Conference Proceedings

Predicting cognitive impairment using novel functional features of spatial proximity and circularity in the digital clock drawing test

Alzheimer’s & Dementia (Supplement; Conference Proceedings) (2025). Abstract Background: The digital clock drawing test (dCDT) is a cognitive screening tool employing a digital pen to capture high-resolution pen movements. Traditional dCDT approaches to predict cognitive outcomes often rely on many summary features (e.g. time to completion, mean pressure, clock face area, etc.) which involve subjective decisions such as feature selection and imputation of missing data. To address these limitations, we introduce novel dCDT features, expressed as mathematical functions.

Lifelong trends in vascular risk factors and cerebral small vessel disease

American Heart Association International Stroke Conference (ISC) 2025 Proceedings (2025). Abstract Background & Hypothesis. Cerebral small vessel disease (CSVD) contributes to stroke and dementia risk and may evolve over decades. The study posited that people follow distinct lifetime trajectories of modifiable vascular risk factors (VRFs) and that these VRF patterns are associated with CSVD burden later in life. Methods. Using Framingham Heart Study participants with at least six lifetime VRF assessments and brain MRI markers of CSVD, the team derived a multi‑marker CSVD score (range 0–5; components included covert infarcts, cerebral microbleeds, extensive white‑matter hyperintensities, cortical superficial siderosis, and high perivascular space burden) and grouped it as 0, 1, or ≥2.

Quantitative Pupillometry Post‑Surgical Decompression Predicts Severe Neurologic Disability in Patients with Acute Brain Injury

Neurocritical Care Society Annual Meeting (2024). Accepted as a poster at the conference.

Low‑parameter supervised learning models can discriminate pseudoprogression and true progression in non‑perfusion‑based MRI

IEEE Engineering in Medicine and Biology Society (EMBS) Annual International Conference (EMBC) Proceedings (2023). Abstract Discrimination of pseudoprogression and true progression is challenging in malignant gliomas. We investigate low‑parametric supervised learning (geographically weighted regression; GWR) on widely available MRI modalities—including ADC—to distinguish pseudoprogression from true progression. Applying GWR to modality pairs is suitable for small samples and novel in this setting. Modality pairs involving ADC and those regressing post‑contrast T1 onto T2 showed promise.

Spatial risk estimation in Tweedie compound Poisson double generalized linear models

International E-Conference on Mathematical and Statistical Sciences: A Selçuk Meeting (ICOMSS 2022) Proceedings Abstract: Tweedie exponential dispersion family constitutes a fairly rich sub-class of the celebrated exponential family. In particular, a member, compound Poisson gamma (CP-g) model has seen extensive use over the past decade for modeling mixed response featuring exact zeros with a continuous response from a gamma distribution. This paper proposes a framework to perform residual analysis on CP-g double generalized linear models for spatial uncertainty quantification.

A dynamical systems approach to systemic risk in a financial network

Indian Control Conference (ICC 2016) Proceedings. Abstract The insolvency of a financial entity such as a bank can trigger a sequence of defaults in a network of financial entities interconnected through mutual financial obligations, thus posing a systemic risk to all the financial entities that make up the network. This paper studies the well-known Eisenberg–Noe model for systemic risk from a dynamical systems perspective. In particular, we model the sequence of defaults in the form of a dynamical system, and provide results on its stability and asymptotic behavior.