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Michael O'Connor

Publications and source records attributed to Michael O'Connor.

6 recordsLinked to original sources
arXiv2025

A Technical Policy Blueprint for Trustworthy Decentralized AI

Decentralized AI systems, such as federated learning, can play a critical role in further unlocking AI asset marketplaces (e.g., healthcare data marketplaces) thanks to increased asset privacy protection. Unlocking this big potential necessitates governance mechanisms that are transparent, scalable, and verifiable. However current governance approaches rely on bespoke, infrastructure-specific policies that hinder asset interoperability and trust among systems. We are proposing a Technical Policy Blueprint that encodes governance requirements as policy-as-code objects and separates asset policy verification from asset policy enforcement. In this architecture the Policy Engine verifies evidence (e.g., identities, signatures, payments, trusted-hardware attestations) and issues capability packages. Asset Guardians (e.g. data guardians, model guardians, computation guardians, etc.) enforce access or execution solely based on these capability packages. This core concept of decoupling policy processing from capabilities enables governance to evolve without reconfiguring AI infrastructure, thus creating an approach that is transparent, auditable, and resilient to change.

cs.CY↗
arXiv2023

Bayesian Learning via Q-Exponential Process

Regularization is one of the most fundamental topics in optimization, statistics and machine learning. To get sparsity in estimating a parameter $u\in\mathbb{R}^d$, an $\ell_q$ penalty term, $\Vert u\Vert_q$, is usually added to the objective function. What is the probabilistic distribution corresponding to such $\ell_q$ penalty? What is the correct stochastic process corresponding to $\Vert u\Vert_q$ when we model functions $u\in L^q$? This is important for statistically modeling large dimensional objects, e.g. images, with penalty to preserve certainty properties, e.g. edges in the image. In this work, we generalize the $q$-exponential distribution (with density proportional to) $\exp{(- \frac{1}{2}|u|^q)}$ to a stochastic process named $Q$-exponential (Q-EP) process that corresponds to the $L_q$ regularization of functions. The key step is to specify consistent multivariate $q$-exponential distributions by choosing from a large family of elliptic contour distributions. The work is closely related to Besov process which is usually defined by the expanded series. Q-EP can be regarded as a definition of Besov process with explicit probabilistic formulation and direct control on the correlation length. From the Bayesian perspective, Q-EP provides a flexible prior on functions with sharper penalty ($q<2$) than the commonly used Gaussian process (GP). We compare GP, Besov and Q-EP in modeling functional data, reconstructing images, and solving inverse problems and demonstrate the advantage of our proposed methodology.

stat.ME↗
arXiv2019

Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework

As molecular scientists have made progress in their ability to engineer nano-scale molecular structure, we are facing new challenges in our ability to engineer molecular dynamics (MD) and flexibility. Dynamics at the molecular scale differs from the familiar mechanics of everyday objects, because it involves a complicated, highly correlated, and three-dimensional many-body dynamical choreography which is often non-intuitive even for highly trained researchers. We recently described how interactive molecular dynamics in virtual reality (iMD-VR) can help to meet this challenge, enabling researchers to manipulate real-time MD simulations of flexible structures in 3D. In this article, we outline efforts to extend immersive technologies to the molecular sciences, and we introduce 'Narupa', a flexible, open-source, multi-person iMD-VR software framework which enables groups of researchers to simultaneously cohabit real-time simulation environments to interactively visualize and manipulate the dynamics of molecular structures with atomic-level precision. We outline several application domains where iMD-VR is facilitating research, communication, and creative approaches within the molecular sciences, including training machines to learn reactive potential energy surfaces (PESs), biomolecular conformational sampling, protein-ligand binding, reaction discovery using 'on-the-fly' quantum chemistry, and transport dynamics in materials. We touch on iMD-VR's various cognitive and perceptual affordances, and how these provide research insight for molecular systems. By synergistically combining human spatial reasoning and design insight with computational automation, technologies like iMD-VR have the potential to improve our ability to understand, engineer, and communicate microscopic dynamical behavior, offering the potential to usher in a new paradigm for engineering molecules and nano-architectures.

physics.chem-ph↗
arXiv2007

Embeddings into Free Heyting Algebras and Translations into Intuitionistic Propositional Logic

We find a translation with particularly nice properties from intuitionistic propositional logic in countably many variables to intuitionistic propositional logic in two variables. In addition, the existence of a possibly-not-as-nice translation from any countable logic into intuitionistic propositional logic in two variables is shown. The nonexistence of a translation from classical logic into intuitionistic propositional logic which preserves ``and'' and ``or'' but not necessarily ``true'' is proven. These results about translations follow from additional results about embeddings into free Heyting algebras.

math.LO↗