About

David L. Clark is a retired Los Alamos National Laboratory (LANL) Fellow and Guest Scientist with the Laboratory’s Glenn T. Seaborg Institute for Actinide Science. He served as LANL’s Director of the National Security Education Center from 2013 to 2025. His research interests include the molecular and electronic structure of actinide materials, applications of synchrotron radiation to nuclear security, behavior of actinide and fission products in the environment, aging effects in nuclear weapons materials, and the education of judges on the methods of science. Clark is an international authority on the chemistry and physics of the actinides, with nearly 200 peer-reviewed publications, encyclopedia, and book chapters. He is the co-Editor of the six-volume Plutonium Handbook, portions of which were written while a CISAC Visiting Scholar in 2015. He served as the inaugural Director of the Los Alamos Glenn T. Seaborg Institute for Transactinium Science from 1997 to 2009 and has contributed as a technical advisor to the Department of Energy on various environmental stewardship projects, including the Rocky Flats cleanup, Savannah River Site waste tank closures, and the DOE High Level Waste Corporate Board. Clark is a Fellow of the American Association for the Advancement of Science and a Los Alamos Laboratory Fellow. His awards include the ACS Nobel Laureate Signature Award in 1988, the Glenn Seaborg Award in Nuclear Chemistry in 2017, and several Defense Programs Awards of Excellence. He holds a B.S. in chemistry from the University of Washington and a Ph.D. from Indiana University, and he was a postdoctoral fellow at the University of Oxford before joining LANL as a J. Robert Oppenheimer Fellow in 1988.

Research topics

• Computer science
• Physics
• Algorithm
• Artificial intelligence
• Astrophysics

Selected publications

• Calculation of Multi-Target Conditional Mean and Covariance Based on Gaussian Random Fields

  2025-07-07

  articleSenior author

  A conditional multi-target mean and covariance are calculated based on a Gaussian random field approximation of point processes. We derive a particular solution based on a multi-target model commonly used for multi-target tracking. The resulting conditional mean is shown to coincide with the classical first-order filter, while the posterior covariance–owing to the Gaussian approximation–exhibits a refined, localized representation of spatial correlations that contrasts with previous point process derivations. The proposed framework opens new avenues for interdisciplinary research in multi-target tracking, bridging point process theory and field theoretic methods.

  PublisherDOI

• A Functional Quadratic Form Distance for Multi-Target Tracking Performance Assessment

  2025-07-07

  article1st authorCorresponding

  A multi-object miss-distance is proposed for performance evaluation of multi-target tracking algorithms based on a quadratic form for functions. The distance is in the form of a bilinear functional. In this article we consider the functional in the context of multi-target tracking applications and errors between the following (i) two sets of points; (ii) a set of points and a point process intensity; and (iii) two point process intensities. The metric is motivated for performance assessment methods for assessing association errors between sets of targets and multi-target tracking estimates.

  PublisherDOI

• Quadratic error for point patterns

  2025-07-24

  preprintOpen access1st authorCorresponding

  Quantification of error for estimation of spatial point patterns is important for a diverse range of applications in machine learning, spatial statistics, and signal processing. While set-based metrics are commonly used for analysis of point patterns, they lack some essential attributes when compared to the Euclidean distance. For instance, they are often based on heuristic optimisation criteria and do not lend themselves to integration into statistical distances. Since the analogue of mean squared error does not exist for point patterns, we propose a kernel-based quadratic form distance that enables development of a mean quadratic error for point processes. Borrowing concepts from quantum field theory, we also develop a generalisation of the standard score for point processes. We demonstrate the approach in worked examples for a number of different point process parametrisations from simple examples including the Poisson and Bernoulli processes to more complex models including the Cox process. Moreover, we illustrate the proposed methodology through a practical case study for an application in tracking multiple targets from sensor data.

  PublisherOA PDFDOI

• Panel Series – An unconventional approach | The role of Queensland in the east coast gas market

  Australian Energy Producers journal. · 2025-06-19

  article1st authorCorresponding

  Queensland has built a world class gas industry on the back of coal seam gas (CSG) exploration and development, underpinned by international investment in liquified natural gas (LNG) capacity – a combination that remains unprecedented globally. There are now over 14,000 CSG wells across the state, serving domestic demand and facilitating the rollout of renewables while at the same time helping meet the energy needs of the region. And the importance of Queensland natural gas is only increasing with gas production in the Bass Strait in steep decline and barriers to exploration and production remaining across the southern states. But we can’t take Queensland gas for granted – continued investment is needed in exploration and production and in the pipelines to get the gas south. New production will also be critical in Victoria and NSW, near to where it is needed. This panel will discuss how government and industry worked together to make Queensland a global gas powerhouse, and what government and industry needs to do – across the east coast – to deliver the gas so urgently needed in Australia and the region.

  PublisherDOI

• A Mahalanobis Distance for Multi-Target Tracking

  2025-07-07

  articleSenior author

  We propose the use of a novel Mahalanobis distance for comparing point processes in the context of multi-target tracking and sensor fusion. In drawing on methods analogous to free field theory, we derive analytic expressions for key models such as the Poisson and Bernoulli point processes. Moreover, we present efficient Monte Carlo integration schemes that enable practical computation in cases where the point processes are represented by Gaussian mixtures. Simulation results confirm that our proposed distance can be applied in practice.

  PublisherDOI

• An Analysis of the Mutual Information Upper Bound for Sensor-Subset Selection

  2024-07-08

  articleSenior author

  The ability to rapidly select an optimal subset of sensors is of critical importance in massive multi-sensor target tracking. Various information metrics exist for selecting the subset of sensors that is most informative with respect to the target being tracked. Moreover, information bounds were proposed as approximate metrics in order to speed up the selection algorithms. In this paper, we provide an analysis on the information loss and its impact on the subset selection problem when employing an information upper bound instead of the exact mutual information metric. We design several greedy sensor-selection algorithms that sequentially evaluate the exact mutual information between a set of sensors and the target. Subsequently, we compare these algorithms with a sensor-selection method that employs an information upper bound and highlight situations where the latter finds sub-optimal solutions.

  PublisherDOI

• Keynote Panel: Energy Superpower | Australia’s role in the region

  Australian Energy Producers journal. · 2024-06-07

  article1st authorCorresponding

  Australia’s world leading LNG industry has underpinned our economy for decades – attracting hundreds of billions in foreign investment, injecting tens of billions into state and federal budgets and employing tens of thousands of Australians. LNG delivers secure, affordable energy to the region while supporting the shift away from coal and the roll-out of renewable energy. Current energy and climate policies in South-East Asia are expected to lead to a 10-fold increase in LNG demand as these economies transform to net zero. Despite this, there are growing perceptions that Australia is no longer the reliable, trusted partner while the US and Qatar and other LNG producing nations ramp up production to meet this growing regional demand. This session discusses the growing importance of LNG in the delivery of secure, lower emissions energy and whether Australia is willing and able to continue as a key energy player in the region. The session also considers if and when low-carbon hydrogen exports and CO2 imports can complement Australia’s LNG sector.

  PublisherDOI

• Centralized multi-sensor multi-target data fusion with tracks as measurements

  2023-06-14 · 2 citations

  article

  Tracking systems often provide sets of tracks rather than raw detections obtained from sensors. Integrating these track sets into other tracking systems is challenging because the usual sensor models do not apply. In this work we present a method for fusing track data from multiple sensors in a central fusion node. The algorithm exploits the covariance intersection algorithm as a pseudo-Kalman filter which is integrated into a multi-sensor multi-target tracker within a Bayesian paradigm. This makes it possible to (i) integrate the proposed fusion method seamlessly into any existing tracker; (ii) modify multi-target trackers to take a set of tracks as a set of measurements; and (iii) perform gating to enable data association between tracks. The described method is demonstrated in simulations using several target trackers within the Stone Soup tracking framework.

  PublisherDOI

• A conceptual multi-object Kalman filter based on an approximate Gaussian point process

  2023-06-28

  article1st authorCorresponding

  A conceptual Kalman filter for random fields is proposed for estimating multiple objects. The result exploits an approximation of point processes with Gaussian random fields. The motivation is to develop a solution for multi-object filtering problem in terms of the first two moments of a random field. Applications are discussed for a multi-target tracking model.

  PublisherDOI

• Stochastic flows – a primer on early multi-object filtering work with point processes

  2023-06-28 · 5 citations

  article1st authorCorresponding

  Multi-object filtering is a generalisation of stochastic filtering to deal with an unknown and time-varying number of targets, largely based on modelling with point processes. Some early works on this topic from the Soviet Union from 1960s-l980s appeared prior to well known results in the contemporary liter-ature. This article reviews some of these historical contributions.

  PublisherDOI

Frequent coauthors

• I. W. Harry

  University of Portsmouth

  234 shared

• J. D. E. Creighton

  229 shared

• B. Barr

  University of Glasgow

  218 shared

• J. van den Brand

  204 shared

• R. L. Ward

  University of Glasgow

  184 shared

• E. Chassande–Mottin

  Laboratoire AstroParticule et Cosmologie

  177 shared

• B. Willke

  Max Planck Institute for Gravitational Physics

  171 shared

• B. F. Schutz

  Max Planck Institute for Gravitational Physics

  170 shared

Awards & honors

• Nobel Laureate Signature Award (1988)
• Glenn Seaborg Award in Nuclear Chemistry (2017)
• Fellow of the American Association for the Advancement of Sc…
• Los Alamos Laboratory Fellow
• Defense Programs Awards of Excellence