Kumar S.

Key Skills

Software

Other

Labelbox

Top Subject Matter

Computer Science Engineering (Data Structures, Algorithms, project mentoring, C++ Programming, evaluations)

Machine Learning for Threat Detection in IoT Networks (Cybersecurity, Machine Learning)

Top Data Types

Computer Code Programming

Image

Text

Top Task Types

Fine-tuning

Computer Programming/Coding

Evaluation/Rating

Classification

Question Answering

Prompt + Response Writing (SFT)

Freelancer Overview

Assistant Professor at Lovely Professional University (Jun 2024–Present). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Master of Technology, National Institute of Technology, Rourkela (2024) and Bachelor of Technology, Rajkiya Engineering College, Bijnor (2020). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Computer Programming, Coding, and Fine-tuning.

Labeling Experience

Assistant Professor at Lovely Professional University (Jun 2024–Present)

Other

Assisted in teaching and mentoring students on building and applying machine learning concepts through programming-based coursework. Covered coding practices, algorithm implementation, and evaluation methodologies aligned to data-driven problem solving. Acted as an academic guide for projects where students translated learning outcomes into working code artifacts. • Delivered instruction in Data Structures & Algorithms (C++), and Design & Analysis of Algorithms • Mentored 500+ students via interactive lectures, labs, and assessments • Guided 30+ students on academic projects, including coding practices and development • Managed examination and evaluation duties such as question papers, invigilation, and grading

2024 - Present

Machine Learning for Threat Detection in IoT Networks project at NIT Rourkela (Jul 2023–Jun 2024)

OtherFine-tuning

Developed a machine learning model for detecting cyberattacks in IoT environments using supervised learning workflows. Implemented and experimented with Random Forest, XGBoost, and Neural Networks to improve detection performance. Performed model development using cloud and ML tooling rather than manual annotation work. • Built threat-detection ML pipeline for IoT networks • Experimented with Random Forest, XGBoost, and Neural Networks • Used Python and TensorFlow for model development • Utilized GCP for ML/deployment-related workflow support

2023 - 2024

Education

Work History