Maneet Chatterjee
Email: maneet2018@gmail.com | 2023meb045.maneet@students.iiests.ac.in
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Github |
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More about me:
Blog |
Coursework |
Ongoing Projects
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I am a third-year undergraduate in Mechanical Engineering at Indian Institute of Engineering Science and Technology Shibpur
with a deep interest in Robotics, Artificial Intelligence and its applications. My work spans robotic manipulation, computer vision, IoT architectures, and simulation-based control, with hands-on
experience in ROS, Isaac Sim, PyBullet, Blender, and embedded systems. Recently, I've been exploring Reinforcement Learning and SLAM in robotics.
I recently completed a Research & Development (R&D) Internship at Synopsys
working with Vinod Singh Rao and Gopal Sharma in the
Synopsys Innovation Group (SIG). My research focused on IoT, digital twins, computer vision and robotic manipulations.
Previously, I was a Research Assistant at IIEST Shibpur with
Dr. Sudip Ghosh, working on photovoltaic cells and their use in hydrogen production for small scale industries.
I am a passionate photographer and spend time clicking pictures around in my city (Kolkata) in my leisure time. I also love reading about finance and geopolitics. I also play football and cricket.
I am always open to collaborations and discussions. Please feel free to reach out!
Other:
Achievements |
Fun/Favorites
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| [Oct 25] |
First Technical Paper "Framework for Digitalization of Brownfield Deploymentsusing IT-OT Integration and Digital Twins", with Synopsys, accepted at AIIoT 2025! [LinkedIn] |
| [Oct 25] |
Our research work at Synopsys was presented at the prestigious Ansys(part of Synopsys) TechCon Conference, Pittsburgh, USA! [LinkedIn] |
| [Oct 25] |
Paper "Beyond CNNs: EfficientMamba Powered Sequence Learning and Curriculum-Guided Domain Adaptation for Fossil Classification" accepted at InGARSS 2025! [LinkedIn] |
| [Jul 25] |
Completed summer internship at Synopsys - SIG! Worked on IT-OT integration, robotics and computer vison. [LinkedIn] |
| [Apr 25] |
Received the prestigious IEEE Geoscience and Remote Sensing Society Travel Grant of US $1,550.0 to present at IGARSS 2025. [LinkedIn] |
| [Mar 25] |
Paper "EEMAMBA: A Hardware-Aware Energy-Efficient State-Space Model for EUROSAT Classification" accepted at IGARSS 2025! [LinkedIn] |
| [June 24] |
First-author paper published in IEEE Xplore! [LinkedIn] |
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Framework for Digitalization of Brownfield Deploymentsusing IT-OT Integration and Digital Twins
Gopal Sharma, Maneet Chatterjee, Harshaditya Punera, Vinod Singh Rao, Aniruddha Mukhopadhyay
To be presented at AIIoT 2025
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Beyond CNNs: EfficientMamba Powered Sequence Learning and Curriculum-Guided Domain Adaptation for Fossil Classification
Angshuman Roy, Anuvab Sen, Samyajit Das, Maneet Chatterjee, Jayanta Paul
The 2025 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) 2025
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EEMAMBA: A Hardware-Aware Energy-Efficient State-Space Model for EUROSAT Classification
Maneet Chatterjee, Anuvab Sen, Subhabrata Roy
The International Geoscience and Remote Sensing Symposium (IGARSS) 2025
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ExoSpikeNet: A Light Curve Analysis Based Spiking Neural Network for Exoplanet Detection
Maneet Chatterjee, Anuvab Sen, Subhabrata Roy
IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT) 2024, 802-807
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R&D Intern
May 2025 - July 2025
Synopsys
Pune, India
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Worked with Vinod Singh Rao and Gopal Sharma in Synopsys-Special Innovation Group (SIG).
Developed a real-time digital twin of a 6-axis robotic manipulator, integrating a low-latency data streaming pipeline for synchronized state transfer between the physical system and simulation. Employed computer vision to enable and evaluate diverse robotic manipulation tasks within the Isaac Sim–based physics-aware simulation environment.
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Research Assistant
Aug 2024 - Dec 2024
IIEST Shibpur
Kolkata, India
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Worked with Prof. Sudip Ghosh on the system-level design and performance analysis of solar-powered hydrogen electrolysis systems, integrating photovoltaic sources with alkaline/PEM electrolyzers.
Designed and optimized DC power conditioning and energy models using real-world irradiance data to estimate hydrogen yield and evaluate system efficiency.
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