Draft:Andrew Moore (Computer scientist)

Early life

Andrew W. Moore grew up in Bournemouth, England, on the country's south coast.^[5]

During his childhood in Bournemouth, he developed an early interest in computing by writing video games for obscure 6502-based personal computers, such as the Tangerine Microtan 65.^[5] This hands-on experience with programming and game development on limited hardware fostered his passion for computer science and influenced his later academic path.

Education

Andrew W. Moore received his undergraduate degree in mathematics and computer science from the University of Cambridge. He remained at Cambridge for his doctoral studies, earning his PhD in computer science in 1991. His thesis, titled Efficient Memory-based Learning for Robot Control, was supervised by William F. Clocksin and explored machine learning techniques applied to robot control, introducing efficient memory-based approaches to enable robots to learn from experience and improve their performance in dynamic environments.^[7][8]

Following completion of his PhD, Moore conducted postdoctoral research at MIT.

Hewlett-Packard Labs and MIT

After completing his undergraduate studies at Cambridge, Andrew W. Moore spent a year working at Hewlett-Packard Research Labs in Bristol, England, before beginning his graduate studies.^[5]

Following his PhD at Cambridge, Moore held a postdoctoral position at the Massachusetts Institute of Technology (MIT) in Chris Atkeson's Robot Learning group. There, he conducted research on robot juggling and manipulation, and demonstrated the use of non-parametric regression to achieve high accuracy in complex tasks such as pool playing.^[5] Moore subsequently joined the faculty at Carnegie Mellon University as an assistant professor.^[5]

Carnegie Mellon University faculty (1993–2006)

Andrew W. Moore joined the Carnegie Mellon University faculty in 1993 as an assistant professor, initially working in the fields of machine learning, reinforcement learning, manufacturing, and algorithms for non-parametric regression.^[3][5]

He was tenured in 2000 and subsequently held professorships in the Computer Science Department, the Machine Learning Department, and the Robotics Institute.^[5][3]

During this period, Moore co-founded a consultancy specializing in statistical data mining applications for manufacturing.^[5]

He also founded the Auton Laboratory at Carnegie Mellon to advance efficient large-scale statistical methods. In 2006, Moore left Carnegie Mellon to join Google.^[5][3]

Google (2006–2014)

In 2006, Andrew W. Moore joined Google as the founding director of its Pittsburgh engineering office, which was established on the Carnegie Mellon University campus.^[9][10]

Under his leadership, the office expanded significantly, growing to employ hundreds of people and becoming an important hub for the company's engineering activities.^[10]

In 2011, Moore was promoted to vice president of engineering for Google Commerce, where he oversaw efforts related to retail-related products.^[10]

During his tenure through 2014, he led essential engineering contributions to several core Google services, including AdWords, Shopping, and Search, along with foundational infrastructure and tools.^[9][10]

Moore departed Google in August 2014 to serve as dean of Carnegie Mellon University's School of Computer Science.^[9][10]

Dean of Carnegie Mellon School of Computer Science (2014–2018)

In August 2014, Andrew W. Moore returned to Carnegie Mellon University as dean of the School of Computer Science (SCS), succeeding Randal Bryant who had served in the role since 2004.^[2] Moore's appointment followed his eight years at Google, where he had founded the company's Pittsburgh engineering office, and was announced in April 2014 by CMU President Subra Suresh, who described Moore as combining "expansive vision, scientific expertise, and leadership strength" well-suited to lead SCS amid computing's growing global importance.^[2]

During his four-year tenure, Moore focused on expanding the school's capacity and diversity in response to surging demand for computer science graduates. The incoming undergraduate class grew from 139 students in 2014 to a record 211 in 2018, with women achieving parity with men in the last three incoming classes.^[3] New undergraduate degrees were introduced in computational biology and artificial intelligence, and outreach programs were launched to engage K–12 students and increase participation from underrepresented minorities in computer science.^[11]

Moore also prioritized leveraging SCS's strengths in artificial intelligence by establishing the CMU AI initiative in 2017. This effort unified AI research across the university, involving more than 200 faculty members from SCS and other departments to collaborate on topics including machine learning, robotics, natural language processing, and societal impacts of AI.^[12][11] Moore advocated for AI's potential benefits while stressing ethical considerations, including testifying before Congress and consulting with government and industry leaders.^[11]

In August 2018, Moore announced he would step down as dean effective at the end of the calendar year to pursue a new professional opportunity, returning to Google.^[3] In his farewell statement, he expressed awe at SCS's ongoing advancements in AI and diversity, crediting students, faculty, and staff for positioning the school as a continuing leader in computer science and robotics.^[3] CMU President Farnam Jahanian praised Moore for building momentum in technology's societal impact and enhancing Pittsburgh's role as a hub of innovation.^[3]

Google Cloud AI (2018–2023)

In September 2018, Google announced that Andrew Moore would rejoin the company to lead Google Cloud AI, succeeding Fei-Fei Li who transitioned to an advisory role and returned to academia.^[13] Moore, who had previously served at Google from 2006 to 2014, stepped down as Dean of Carnegie Mellon University's School of Computer Science at the end of 2018 to assume this position, remaining based in Pittsburgh.^[14]

He began advising immediately and took on full-time leadership of Google Cloud AI in January 2019, focusing on expanding the division's AI technologies and making them accessible to customers across industries. Moore emphasized his commitment to democratizing AI, stating: "I am bursting with excitement about this. I have always deeply believed in the power of technology to improve the state of the world, so for me it's a big opportunity to help Google bring useful AI to all the other industry verticals."^[13]

During his tenure through 2023, Moore oversaw Google Cloud AI as its head (also referred to in some contexts as Vice President and General Manager of the AI division), advancing cloud-based AI offerings and supporting the company's broader mission to foster innovation through responsible AI development. He departed Google Cloud in 2023 to co-found Lovelace AI.

Lovelace AI (2023–present)

In January 2023, Andrew W. Moore co-founded Lovelace AI, a Pittsburgh-based startup, where he serves as CEO.^[15][1]

The company develops AI technologies focused on crisis-ready applications, synthesizing data from diverse sources such as satellite imagery, sensor readings, and text reports to produce reliable, actionable insights for high-stakes decision-making in areas including defense, disaster response, counterterrorism, and protection against adversarial AI systems.^[1][15]

Lovelace AI prioritizes high reliability and rapid processing to support human safety in complex, high-risk environments, with applications for military commanders, risk managers, and other critical users. Moore has described the core challenge as combining AI with rigorous mathematical statistics to interpret hopelessly complex situations quickly and accurately enough to protect lives.^[1]

In April 2023, shortly after the company's founding, Moore was appointed as the first-ever adviser for artificial intelligence, robotics, and autonomous systems to the United States Central Command.^[16]

In 2025, Lovelace AI secured a seed round of approximately $16 million led by RRE Ventures, with funds directed toward product development, talent acquisition, and expanded deployment across defense and commercial sectors. The company collaborates with partners including Nvidia and the National Security Innovation Network.^[1]

Moore's expertise in machine learning and robotics informs Lovelace AI's mission to create trustworthy AI systems for mission-critical use.^[1][15]

Boards

In December 2023, Moore was appointed to the Dropbox Board of Directors. Dropbox cited his expertise in AI, machine learning, and robotics, noting that his experience building AI-powered products would offer perspective as the company invested in AI across its product portfolio and through Dropbox Ventures.^[17]

Statistical machine learning and big data

Andrew W. Moore's research has focused on statistical machine learning and the application of computational statistics to big data, emphasizing efficient algorithms capable of handling massive datasets to uncover patterns and extract meaningful information. His work applies statistical methods and mathematical formulations to large volumes of data from diverse sources, including web searches, astronomy, and medical records, enabling the identification of subtle patterns and the derivation of actionable insights.^[10]

A key aspect of Moore's contributions lies in developing scalable techniques for statistical data mining and computational statistics, particularly through the founding of the Auton Lab at Carnegie Mellon University in 1993. The lab has pioneered methods for performing large-scale statistical operations efficiently, often achieving improvements over prior state-of-the-art performance by several orders of magnitude. These advances have supported applications in areas such as Bayesian networks, data mining, medical informatics, and social network analysis.^[5][18]

Moore has advanced non-parametric regression and related techniques, including kernel methods and locally weighted learning, which provide flexible modeling without rigid parametric assumptions and are well-suited to complex, high-dimensional data. His research also encompasses density estimation, Gaussian mixture models, and probabilistic frameworks for large-scale inference.^[5][19]

To disseminate knowledge in these fields, Moore created extensive online tutorials covering foundational and advanced topics in statistical machine learning and big data. These include probability and density estimation, Bayesian networks (with coverage of inference, structure learning, and naive Bayes classifiers), non-parametric methods such as instance-based learning, and efficient algorithms for tasks like clustering and regression. These resources have been widely accessed and used in education and practice.^[19] As of early 2025, Moore's work has been cited over 48,700 times according to Google Scholar, making him one of the most-cited researchers in machine learning and artificial intelligence.^[20]

Robotics and reinforcement learning

Andrew W. Moore's contributions to robotics and reinforcement learning began during his doctoral studies at the University of Cambridge, where he focused on efficient machine learning methods for robot control. In his 1991 PhD thesis, Efficient Memory-based Learning for Robot Control, Moore developed memory-based techniques that allowed robots to learn control policies directly from sensory data and experience, emphasizing fast, instance-based generalization over parametric models. These methods were demonstrated through experiments including simulated robot juggling tasks, where the system learned to maintain stable ball manipulation with relatively few trials.^[21][20]

After joining Carnegie Mellon University, Moore advanced reinforcement learning techniques tailored to the challenges of robotic systems, such as high-dimensional state spaces and real-time constraints. In 1993, he co-authored "Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time" with Christopher G. Atkeson, introducing an efficient model-based approach that prioritizes updates to state values with the largest expected change, significantly reducing the number of required interactions and computations compared to standard methods. This algorithm improved the practicality of reinforcement learning for control tasks.^[22]

Moore further addressed scalability in complex environments through variable resolution reinforcement learning. His 1995 paper "Variable Resolution Reinforcement Learning" presented the parti-game algorithm, which adaptively partitions state space using kd-trees and incorporates a continuity assumption to minimize unnecessary exploration. The method was validated on robotic tasks, including navigation of a 9-joint snakelike manipulator around obstacles and control of a puck on a non-linear bumpy surface, where it discovered effective strategies with fewer partitions than uniform discretization approaches. These contributions enhanced robots' ability to sense their surroundings and generate appropriate responses in dynamic, high-dimensional settings.^[23][20]

Auton Laboratory

The Auton Laboratory was founded by Andrew W. Moore in 1993 at Carnegie Mellon University.^[18] Moore has served as a founder and director of the lab, which is co-directed by Artur Dubrawski and Jeff Schneider.^[24]

The lab develops efficient algorithms, intelligent data structures, and learning methods for large-scale statistical operations in machine learning and statistical data mining.^[18][24] It emphasizes practical, scalable approaches to detecting patterns in massive datasets while addressing real-world constraints in AI trustworthiness, interpretability, and data readiness.^[18]

The Auton Lab has grown into one of the largest applied machine learning research groups in academia and has produced work deployed across commercial, university, and government settings.^[18] Its algorithms and systems have been applied through collaborations with government agencies including the CDC, the USDA, and the Allegheny County Health Department on initiatives such as monitoring food-borne illness outbreaks, forecasting COVID-19 spread via wastewater analysis, and radiological nuclear threat evaluation.^[25][26] The lab has also partnered with industrial research groups, supported predictive maintenance in safety-critical systems, and spun out concepts into successful startups.^[25][26] Its contributions include anomaly detection and biosurveillance techniques applied in public health and security contexts.^[26]

Notable applications and projects

Moore's work through the Auton Lab has produced several high-impact real-world applications of statistical machine learning and data mining techniques.

In astronomy, Auton Lab algorithms enabled efficient detection of asteroids in the Pan-STARRS telescope project, processing tens of billions of noisy data points to identify potential threats from near-Earth objects.^[5]

In biosurveillance and public health, the lab developed anomaly detection systems for DARPA programs and contributed spatial scan algorithms to the Real-time Outbreak and Disease Surveillance (RODS) system, which monitored hospital admissions and national retail data to detect disease outbreaks. These tools also supported homeland security efforts by enabling tractable searches over trillions of spatial regions daily for anomaly detection.^[5]

For food safety, the lab collaborated with the USDA on the Tip Monitor system, which analyzes patterns in consumer food complaints to identify potential issues in the supply chain.^[5]

In industry, Auton Lab methods supported Mars Inc. with massive-scale inventory management optimization and early-warning systems for detecting changes in product characteristics. For Unilever, the lab developed a system to identify novel marketing segments from consumer data.^[5]

Citizenship

Andrew W. Moore is a naturalized United States citizen, having become a U.S. citizen in 2003.^[5] Moore grew up in Bournemouth, England.^[5]

Residence

Andrew W. Moore resides in Pittsburgh, Pennsylvania.^[27] He has maintained a long-term professional and personal connection to the city through his career at Carnegie Mellon University and subsequent ventures.

Books

Wagner, M.M., Moore, A.W., and Aryel, R.M., eds. (2006). The Handbook of Biosurveillance. Academic Press.

Selected Articles

Moore, A.W. and Atkeson, C.G. (1993). "Prioritized Sweeping: Reinforcement Learning with Less Data and Less Real Time." Machine Learning, 13(1): 103-130.

Wong, W.K., Moore, A.W., Cooper, G.F., and Wagner, M.M. (2005). "What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks." Journal of Machine Learning Research, 6: 1961-1998.

Liu, T., Moore, A.W., and Gray, A. (2006). "New Algorithms for Efficient High-Dimensional Nonparametric Classification." Journal of Machine Learning Research, 7: 1135-1158.

Moore, A.W. (1991). "An Introductory Tutorial on Kd-trees" (from Ph.D. thesis: Efficient Memory-based Learning for Robot Control). University of Cambridge.