Pooyan Jamshidi

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I am an assistant professor in the Computer Science and Engineering Department at the University of South Carolina. My goal is to advance a scientific, principled understanding of machine learning systems, with an eye towards computer systems analysis (e.g., understanding their performance behavior, reasoning about qualities and making tradeoff) informed by careful empirical work. Naturally, I am also interested in connections among software engineering, computer systems, and machine learning.

machine learning cloud architecture transfer learning self-adaptive software performance tuning highly configurable software configuration optimization devops artificial intelligence big data cloud computing software engineering software architecture uncertainty auto-scaling machine learning systems performance analysis performance engineering apache storm apache cassandra microservices migration pattern elasticity ai causality causal inference systems robotics model learning model prediction predictive models bayesian optimization response surface stream processing system design of experiment apache kafka apache hadoop gaussian process fuzzy control cloud resource provisioning adaptive system autonomic computing multi-cloud cloud architecture pattern windows azure cloud migration software architecture evolution uai2020 chain graphs thesis dissertation viva phd planning ai security deep learning adversarial machine learning highly-configurable systems model-based adaptation darpa brass architectural tradeoffs distributed systems software performance models exploratory study sensitivity analysis adaptive robotic software auto-tuning cassandra software testing storm feedback-driven development continuous delivery knowledge sharingcloud computing reinforcement learning knowledge evolution self-learning software connectors cloud auto-scaling runtime adaptation dynamic resource provisioning amazon cloud application modernization openstack google cloud cloud design pattern method engineering elastic software evidence-based and empirical study systematic literature review architecture-centric software evolution software evolution change pattern pattern-based software evolution model driven development

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Pooyan Jamshidi

A Framework for Model-Driven Evolution in Families of Software Architectures

An Information Retrieval Based Approach for Measuring Service Conceptual Cohesion

Transfer Learning for Performance Analysis of Machine Learning Systems

Transfer Learning for Performance Analysis of Highly-Configurable Software

Workload Patterns for Quality-driven Dynamic Cloud Service Configuration and Auto-scaling

Configuration Optimization for Big Data Software

Learning LWF Chain Graphs: A Markov Blanket Discovery Approach

Transfer Learning for Improving Model Predictions in Robotic Systems

I Bet You Did Not Know That Software Can Adapt Itself On-the-fly?

Learning to Sample

Configuration Optimization Tool

Integrated Model Discovery and Self-Adaptation of Robots

Ensembles of Many Diverse Weak Defenses can be Strong: Defending Deep Neural Networks Against Adversarial Attacks

Transfer Learning for Performance Analysis of Configurable Systems:A Causal Analysis

A Framework for Robust Control of Uncertainty in Self-Adaptive Software Connectors

Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots