We are pleased to announce a diverse set of perspectives represented through our panel, covering topics related to privacy evaluations: privacy-preserving machine learning, law and AI privacy, confidential computation, and evaluating AI models.

As you meet the speakers, we welcome questions sent to us. Some potential questions for NeurIPS audience

  • What is the state of each of the technologies (Homomorphic encryption for machine learning, secure-mpc or on-device training, private evaluations.) now?
  • What do you say to the claim that “privacy is dead”, or “we don’t need privacy”?
  • What are the limitations that you think are solvable in the near term?

Niloofar Mireshghallah

headshot_niloofar Niloofar Mireshghallah is a post-doctoral scholar at the Paul G. Allen Center for Computer Science & Engineering at University of Washington. She received her Ph.D. from the CSE department of UC San Diego in 2023. Her research interests are Trustworthy Machine Learning and Natural Language Processing. She is a recipient of the National Center for Women & IT (NCWIT) Collegiate award in 2020 for her work on privacy-preserving inference, a finalist of the Qualcomm Innovation Fellowship in 2021 and a recipient of the 2022 Rising star in Adversarial ML award.

Kim Laine

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Kim Laine is a Principal Researcher at Microsoft Research’s Cryptography Group. He is particularly interested in privacy and compliance issues that arise from modern machine learning practices. He works on practical systems for key distributions in E2EE communication, including key transparency systems. He is also working on reputation and accountability systems that can ensure reliable signals in adversarial environments.

With broad interests in applied cryptography, privacy, and security, much of his past work developed and applied homomorphic encryption. For example, he built much of the Microsoft SEAL homomorphic encryption library and of the APSI private set intersection library, co-organizers of the HomomorphicEncryption.org standardization initiative. He graduated with an MSc in mathematical physics from University of Helsinki and with a PhD in mathematics from UC Berkeley.

Sara Hooker

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Sara Hooker leads Cohere For AI, the dedicated research arm of Cohere. Cohere For AI seeks to solve complex machine learning problems and supports fundamental research that explores the unknown. With a long track-record of impactful research at Google Brain, Sara brings a wealth of knowledge from across machine learning. Her work has focused on model efficiency training techniques and optimizing for models that fulfill multiple desired criteria – interpretable, efficient, fair and robust. Sara leads a team of researchers and engineers working on making large language models more efficient, safe and grounded. Sara is currently on Kaggle’s ML Advisory Research Board and serves on the World Economic Forum council on the Future of Artificial Intelligence.

Daniel Coelho de Castro

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Daniel Coelho de Castro is a senior researcher in the Biomedical Imaging team at Microsoft Research Health Futures, in Cambridge, UK. He has worked on a variety of applications of deep learning in medical image analysis—including chest radiography, computational pathology, and neuroimaging—and is particularly interested in integration of multimodal data sources. Daniel has a strong focus on combining methodological rigour, domain knowledge, and interdisciplinary collaboration to ensure reliability of machine-learning models in healthcare. Prior to joining Microsoft Research, he completed his MRes and PhD work in machine learning for medical imaging at Imperial College London, after graduating from École Centrale Paris (Dipl. Ing.) and PUC-Rio (BSc).