465 citations · 910 across the 22 of their papers we have counts for
23 papers
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
Hector Zenil, Jesper Tegnér, Felipe S. Abrahão +17
Recent advances in machine learning and AI, including Generative AI and LLMs, are disrupting technological innovation, product development, and society as a whole. AI's contributio…
Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models
Lijing Wang, Takuya Kurihana, Aurelien Meray +6
Soil and groundwater contamination is a pervasive problem at thousands of locations across the world. Contaminated sites often require decades to remediate or to monitor natural at…
Physical Computing for Materials Acceleration Platforms
Erik Peterson, Alexander Lavin
A ''technology lottery'' describes a research idea or technology succeeding over others because it is suited to the available software and hardware, not necessarily because it is s…
The Unreasonable Effectiveness of Deep Evidential Regression
Nis Meinert, Jakob Gawlikowski, Alexander Lavin
There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncer…
Simulation Intelligence: Towards a New Generation of Scientific Methods
Alexander Lavin, David Krakauer, Hector Zenil +21
The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of comput…
Digital Twin Earth -- Coasts: Developing a fast and physics-informed surrogate model for coastal floods via neural operators
Peishi Jiang, Nis Meinert, Helga Jordão +8
Developing fast and accurate surrogates for physics-based coastal and ocean models is an urgent need due to the coastal flood risk under accelerating sea level rise, and the comput…
Multivariate Deep Evidential Regression
Nis Meinert, Alexander Lavin
There is significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncerta…
Generating Physically-Consistent Satellite Imagery for Climate Visualizations
Björn Lütjens, Brandon Leshchinskiy, Océane Boulais +11
Deep generative vision models are now able to synthesize realistic-looking satellite imagery. But, the possibility of hallucinations prevents their adoption for risk-sensitive appl…
Technology Readiness Levels for Machine Learning Systems
Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic +12
The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of dilig…
Learnings from Frontier Development Lab and SpaceML -- AI Accelerators for NASA and ESA
Siddha Ganju, Anirudh Koul, Alexander Lavin +3
Research with AI and ML technologies lives in a variety of settings with often asynchronous goals and timelines: academic labs and government organizations pursue open-ended resear…
Physics-informed GANs for Coastal Flood Visualization
Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa +8
As climate change increases the intensity of natural disasters, society needs better tools for adaptation. Floods, for example, are the most frequent natural disaster, but during h…
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels
Alexander Lavin
We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural…
Manifolds for Unsupervised Visual Anomaly Detection
Louise Naud, Alexander Lavin
Anomalies are by definition rare, thus labeled examples are very limited or nonexistent, and likely do not cover unforeseen scenarios. Unsupervised learning methods that don't nece…
Technology Readiness Levels for AI & ML
Alexander Lavin, Gregory Renard
The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence…
Fine-Grain Few-Shot Vision via Domain Knowledge as Hyperspherical Priors
Bijan Haney, Alexander Lavin
Prototypical networks have been shown to perform well at few-shot learning tasks in computer vision. Yet these networks struggle when classes are very similar to each other (fine-g…
Doubly Bayesian Optimization
Alexander Lavin
Probabilistic programming systems enable users to encode model structure and naturally reason about uncertainties, which can be leveraged towards improved Bayesian optimization (BO…
Cortical Microcircuits from a Generative Vision Model
Dileep George, Alexander Lavin, J. Swaroop Guntupalli +3
Understanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence. The theoretical setting of Bayesian infer…
Clustering Time-Series Energy Data from Smart Meters
Alexander Lavin, Diego Klabjan
Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage p…
Finite Element-Based Structural Optimization of Large System Models Under Buckling Constraints
Alexander Lavin, Giovanni Greco, Kenjji Shimada
Optimization of large structures of multiple components is essential to many industries for minimizing mass, especially the design of aerospace vehicles. Optimizing a single primar…
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark
Alexander Lavin, Subutai Ahmad
Much of the world's data is streaming, time-series data, where anomalies give significant information in critical situations; examples abound in domains such as finance, IT, securi…
A Pareto Optimal D* Search Algorithm for Multiobjective Path Planning
Alexander Lavin
Path planning is one of the most vital elements of mobile robotics, providing the agent with a collision-free route through the workspace. The global path plan can be calculated wi…
Optimized Mission Planning for Planetary Exploration Rovers
Alexander Lavin
The exploration of planetary surfaces is predominately unmanned, calling for a landing vehicle and an autonomous and/or teleoperated rover. Artificial intelligence and machine lear…
A Pareto Front-Based Multiobjective Path Planning Algorithm
Alexander Lavin
Path planning is one of the most vital elements of mobile robotics. With a priori knowledge of the environment, global path planning provides a collision-free route through the wor…