Paul Bogdan

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Assistant Professor

University of Southern California

United States

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About

Paul Bogdan is an Assistant Professor at the University of Southern California, USA. His research areas include the spiking dynamics of neurons, deep learning applications in brain aging and neurocognitive changes, and machine learning for plant disease prediction. He is also interested in dynamical systems and computational frameworks for analyzing network behaviors and developing programmable computing systems.

Recent Grants

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CAREER: Embracing Complexity: A Fractal Calculus Approach to the Modeling and Optimization of Medical Cyber-Physical Systems

Open Date: 2015-05-15

Close Date: 2020-04-30

Articles (15)

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2020

Collaborators (6)

Emily Pereira

Assistant Professor

Texas Tech University

UNITED STATES

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David Cook

Associate Professor

University of Kansas

UNITED STATES

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Guilherme Ramos

University of Lisbon

PORTUGAL

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Mark Hammig

University of Michigan

UNITED STATES

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Anne Thessen

Visiting Associate Professor

University of Colorado Anschutz Medical Campus

UNITED STATES

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Sérgio Pequito

Uppsala University

SWEDEN

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Paul Bogdan

Research Interests

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About

Recent Grants

Articles (15)

Machine learning‐based identification of general transcriptional predictors for plant disease

Spiking dynamics of individual neurons reflect changes in the structure and function of neuronal networks

Deep learning to quantify the pace of brain aging in relation to neurocognitive changes

A Graph-Based Learning Framework for Compiler Loop Auto-Vectorization

Phasor Measurement Unit Change-Point Detection of Frequency Hurst Exponent Anomaly With Time-to-Event

A Scalable Distributed Dynamical Systems Approach to Learn the Strongly Connected Components and Diameter of Networks

Fractional Dynamics Foster Deep Learning of COPD Stage Prediction

Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment

The role of long-term power-law memory in controlling large-scale dynamical networks

End-to-end programmable computing systems

Spanning Network Gels from Nanoparticles and Graph Theoretical Analysis of Their Structure and Properties

Hidden network generating rules from partially observed complex networks

A stochastic quantum program synthesis framework based on Bayesian optimization

From Reductionism to Reintegration: Solving society’s most pressing problems requires building bridges between data types across the life sciences

Uncovering New Drug Properties in Target-Based Drug–Drug Similarity Networks

Collaborators (6)

Emily Pereira

David Cook

Guilherme Ramos

Mark Hammig

Anne Thessen

Sérgio Pequito

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