Petar M Djuric

Stony Brook University | Stony Brook · Department of Electrical and Computer Engineering

Causal discovery with time series data remains a challenging yet increasingly important task across many scientific domains. Convergent cross mapping (CCM) and related methods have been proposed to study time series that are generated by dynamical systems, where traditional approaches like Granger causality are unreliable. However, CCM often yields...

Random feature latent variable models (RFLVMs) represent the state-of-the-art in latent variable models, capable of handling non-Gaussian likelihoods and effectively uncovering patterns in high-dimensional data. However, their heavy reliance on Monte Carlo sampling results in scalability issues which makes it difficult to use these models for datas...

Normal pressure hydrocephalus (NPH) represents a unique form of hydrocephalus characterised by the paradox of ventriculomegaly without significant elevations in intracranial pressure, with the clinical triad of gait instability, cognitive impairment, and urinary incontinence. A myriad of neurobiological correlates have been implicated in its pathop...

Transportation infrastructure experiences distress due to aging, overuse, and climate changes. To reduce maintenance costs and labor, researchers have developed various structural health monitoring systems. However, the existing systems are designed for short-term monitoring and do not quantify structural parameters. A long-term monitoring system t...

Background and objective: Abnormal ventricular potentials (AVPs) in intracardiac electrograms (EGMs) are frequently considered as markers of arrhythmogenic sites in post-ischemic ventricular tachycardia (VT) during electroanatomic mapping (EAM) procedures. Their detection is strongly operator-dependent and time-consuming. This work explores the ado...

Estimates of movement costs are essential for understanding energetic and life-history trade-offs. Although overall dynamic body acceleration (ODBA) derived from accelerometer data is widely used as a proxy for energy expenditure (EE) in free-ranging animals, its utility has not been tested in species that predominately use body rotations or exploi...

Counterfactual reasoning allows us to explore hypothetical scenarios in order to explain the impacts of our decisions. However, addressing such inquires is impossible without establishing the appropriate mathematical framework. In this work, we introduce the problem of counterfactual reasoning in the context of vector autoregressive (VAR) processes...

Online prediction of time series under regime switching is a widely studied problem in the literature, with many celebrated approaches. Using the non-parametric flexibility of Gaussian processes, the recently proposed INTEL algorithm provides a product of experts approach to online prediction of time series under possible regime switching, includin...

In this paper, we consider a scenario with one UAV equipped with a ULA, which sends combined information and sensing signals to communicate with multiple GBS and, at the same time, senses potential targets placed within an interested area on the ground. We aim to jointly design the transmit beamforming with the GBS association to optimize communica...

In this paper, we propose novel Gaussian process-gated hierarchical mixtures of experts (GPHMEs). Unlike other mixtures of experts with gating models linear in the input, our model employs gating functions built with Gaussian processes (GPs). These processes are based on random features that are non-linear functions of the inputs. Furthermore, the...

In this video article, accompanying the paper “An approach to learning the hierarchical organization of the frontal lobe”, we discuss a data driven approach to learning brain connectivity. Hierarchical models of brain connectivity are useful to understand how the brain can process sensory information, make decisions, and perform other high-level ta...

We introduce Dagma-DCE , an interpretable and model-agnostic scheme for differentiable causal discovery. Current non- or over-parametric methods in differentiable causal discovery use opaque proxies of “independence” to justify the inclusion or exclusion of a causal relationship. We show theoretically and empirically that these proxies may be arb...

In machine learning applications, data are often high-dimensional and intricately related. It is often of interest to find the underlying structure and Granger causal relationships among the data and represent these relationships with directed graphs. In this paper, we study multivariate time series, where each series is associated with a node of a...

In the study of causality, we often seek not only to detect the presence of cause-effect relationships, but also to characterize how multiple causes combine to produce an effect. When the response to a change in one of the causes depends on the state of another cause, we say that there is an interaction or joint causation between the multiple cause...

In this paper, we consider a scenario with one unmanned aerial vehicle (UAV) equipped with a uniform linear array (ULA), which sends combined information and sensing signals to communicate with multiple ground base stations (GBSs) and, at the same time, senses potential targets placed within an interested area on the ground. We aim to jointly desig...

OBJECTIVE Traditional models of intracranial dynamics fail to capture several important features of the intracranial pressure (ICP) pulse. Experiments show that, at a local amplitude minimum, the ICP pulse normally precedes the arterial blood pressure (ABP) pulse, and the cranium is a band-stop filter centered at the heart rate for the ICP pulse wi...

In this chapter, we present Gaussian process-based machine learning solutions to important tasks in computerized fetal heart rate analysis. Gaussian processes provide a powerful Bayesian machinery for learning functions or mappings and have inherent connections with many popular machine learning methods such as support vector machines and neural ne...

In this paper, we propose novel Gaussian process-gated hierarchical mixtures of experts (GPHMEs) that are used for building gates and experts. Unlike in other mixtures of experts where the gating models are linear to the input, the gating functions of our model are inner nodes built with Gaussian processes based on random features that are non-line...

We consider the problem of sequential estimation of the unknowns of state-space and deep state-space models that include estimation of functions and latent processes of the models. The proposed approach relies on Gaussian and deep Gaussian processes that are implemented via random feature-based Gaussian processes. With this model, we have two sets...

Lip‐reading provides an effective speech communication interface for people with voice disorders and for intuitive human–machine interactions. Existing systems are generally challenged by bulkiness, obtrusiveness, and poor robustness against environmental interferences. The lack of a truly natural and unobtrusive system for converting lip movements...

Introduction: During labor, fetal heart rate (FHR) and uterine activity (UA) can be continuously monitored using Cardiotocography (CTG). This is the most widely adopted approach for electronic fetal monitoring in hospitals. Both FHR and UA recordings are evaluated by obstetricians for assessing fetal well-being. Due to the complex and noisy nature...

We are delighted to present you the Proceedings of the 2022 CNS meeting. The CNS meeting encourages approaches that combine theoretical, computational, and experimental work in the neurosciences, and provides an opportunity for participants to share their views. The abstracts corresponding to speakers' talks and posters are what you find collected...

Convergent cross mapping is a principled causal discovery technique for signals, but its efficacy depends on a number of assumptions about the systems that generated the signals. In this work, we present a self-contained introduction to the theory of causality in state-spaces, Takens' theorem, and cross maps, and we propose conditions to check if a...

Dynamic radar networks, usually composed of flying UAVs, have recently attracted great interest for time-critical applications, such as search-and-rescue operations, involving reliable detection of multiple targets and situational awareness through environment radio mapping. Unfortunately, the time available for detection is often limited, and in m...

We consider the problem of sequential estimation of the unknowns of state-space and deep state-space models that include estimation of functions and latent processes of the models. The proposed approach relies on Gaussian and deep Gaussian processes that are implemented via random feature-based Gaussian processes. In these models, we have two sets...

This paper addresses over-the-air synchronization of two distributed receivers connected to a fusion center via digital links. We distinguish between received signals’ synchronization and receiving channels’ synchronization. The applications are digital receive beamforming (BF) and radio source localization, respectively. We introduce the system an...

Arrhythmogenic sites in post-ischemic ventricular tachycardia (VT) are usually identified by looking for abnormal ventricular potentials (AVPs) in intracardiac electrograms (EGMs). Unfortunately, the accurate recognition of AVPs is a challenging problem for different reasons, including the intrinsic variability in the AVP waveform. Given the high p...

Neurological and psychiatric disorders typically result from dysfunction across multiple neural circuits. Most of these disorders lack a satisfactory neuromodulation treatment. However, deep brain stimulation (DBS) has been successful in a limited number of disorders; DBS typically targets one or two brain areas with single contacts on relatively l...

Future networks of unmanned aerial vehicles (UAVs) will be tasked to carry out ever-increasing complex operations that are time-critical and that require accurate localization performance, such as tracking the position of a malicious user. Since there is the need to preserve low UAV complexity while tackling the challenging goals of missions in eff...

We propose a sensing system comprising a large network of tiny, battery-less, Radio Frequency (RF)-powered sensors that use backscatter communication. The sensors use an entirely passive technique to 'sense' the parameters of the wireless channel between themselves. Since the material properties influence RF channels, this fine-grain sensing can un...

We utilized machine learning (ML) methods on data from the PROMOTE, a novel psychosocial screening tool, to quantify risk for prenatal depression for individual patients and identify contributing factors that impart greater risk for depression. Random forest algorithms were used to predict likelihood for being at high risk for prenatal depression (...

Multi-agent robotic networks allow simultaneous observations at different positions while avoiding a single point of failure, which is essential for emergency and time-critical applications. Autonomous navigation is vital to the task accomplishment of a multi-agent network in challenging global navigation satellite systems (GNSS)-denied environment...

Low umbilical artery pH is a marker for neonatal acidosis and is associated with an increased risk for neonatal complications. The phase-rectified signal averaging (PRSA) features have demonstrated superior discriminatory or diagnostic ability and good interpretability in many biomedical applications including fetal heart rate analysis. However, th...

The computer-aided interpretation of fetal heart rate (FHR) and uterine contraction (UC) has not been developed well enough for wide use in delivery rooms. The main challenges still lie in the lack of unclear and nonstandard labels for cardiotocography (CTG) recordings, and the timely prediction of fetal state during monitoring. Rather than traditi...

During the process of childbirth, fetal distress caused by hypoxia can lead to various abnormalities. Cardiotocography (CTG), which consists of continuous recording of the fetal heart rate (FHR) and uterine contractions (UC), is routinely used for classifying the fetuses as hypoxic or non-hypoxic. In practice, we face highly imbalanced data, where...

INTRODUCTION Pseudotumor cerebri is characterized by elevated intracranial pressure (ICP) without ventricular dilation. The pathophysiology of pseudotumor is traditionally viewed as chronic cerebrovenous hypertension which causes impaired cerebrospinal fluid (CSF) resorption. Increased resistance to CSF absorption results in ventriculomegaly in obs...

Fusing probabilistic information is a fundamental task in signal and data processing with relevance to many fields of technology and science. In this work, we investigate the fusion of multiple probability density functions (pdfs) of a continuous random variable or vector. Although the case of continuous random variables and the problem of pdf fusi...

Objective: Pseudotumor cerebri is a disorder of intracranial dynamics characterized by elevated intracranial pressure (ICP) and chronic cerebral venous hypertension without structural abnormalities. A perplexing feature of pseudotumor is the absence of the ventriculomegaly found in obstructive hydrocephalus, although both diseases are associated w...

Fusing probabilistic information is a fundamental task in signal and data processing with relevance to many fields of technology and science. In this work, we investigate the fusion of multiple probability density functions (pdfs) of a continuous random variable or vector. Although the case of continuous random variables and the problem of pdf fusi...

We address the problem of localizing an (unauthorized) transmitter using a distributed set of sensors. Our focus is on developing techniques that perform the transmitter localization in an efficient manner, wherein the efficiency is defined in terms of the number of sensors used to localize. Localization of unauthorized transmitters is an important...

Background: Traditional models of intracranial dynamics fail to capture several important features of the cerebral windkessel. Experiments show that the cerebral windkessel is a band-stop filter tuned to the heart rate, which is not consistent with existing pressure-volume compartment models. Flow MRI studies in humans reveal expansion and relaxati...

Major theories of consciousness predict that complex electroencephalographic (EEG) activity is required for consciousness, yet it is not clear how such activity arises in the corticothalamic system. The thalamus is well-known to control cortical excitability via interlaminar projections, but whether thalamic input is needed for complexity is not kn...

The ability to quantify the strength of an interaction between events represented by random variables is important in many applications such as medicine and environmental science. We present the problem of measuring the strength of a causal interaction, starting from the linear perspective and generalizing to a nonlinear measure of causal influence...

Background Psychosocial vulnerabilities (e.g. inadequate social support, financial insecurity, stress) and substance use elevate risks for adverse perinatal outcomes and maternal mental health morbidities. However, various barriers, including paucity of validated, simple and usable comprehensive instruments, impede execution of the recommendations...

We investigate the performance of a class of particle filters (PFs) that can automatically tune their computational complexity by evaluating online certain predictive statistics which are invariant for a broad class of state-space models. To be specific, we propose a family of block-adaptive PFs based on the methodology of Elvira et al. (IEEE Trans...

The return of consciousness after traumatic brain injury (TBI) is associated with restoring complex cortical dynamics; however, it is unclear what interactions govern these complex dynamics. Here, we set out to uncover the mechanism underlying the return of consciousness by measuring local field potentials (LFP) using invasive electrophysiological...

During the process of childbirth, fetal distress caused by hypoxia can lead to various abnormalities. Cardiotocography (CTG), which consists of continuous recording of the fetal heart rate (FHR) and uterine contractions (UC), is routinely used for classifying the fetuses as hypoxic or non-hypoxic. In practice, we face highly imbalanced data, where...

Detection of anomalies in time series is still a challenging problem. In this paper, we provide a new approach to unsupervised detection of anomalies in time series based on the concept of phase space reconstruction and manifolds. We propose a rotation-insensitive metric for quantifying the similarity of manifolds and a method that uses it for esti...

Future networks of unmanned aerial vehicles (UAVs) will be tasked to carry out ever-increasing complex operations that are time-critical and that require accurate localization performance (e.g., tracking the state of a malicious user). Since there is the need to preserve low UAV complexity while tackling the challenging goals of missions in effecti...

Applications towards 6G have brought a huge interest towards arrays with a high number of antennas and operating within the millimeter and sub-THz bandwidths for joint communication, sensing, and localization. With such large arrays, the plane wave approximation is often not accurate because the system may operate in the (radiating) near-field prop...