Arnab Bhowmik

A motivated and quality-driven computer scientist with 6 years of experience in machine learning,

developing models and algorithms in the field of data analysis and image processing, as well as fluent in programming languages like Python C, C++ and R. A quick learner and a team player with excellent communication skills, seeking to contribute to the field of AI, image processing, mobile networking, Automation and Robotics.

System Engineer, IMMS GmbH, Ilmenau, DE 09.22 - Current

• Designing and developing advanced automation solutions for automotive manufacturing processes and anomaly detection by using time series models.
• Implementing MLOps, Industry 4.0 technologies such as IoT-based sensory systems, AI, and robotics to enhance production efficiency.

INTERNSHIP, Fraunhofer IOSB-AST, Ilmenau, DE. 11.21 - 02.23

• Developed and executed machine learning algorithms for real-time control and navigation systems of self-driving robots, resulting in a 25% enhancement in system efficiency.
• Used extensively Oracle SQL-based backend and implemented developed algorithms using C#.
• Identified and assessed cutting-edge technologies to incorporate deep learning models in designing control systems.

RESEARCH ASSISTANT, Fraunhofer IOSB-AST, Ilmenau, DE 02.22 -12.22

• Image data analysis and statistical analysis.
• Successful testing in Robot Operating System (ROS) simulation.
• Database management
  • Satellite-based image segmentation and real-time data analysis (Images with 13 channels).

INTERNSHIP, German Aerospace Center, Munich, DE 02.21 - 03.23

• Huge data-compatible algorithm development and successful Implementation.

TECHNICAL PROJECTS

MASTER THESIS, IoT Engineering, TU Ilmenau 11.23 - 04.24

Title: Handover in Dynamic Mobile Base Station Scenarios

• Conducting research on handover parameters (Radio Link Failure (RLF), Packet Size, Handover Latency) using OMNET++, INET, Veins, Simu5G.
• Developing base stations mobile in Simu5G and observing the handover parameters and the efficiency and optimization of the handover algorithm.

RESEARCH PROJECT, Informatik und Automation, TU Ilmenau 03.23 - 06.23

Title: Application of Variational Inference in Topic Model-based Image Segmentation

• Testing Satellite-based Image segmentation using Latent Dirichlet Allocation (LDA) method is more efficient than the basic deep learning method for image segmentation. 

EDUCATION

Master of Science (M.Sc.), Research in Computer and Systems Engineering 10.20 - 05.24

Technische Universität Ilmenau, Germany

Bachelor of Technology (B. Tech.), Computer Science and Engineering 08.16 - 07.20

University of Engineering & Management, India