# IDSS Published articles for IDSS. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Naoki Egami's Research on Political Methodology and External Validity DevFeed: [Naoki Egami's Research on Political Methodology and External Validity]() Original publisher: [Read original article]() Author: Peter Dizikes | MIT News Published: 2026-09-16T04:00:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Statistics](), [Data Science](), [AI Research]() Tags: [artificial-intelligence](), [external-validity](), [faculty](), [idss](), [mit-political-science](), [mit-schwarzman-college-of-computing](), [naoki-egami](), [political-methodology](), [political-science](), [profile](), [research](), [school-of-humanities-arts-and-social-sciences](), [science](), [social-sciences](), [statistics](), [technology-and-society](), [voting-and-elections]() ### AI overview An MIT profile of political scientist Naoki Egami, whose research examines research methodology, external validity, and the mathematical and statistical challenges of studying civic and political phenomena. It also discusses his work on the use of AI tools in research. ### Source excerpt Naoki Egami has become a standout in political methodology, helping refine tools that give scholars durable results. ## MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications DevFeed: [MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-14T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Generative AI](), [AI Models](), [Algorithm](), [Requirements](), [Computer vision](), [Robotics]() Tags: [ai](), [ai-models](), [algorithm](), [algorithms](), [artificial-intelligence](), [computer-science-and-technology](), [computer-vision](), [diffusion-models](), [electrical-engineering-and-computer-science-eecs](), [flow-matching](), [generative-ai](), [hard-constrained-sampling](), [hardflow](), [idss](), [kaveh-alim](), [laboratory-for-information-and-decision-systems-lids](), [machine-learning](), [mechanical-engineering](), [mit-schwarzman-college-of-computing](), [navid-azizan](), [optimal-control](), [paper](), [requirements](), [research](), [robotics](), [safe-ai](), [school-of-engineering](), [trajectory-optimization](), [zeyang-li]() ### AI overview MIT researchers developed a deployment-time technique that lets pretrained generative AI models explore solutions while enforcing hard constraints on final outputs. Experiments in robotics, physical-process control, and computer vision found that the method satisfied required constraints and identified better solutions than existing techniques. ### Source excerpt The "HardFlow" algorithm could help generative AI models produce high-quality outputs that obey strict requirements when "pretty close" doesn't cut it. ## Generating scenarios for extreme events, without extreme data DevFeed: [Generating scenarios for extreme events, without extreme data]() Original publisher: [Read original article]() Author: Jennifer Chu | MIT News Published: 2026-08-24T18:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Machine learning](), [Algorithms](), [Critical Infrastructure](), [data]() Tags: [algorithm](), [algorithms](), [artificial-intelligence](), [center-for-computational-science-and-engineering](), [climate](), [climate-risk-assessment](), [computer-modeling](), [critical-infrastructure](), [data](), [extreme-event-aware](), [extreme-weather](), [fire](), [heat](), [idss](), [kai-chang](), [learning-fefb62e9fa83](), [machine-learning](), [mechanical-engineering](), [mit-meche](), [mit-schwarzman-college-of-computing](), [natural-disasters](), [research](), [risk](), [school-of-engineering](), [storm](), [sustainability](), [themis-sapsis](), [weather](), [weather-prediction]() ### AI overview MIT engineers developed a machine-learning algorithm that generates plausible future extreme-event scenarios without requiring past extreme events in the training data. It learns from available records, filters out implausible weather scenarios, and estimates events' frequency, size, intensity, duration, and area of impact to help planners prepare. ### Source excerpt A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for. ## Alexander Rakhlin named director of the MIT Statistics and Data Science Center DevFeed: [Alexander Rakhlin named director of the MIT Statistics and Data Science Center]() Original publisher: [Read original article]() Author: Institute for Data, Systems, and Society Published: 2026-08-03T19:50:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Statistics](), [Data Science](), [Machine learning]() Tags: [ai-in-statistics](), [alexander-sasha-rakhlin](), [alumni-ae](), [ankur-moitra](), [artificial-intelligence](), [brain-and-cognitive-sciences](), [computer-science-and-technology](), [data-science](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [fotini-christia](), [idss](), [laboratory-for-information-and-decision-systems-lids](), [leadership](), [lids](), [machine-learning](), [mit-bcs](), [mit-faculty-appointments](), [mit-idss](), [mit-leadership](), [mit-schwarzman-college-of-computing](), [mit-statistics-and-data-science-center-sdsc](), [philippe-rigollet](), [richard-dick-larson](), [school-of-engineering](), [school-of-science](), [statistics]() ### AI overview Alexander Rakhlin has been named the next director of MIT's Statistics and Data Science Center, succeeding Ankur Moitra. Rakhlin has been affiliated with the center since 2016 and has led its interdisciplinary doctoral program, overseeing more than 75 successful PhD defenses. ### Source excerpt An expert in machine learning, statistics, and computation, Rakhlin succeeds Professor Ankur Moitra. ## Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making DevFeed: [Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making]() Original publisher: [Read original article]() Author: David Chandler | Laboratory for Information and Decision Systems Published: 2026-07-14T20:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [decision-making](), [Structured-data](), [AI Models](), [data](), [Time Series](), [Computer science](), [Electrical engineering and computer science (EECS)](), [Temporal data](), [systems]() Tags: [ai](), [ai-and-decision-making](), [ai-in-business-planning](), [ai-models](), [algorithms](), [artificial-intelligence](), [business-and-management](), [business-modeling](), [celonis-context-model](), [computer-science-and-technology](), [data](), [data-systems](), [decision-making](), [devavrat-shah](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [forecasting](), [idss](), [ikigailabs](), [laboratory-for-information-and-decision-systems-lids](), [mit-eecs](), [mit-idss](), [mit-intellectual-property](), [mit-lids](), [mit-schwarzman-college-of-computing](), [predictions](), [profile](), [real-time](), [research](), [school-of-engineering](), [startups](), [structured](), [tabular-data](), [time-series-data]() ### AI overview MIT Professor Devavrat Shah's research led to a foundation model for tabular and time-series enterprise data. Developed with Ikigai Labs, the system continuously tests predictions against real outcomes to support large-scale forecasting and decision-making. ### Source excerpt Through research and entrepreneurship, Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources. ## Jesse Thaler named director of the Laboratory for Nuclear Science DevFeed: [Jesse Thaler named director of the Laboratory for Nuclear Science]() Original publisher: [Read original article]() Author: Julia C. Keller | School of Science Published: 2026-07-07T14:45:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Research](), [AI, ML & Data Engineering]() Tags: [ai](), [ai-in-physics](), [artificial-intelligence](), [bolek-wyslouch](), [data](), [department-of-energy-doe](), [faculty](), [idss](), [jesse-thaler](), [laboratory-for-nuclear-science](), [leadership](), [mit-center-for-theoretical-physics-ctp](), [mit-ctp-li](), [mit-faculty-appointments](), [mit-iaifi](), [mit-idss](), [mit-leadership](), [mit-lns](), [national-science-foundation-nsf](), [nergis-mavalvala](), [particle-physics](), [physics](), [quantum](), [research](), [school-of-science](), [science](), [tracey-slatyer]() ### AI overview Professor Jesse Thaler has been named director of MIT's Laboratory for Nuclear Science, effective Aug. 1. He succeeds Bolek Wyslouch and will lead the laboratory while continuing research combining particle physics and machine learning. ### Source excerpt The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.