Vijaya Kolachalama

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Associate Professor

Boston University

United States

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About

Dr. Vijaya Kolachalama is an Associate Professor at Boston University in the United States. His research focuses on neuroimaging abnormalities, cognitive assessment tools, and the fusion of multimodal data for disease biomarker evaluation, particularly in Alzheimer's disease and cancer. His recent work also includes investigating the use of machine learning to analyze gait alterations and the effects of treatments on patient outcomes.

Recent Grants

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Integrated Digital Technology Platform for Optimization of Precision Brain Health

Open Date: 2020-07-01

Close Date: 2024-06-30

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Collaborators (16)

Ludy Shih

Boston University

UNITED STATES

Vijaya Kolachalama

Research Interests

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About

Recent Grants

Articles (25)

Anatomy‐Guided, Modality‐Agnostic Segmentation of Neuroimaging Abnormalities

Practice effects on digital cognitive assessment tools: insights from the defense automated neurobehavioral assessment battery

AI-driven fusion of multimodal data for Alzheimer’s disease biomarker assessment

Obfuscation via pitch‐shifting for balancing privacy and diagnostic utility in voice‐based cognitive assessment

Graph Attention-Based Fusion of Pathology Images and Gene Expression for Prediction of Cancer Survival

Graph Perceiver Network for Lung Tumor and Bronchial Premalignant Lesion Stratification from Histopathology

Alleviating iatrogenic effects of paclitaxel via antiinflammatory treatment

Survival analysis on subchondral bone length for total knee replacement

Connected Speech Fluency in Poststroke and Progressive Aphasia: A Scoping Review of Quantitative Approaches and Features

Gait Alterations and Association With Worsening Knee Pain and Physical Function: A Machine Learning Approach With Wearable Sensors in the Multicenter Osteoarthritis Study

Disease‐driven domain generalization for neuroimaging‐based assessment of Alzheimer's disease

Hydrophilic Coating Microstructure Mediates Acute Drug Transfer in Drug-Coated Balloon Therapy

Adversarial Learning for MRI Reconstruction and Classification of Cognitively Impaired Individuals

Global amyloid burden enhances network efficiency of tau propagation in the brain

Gait, physical activity and tibiofemoral cartilage damage: a longitudinal machine learning analysis in the Multicenter Osteoarthritis Study

Machine Learning in Clinical Trials: A Primer with Applications to Neurology

Interpretable Machine Learning on Metabolomics Data Reveals Biomarkers for Parkinson’s Disease

Understudied factors in drug‐coated balloon design and evaluation: A biophysical perspective

Large Language Models in Neurology Research and Future Practice

A Graph-Transformer for Whole Slide Image Classification

Machine learning and pre-medical education

Carotid Intima-Media Thickness and Midlife Cognitive Function

Deep learning for subtyping the Alzheimer’s disease spectrum

Detection of dementia on voice recordings using deep learning: a Framingham Heart Study

Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

Collaborators (16)

Ludy Shih

Yi Zheng

Kerry Costello

David Felson

Ali Guermazi

Ioannis Ch. Paschalidis

Frank W. Roemer

Deepak Kumar

S. Reza Jafarzadeh

William Donald

Neil Segal

Swathi Kiran

Owen Carmichael

Cara Lewis

Mark Uline

Jennifer Beane-Ebel

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