Variational is a research initiative exploring the intersection of artificial intelligence, cloud computing, and data systems. We develop novel approaches to distributed intelligence, adaptive systems, and scalable AI infrastructure.
Our work is organized around three interconnected research pillars that define our approach to AI and cloud intelligence
Developing probabilistic machine learning methods that enable efficient inference and uncertainty quantification in distributed cloud environments.
Researching self-optimizing cloud systems that dynamically allocate resources based on workload characteristics and learning algorithm requirements.
Exploring privacy-preserving techniques for collaborative machine learning across organizational boundaries without centralized data collection.
Selected research papers and preprints from our team and collaborators
A framework for distributed Bayesian inference that maintains uncertainty estimates while preserving data privacy across organizational boundaries.
Read PaperDynamic resource allocation algorithms that optimize GPU memory and compute utilization for training large neural networks in cloud environments.
Read PaperModifications to variational autoencoder architectures that significantly reduce energy consumption during training and inference phases.
Read PaperOur research spans across major cloud platforms to ensure practical applicability and broad impact
Research on SageMaker optimizations, Lambda-based inference, and distributed training on EC2 GPU clusters.
SageMaker LambdaWork on Vertex AI optimizations, TPU utilization strategies, and BigQuery ML integration patterns.
Vertex AI TPUResearch on Azure Machine Learning, distributed training with Azure Kubernetes, and cognitive services integration.
Azure ML AKSOur approach combines theoretical rigor with practical experimentation in real-world cloud environments
Identifying fundamental challenges in AI systems when deployed at scale in cloud environments, focusing on efficiency, scalability, and reliability.
Developing mathematical frameworks and algorithms that address identified challenges, with particular focus on variational methods and probabilistic approaches.
Building prototype systems on major cloud platforms, conducting rigorous experimentation, and validating theoretical predictions with empirical data.
Publishing findings in peer-reviewed venues, releasing open-source implementations, and collaborating with industry and academic partners.
Our interdisciplinary team brings together expertise in machine learning, distributed systems, and cloud infrastructure
Formerly at Google Brain, focuses on distributed machine learning and variational inference methods for large-scale systems.
Expert in cloud-native architectures and resource optimization for AI workloads across multiple cloud providers.
Specializes in privacy-preserving machine learning, differential privacy, and secure distributed computation.
Conferences, workshops, and seminars where our research will be presented
Presentation of our latest work on variational federated learning and adaptive cloud resource allocation for large-scale model training.
Event DetailsKeynote presentation on the future of variational methods in cloud-native AI systems and their implications for industry applications.
Event DetailsDatasets, code, and educational materials produced by our research initiatives
Open-source implementations of our research algorithms and frameworks for variational inference in cloud environments.
View on GitHubCurated datasets for benchmarking distributed learning algorithms and cloud AI performance across different infrastructure configurations.
Access DatasetsEducational materials covering variational methods, cloud AI deployment, and distributed machine learning concepts.
Learn MoreWe welcome research collaborations with academic institutions, industry partners, and fellow researchers interested in advancing cloud intelligence through variational methods.
Contact Research Team