Neeraj Julka Senior Research Engineer
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I hold a Doctorate degree in Engineering with a specialization in Electronics and Communication Engineering (ECE), which has equipped me with a strong foundation in technical expertise. With over six years of experience in teaching, I have honed my ability to effectively convey complex concepts.

My career spans more than four years of extensive experience in data science and analysis. I have a deep understanding of machine learning and deep learning algorithms, allowing me to navigate the evolving landscape of AI effortlessly. Furthermore, I am well-versed in the complete model lifecycle, including deployment and monitoring, and possess hands-on proficiency in Amazon Web Services (AWS). My expertise extends to creating datasets using industrial-grade cameras, such as the SONY XCD-X710 Monochrome Camera and Basler Color Camera. I have contributed to the field by creating and donating a Wheat seeds Dataset to the UCI repository. Additionally, I have managed cross-functional teams comprising data scientists, data analysts, Python developers, Java developers, and UI/UX specialists for robust tool development.

My technical skill set includes proficiency in Python, R, Django, Flask API, Hug Framework, Stream lit, MATLAB, LABVIEW, NI VISION ASSISTANT, and NI VISION BUILDER, covering a wide range of tools and frameworks used in data science and engineering. I have successfully developed advanced Natural Language Processing (NLP) and Machine Learning (ML) models for clinical data, reflecting my ability to tackle complex problems in the healthcare domain. Furthermore, I've leveraged my skills in computer vision to develop algorithms for wheat classification and tomato leaf disease prediction using Convolutional Neural Networks (CNN). My diverse skill set and contributions to the field make me a valuable candidate for roles in academia, research, data science, engineering, or leadership positions within organizations seeking expertise in machine learning, data analysis, and engineering.

Subjects

  • E-Learning Beginner-Intermediate

  • Python Basics Beginner-Intermediate

Experience

  • Analyst Data Science (Jun, 2021–Dec, 2023) at GenInvo Technologies Pvt. Ltd. Mohali
    Designed and developed Customized NER model Tool for extraction of Medical Entities. The tool aims to reduce time-consuming cycles of manual review that affects drug discovery and publishing process. Developed the backend architecture and core processing engine that includes Intelligent Recognition of medical entities using natural language processing techniques. Programmed logic for QC checks and implemented NER model for intelligent recognition disease name, symptoms, mapping of original data to mapped data and simulating process covering multiple scenarios.
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    Designed case study / POC solution for automating translation for applying / importing strategies from English document to non-English documents. The main aims to reduce the extra repetitive task to anonymize or redact the document personal or important information. Developed and implemented the model for QC checks for clinical documents. This was designed as two stage system. The first stage involves translation based on applied strategies on English document. The second stage involves the finding the similar sentence based on score in non-English document.
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    Customized Deep learning-based tool for extraction of signatures. The main motive behind this tool to reduce the manual efforts as a part of QC check on clinical documents in English language. It automatically identifies and detects the signatures based on coordinates of a text. The Deep learning model was used for detecting the signatures. Achieved accuracy of 98.01 %.
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    Conceptualized, designed and implemented data deid strategy for clinical documents for redaction of partial data in R.Designed and developed Customized NER model Tool for extraction of Medical Entities. The tool aims to reduce time-consuming cycles of manual review that affects drug discovery and publishing process. Developed the backend architecture and core processing engine that includes Intelligent Recognition of medical entities using natural language processing techniques. Programmed logic for QC checks and implemented NER model for intelligent recognition disease name, symptoms, mapping of original data to mapped data and simulating process covering multiple scenarios. • Designed case study / POC solution for automating translation for applying / importing strategies from English document to non-English documents. The main aims to reduce the extra repetitive task to anonymize or redact the document personal or important information. Developed and implemented the model for QC checks for clinical documents. This was designed as two stage system. The first stage involves translation based on applied strategies on English document. The second stage involves the finding the similar sentence based on score in non-English document. • Customized Deep learning-based tool for extraction of signatures. The main motive behind this tool to reduce the manual efforts as a part of QC check on clinical documents in English language. It automatically identifies and detects the signatures based on coordinates of a text. The Deep learning model was used for detecting the signatures. Achieved accuracy of 98.01 %. • Conceptualized, designed and implemented data deid strategy for clinical documents for redaction of partial data in R.
    Skills: Neural Networks · Deep Learning · Natural Language Processing (NLP) · Data Annotation · Python (Programming Language) · Data Science

Education

  • Ph.D (ECE) (Jul, 2016–Oct, 2021) from Sant Longowal Institute of Engineering & Technology, Longowal

Fee details

    ₹1,000–1,200/hour (US$11.89–14.27/hour)

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