08.02.22 2:58 Closing remarks: Causal Machine Learning
Speaker: Emre Kiciman, Senior Principal Researcher, Microsoft Research Redmond Learn more about the 2021 Microsoft Research Summit: Aka.ms/researchsummit
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08.02.22 21:32 Demo: Enabling end-to-end causal inference at scale
Speakers: Eleanor Dillon, Principal Economist, Microsoft Research New England Amit Sharma, Senior Researcher, Microsoft Research India This session will present the two popular open-source tools
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08.02.22 16:29 Research Talk: Enhancing the robustness of massive language models via invariant risk minimization
Speaker: Robert West, Tenure-Track Assistant Professor, EPFL Despite the dramatic recent progress in natural language processing (NLP) afforded by large pretrained language models, important
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08.02.22 16:53 Research talk: Post-contextual-bandit inference
Speaker: Nathan Kallus, Associate Professor, Cornell University Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-commerce, healthcare, and policymaking because
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08.02.22 5:55 Research talk: Causal ML and fairness
Speaker: Allison Koenecke, Postdoc, Microsoft Research New England Observing heterogeneous treatment effects across different demographic groups is an important mechanism for evaluating fairness.
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08.02.22 4:16 Research talk: Causality for medical image analysis
Speaker: Daniel Coelho de Castro, Senior Researcher, Microsoft Research Cambridge Machine learning has huge potential to augment medical image analysis workflows and improve patient care.
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08.02.22 4:32 Research talk: Causal learning: Discovering causal relations for out-of-distribution generalization
Speaker: Wei Chen, Principal Researcher, Microsoft Research Asia Machine learning models should be explainable and robust on out-of-distribution samples, especially on safety-critical tasks such as
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08.02.22 6:11 Research talk: Can causal learning improve the privacy of ML models?
Speaker: Shruti Tople, Senior Researcher, Microsoft Research Cambridge Ensuring privacy of data used to train machine learning models is important for safe and responsible deployment of these models.
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08.02.22 5:52 Research talk: Causal ML and business
Speaker: Jacob LaRiviere, Economist, Microsoft Research Redmond Using machine learning for causal inference can, in a subset of cases with rich data, replicate results from A/B experimentation.
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08.02.22 30:57 Research talk: Challenges and opportunities in causal machine learning
Speakers: Amit Sharma, Senior Researcher, Microsoft Research India Cheng Zhang, Principal Researcher, Microsoft Research Cambridge Emre Kiciman, Senior Principal Researcher, Microsoft Research
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08.02.22 3:32 Opening remarks: Causal Machine Learning
Speaker: Cheng Zhang, Principal Researcher, Microsoft Research Cambridge Causal machine learning is an increasingly important, but not well understood, technology.
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08.02.22 2:07 Closing remarks: The Future of Privacy and Security
Speaker: Robert Sim, Principal Research Manager, Microsoft Research Redmond Learn more about the 2021 Microsoft Research Summit: Aka.ms/researchsummit
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08.02.22 18:28 Demo: Generating formally proven low-level parsers with EverParse
Speaker: Aseem Rastogi, Principal Researcher, Microsoft Research India DARPA and MITRE estimate that 80 percent of software security vulnerabilities have incorrect input validation as their root
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08.02.22 28:51 Demo: EverParse: Automatic generation of formally verified secure parsers for cloud integrity
Speaker: Tahina Ramananandro, Principal Research Software Development Engineer, Microsoft Research Redmond DARPA and MITRE estimate that 80 percent of software security vulnerabilities have
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08.02.22 31:11 Research talk: DARPA SafeDocs: an approach to secure parsing and information interchange formats
Speaker: Sergey Bratus, Program Manager, DARPA DARPA and MITRE estimate that 80 percent of software security vulnerabilities have incorrect input validation as their root cause.
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08.02.22 34:15 Research talk: Privacy in machine learning research at Microsoft
Speaker: Melissa Chase, Principal Researcher, Microsoft Research Redmond Training modern machine learning models requires large amounts of data, and often that data may be private or confidential.
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08.02.22 30:26 Research talk: Towards bridging between legal and technical approaches to data protection
Speaker: Kobbi Nissim, Professor, Georgetown University As computer systems become integrated into almost every aspect of society and are increasingly making decisions of legal significance, the
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08.02.22 28:44 Research talk: Building towards a responsible data economy
Speaker: Dawn Song, Professor, UC Berkeley Data is a key driver of the modern economy and AI/machine learning.
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08.02.22 22:13 Keynote: Unlocking exabytes of training data through privacy preserving machine learning
Speaker: Jim Kleewein, Technical Fellow, Microsoft Data is the lifeblood of AI and machine learning. The better the model, and the better the data that model is trained on, the better the outcome.
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08.02.22 3:02 Closing remarks: Responsible AI
Speaker: Ece Kamar, Partner Research Manager, Microsoft Research Redmond As we’ve seen in countless media articles, AI systems can behave unfairly or unreliably.
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08.02.22 5:20 Opening remarks: The Future of Privacy and Security
Speaker: Robert Sim, Principal Research Manager, Microsoft Research Redmond Security and privacy are key components to building trust in the technologies that we use, whether that be for
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08.02.22 39:39 Tutorial: Create human-centered AI with the Human-AI eXperience (HAX) Toolkit
Speakers: Saleema Amershi, Senior Principal Research Manager, Microsoft Research Mihaela Vorvoreanu, Director, Aether UX Research & RAI Education There’s been a push to build AI technologies that
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08.02.22 44:40 Panel: Maximizing benefits and minimizing harms with language technologies
Speakers: Hal Daumé III, Sr Principal Researcher, Microsoft Research NYC Steven Bird, Professor, Charles Darwin University Su Lin Blodgett, Postdoctoral Researcher, Microsoft Research Montréal
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08.02.22 21:09 Lightning talks: Advances in fairness in AI: New directions
Over the past few years, we’ve seen that artificial intelligence (AI) and machine learning (ML) provide us with new opportunities, but they also raise new challenges.
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08.02.22 3:15 Closing remarks: Tech for resilient communities
Speaker: Mary Gray, Senior Principal Researcher, Microsoft Research New England Learn more about the 2021 Microsoft Research Summit: Aka.ms/researchsummit
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08.02.22 20:46 Lightning talks: Advances in fairness in AI: From research to practice
Over the past few years, we’ve seen that artificial intelligence (AI) and machine learning (ML) provide us with new opportunities, but they also raise new challenges.
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08.02.22 41:00 Panel: Content moderation beyond the ban: Reducing toxic, misleading, and low-quality content
Speakers: Tarleton Gillespie, Senior Principal Researcher, Microsoft Research New England Zoe Darmé, Senior Manager, Google Ryan Calo, Lane Powell and D.
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08.02.22 24:30 Technology demo: Using technology to combat human trafficking
Speaker: Darren Edge, Director of Societal Resilience, Microsoft Research Cambridge Microsoft is a founding member of Tech Against Trafficking (TAT) – a coalition of organizations working to combat
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08.02.22 19:48 Technology demo: Project Eclipse: Hyperlocal air quality monitoring for cities
Speaker: Scott Counts, Sr. Principal Research Manager, Microsoft Research Redmond Project Eclipse is a full-stack solution for neighborhood scale air quality monitoring in cities.
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08.02.22 19:57 Research talk: Bucket of me: Using few-shot learning to realize teachable AI systems
Speaker: Daniela Massiceti, Senior Researcher, Microsoft Research We’re entering a technological era that is all about “me”—from personalized shopping recommendations to avatars, and even bespoke
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