Purva R. Gawde

Email: add2bv@r.postjobfree.com

Cell-phone: 437-***-****

LinkedIn Profile: https://www.linkedin.com/in/purva-gawde-b1a11b46/ Skype-id: purva.rajendra.gawde

Objective: Online and In-Person teaching position and computer science course development position Research Interests:

Machine Learning, Artificial Intelligence, Health Informatics, Biosignal analysis, Data Science Education:

Ph.D. Computer Science, Department of Computer Science, Kent State University, Ohio, USA, December 2018. Phd Advisor: Professor Arvind Bansal, Department of Computer Science, Kent State University, Ohio Dissertation Title: “Integrated Analysis of Temporal and Morphological features Using Machine Learning Techniques for Real-Time Diagnosis of Arrhythmia and Irregular Beats”. M.S. Computer Science, Department of Computer Science, Kent State University, Ohio, USA, May 2013 B.S. Computer Engineering, S. S. Jondhale engineering (Mumbai University), Mumbai, India. May 2010 Programming languages skills:

C, C++, java, .NET, MATLAB, Simulink, Visual Basics, Python, JavaScript, CLISP, SCALA PhD Dissertation Abstract

Heart diseases are the major causes of morbidity and fatality in senior age group which affect their productivity and lifestyle significantly. ECG is a noninvasive means of maintaining healthy heart. One of the major abnormalities of heart is arrhythmia that consists of irregular heartbeats due to ectopic nodes. Currently available systems lack enough accuracy and finer real time classification, which affects the treatment. In this research, machine learning and parallelization techniques have been developed for the real-time analysis of ECG for diagnosing the finer classes of arrhythmias and irregular heartbeats. An integrated approach combining Markov model and bivariate Gaussian distribution has been proposed for an integrated analysis of the temporal and morphological features. Area subtraction techniques have been proposed for detecting the embedded waveforms. The analysis has been extended with a look- ahead pattern analysis algorithm for identifying different classes of irregular beats. The execution efficiency has been further improved to accommodate diagnosis of other heart-diseases in real-time by exploiting GPU based SIMT parallelism that performs beat level analysis concurrently. The implementation results show very high accuracy. Academic Employment & Professional Experience:

Graduate Teaching Assistant September 2012 To February 2019 Professional Reviewing Activity:

• Reviewer, AMIA Annual symposium of Medical Informatics 2017

• Reviewer, AMIA Annual Symposium of Medical Informatics 2018 Refereed Research Publications:

In print

1. P. R. Gawde, A. K. Bansal, and J. Nielson, "Applying Markov Model for Automated Classification of Supraventricular Dysrhythmia," International Conference on Health Informatics and Medical Systems (HIMS), Eds: H. R. Arabnia and L. Deligiannidid, Las Vegas, July 2015, pp. 10-16 2. P. R. Gawde, A. K. Bansal, and J. Nielson, “Integrating Markov model and morphology analysis for finer classification of Ventricular Arrhythmia in real time”, IEEE International conference on Biomedical and Health Informatics (BHI 2017), Orlando, Florida, USA, February 2017, pp. 409-412 3. Purva R. Gawde, Arvind K. Bansal, Jeffrey A. Nielson, “Bivariate Markov Model Based Analysis of ECG for Accurate Identification and Classification of Premature Heartbeats and Irregular Beat-Patterns”, IEEE Conference Intelligent Systems (IntelliSys) 2018, London, UK, 6-7 September 2018, pp. 850-859, 2018. 4. P. R. Gawde, A. K. Bansal, and J. A. Nielson,“Integrating Markov Model, Bivariate Gaussian Distribution and GPU based Parallelization for Accurate Real time Diagnosis of Arrhythmia Subclasses,” in 2018 IEEE Technically Sponsored Future Technologies Conference, FTC 2018, Vancouver, Canada, November 2018. To be submitted

5. Purva R. Gawde, Arvind K. Bansal, Jeffrey A. Nielson, “Integrating Markov model and morphology analysis with clustering for finer arrhythmia classification in real time,” submitted to a tier 1 journal, under review. 6. Purva R. Gawde and Arvind K. Bansal, “Real time ECG signal classification into finer arrhythmia subclasses Exploiting CUDA based parallelization on GPU,” to be submitted to a parallel processing conference. 7. Purva R. Gawde, Arvind K. Bansal and Gokarna P. Sharma, “Parallelizing Cluster Analysis and Markov Model based Analysis of ECG for Real-time Identification of Regularly Irregular Beat Patterns in Arrhythmia, to be submitted to a parallel processing conference.

8. Purva R. Gawde, Arvind K. Bansal, Jeffrey A. Nielson, Javed I. Khan and Gokarna P. Sharma, “A Parallel Technique for Integrating Temporal and Morphological Analysis of ECG Waves to Derive Irregularly Regular Beat Patterns in Real-time,” to be submitted to a Health Informatics journal. Conference Publication:

• “Integrating Markov model and morphology analysis for finer classification of Ventricular Arrhythmia in real time”, IEEE International conference on Biomedical and Health Informatics (BHI 2017), Orlando, Florida, USA, February 2017.

Teaching Experience:

• Developed online course Computational Health Informatics (Audio of lecture recordings with PowerPoint and video explanations)

• Instructor for online course Structure of Programming Languages (with programming labs)

• Instructor for online course Structure of Programming Languages (with programming labs)

• Instructor for Computational Health Informatics, Senior undergraduate level course

• Instructor for Hybrid (combination of Online teaching and In-class tutorials and labs) offering of Structure of Programming Languages (with programming labs)

• Instructor for In-class Structure of Programming Languages (with programming labs)

• Teaching Assistant: 1. Embedded Computing; and 2. Multimedia and Biometrics, both graduate (MS/PhD level) courses

• Teaching Assistant: 1. Advanced Artificial Intelligence; and 2. Health Informatics, both graduate (MS / PhD) level courses

• Lab instructor, Computer Science Programming and Problem Solving, taught advanced C++ programming, C++ data structures in a lab setting

• Lab instructor, “Introduction to Computer Science”, Taught introductory C++ programming, HTML and Javascript, in a lab setting

Invited Guest Lectures

Advanced Artificial Intelligence for lectures regarding automated ECG analysis Fall 2018 Embedded Computing, substituted instructor for two weeks during his medical leave Fall 2017 Computational Health Informatics, substituted instructor for two weeks during his professional university trip. Spring 2016 Three lectures on ECG Analysis in “Computational Health Informatics” Fall 2015 Two lectures on gesture analysis in “Biometrics and Multimedia” Fall 2015 Three lectures in Advanced Artificial Intelligence of machine learning Spring 2015 Awards and Professional Memberships

1) Graduate senate research travel award, Kent State University, 2017 2) Departmental research travel award, Department of Computer Science, Kent State University, 2017 3) Member, IEEE, since 2017

4) Member, ACM (Association of Computing Machinery), since 2017 REFERENCES

Dr. Arvind K. Bansal,

PhD advisor and Full Professor,

Department of Computer Science,

Kent State University, Kent, OH 44242

Website: http://www.cs.kent.edu/~arvind

Email: add2bv@r.postjobfree.com

Dr. Javed I. Khan

Full Professor and Chairman

Department of Computer Science

Kent State University Kent, OH 44242, USA

Website: http://www.cs.kent.edu/~javed

Email: add2bv@r.postjobfree.com

Dr. Jeffrey A. Nielson, MD

Associate Professor of Emergency Medicine

Northeast Ohio Medical University

4209 St. Rt. 44, PO Box 95

Rootstown, Ohio 44272

Email: add2bv@r.postjobfree.com

Dr. Cheng Chang Lu

Full professor and Assistant Chairman

Department of Computer Science

Kent State University Kent, OH 44242, USA

Website: http://www.cs.kent.edu/~lucc/

Email: add2bv@r.postjobfree.com

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