse.zoom.us\/j\/92215066845<\/p>\n","protected":false},"excerpt":{"rendered":"

Location: Shaffer 3 When: February 5th at 1:30 p.m. Title: Designing for Society: AI in Networks, Markets, and Platforms\u00a0 Abstract: AI systems increasingly reshape our networks, markets, and platforms. 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