Vijaya B. Kolachalama, PhD, FAHA
Assistant Professor
Boston University School of Medicine
Dept of Medicine
Computational Biomedicine

BS, Indian Institute of Technology, Kharagpur, India
PhD, University of Southampton, UK




The Kolachalama laboratory is broadly focused on developing advanced machine learning algorithms to tackle complex biomedical datasets, with particular emphasis on medical imaging.

Phenotyping neurodegeneration using machine learning: We build machine learning frameworks to process multimodal data and identify specific signatures of neurodegeneration. We have experience in dealing with large data cohorts such as the Framingham Heart Study and established several computational pipelines to efficiently process volumetric images of the brain, neuropathology and other modes of data and use them for further analysis.

Digital pathology: We develop computational frameworks based on deep learning to assist the pathologist. Our current application areas include kidney disease, lung cancer and colorectal cancer. 

Machine learning for musculoskeletal diseases: We develop machine learning frameworks to bring efficiency to the analysis of large-scale studies such as Osteoarthritis Initiative (OAI) and Multicenter osteoarthritis (MOST) study. We are particularly interested to quantify structures that are responsible for pain and factors that contribute to the progression of knee osteoarthritis.

Devices, drugs and interfaces: We use a multidisciplinary approach to quantify device-artery interactions and the interfacial mechanisms driving the performance of endovascular devices. Previous studies by us and our collaborators have explained the role of physiologic factors in modulating spatiotemporal arterial distribution patterns for drug-eluting devices as a function of intrinsic device design, relative device position and pulsatile nature of blood flow. We have extended models simulating idealized settings of physiology to real world issues and further examine arterial tissue response that varies due to procedural settings, device composition, arterial wall ultrastructure and disease, physiologic changes within complex vascular anatomies, vascular injury and the mode of drug delivery.

Member
Boston University
Whitaker Cardiovascular Institute


Member
Boston University
Evans Center for Interdisciplinary Biomedical Research


Founding Assistant Professor
Boston University School of Medicine, Graduate Medical Sciences
Computing & Data Sciences Administration



2021 Karen Toffler Charitable Trust: Toffler Scholar in Neuroscience
2020 Faculty of Computing & Data Sciences, Boston University: Founding Member
2019 Institute for Health System Innovation & Policy, Boston University: Institute Fellow
2019 American Heart Association: Fellow
2018 Hariri Institute for Computing and Computational Science & Engineering, Boston University: Junior Faculty Fellow
2008 United States Food and Drug Administration: ORISE Fellowship
2005 Massachusetts Institute of Technology: Young Researcher Fellowship


Integrated Digital Technology Platform for Optimization of Precision Brain Health
07/01/2020 - 06/30/2024 (Multi-PI)
PI: Vijaya B. Kolachalama, PhD, FAHA
American Heart Association


PRISTINE: Pre-cancer histology identification of Endobronchial biopsies using deep learning
09/01/2020 - 08/31/2022 (Multi-PI)
PI: Vijaya B. Kolachalama, PhD, FAHA
NIH/National Cancer Institute
1R21CA253498-01

Biomarkers of lung squamous premalignant progression using deep learning on pathology images
06/01/2020 - 12/31/2021 (Multi-PI)
PI: Vijaya B. Kolachalama, PhD, FAHA
Johnson & Johnson Enterprise Innovation, Inc.


Mechanisms of drug-coated balloon therapy
07/01/2017 - 06/30/2021 (PI)
American Heart Association


Novel antibody design using deep learning
01/01/2018 - 12/31/2019 (PI)
Visterra, Inc.




Title


Yr Title Project-Sub Proj Pubs

Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.

iCite Analysis       Copy PMIDs To Clipboard

  1. Xue C, Karjadi C, Paschalidis IC, Au R, Kolachalama VB. Detection of dementia on voice recordings using deep learning: a Framingham Heart Study. Alzheimers Res Ther. 2021 08 31; 13(1):146.View Related Profiles. PMID: 34465384; PMCID: PMC8409004; DOI: 10.1186/s13195-021-00888-3;
     
  2. Zhang JD, Baker MJ, Liu Z, Kabir KMM, Kolachalama VB, Yates DH, Donald WA. Medical diagnosis at the point-of-care by portable high-field asymmetric waveform ion mobility spectrometry: a systematic review and meta-analysis. J Breath Res. 2021 Jul 28; 15(4). PMID: 34252887
     
  3. Xu D, Zhou F, Sun W, Chen L, Lan L, Li H, Xiao F, Li Y, Kolachalama VB, Li Y, Wang X, Xu H. Relationship Between Serum Severe Acute Respiratory Syndrome Coronavirus 2 Nucleic Acid and Organ Damage in Coronavirus 2019 Patients: A Cohort Study. Clin Infect Dis. 2021 07 01; 73(1):68-75. PMID: 32720678; PMCID: PMC7454386; DOI: 10.1093/cid/ciaa1085;
     
  4. Zheng Y, Cassol CA, Jung S, Veerapaneni D, Chitalia VC, Ren KYM, Bellur SS, Boor P, Barisoni LM, Waikar SS, Betke M, Kolachalama VB. Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies. Am J Pathol. 2021 08; 191(8):1442-1453.View Related Profiles. PMID: 34033750
     
  5. Weizenbaum EL, Fulford D, Torous J, Pinsky E, Kolachalama VB, Cronin-Golomb A. Smartphone-Based Neuropsychological Assessment in Parkinson's Disease: Feasibility, Validity, and Contextually Driven Variability in Cognition. J Int Neuropsychol Soc. 2021 May 17; 1-13.View Related Profiles. PMID: 33998438
     
  6. Chang GH, Park LK, Le NA, Jhun RS, Surendran T, Lai J, Seo H, Promchotichai N, Yoon G, Scalera J, Capellini TD, Felson DT, Kolachalama VB. Subchondral bone length in knee osteoarthritis: A deep learning derived imaging measure and its association with radiographic and clinical outcomes. Arthritis Rheumatol. 2021 May 11.View Related Profiles. PMID: 33973737
     
  7. Zhou X, Qiu S, Joshi PS, Xue C, Killiany RJ, Mian AZ, Chin SP, Au R, Kolachalama VB. Enhancing magnetic resonance imaging-driven Alzheimer's disease classification performance using generative adversarial learning. Alzheimers Res Ther. 2021 03 14; 13(1):60.View Related Profiles. PMID: 33715635; PMCID: PMC7958452; DOI: 10.1186/s13195-021-00797-5;
     
  8. Amini S, Zhang L, Hao B, Gupta A, Song M, Karjadi C, Lin H, Kolachalama VB, Au R, Paschalidis IC. An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test. J Alzheimers Dis. 2021; 83(2):581-589.View Related Profiles. PMID: 34334396
     
  9. Chang GH, Felson DT, Qiu S, Guermazi A, Capellini TD, Kolachalama VB. Correction to: Assessment of knee pain from MR imaging using a convolutional Siamese network. Eur Radiol. 2020 Dec; 30(12):6968.View Related Profiles. PMID: 32700018
     
  10. Qiu S, Joshi PS, Miller MI, Xue C, Zhou X, Karjadi C, Chang GH, Joshi AS, Dwyer B, Zhu S, Kaku M, Zhou Y, Alderazi YJ, Swaminathan A, Kedar S, Saint-Hilaire MH, Auerbach SH, Yuan J, Sartor EA, Au R, Kolachalama VB. Development and validation of an interpretable deep learning framework for Alzheimer's disease classification. Brain. 2020 06 01; 143(6):1920-1933.View Related Profiles. PMID: 32357201; PMCID: PMC7296847; DOI: 10.1093/brain/awaa137;
     
Showing 10 of 52 results. Show More

This graph shows the total number of publications by year, by first, middle/unknown, or last author.

Bar chart showing 52 publications over 14 distinct years, with a maximum of 10 publications in 2019

YearPublications
20072
20082
20092
20112
20123
20132
20142
20152
20164
20172
20186
201910
20205
20218

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72 East Concord Street, Evans 636
Boston MA 02118
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