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Hi! I am Arihant

Data Scientist and Engineer

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About

Let me introduce myself.

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I am a data + software engineer with experience in healthcare industry. I enjoy machine learning, web programming and everything about data science. I believe that data can be used to make better decisions. My core competencies includes building applications using Natural Language processing and Machine Learning. Currently working as a Independent Software Consultant, helping organizations in building intelligent tools by using vast array of technologies.

Profile

An engineer with 6+ years of experience in mutliple disciplines especially healthcare, government related informatics, active and data adept at building innovative applications to improve efficiency.

Skills

Highly skilled in using NLP and machine learning with 5 years of research experience. Experienced developer with hands on various programming languages.

  • 85%
    Machine Learning
  • 90%
    Natural Language Processing
  • 90%
    Software Engineering
  • 85%
    Python
  • 80%
    Core Java
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More of my credentials.

Experienced in developing end to end machine learning based software applications. I have strong background in computer vision, natural language processing and with other deep learning based applications. Solid skills in statistics and algorithms. Commitment to providing support and essential information about trends to companies in a variety of industries.

Work Experience

Principal Architect AI/ML

May 2019 - Present

Tamil Nadu E-governance Agency (TNeGA), Tamil Nadu, India

TNeGA is a central agency to cater technical requirements of all government departments. As part of TNeGA, I am involved understanding requirements, designing and implementing intelligent systems which can ease processes and ultimately help them in better governance. - Own the short-, medium term road map for ML architectures . - Manage teams responsible for Machine Learning development. - Work across all functions for TNeGA and provide ML/AI direction. - Work with internal/external ecosystem to coordinate direction.

Software Engineer

July 2016 - April 2019

Motive Medical Intelligence, CA

Motive Medical Intelligence is involved in improving quality of care by intelligent-evidence based systems. Worked as an integral part of team to build and enhance a Clinical Decision Support System (CDSS) as a service.

NLP Applications Engineer

Feb 2016 - May 2016

City of Hope, CA

City of Hope is a cancer research centre. Worked for informatics division in Research Informatics Applications group and was involved in developing a semantic based search application for clinical trials

Graduate Researcher

Jan 2015 - Jan 2016

Institute of Neuro-Imaging and Informatics, USC

Part of the Global Alzheimer's Association Interactive Network(GAAIN). Worked on data integration and data distribution technology to improve effective data access for Alzheimer’s research community.

Software Engineer Intern and Volunteer

Jun 2015 - Dec 2016

Meliorix Incorporated, San Diego, CA

Meliorix is a health informatics based company specialised in clinical decision support service (CDSS). Worked closely in a team to identify data extraction and translation requirements. Designed a model to manage rules for data extraction.

Education

Master Degree

July 2014 - May 2016

University of Southern California

MS in Engineering Management (Minor Data Informatics)

Master Degree

July 2009 - June 2014

Vellore Institute of Technology

Master's in Software Engineering

Publications

Association for Computing Machinery

November, 2019

Automatic License Plate Recognition for Indian Roads Using Faster-RCNN.

In this work, we developed algorithms to read number plates using Convolutional Neural Network (CNN) and FRCNN in Indian context. This algorithm was developed keeping in mind variations found in number plates like shape of the plate, shape of the characters, other unrequired characters present on plate, advertisement on vehicles etc. Our system efficiently detects plates and accurately detects Licence plate number. This approach when test on foreign number plates works well. We futher intend to improve it as there are various day-to-day usecases where it can be helpful to ensure secure community.

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Machine Reading of Biomedical Data Dictionaries.

As part of my research at LONI, we developed a mechanism to automatically read biomedical data dictionaries. Automated reading is the process of extracting element details for each of the data elements from a data dictionary in a document format (such as PDF) to a completely structured representation. Considering different formats of data dictionaries, We had a particular focus on the most challenging format with a machine-learning classification solution to the problem using conditional random field classifiers. We presented an evaluation using several actual data dictionaries, demonstrating the effectiveness of our approach.

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Association for Computing Machinery

October 2016

Name Similarity for Composite Element Name Matching

Matching corresponding data elements is a critical problem in biomedical data harmonization for data sharing. The similarity of the element names is one of the many factors employed in determining data element matches. Determining name similarity is complicated by the fact that data element names in biomedical data are composite i.e., composed of multiple components. We provide a better approach to determining element name similarity for composite element names.
Our approach is effective and has been integrated as part of a more comprehensive "schema-mapping" system, which we have developed for harmonizing biomedical datasets.

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Springer International Publishing

July 2015

GAAIN Virtual Appliances: Virtual Machine Technology for Scientific Data Analysis

At LONI, Our focus is to work with various platforms and experience technologies for developing virtual machines and virtual appliances. Our goal was to facilitate scientific workflow sharing and analysis for scientific investigation using virtual machine technology. We worked on researching and building an efficient data sharing and analysis network for data providers as well as scientific investigators in the domain of Alzheimer’s Disease (AD) research. We successfully identified pros and cons for using different tools/technologies like Virtual Machines (VmWare Virtual Box), Docker etc which helped us to provide efficient data sharing and secure analysis mechanism.

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Contact

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Interested in working together? Fill out the form below and I will revert back to you as soon as possible.


Where to find me

On Earth!

Email Me At

apatawari.hit@gmail.com
patawari@usc.edu