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SUMMARY:ISyE - Bridging Machine Learning and Optimization for Human-Centered AI
DESCRIPTION:From healthcare delivery to resilient power grid management\, predictive and prescriptive analytics tools have the potential to improve decision-making for some of today’s most pressing problems\, yet their impact is often limited by the technical barriers required to access these tools and to interpret and trust their results. This talk will explore how the synthesis of machine learning and optimization can lower these barriers to advance human-centered artificial intelligence (AI). The first part of the talk will demonstrate how generative AI can broaden access to optimization tools through an interactive decision-support framework\, developed in collaboration with Microsoft Outlook\, that leverages large language models to translate natural-language user requests into underlying constraint programming models. The second part of the talk will focus on trust\, showing how optimization can identify regions where machine learning models make fixed predictions that preclude individuals from changing their outcomes\, such as a loan applicant who can never be approved regardless of their actions. We will conclude by outlining broader opportunities for integrating AI and optimization\, moving toward a future in which advanced analytics tools are as accessible and trustworthy for managers at a local food bank as they are for applied scientists at Amazon. \n\n\n\n\n\nBio: Connor Lawless is a Postdoctoral Fellow at the Stanford Institute for Human- Centered Artificial Intelligence advised by Ellen Vitercik and Madeleine Udell. His research blends tools from optimization\, machine learning\, and human-computer interaction to make advanced analytics tools more accessible and trustworthy. He received his PhD in Operations Research from Cornell University where he was advised by Oktay Gunluk\, and previously spent time at Microsoft Research\, IBM Research\, and the Royal Bank of Canada.
URL:https://engineering.wisc.edu/event/isye-bridging-machine-learning-and-optimization-for-human-centered-ai/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/02/cohengraphic.avif
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SUMMARY:ISyE - Online Fault Detection for High-dimensional Data Streams under Resource Constraints
DESCRIPTION:With the rapid advances in sensing and communication technologies\, most complex systems are continuously monitored by sensors that provide a variety of streaming data with rich information about the system’s performance. Monitoring such high-dimensional streaming data in real-time is critical to detect anomalies and system failures. Nonetheless\, resource constraints on sensing\, computation\, and communication make traditional monitoring and anomaly detection methods impractical. This talk introduces a family of adaptive and active learning strategies for online fault detection that explicitly account for the limitations associated with resource constraints. By dynamically selecting which data to sample\, process\, or transmit\, these methods achieve efficient monitoring without sacrificing statistical reliability. I will discuss applications in networked and partially observed systems\, real-time anomaly detection with mobile sensors\, and online batch fault diagnosis. The unifying theme is the integration of statistical learning\, sequential decision-making\, and uncertainty quantification to enable scalable\, data-efficient online monitoring under resource constraints. \n\n\n\n\n\nBio: Ana Maria Estrada Gomez is an assistant professor at the Edwardson School of Industrial Engineering at Purdue University. She received a B.Sc. in industrial engineering and a B.Sc. in mathematics from la Universidad de los Andes\, Bogota\, Colombia\, in 2013 and 2015\, respectively. She also holds a M.Sc. in industrial engineering from la Universidad de los Andes (2015)\, and a M.Sc. in statistics from Georgia Tech (2018). In 2021\, she received her PhD in industrial engineering with a specialization in statistics from Georgia Tech. Her research interests lie in developing efficient methodologies and algorithms for modeling\, monitoring\, and diagnosing complex systems that collect high-dimensional data\, using statistics and machine learning tools. She is the recipient of the SPES + Q&P Best Student Paper Award from ASA\, the QSR Best Poster Award from INFORMS\, and the IISE Doctoral Colloquium Best Poster Award. She has also been appointed as a Latina Trailblazer in Engineering Fellow by Purdue’s College of Engineering.
URL:https://engineering.wisc.edu/event/isye-online-fault-detection-for-high-dimensional-data-streams-under-resource-constraints/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/02/cohengraphic-1.avif
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