Narges Razavian

Assistant Professor (Research), Center for Healthcare Innovation, NYU Langone Medical Center

Jingshu Liu

Graduate Student, NYU Department of Computer Science

Research: Deep Learning and Natural Language Processing for Clinical Notes

Zachariah Zhang

Graduate Student, NYU Center for Data Science

Research: Deep Learning and Natural Language Processing for Clinical Notes

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Xianzhi (Viola) Cao

Graduate Student, NYU Center for Data Science

Research: Visualization for Deep Learning Models

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Nicolas Coudray

Image Analysis Specialist, Applied Bioinformatics Laboratories, NYU Lagnone Medical Center

Research: Deep Learning for Histopathology and Medical Imaging

Shaivi Kochar

Graduate Student, NYU Tandon School of Engineering

Research: Visualization of Deep Learning Models and Generative Adversarial Networks for Histopathology Models

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Sheng Liu

Graduate Student, NYU Center for Data Science

Research: Deep Learning and Natural Language Processing for Clincial Notes on ICU data

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Jun Chen

Graduate Student, NYU Tandon School of Engineering

Research: Interpretable Deep Learning Models for Classification of Medical Outcomes

Rob Hammond

Graduate Student, NYU Center for Data Science

Data Scientist, NYU Langone Medical Center

Research: Machine Learning Models for Electronic Health Records, Predicting Childhood Obesity

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Anant Gupta

Graduate Student, Courant Institute, Department of Computer Science, NYU

Research: Deep Learning and Machine Learning on Electronic Health Records, Predicting Preventable Diseases

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Deep Learning for Medical Imaging

In collaboration with Bioinformatics Lab, NYU Department of Pathology, and NYU Department of Radiology, we have several projects underway. Projects include but not limited to:

Histopathology Classification
Image Reconstruction for MRI
Classification of Neurological disorders using MRI
etc.

Our recent publications include:
Coudray, N., Moreira, A. L., Sakellaropoulos, T., Fenyo, D., Razavian, N., Tsirigos, A. (2017). Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images using Deep Learning. bioRxiv, https://doi.org/10.1101/197574

We are hiring for the MRI Image Reconstructions Project! Experience with GANs is a plus. Email me for more info.


Machine Learning for Disease Prediction using Electronic Health Records and Claims Data

As part of NYU Predictive Analytics Unit, we continuously focus on improving detection of undiagnosed diseases and early prediction of preventable diseases.

For this purpose, we build models of patient time series of electronic health records, which includes labs, medications, disease history, procedures and clinical notes. Email me if you are interested to work in this area.

Our recent publications include:
Narges Razavian, David Sontag, Temporal Convolutional Neural Networks for Diagnosis from Lab Tests, International Conference on Representation Learning, 2016.
Narges Razavian, Jake Marcus, David Sontag. Multi-task Prediction of Disease Onsets from Longitudinal Laboratory Tests. In Machine Learning for Healthcare Conference, 2016


Natural Language Processing for Clinical Notes

As part of our research on predicting preventable conditions, we are building various NLP models. Email me if you are interested to work in this area.


Recent Publications

Narges Razavian, Saul Blecker, Ann Marie Schmidt, Aaron Smith-McLallen, Somesh Nigam, and David Sontag, "Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors", Big Data. January 2016.

Narges Razavian, David Sontag, "Temporal Convolutional Models of Biomarkers for Disease Diagnosis",2nd Workshop on Data Mining for Medical Informatics: Predictive Analytics, 2015.[PDF]

Narges Razavian, Saul Blecker, David Sontag “Gaussian Processes for interpreting Multiple Prostate Specific Antigen measurements for Prostate Cancer Prediction”, American Medical Association Annual Meeting, November 2015.

Josua Krause, Narges Razavian, Enrico Bertini and David Sontag, “Visual Inspection of Longitudinal Electronic Medical Records”, IEEE Workshop on Visual Analytics in Healthcare, 2015. [code on github]

Josua Krause, Narges Razavian, Enrico Bertini and David Sontag, “Visual Exploration of Temporal Data in Electronic Medical Records”, American Medical Association Annual Meeting, November 2015.

Narges Razavian, Aaron Smith-McLallen, Somesh Nigam, Saul Blecker, Ann-Marie Schmidt, David Sontag, “Predicting Chronic Comorbid Conditions Of Type 2 Diabetes In Newly-Diagnosed Diabetic Patients”, 20th International Society for Pharmaeconomics and Outcomes Research Annual Conference. [Winner of best new investigator poster award]

Narges Razavian,Aaron Smith-McLallen, Somesh Nigam, Saul Blecker, Ann-Marie Schmidt, David Sontag, “Population-level Prediction of Type 2 Diabetes from Insurance Claims and Analysis of Risk Factors” 75th American Diabetes Association Annual Meeting, June 2015.

Rahul Krishnan, Narges Razavian, YD Choi, Somesh Nigam, Saul Blecker, AnnMarie Schmidt, David Sontag "Early Detection of Diabetes from Health Claims" Machine Learning in Healthcare Workshop at NIPS 2013

Narges Razavian, "Continuous Graphical Models for Static and Dynamic Distributions: Application to Structural Biology" PhD thesis proposal, December 2012

Narges Razavian, Christopher J. Langmead, "Kernels for Protein Structure Prediction", NIPS workshop on the Confluence between Kernel Methods and Graphical Models, December 2012 [video]

Narges Razavian, Hetu Kamisetty, Christopher J. Langmead, "Learning generative models of molecular dynamics", Tenth Asia Pacific Bioinformatics Conference (APBC 2012), also in BMC Genomics 2012

Narges Razavian, Hetu Kamisetty, Christopher Langmead, “Expectation Propagation for von-Mises Graphical Models”, NIPS Workshop, Machine Learning in Computational Biology, December 2012

Narges Razavian, Hetu Kamisetty, Christopher Langmead, “ The von Mises Graphical Model: Structure Learning”, NIPS Workshop on Machine Learning in Computational Biology, December 2011

Narges Razavian, Subhodeep Moitra, Hetu Kamisetty, Arvind Ramanathan, Christopher J. Langmead, “Time-Varying Gaussian Graphical Models of Molecular Dynamics Data” Proceedings of 3DSIG 2010 Structural Bioinformatics and Computational Biophysics, Boston, MA. July 9-10, 2010.

Narges Razavian, Selen Uguroglu, Andreas Zollmann. “Species Selection for Phylogeny-Based Motif Detection”, Computational Genomics Technical Report, June 2009

Narges Razavian, Andreas Zollmann. “An Overview of Nonparametric Bayesian Models and Applications to Natural Language Processing”, Languages and Statistics II project report, January 2009

Narges Razavian, Stephan Vogel,“Fixed Length Word Suffix as New Factors in Factored Statistical Machine Translation”, ACL 2010, also presented in LTI Student Research Symposium, September 2010

Narges Razavian, Stephan Vogel,“The Web as a Platform to Build Machine Translation Resources”, International Workshop on Intercultural Collaboration(IWIC2008), Stanford, USA February 2009

Narges Razavian, Fattaneh Taghiyareh,“Embedding Corporate Blogging System in CRM Solutions” Information Technology: New Generations (ITNG), Las Vegas, USA, April 2008.

Mostafa Keikha, Narges Razavian, Farhad Oroumchian, Hassan Seyedrazi,“Document Representation and Quality of Text: An Analysis”, Survey of Text Mining: Clustering, Classification, and Retrieval, Second Edition, Chapter 12, pp219-232,Springer London, ISBN 978-1-84800-046-9, July 2007.


Teachings

Spring 2018 - Deep Learning in Medicine (BMSC-GA 4493, BMIN-GA 3007)

Link to the class syllabus

Link to the class github page: https://github.com/nyumc-dl/BMSC-GA-4493-Spring2018


Open Positions

1) We are continuously looking for new research assistants with good Python programming skills (critical) and experience in deep learning, machine learning and with interest in medical machine learning. Occasionally we also have funding for it, but not always. We also have a postdoc position at medical school available. Send me an email with your resume if you are looking to join my lab!

2) If you are interested in being a TA or Grader for class 'Deep Learning for Medicine' in Spring semester, send me an email.