7 Search Results for "Williams, Matt"

Document
DOI: 10.4230/OASIcs.NINeS.2026.20
OpenFLAME: A Federated Spatial Naming Infrastructure

Authors: Sagar Bharadwaj, Ziyong Ma, Ivan Liang, Michael Farb, Anthony Rowe, and Srinivasan Seshan

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Spatial applications, i.e., applications that tie digital information with the physical world, have improved many of our daily activities, such as navigation and ride-sharing. This class of applications also holds significant promise of enabling new industries such as augmented reality and robotics. The development of these applications is enabled by a system that can resolve real-world locations to names, or a spatial naming system. Today, mapping platforms provided by organizations like Google and Apple serve as spatial naming systems. These maps are centralized and primarily cover outdoor spaces. We envision that future spatial applications, such as persistent world-scale augmented reality, would require detailed and precise spatial data across indoor and outdoor spaces. The scale of cartography efforts required to survey indoor spaces and their privacy needs inhibit existing centralized maps from incorporating such spaces into their platform. In this paper, we present the design and implementation of OpenFLAME, a federated spatial naming system, or in other words, a federated mapping infrastructure. OpenFLAME enables independent parties to manage and serve their own maps of physical regions. This unlocks scalability of map management, isolation, and privacy of maps. The discovery system that identifies maps hosted at a given location is a primary component of OpenFLAME. We implement OpenFLAME on top of the existing Domain Name System (DNS), which enables us to leverage its existing infrastructure. We implement map services such as address-to-location mapping, routing, and localization on top of our federated mapping infrastructure.
Cite as

Sagar Bharadwaj, Ziyong Ma, Ivan Liang, Michael Farb, Anthony Rowe, and Srinivasan Seshan. OpenFLAME: A Federated Spatial Naming Infrastructure. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 20:1-20:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{bharadwaj_et_al:OASIcs.NINeS.2026.20,
  author =	{Bharadwaj, Sagar and Ma, Ziyong and Liang, Ivan and Farb, Michael and Rowe, Anthony and Seshan, Srinivasan},
  title =	{{OpenFLAME: A Federated Spatial Naming Infrastructure}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{20:1--20:26},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.20},
  URN =		{urn:nbn:de:0030-drops-256051},
  doi =		{10.4230/OASIcs.NINeS.2026.20},
  annote =	{Keywords: Geographic naming system, Augmented reality, Robotics}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.5
OrbitalBrain: A Distributed Framework for Training ML Models in Space

Authors: Om Chabra, Chenning Li, Kevin Hsieh, Santiago Segarra, Behnaz Arzani, Peder Olsen, and Ranveer Chandra

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Earth observation nanosatellites capture high-resolution photos of the Earth in near real-time. These images increasingly support ML applications that are critical for safety and response, such as forest fire and flood detection. However, the downlink bandwidth is limited, resulting in days or weeks of delay from image capture to training. In this work, we propose OrbitalBrain, an efficient in-space distributed ML training framework that leverages limited and predictable satellite compute, bandwidth, and power to intelligently balance data transfer, model aggregation, and local training. Our evaluations demonstrate that OrbitalBrain achieves 1.52×-12.4× speedup in time-to-accuracy while always reaching a higher final model accuracy compared to state-of-the-art ground-based or federated learning baselines. Furthermore, our approach is complementary to satellite imagery capturing and downloading, enhancing the overall efficiency of satellite-based applications.
Cite as

Om Chabra, Chenning Li, Kevin Hsieh, Santiago Segarra, Behnaz Arzani, Peder Olsen, and Ranveer Chandra. OrbitalBrain: A Distributed Framework for Training ML Models in Space. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 5:1-5:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{chabra_et_al:OASIcs.NINeS.2026.5,
  author =	{Chabra, Om and Li, Chenning and Hsieh, Kevin and Segarra, Santiago and Arzani, Behnaz and Olsen, Peder and Chandra, Ranveer},
  title =	{{OrbitalBrain: A Distributed Framework for Training ML Models in Space}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{5:1--5:32},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.5},
  URN =		{urn:nbn:de:0030-drops-255907},
  doi =		{10.4230/OASIcs.NINeS.2026.5},
  annote =	{Keywords: Satellite networks, Distributed machine learning, Federated learning, Earth observation, In-orbit computing}
}
Document
DOI: 10.4230/OASIcs.DX.2025.10
Automating Control System Design: Using Language Models for Expert Knowledge in Decentralized Controller Auto-Tuning

Authors: Marlon J. Ares-Milian, Gregory Provan, and Marcos Quinones-Grueiro

Published in: OASIcs, Volume 136, 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)

Abstract
Fully-automated optimal controller design for engineering systems is a challenging task. While, optimization-based, automated control parameter tuning techniques have been widely discussed in the literature, most works do not discuss expert knowledge requirements for system design, which result in significant human intervention. In this work, we discuss a multistage controller tuning framework for decentralized control that highlights expert knowledge requirements in automated controller design. We propose a methodology to automate the input-output pairing and stage definition steps in the framework using Large Language Models (LLMs) for a family of multi-tank benchmarks. We achieve this by proposing a mathematical language to describe the system and design an algorithm to bind this mathematical representation to the input prompt space of an LLM. We demonstrate that our methodology can produce consistent expert knowledge outputs from the LLM with over 97% accuracy for the multi-tank benchmarks. We also empirically show that, correct stage definition by the LLM can improve tuned controller performance by up to 52%.
Cite as

Marlon J. Ares-Milian, Gregory Provan, and Marcos Quinones-Grueiro. Automating Control System Design: Using Language Models for Expert Knowledge in Decentralized Controller Auto-Tuning. In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 10:1-10:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{aresmilian_et_al:OASIcs.DX.2025.10,
  author =	{Ares-Milian, Marlon J. and Provan, Gregory and Quinones-Grueiro, Marcos},
  title =	{{Automating Control System Design: Using Language Models for Expert Knowledge in Decentralized Controller Auto-Tuning}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{10:1--10:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-394-2},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{136},
  editor =	{Quinones-Grueiro, Marcos and Biswas, Gautam and Pill, Ingo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2025.10},
  URN =		{urn:nbn:de:0030-drops-247996},
  doi =		{10.4230/OASIcs.DX.2025.10},
  annote =	{Keywords: controller auto-tuning, automated system design, large language models}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2025.32
Light Edge Fault Tolerant Graph Spanners

Authors: Greg Bodwin, Michael Dinitz, Ama Koranteng, and Lily Wang

Published in: LIPIcs, Volume 334, 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)

Abstract
There has recently been significant interest in fault tolerant spanners, which are spanners that still maintain their stretch guarantees after some nodes or edges fail. This work has culminated in an almost complete understanding of the three-way tradeoff between stretch, sparsity, and number of faults tolerated. However, despite some progress in metric settings, there have been no results to date on the tradeoff in general graphs between stretch, lightness, and number of faults tolerated. We initiate the study of light edge fault tolerant (EFT) graph spanners, obtaining the first such results. First, we observe that lightness can be unbounded if we use the traditional definition (normalizing by the MST). We then argue that a natural definition of fault-tolerant lightness is to instead normalize by a min-weight fault tolerant connectivity preserver; essentially, a fault-tolerant version of the MST. However, even with this, we show that it is still not generally possible to construct f-EFT spanners whose weight compares reasonably to the weight of a min-weight f-EFT connectivity preserver. In light of this lower bound, it is natural to then consider bicriteria notions of lightness, where we compare the weight of an f-EFT spanner to a min-weight (f' > f)-EFT connectivity preserver. The most interesting question is to determine the minimum value of f' that allows for reasonable lightness upper bounds. Our main result is a precise answer to this question: f' = 2f. In particular, we show that the lightness can be untenably large (roughly n/k for a k-spanner) if one normalizes by the min-weight (2f-1)-EFT connectivity preserver. But if one normalizes by the min-weight 2f-EFT connectivity preserver, then we show that the lightness is bounded by just O(f^{1/2}) times the non-fault tolerant lightness (roughly n^{1/k} for a (1+ε)(2k-1)-spanner).
Cite as

Greg Bodwin, Michael Dinitz, Ama Koranteng, and Lily Wang. Light Edge Fault Tolerant Graph Spanners. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 32:1-32:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bodwin_et_al:LIPIcs.ICALP.2025.32,
  author =	{Bodwin, Greg and Dinitz, Michael and Koranteng, Ama and Wang, Lily},
  title =	{{Light Edge Fault Tolerant Graph Spanners}},
  booktitle =	{52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)},
  pages =	{32:1--32:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-372-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{334},
  editor =	{Censor-Hillel, Keren and Grandoni, Fabrizio and Ouaknine, Jo\"{e}l and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2025.32},
  URN =		{urn:nbn:de:0030-drops-234093},
  doi =		{10.4230/LIPIcs.ICALP.2025.32},
  annote =	{Keywords: Fault Tolerant Spanners, Light Spanners}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2025.9
On the Quantum Time Complexity of Divide and Conquer

Authors: Jonathan Allcock, Jinge Bao, Aleksandrs Belovs, Troy Lee, and Miklos Santha

Published in: LIPIcs, Volume 334, 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)

Abstract
In this work, we initiate a systematic study of the time complexity of quantum divide and conquer (QD&C) algorithms for classical problems, and propose a general framework for their analysis. We establish generic conditions under which search and minimization problems with classical divide and conquer algorithms are amenable to quantum speedup, and apply these theorems to various problems involving strings, integers, and geometric objects. These include Longest Distinct Substring, Klee's Coverage, several optimization problems on stock transactions, and k-Increasing Subsequence. For most of these problems our quantum time upper bounds match the quantum query lower bounds, up to polylogarithmic factors. We give a structured framework for describing and classifying a wide variety of QD&C algorithms so that quantum speedups can be more easily identified and applied, and prove general statements on QD&C time complexity covering a range of cases, accounting for the time required for all operations. In particular, we explicitly account for memory access operations in the commonly used QRAM (read-only) and QRAG (read-write) models, which are assumed to take unit time in the query model, and which require careful analysis when involved in recursion. Our generic QD&C theorems have several nice features. 1) To apply them, it suffices to come up with a classical divide and conquer algorithm satisfying the conditions of the theorem. The quantization of the algorithm is then completely handled by the theorem. This can make it easier to find applications which admit a quantum speedup, and contrast with dynamic programming algorithms which can be difficult to quantize due to their highly sequential nature. 2) As these theorems give bounds on time complexity, they can be applied to a greater range of problems than those based on query complexity, e.g., where the best-known quantum algorithms require super-linear time. 3) It can handle minimization problems as well as boolean functions, which allows us to improve on the query complexity result of Childs et al. [Childs et al., 2025] for k-Increasing Subsequence by a logarithmic factor.
Cite as

Jonathan Allcock, Jinge Bao, Aleksandrs Belovs, Troy Lee, and Miklos Santha. On the Quantum Time Complexity of Divide and Conquer. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 9:1-9:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{allcock_et_al:LIPIcs.ICALP.2025.9,
  author =	{Allcock, Jonathan and Bao, Jinge and Belovs, Aleksandrs and Lee, Troy and Santha, Miklos},
  title =	{{On the Quantum Time Complexity of Divide and Conquer}},
  booktitle =	{52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)},
  pages =	{9:1--9:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-372-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{334},
  editor =	{Censor-Hillel, Keren and Grandoni, Fabrizio and Ouaknine, Jo\"{e}l and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2025.9},
  URN =		{urn:nbn:de:0030-drops-233863},
  doi =		{10.4230/LIPIcs.ICALP.2025.9},
  annote =	{Keywords: Quantum Computing, Quantum Algorithms, Divide and Conquer}
}
Document
DOI: 10.4230/DagRep.12.9.150
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382)

Authors: Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, and Jessica Montgomery

Published in: Dagstuhl Reports, Volume 12, Issue 9 (2023)

Abstract
This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling". Today’s scientific challenges are characterised by complexity. Interconnected natural, technological, and human systems are influenced by forces acting across time- and spatial-scales, resulting in complex interactions and emergent behaviours. Understanding these phenomena - and leveraging scientific advances to deliver innovative solutions to improve society’s health, wealth, and well-being - requires new ways of analysing complex systems. The transformative potential of AI stems from its widespread applicability across disciplines, and will only be achieved through integration across research domains. AI for science is a rendezvous point. It brings together expertise from AI and application domains; combines modelling knowledge with engineering know-how; and relies on collaboration across disciplines and between humans and machines. Alongside technical advances, the next wave of progress in the field will come from building a community of machine learning researchers, domain experts, citizen scientists, and engineers working together to design and deploy effective AI tools. This report summarises the discussions from the seminar and provides a roadmap to suggest how different communities can collaborate to deliver a new wave of progress in AI and its application for scientific discovery.
Cite as

Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, and Jessica Montgomery. Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382). In Dagstuhl Reports, Volume 12, Issue 9, pp. 150-199, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{berens_et_al:DagRep.12.9.150,
  author =	{Berens, Philipp and Cranmer, Kyle and Lawrence, Neil D. and von Luxburg, Ulrike and Montgomery, Jessica},
  title =	{{Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382)}},
  pages =	{150--199},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{9},
  editor =	{Berens, Philipp and Cranmer, Kyle and Lawrence, Neil D. and von Luxburg, Ulrike and Montgomery, Jessica},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.9.150},
  URN =		{urn:nbn:de:0030-drops-178125},
  doi =		{10.4230/DagRep.12.9.150},
  annote =	{Keywords: machine learning, artificial intelligence, life sciences, physical sciences, environmental sciences, simulation, causality, modelling}
}
Document
DOI: 10.4230/DFU.Vol2.SciViz.2011.240
Reflections on QuestVis: A Visualization System for an Environmental Sustainability Model

Authors: Tamara Munzner, Aaron Barsky, and Matt Williams

Published in: Dagstuhl Follow-Ups, Volume 2, Scientific Visualization: Interactions, Features, Metaphors (2011)

Abstract
We present lessons learned from the iterative design of QuestVis, a visualization interface for the QUEST environmental sustainability model. The QUEST model predicts the effects of policy choices in the present using scenarios of future outcomes that consist of several hundred indicators. QuestVis treats this information as a high-dimensional dataset, and shows the relationship between input choices and output indicators using linked views and a compact multilevel browser for indicator values. A first prototype also featured an overview of the space of all possible scenarios based on dimensionality reduction, but this representation was deemed to be be inappropriate for a target audience of people unfamiliar with data analysis. A second prototype with a considerably simplified and streamlined interface was created that supported comparison between multiple scenarios using a flexible approach to aggregation. However, QuestVis was not deployed because of a mismatch between the design goals of the project and the true needs of the target user community, who did not need to carry out detailed analysis of the high-dimensional dataset. We discuss this breakdown in the context of a nested model for visualization design and evaluation.
Cite as

Tamara Munzner, Aaron Barsky, and Matt Williams. Reflections on QuestVis: A Visualization System for an Environmental Sustainability Model. In Scientific Visualization: Interactions, Features, Metaphors. Dagstuhl Follow-Ups, Volume 2, pp. 240-259, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InCollection{munzner_et_al:DFU.Vol2.SciViz.2011.240,
  author =	{Munzner, Tamara and Barsky, Aaron and Williams, Matt},
  title =	{{Reflections on QuestVis: A Visualization System for an Environmental Sustainability Model }},
  booktitle =	{Scientific Visualization: Interactions, Features, Metaphors},
  pages =	{240--259},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-26-2},
  ISSN =	{1868-8977},
  year =	{2011},
  volume =	{2},
  editor =	{Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DFU.Vol2.SciViz.2011.240},
  URN =		{urn:nbn:de:0030-drops-32970},
  doi =		{10.4230/DFU.Vol2.SciViz.2011.240},
  annote =	{Keywords: high-dimensional visualization, dimensionality reduction, linked views, simulation visualization, design study}
}
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