Mudit Bhargava

Based in India

Open to opportunities worldwide

muditbhargava666@gmail.com

Welcome! I’m Mudit Bhargava, an engineer with a deep interest in bridging the gap between hardware and software. My work spans signal processing, financial modeling, machine learning, and FPGA development—areas that constantly push me to think across disciplines.

What I Work On

My projects typically involve:

Signal Processing — Developing algorithms for real-time data analysis and embedded applications
Financial Modeling — Building quantitative models and exploring algorithmic trading strategies
Machine Learning — Designing neural networks and intelligent systems for practical problems
FPGA Development — Creating high-performance hardware accelerators and embedded systems

My Philosophy

I believe the most interesting problems live at the intersection of different fields. Combining rigorous signal processing with modern machine learning, then implementing it on custom hardware—that’s where I find the work most rewarding. I enjoy taking ideas from theory all the way to working implementations.

Current Interests

Right now, I’m focused on:

Hardware acceleration of AI algorithms on FPGAs
Real-time signal processing for financial applications
Contributing to open-source projects in the FPGA and signal processing space
Exploring the practical applications of ML in embedded systems

Get in Touch

I’m always open to conversations about interesting technical challenges. If you’re working on something in signal processing, need help with FPGA implementations, or want to discuss ML applications in finance, I’d be happy to connect.

Take a look at my projects to see recent work, or browse my publications for research I’ve been involved with.

news

Feb 15, 2026	🎉 Our paper SCREAM: A Systematic Framework for Analyzing Security Threats in Digital Manufacturing has been accepted at the 21st ACM Asia Conference on Computer and Communications Security (ASIACCS 2026)! Looking forward to presenting our framework for threat modeling in digital manufacturing.
Jan 15, 2024	🚀 Excited to share my new open-source FPGA accelerator for real-time signal processing! Check it out on GitHub.

latest posts

Dec 25, 2024	Building an LLVM-Based Deep Learning Optimizer from Scratch
Dec 24, 2024	Implementing Extended LSTM (xLSTM) - A Modern Take on Recurrent Networks
May 01, 2024	a post with tabs

selected publications

ASIACCS

SCREAM: Secure Channels for Real-time Evaluation of Additive Manufacturing

Prithwish Basu Roy, Jason Blocklove, Mudit Bhargava, and 6 more authors

In Proceedings of the ACM Asia Conference on Computer and Communications Security , Bangalore, India, 2026

Abs

Additive Manufacturing (AM), also known as 3D printing, offers several advantages, including on-site production, enhanced throughput, and efficient use of raw materials. However, the rise in its usage has also led to an increase in potential threats that aim to disrupt the printing process. These attacks can subtly alter the design (CAD or STL) files or machine instructions (g-code), which can cause significant economic and reputational harm to the victim company. Current detection techniques, based on acoustic, magnetic, and accelerationbased side-channel analysis, have proven to be ineffective. Although power side-channel analysis is more effective than other means, it is expensive and not scalable. This paper proposes a novel detection method, SCREAM, that assumes the user has access to a trusted STL source and an untrusted g-code. SCREAM leverages the pulse trains sent to the motors to reconstruct the executing g-code. To ensure the safe and accurate execution of g-code, a three-level comparison is performed between recovered and untrusted g-code, as well as trusted STL ensuring successful detection of any anomalies present in the executing g-code. Our testing has shown that this method can detect a range of existing attacks on AM, including malicious firmware manipulation, FLAW3D, and Needle in a Haystack.

J. Phys.

Towards development of performance metrics for benchmarking SLAM algorithms

Mudit Bhargava, Rushad Mehta, Chandan Das Adhikari, and 1 more author

Journal of Physics: Conference Series, 2021

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This paper presents a comprehensive approach to developing performance metrics for benchmarking Simultaneous Localization and Mapping (SLAM) algorithms. We propose standardized evaluation criteria that enable fair comparison across different SLAM implementations, addressing key aspects such as accuracy, robustness, and computational efficiency.

@article{bhargava2021slam,
  title = {Towards development of performance metrics for benchmarking SLAM algorithms},
  author = {Bhargava, Mudit and Mehta, Rushad and Adhikari, Chandan Das and Sivanathan, K.},
  journal = {Journal of Physics: Conference Series},
  volume = {1964},
  number = {6},
  pages = {062115},
  year = {2021},
  publisher = {IOP Publishing},
  doi = {10.1088/1742-6596/1964/6/062115},
  url = {https://iopscience.iop.org/article/10.1088/1742-6596/1964/6/062115},
  google_scholar_id = {u5HHmVD_uO8C},
  dimensions = {true},
}

arXiv
A survey of Digital Manufacturing Hardware and Software Trojans

Prithwish Basu Roy, Mudit Bhargava, Chia-Yun Chang, and 4 more authors

arXiv preprint, 2023

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Digital manufacturing systems are increasingly vulnerable to hardware and software Trojans that can compromise the integrity, confidentiality, and availability of manufactured products. This comprehensive survey examines the landscape of Trojans in digital manufacturing, analyzing attack vectors, detection methods, and defense mechanisms across the manufacturing lifecycle.
@article{basuroy2023trojans, title = {A survey of Digital Manufacturing Hardware and Software Trojans}, author = {Basu Roy, Prithwish and Bhargava, Mudit and Chang, Chia-Yun and Hui, Ellen and Gupta, Nikhil and Karri, Ramesh and Pearce, Hammond}, journal = {arXiv preprint}, year = {2023}, url = {https://arxiv.org/abs/2301.10336}, google_scholar_id = {d1gkVwhDpl0C}, dimensions = {true}, }

IEEE-NANO

Performance Enhancement of MEMS based Piezoelectric Pressure Sensor using Multistep Configuration

Mudit Bhargava, Alkausil Tamboli, and K. Sivanathan

In 2019 IEEE 19th International Conference on Nanotechnology (IEEE-NANO) , 2019

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This paper presents a novel multistep configuration approach for enhancing the performance of MEMS-based piezoelectric pressure sensors. The proposed design demonstrates improved sensitivity and linearity compared to conventional single-step configurations, making it suitable for precision measurement applications.

@inproceedings{bhargava2019mems,
  title = {Performance Enhancement of MEMS based Piezoelectric Pressure Sensor using Multistep Configuration},
  author = {Bhargava, Mudit and Tamboli, Alkausil and Sivanathan, K.},
  booktitle = {2019 IEEE 19th International Conference on Nanotechnology (IEEE-NANO)},
  pages = {576--579},
  year = {2019},
  organization = {IEEE},
  doi = {10.1109/NANO46743.2019.8993875},
  url = {https://ieeexplore.ieee.org/abstract/document/8993875},
  google_scholar_id = {9yKSN-GCB0IC},
  dimensions = {true},
}