About

Brian Murray is a Research Professor in the Division of Environmental Social Systems at Duke University and serves as the Director of the Nicholas Institute for Energy, Environment & Sustainability. He is also a Research Professor in the Sanford School of Public Policy and an Associate of the Duke Initiative for Science & Society. His work involves discussions on carbon taxes and their role in curbing emissions and combating climate change, as evidenced by his participation in Sanford's Policy 360 podcast. His contact information is available at Duke University, located at 140 Science Drive, Gross Hall, Ste 101, Durham, NC 27708.

Research topics

• Data Mining
• Computer Science
• Artificial Intelligence
• Machine Learning
• Physics
• Engineering

Selected publications

• Effectiveness of a Standardized Approach to Repeat Paroxysmal Atrial Fibrillation Ablation: Insights Into the Value of Extrapulmonary Vein Targets

  Canadian Journal of Cardiology · 2025-03-04 · 2 citations

  articleOpen access

  BACKGROUND: The optimal approach to repeat catheter ablation for recurrent paroxysmal atrial fibrillation (PAF) is unknown. METHODS: Consecutive patients undergoing repeat PAF ablation were studied. The following 6-step approach was used in all cases: re-isolation of reconnected pulmonary veins (PVs); ablation of left atrial low-voltage areas (LVAs); targeted ablation of clinical or inducible atrial flutter/tachycardia; non-PV trigger ablation; ablation of inducible supraventricular tachycardia; and additional empirical ablation based on operator judgement. The primary study outcome was atrial arrhythmia-free survival at 1 year. RESULTS: One hundred thirteen patients were included in the study (mean age 63.7 ± 8.6 years, 28.3% women). In this cohort, 73.5% had PV reconnection(s), 31.9% had LVAs, 10.6% had identifiable non-PV triggers, 5.3% had inducible atrioventricular nodal re-entrant tachycardia, 31.9% underwent atrial flutter/tachycardia ablation, and 12.4% had additional empirical ablation performed. Arrhythmia-free survival at 1 year was 53.1%. Patients with arrhythmia recurrence were more likely to be older, female, have hypertension, have durably isolated PVs, and to have undergone LVA ablation. In multivariable analysis, female sex and LVA ablation remained predictive of arrhythmia recurrence. Among patients with durably isolated PVs, only female sex was (negatively) associated with procedural success. CONCLUSIONS: A comprehensive protocol for repeat PAF ablation resulted in arrhythmia-free survival at 1 year in 53% of patients. Durably isolated PVs were observed in 26.5% of cases. None of the ablation protocol's steps was suggested to independently improve procedural success. Further research to determine the optimal ablation strategy in patients undergoing repeat ablation for PAF is needed, a growing proportion of whom are expected to have durably isolated PVs.

  PublisherOA PDFDOI

• Protected areas can provide net benefits without reducing the loss of ecosystem area

  Research Square · 2025-06-20

  preprintOpen access
  PublisherOA PDFDOI

• Carrots, Sticks, and the Evolution of U.S. Climate Policy

  Texas A&M Law Review · 2024-05-01 · 3 citations

  articleOpen access1st authorCorresponding

  The Inflation Reduction Act (IRA), enacted by Congress in 2022, is the most significant federal investment in decarbonization in U.S. history. The law makes hundreds of billions of dollars available for clean energy tax credits, grants to state and local governments, and other financial incentives for public and private investments. The IRA’s focus on incentives, or “carrots,” marks a significant departure from the emphasis on prescriptive regulations and penalties, or “sticks,” that are prominent in federal and state climate policies that predate the IRA. This Article situates the IRA within the existing climate policy framework and explores the long-term impacts of the new law. The Article begins with an overview of regulations and tax incentives to reduce greenhouse gas emissions leading up to 2007. The Article then discusses the emphasis on pricing carbon through federal Cap-and-Trade legislation from 2003 to 2011, and the return to prescriptive regulation under the Clean Air Act when those federal bills failed. The Article contrasts these efforts with the positive financial incentives included in the IRA, tracking the evolution of the bill and the political and economic circumstances that created the policy window for Congress to pass such an impactful law. The Article concludes with a discussion of the lasting impacts of the IRA and the interplay between the existing policy instruments.

  PublisherOA PDFDOI

• Data-driven construction of maritime traffic networks for AI-based route prediction

  Journal of Physics Conference Series · 2024-10-01 · 2 citations

  articleOpen access

  Abstract Predicting the routes of maritime traffic can improve economic efficiency, decrease ecological impact, and improve safety at sea. Over scales that are small (few hundred meters) and large (dozens to hundreds of kilometers), vessel trajectories have successfully been predicted by deep learning and (static) network-based approaches, respectively. We present an approach for medium to large scales (few kilometers) where (a) a maritime traffic network is automatically constructed from AIS messages, and (b) vessel trajectories are predicted as most likely paths through the network. Using three regions (Stavanger, Tromsø, and Oslo), we show that the network can capture up to ∼ 90 per cent of all maritime traffic (excluding pleasure craft) with a median absolute error of ∼ 80 meters. Vessel paths are sequences of waypoints and legs (nodes and edges) and are map-matched onto the network from vessel trajectories. Once mapped, we predict future paths for two subproblems – (i) known destination, and (ii) unknown destination. We use four algorithms (Dijkstra, Markov, MOGen, GRETEL). For known destinations, we find that Dijkstra performs best. In Stavanger (Tromsø, Oslo), Dijkstra predicts 64 (42, 68) per cent of path segments correctly and keeps the median path error below 15 (33 and 55) meters. For unknown destinations, performance depends on the forecast horizon (the number of legs k to predict). For k ≤ 5, Markov is best and predicts 62 (48, 72) per cent of legs correctly. For k > 5, GRETEL performs best and predicts 54 (47, 63) per cent of legs correctly. For some types of vessels, models improve by considering vessel type. For passenger vessels, models specific to them predict ∼ 10 per cent better paths with half the distance error. For tankers, paths (and distance errors) are 6 (20) per cent worse. For auxiliary vessels, path quality is unchanged, but distance error improves ∼ 36 per cent.

  PublisherDOI

• Associations between cerebral small vessel disease and obstructive sleep apnea in patients with ischemic stroke and TIA (S6.006)

  Neurology · 2023-04-25

  article

  <h3>Objective:</h3> (1) To examine the relationship between OSA severity and Small Vessel Disease (SVD) in patients with ischemic stroke/TIA. (1a) To assess whether these associations may be present only in select brain regions with specific types of SVD. (2) To examine the relationship between OSA, SVD and cognition. <h3>Background:</h3> Cerebral small vessel disease (SVD) is the most common cause of vascular dementia. On MRI SVD manifests as White Matter Hyperintensities (WMH), lacunes, enlarged perivascular spaces and microbleeds. Obstructive Sleep Apnea (OSA) is the most common sleep disorder and meta-analytic data supports a relationship between OSA and SVD. The purpose of this study is to examine relationships between: (1) OSA severity and SVD (2) OSA severity, SVD and cognition. <h3>Design/Methods:</h3> Patients with ischemic stroke/TIA were prospectively recruited across three independent cohort studies. Years of education, vascular risk factors, stroke severity and Montreal Cognitive Assessment scores were collected. All patients completed MRI and either an in-laboratory polysomnography (PSG) or Home Sleep Apnea Test (HSAT). OSA severity was quantified using the Apnea Hypopnea Index (AHI). The burden of small vessel disease was quantified using validated visual rating scales. Ordinal logistic regression models examined relationships between OSA and SVD, while controlling for covariates. <h3>Results:</h3> In 237 patients increasing AHI was associated with a greater burden of periventricular WMH (pWMH) OR=1.02 (CI:1.01 to 1.04, p=0.02), deep microbleeds OR=1.03 (CI:1.01 to 1.05, p=0.002) and lobar microbleeds OR=1.02 (CI:1.01 to 1.04, p=0.03). Finally, in an ordinal logistic regression model, lower cognitive scores were related to cerebral microbleeds OR=1.09 (CI:1.01 to 1.18, p = 0.03) while controlling for covariates. <h3>Conclusions:</h3> OSA severity is associated with greater periventricular WMH and cerebral microbleeds. Cerebral microbleeds are predictive of lower cognitive scores. The relationship between OSA and both lobar and deep microbleeds suggests potential associations with nocturnal hypertension and cerebral amyloid clearance. <b>Disclosure:</b> Dr. Muir has nothing to disclose. Ms. Dharmakulaseelan has nothing to disclose. Dr. Black has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Hoffmann-La Roche. Dr. Black has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Biogen. Dr. Black has received personal compensation in the range of $5,000-$9,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Hoffmann-La Roche. Dr. Black has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Biogen. Dr. Black has received personal compensation in the range of $10,000-$49,999 for serving on a Speakers Bureau for Biogen. The institution of Dr. Black has received research support from Hoffmann-La Roche. The institution of Dr. Black has received research support from Biogen. The institution of Dr. Black has received research support from GE Healthcare. The institution of Dr. Black has received research support from Eli Lilly. The institution of Dr. Black has received research support from Genentech. The institution of Dr. Black has received research support from NovoNordisk. The institution of Dr. Black has received research support from UCB Biopharma. The institution of Dr. Black has received research support from Alkahest Inc. The institution of Dr. Black has received research support from University of Southern California - AHEAD 3-45 Study. The institution of Dr. Murray has received research support from Wake Up Narcolepsy. Dr. Murray has received publishing royalties from a publication relating to health care. Dr. Boulos has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Jazz Pharmaceuticals. Dr. Boulos has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Paladin Labs. Dr. Boulos has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Eisai. Dr. Boulos has received research support from Interaxon. The institution of Dr. Boulos has received research support from The Mahaffy Family Research Fund. The institution of Dr. Boulos has received research support from Canadian Institutes of Health Research. The institution of Dr. Boulos has received research support from Slamen-Fast New Initiatives in Neurology Award. The institution of Dr. Boulos has received research support from Green Mountain . Dr. Boulos has received personal compensation in the range of $5,000-$9,999 for serving as a speaker with Jazz Pharmaceuticals.

  PublisherDOI

• Collision Risk Assessment and Forecasting on Maritime Data

  2023-11-13 · 4 citations

  articleOpen access

  The wide spread of the Automatic Identification System (AIS) and related tools has motivated several maritime analytics operations. One of the most critical operations for the purpose of maritime safety is the so-called Vessel Collision Risk Assessment and Forecasting (VCRA/F), with the difference between the two lying in the time horizon when the collision risk is calculated: either at current time by assessing the current collision risk (i.e., VCRA) or in the (near) future by forecasting the anticipated locations and corresponding collision risk (i.e., VCRF). Accurate VCRA/F is a difficult task, since maritime traffic can become quite volatile due to various factors, including weather conditions, vessel manoeuvres, etc. Addressing this problem by using complex models introduces a trade-off between accuracy (in terms of quality of assessment / forecasting) and responsiveness. In this paper, we propose a deep learning-based framework that discovers encountering vessels and assesses/predicts their corresponding collision risk probability, in the latter case via state-of-the-art vessel route forecasting methods. Our experimental study on a real-world AIS dataset demonstrates that the proposed framework balances the aforementioned trade-off while presenting up to 70% improvement in R2 score, with an overall accuracy of around 96% for VCRA and 77% for VCRF.

  PublisherOA PDFDOI

• Autoencoder-Based Anomaly Detection for Safe Autonomous Ship Operations

  2023-01-01 · 4 citations

  articleOpen access1st authorCorresponding

  The development of autonomous ships is advancing, but ensuring their safe operation remains a challenge.To aid safe operations, autonomous ships are expected to be monitored by humans in a remote operation center.A key challenge is ensuring that human operators remain alert and ready to take control of the system when necessary.Maritime traffic poses a potential hazard to autonomous vessels, and systems to aid the operator in identifying abnormal ship behavior in time should be in place.This study develops deep learning models that automatically detect anomalous ship behavior to aid human operators.A case study related to the remote operation center in Horten, Norway is conducted, where four various autoencoder architectures have been trained on historical Automatic Identification System data to detect maritime traffic anomalies in the Oslo fjord.The models are trained in an unsupervised manner, such that they are able to automatically identify anomalies, without the need for manual labelling.The results indicate that a recurrent autoencoder is the most promising architecture for decision support of remote operators, as it is able to identify a variety of anomalies, with fewer false positives.

  PublisherOA PDFDOI

• CJN volume 50 issue 4 Cover and Front matter

  Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2023-06-29

  articleOpen access

  An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

  PublisherOA PDFDOI

• OnabotulinumtoxinA, Nerve Stimulation Devices, Mirabegron, and Anticholinergics Versus Best Supportive Care for Overactive Bladder: An Updated US Cost-Effectiveness Analysis

  Toxicon · 2022-07-01

  article1st authorCorresponding
  PublisherDOI

• Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis

  arXiv (Cornell University) · 2022-02-18

  reviewOpen access

  High quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, actionable information about energy systems is often of limited availability, incomplete, or only accessible for a substantial fee or through a non-disclosure agreement. Recently, remotely sensed data (e.g., satellite imagery, aerial photography) have emerged as a potentially rich source of energy systems information. However, the use of these data is frequently challenged by its sheer volume and complexity, precluding manual analysis. Recent breakthroughs in machine learning have enabled automated and rapid extraction of useful information from remotely sensed data, facilitating large-scale acquisition of critical energy system variables. Here we present a systematic review of the literature on this emerging topic, providing an in-depth survey and review of papers published within the past two decades. We first taxonomize the existing literature into ten major areas, spanning the energy value chain. Within each research area, we distill and critically discuss major features that are relevant to energy researchers, including, for example, key challenges regarding the accessibility and reliability of the methods. We then synthesize our findings to identify limitations and trends in the literature as a whole, and discuss opportunities for innovation. These include the opportunity to extend the methods beyond electricity to broader energy systems and wider geographic areas; and the ability to expand the use of these methods in research and decision making as satellite data become cheaper and easier to access. We also find that there are persistent challenges: limited standardization and rigor of performance assessments; limited sharing of code, which would improve replicability; and a limited consideration of the ethics and privacy of data.

  PublisherDOI

Frequent coauthors

• Bruce A. McCarl

  Thammasat University

  47 shared

• Gregory S. Merrick

  26 shared

• Robert B. Jackson

  Stanford University

  21 shared

• Estéban G. Jobbágy

  Centro Científico Tecnológico - San Luis

  19 shared

• Robert W. Galbreath

  Ohio University

  19 shared

• Kent E. Wallner

  University of Washington

  18 shared

• Somnath Baidya Roy

  Indian Institute of Technology Delhi

  17 shared

• Roni Avissar

  University of Miami

  17 shared