Petar M. Djuric

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SUNY Distinguished Professor

Stony Brook University

United States

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About

Petar M. Djuric is a SUNY Distinguished Professor at Stony Brook University in the United States. His research areas encompass machine learning applications in neuroscience, dual-function radar-communication systems, and the use of explainable deep learning for identifying arrhythmogenic sites. He has contributed to advancements in RF backscatter networks, smart transportation systems, and causal discovery methodologies.

Recent Grants

Grant: Close

CNS Core: Medium: RF-Based Analytics with Intelligent Backscattering in Passive Tag-to-Tag Networks

Open Date: 2019-10-01

Close Date: 2023-09-30

Grant: Close

Rethinking Electronic Fetal Monitoring to Improve Perinatal Outcomes and Reduce Frequency of Operative Vaginal and Cesarean Deliveries

Open Date: 2019-05-10

Close Date: 2024-03-31

Grant: Close

NeTS: Medium: Collaborative Research: Passive Network of Tags for Smart Spaces

Open Date: 2018-08-01

Close Date: 2022-07-31

Grant: Close

EARS: SpecSense: Bringing Spectrum Sensing to the Masses

Open Date: 2016-10-01

Close Date: 2020-09-30

Grant: Close

CIF: Small: Dynamic Networks: Learning, Inference, and Prediction with Nonparametric Bayesian Methods

Open Date: 2016-07-01

Close Date: 2019-06-30

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AUSTRIA

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Heidi Preis

Adjunct Lecturer

Stony Brook University

UNITED STATES

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Petar M. Djuric

Research Interests

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Contact this professor

About

Recent Grants

Articles (22)

Exploring synergies: Advancing neuroscience with machine learning

Arrhythmogenic sites identification in post-ischemic ventricular tachycardia electrophysiological studies by explainable deep learning

Robust and Energy-Efficient Channel Estimation in RF Backscatter Tag-to-Tag Network

Transdimensional Model Learning With Online Feature Selection Based on Predictive Least Squares

A Vision and Proof of Concept for New Approach to Monitoring for Safer Future Smart Transportation Systems

A Deep-NN Beamforming Approach for Dual Function Radar-Communication THz UAV

Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery

Gaussian Process-Gated Hierarchical Mixtures of Experts

Measuring Strength of Joint Causal Effects

Topology Inference of Directed Graphs by Gaussian Processes With Sparsity Constraints

On Causal Discovery With Convergent Cross Mapping

Sequential Estimation of Gaussian Process-Based Deep State-Space Models

Reinforcement Learning for Joint Detection & Mapping using Dynamic UAV Networks

Over-the-Air Synchronization of Time, Phase and Frequency in a Distributed Receiving System

Networks of UAVs of Low Complexity for Time-Critical Localization

Applying machine learning methods to psychosocial screening data to improve identification of prenatal depression: Implications for clinical practice and research

Fusion of Probability Density Functions

Selection of Sensors for Efficient Transmitter Localization

A Differential Measure of the Strength of Causation

Exploiting Causality for Improved Prediction of Patient Volumes by Gaussian Processes

Bachelor of Science in Electrical Engineering Online: A Journey of Challenges and Triumphs

Bayesian Reconstruction of Fourier Pairs

Collaborators (6)

Giulia Baldazzi

Davide Dardari

Paolo Banelli

Danilo Pani

Franz Hlawatsch

Heidi Preis

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