Rahul Kumar Sevakula, Ph.D.

Rahul Kumar Sevakula, Ph.D.

Harvard University

H-index: 17

North America-United States

About Rahul Kumar Sevakula, Ph.D.

Rahul Kumar Sevakula, Ph.D., With an exceptional h-index of 17 and a recent h-index of 13 (since 2020), a distinguished researcher at Harvard University, specializes in the field of Machine Learning, Signal Processing, Precision Healthcare, Deep learning, Wearables.

His recent articles reflect a diverse array of research interests and contributions to the field:

MVPC—A Classifier with Very Low VC Dimension

Stacked Denoising Sparse Autoencoder-Based Fuzzy Rule Classifiers

Framework for Reliable Fault Detection with Sensor Data

Methods Used to Improve Generalization Performance

Membership Functions for Fuzzy Support Vector Machine in a Noisy Environment

Improving Classifier Generalization: Real-Time Machine Learning Based Applications

Real-Time Arrhythmia Detection in Intensive Care Unit Using a Hybrid Convolutional Neural Network Approach

Ambulatory monitoring promises equitable personalized healthcare delivery in underrepresented patients

Rahul Kumar Sevakula, Ph.D. Information

University

Position

Instructor at Harvard Medical School, Masachusetts General Hospital

Citations(all)

1039

Citations(since 2020)

784

Cited By

590

hIndex(all)

17

hIndex(since 2020)

13

i10Index(all)

25

i10Index(since 2020)

19

Email

University Profile Page

Harvard University

Google Scholar

View Google Scholar Profile

Rahul Kumar Sevakula, Ph.D. Skills & Research Interests

Machine Learning

Signal Processing

Precision Healthcare

Deep learning

Wearables

Top articles of Rahul Kumar Sevakula, Ph.D.

Title

Journal

Author(s)

Publication Date

MVPC—A Classifier with Very Low VC Dimension

Rahul Kumar Sevakula

Nishchal K Verma

2022/9/30

Stacked Denoising Sparse Autoencoder-Based Fuzzy Rule Classifiers

Rahul Kumar Sevakula

Nishchal K Verma

2022/9/30

Framework for Reliable Fault Detection with Sensor Data

Rahul Kumar Sevakula

Nishchal K Verma

2022/9/30

Methods Used to Improve Generalization Performance

Rahul Kumar Sevakula

Nishchal K Verma

2022/9/30

Membership Functions for Fuzzy Support Vector Machine in a Noisy Environment

Rahul Kumar Sevakula

Nishchal K Verma

2022/9/30

Improving Classifier Generalization: Real-Time Machine Learning Based Applications

Rahul Kumar Sevakula

Nishchal K Verma

2022/9/29

Real-Time Arrhythmia Detection in Intensive Care Unit Using a Hybrid Convolutional Neural Network Approach

Circulation

Sandeep Chandra Bollepalli

Rahul K Sevakula

Wan-Tai M Au-Yeung

Mohamad Kassab

Faisal M Merchant

...

2021/11/16

Ambulatory monitoring promises equitable personalized healthcare delivery in underrepresented patients

Kanchan Kulkarni

Rahul Kumar Sevakula

Mohamad B Kassab

John Nichols

Jesse D Roberts Jr

...

2021/9/1

Real-time machine learning-based intensive care unit alarm classification without prior knowledge of the underlying rhythm

European Heart Journal-Digital Health

Wan-Tai M Au-Yeung

Rahul K Sevakula

Ashish K Sahani

Mohamad Kassab

Richard Boyer

...

2021/9/1

On fine-tuning of Autoencoders for Fuzzy rule classifiers

arXiv preprint arXiv:2106.11182

Rahul Kumar Sevakula

Nishchal Kumar Verma

Hisao Ishibuchi

2021/6/21

Machine learning for failure event identification and prediction

2021/1/28

State of the art in artificial intelligence and machine learning techniques for improving patient outcomes pertaining to the cardiovascular and respiratory systems

Wan-Tai M Au-Yeung

Rahul Kumar Sevakula

Jagmeet P Singh

E Kevin Heist

Eric M Isselbacher

...

2021

Real‐time arrhythmia detection using hybrid convolutional neural networks

Journal of the American Heart Association

Sandeep Chandra Bollepalli

Rahul K Sevakula

Wan‐Tai M Au‐Yeung

Mohamad B Kassab

Faisal M Merchant

...

2021/12/7

Machine Learning Based Estimation of Systolic and Diastolic Arterial Blood Pressure From the Electrocardiogram and Oxygen Saturation Waveforms

Circulation

Rahul Kumar Sevakula

Mohamad Kassab

Sandeep Chandra Bollepalli

Eric M Isselbacher

Antonis Armoundas

2020/11/17

Balanced binary search tree multiclass decomposition method with possible non-outliers

SN Applied Sciences

Rahul Kumar Sevakula

Nishchal Kumar Verma

2020/6

State‐of‐the‐art machine learning techniques aiming to improve patient outcomes pertaining to the cardiovascular system

Rahul Kumar Sevakula

Wan‐Tai M Au‐Yeung

Jagmeet P Singh

E Kevin Heist

Eric M Isselbacher

...

2020/2/18

See List of Professors in Rahul Kumar Sevakula, Ph.D. University(Harvard University)

Co-Authors

H-index: 81
Eric Isselbacher

Eric Isselbacher

Harvard University

H-index: 38
Antonis Armoundas

Antonis Armoundas

Harvard University

H-index: 37
Vikas Singh

Vikas Singh

University of Louisville

H-index: 29
Nishchal K Verma

Nishchal K Verma

Indian Institute of Technology Kanpur

H-index: 13
Ashish Sahani

Ashish Sahani

Indian Institute of Technology Ropar

H-index: 13
Mohamad B. Kassab, MD

Mohamad B. Kassab, MD

Harvard University

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