Md Zesun Ahmed Mia

PhD Candidate, Electrical Engineering

Pennsylvania State University

Ex-Intel Graduate Technical Intern

About Zesun

“Curiosity drives me to seek new questions and create new knowledge. I believe progress in science comes from collaboration, open-mindedness, and the courage to explore beyond boundaries.”

I am a PhD candidate in Electrical Engineering at Pennsylvania State University, specializing in neuromorphic computing, machine learning hardware, and emerging semiconductor devices. Previously, I served as a Graduate Technical Intern at Intel Corporation (May-July 2025), where I worked on cutting-edge thin film process development and device integration for next-generation computing systems.

Research Focus

My research centers on brain-inspired computing architectures that bridge the gap between biological neural networks and artificial intelligence hardware. I am particularly interested in:

Neuromorphic Computing & Brain-Inspired AI:

Developing astromorphic transformers that incorporate astrocyte-neuron interactions
Creating bio-inspired machine learning algorithms for efficient long-context processing
Advancing spiking neural networks (SNNs) and algorithm-device co-design

Machine Learning Hardware & AI Accelerators:

Designing in-memory computing architectures for edge AI applications
Optimizing ML accelerator designs for neuromorphic workloads
Developing energy-efficient AI hardware solutions

Emerging Devices & Semiconductor Technology:

Investigating ferroelectric devices (FeFET) for neuromorphic applications
Researching spintronics and non-volatile memory (NVM) technologies
Advancing device-circuit co-design methodologies

Academic Background

I am currently pursuing a Doctor of Philosophy (PhD) in Electrical Engineering at Pennsylvania State University, focusing on neuromorphic computing and brain-inspired AI hardware. My doctoral research centers on developing novel astromorphic computing architectures that integrate astrocyte-neuron interactions to enhance machine learning efficiency and long-context processing capabilities.

I completed my Master of Science in Electrical Engineering at Penn State with a perfect 4.00 GPA, specializing in neuromorphic computing for lifelong learning. My graduate coursework and research provided deep expertise in device-circuit co-design, machine learning hardware, and emerging semiconductor technologies.

My undergraduate degree in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (BUET) provided a strong foundation in semiconductor device physics, circuit design, and electronics fundamentals. This rigorous program established my core knowledge in electrical engineering principles and prepared me for advanced graduate research.

Industry Experience

As a Graduate Technical Intern at Intel Corporation (05/2025 - 07/2025), I worked on thin film deposition process development for advanced technology nodes. I designed and executed Design of Experiments (DOE) for exploratory deposition projects, conducted material characterization (DSIMS, XRR, stress analysis), and developed AI-driven predictive models to assess process impact on circuit electrical parameters and device reliability. This experience bridged my semiconductor process knowledge with ML-based process-circuit co-optimization.

Technical Expertise

ML Systems & Accelerators:

Hardware-aware ML, ML accelerator/ASIC design, neural network accelerator architecture
Transformer/LLM acceleration, model compression (PTQ/QAT, pruning, distillation), mixed-precision (FP16/INT8)
Device-circuit-ML co-design, dataflow optimization, PIM, near-memory computing

Programming & ML Frameworks:

Python, CUDA, C/C++, MATLAB, Verilog, Shell/Bash, Git, Docker
PyTorch, TensorFlow, JAX, ONNX, TensorRT; experiment tracking (W&B, Matplotlib); data tooling (Pandas, NumPy, Jupyter)

Digital/ASIC Design:

RTL design, CMOS/FinFET circuit design, SRAM design, sense amplifier design, memory peripheral circuits
FPGA prototyping, synthesis, P&R, DFT, static timing analysis, timing closure

EDA Tools & Device Simulation:

Cadence Virtuoso, Spectre, HSPICE, TCAD Sentaurus, COMSOL Multiphysics
ModelSim, Quartus, Vivado, Synopsys (Design Compiler, PrimeTime, VCS)
ADC/DAC design, mixed-signal, CIM systems

Device Characterization & Fabrication:

AFM, SEM, TEM, XRD, XRR, Probe station measurements, Hall effect characterization
Lithography: Optical (MLA150) and E-beam (EBPG5200); Etching: Ion beam dry and wet chemical
Deposition: CVD, PVD, sputtering; Magnetic Probe Station (SemiProbe), Keithley/Keysight with LabVIEW

Process Integration & DTCO:

Semiconductor process integration, thin film deposition (CVD, HDP-CVD, PVD), yield optimization
Design-technology co-optimization (DTCO), system-technology co-optimization (STCO), cross-layer PPA

AI & Advanced Computing:

Advanced proficiency in generative AI tools (Cursor, GitHub Copilot, Cline) for research and code development
Prompt engineering, AI-assisted development, edge AI optimization

Research Impact & Publications

My research has been published in prestigious journals including IEEE Transactions on Cognitive and Developmental Systems and Matter (Cell Press). I have presented work at international conferences such as IEEE ISIEA, ICECE, and iCACCESS, covering topics from 3nm GAAFETs to electronic braille devices for accessibility technology.

Teaching & Mentorship

As a Graduate Teaching Assistant at Penn State, I mentor students in analog circuit design, Cadence Virtuoso, and semiconductor device physics. I am passionate about making complex engineering concepts accessible and inspiring the next generation of researchers in neuromorphic computing.

Vision & Future Directions

I envision a future where brain-inspired computing revolutionizes artificial intelligence by creating energy-efficient, adaptive, and fault-tolerant systems. My goal is to develop neuromorphic architectures that not only match but exceed the efficiency of biological neural networks while enabling autonomous learning and real-time adaptation in edge computing environments.

Contact & Academic Identity

📞 Phone: 814-280-7244
📧 Email: zesun.ahmed@psu.edu
🆔 ORCID: 0009-0004-3509-8455
🎓 Google Scholar: View Publications
💼 LinkedIn: Connect with me

Feel free to explore my publications, ongoing projects, and recent news. I welcome opportunities for collaboration and discussion about research in neuromorphic computing, ML hardware, and emerging semiconductor technologies.

Keywords: neuromorphic computing, machine learning hardware, spintronics, semiconductor devices, AI accelerators, brain-inspired computing, emerging devices, FeFET, Penn State, electrical engineering, PhD research, Intel Corporation, edge AI, spiking neural networks, device-circuit co-design

News

Aug 07, 2025	📄 New preprint available! Our paper “Neuromorphic Cybersecurity with Semi-supervised Lifelong Learning” is now on arXiv:2508.04610. This work was presented at ICONS 2025 (July 29-31, 2025) and explores brain-inspired approaches to cybersecurity challenges.
Jul 30, 2025	🗣️ Presented our work on “Neuromorphic Cybersecurity with Semi-supervised Lifelong Learning” at ACM ICONS 2025 in Seattle! Exciting discussions about brain-inspired approaches to cybersecurity and the future of neuromorphic computing. Great to connect with the neuromorphic research community at this premier venue.
Jul 25, 2025	🎯 Successfully completed my Graduate Technical Internship at Intel Corporation! Over the past 3 months, I contributed to cutting-edge thin film deposition projects and advanced technology node development. Grateful for the incredible learning experience in semiconductor manufacturing and AI-driven process optimization.
Jun 20, 2025	🎓 Grateful to be awarded The Wormley Family Graduate Fellowship for 2025! This fellowship recognizes outstanding graduate students and will enable me to focus on advancing astromorphic computing research.
Jun 15, 2025	🏆 Honored to receive the Harry G. Miller Fellowships in Engineering for 2025! This fellowship will support my continued research in neuromorphic computing and brain-inspired AI systems.

Latest Posts

Jul 26, 2025	Bridging Academia and Industry: My Intel Internship Journey in AI-Driven Semiconductor Manufacturing
Jan 26, 2025	Introduction to Neuromorphic Computing: The Future of Brain-Inspired AI
Jan 15, 2025	My PhD Journey: Exploring Neuromorphic Computing and Bio-Inspired AI

Selected Publications

IEEE TCDS
Delving deeper into astromorphic transformers

Md Zesun Ahmed Mia, Malyaban Bal, and Abhronil Sengupta

IEEE Transactions on Cognitive and Developmental Systems, 2025

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Preliminary attempts at incorporating the critical role of astrocytes—cells that constitute more than 50% of human brain cells—in brain-inspired neuromorphic computing remain in infancy. This paper seeks to delve deeper into various key aspects of neuron-synapse-astrocyte interactions to mimic self-attention mechanisms in Transformers. The crosslayer perspective explored in this work involves bioplausible modeling of Hebbian and presynaptic plasticities in neuronastrocyte networks, incorporating effects of non-linearities and feedback along with algorithmic formulations to map the neuronastrocyte computations to self-attention mechanism and evaluating the impact of incorporating bio-realistic effects from the machine learning application side. Our analysis on sentiment and image classification tasks (IMDB and CIFAR10 datasets) highlights the advantages of Astromorphic Transformers, offering improved accuracy and learning speed. Furthermore, the model demonstrates strong natural language generation capabilities on the WikiText-2 dataset, achieving better perplexity compared to conventional models, thus showcasing enhanced generalization and stability across diverse machine learning tasks.
@article{mia2025delving, title = {Delving deeper into astromorphic transformers}, author = {Mia, Md Zesun Ahmed and Bal, Malyaban and Sengupta, Abhronil}, journal = {IEEE Transactions on Cognitive and Developmental Systems}, year = {2025}, publisher = {IEEE}, url = {https://ieeexplore.ieee.org/document/10976578/}, }
Matter
Self-sensitizable neuromorphic device based on adaptive hydrogen gradient

Tao Zhang, Mingjie Hu, Md Zesun Ahmed Mia, and 8 more authors

Matter, 2024

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Neuromorphic computing aims to achieve superior efficiency by simulating the human brain to surpass traditional computers. It is crucial for neuromorphic devices to possess excellent biological characteristics, offering a vital way to address existing technical bottlenecks in artificial intelligence (AI). Here, we demonstrate adaptive self-sensitization based on a hydrogen gradient in artificial neurons, empowering neural networks to effectively tackle long-standing challenges of model failure in unknown situations beyond pre-defined boundaries. This advancement propels the design of adaptive neuromorphic devices to process unforeseeable signals autonomously. It significantly promotes the development of AI capable of interacting with complex environments, enhancing capabilities to perform tasks such as autonomous navigation, disaster rescue, and outer space exploration and opening up new possibilities for self-guided cognitive systems.
@article{zhang2024self, title = {Self-sensitizable neuromorphic device based on adaptive hydrogen gradient}, author = {Zhang, Tao and Hu, Mingjie and Mia, Md Zesun Ahmed and Zhang, Hao and Mao, Wei and Fukutani, Katsuyuki and Matsuzaki, Hiroyuki and Wen, Lingzhi and Wang, Cong and Zhao, Hongbo and others}, journal = {Matter}, volume = {7}, number = {5}, pages = {1799--1816}, year = {2024}, publisher = {Elsevier}, url = {https://www.cell.com/matter/fulltext/S2590-2385(24)00108-5}, }

ICONS

Neuromorphic Cybersecurity with Semi-Supervised Lifelong Learning

Md Zesun Ahmed Mia, Malyaban Bal, Sen Lu, and 4 more authors

In Proceedings of the International Conference on Neuromorphic Systems (ICONS ’25), 2025

DOI Bib PDF

@inproceedings{mia2025neuromorphic,
  title = {Neuromorphic Cybersecurity with Semi-Supervised Lifelong Learning},
  author = {Mia, Md Zesun Ahmed and Bal, Malyaban and Lu, Sen and Nishibuchi, George M. and Chelian, Suhas and Vasan, Srini and Sengupta, Abhronil},
  booktitle = {Proceedings of the International Conference on Neuromorphic Systems (ICONS '25)},
  pages = {235--238},
  year = {2025},
  publisher = {IEEE Press},
  doi = {10.1109/ICONS69015.2025.00043},
  url = {https://doi.org/10.1109/ICONS69015.2025.00043},
}

ICCIT
Irfd: A feature engineering based ensemble classification for detecting electricity fraud in traditional meters

Md Zesun Ahmed Mia, Md Moinul Islam, Monjurul Haque, and 2 more authors

In 2021 24th International Conference on Computer and Information Technology (ICCIT), 2021

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Nations have suffered significant economic losses as a result of non-technical electric losses resulting from power fraud. It is a criminal act of stealing electricity by applying various mechanisms that incorporate unauthorized tapping to the power line, bypassing the smart meter, etc. Electricity theft is a significant concern for not only developing countries but also developed countries as well. However, for most developing countries, the implications are catastrophic, given that their usage is always less than their demands. Electricity theft must be detected precisely and quickly in order to be mitigated. In our study, we have proposed a method of predictive ensemble machine learning techniques (IRFD) with a novel combination of feature distinction methods to detect electricity theft. In our proposed model, we have combined feature selection technique, Recursive Feature Elimination with Stratified 10-Fold cross-validation (RFECV) and Isolation Forest (IF), to identify and remove outliers along with several machine learning classifiers to forecast the theft of electricity. This study additionally enhances the management of highly imbalanced fraudulent data with Borderline-SMOTE with SVM (SVMSMOTE) and feature scaling with StandardScaler. Following the study, the Random Forest classifier observed a higher degree of accuracy (97.06%) with higher precision, recall, and F1-Score. To evaluate the efficacy of our proposed model, comparative analysis of the classification metrics is also assessed with several machine learning classifiers like Logistic Regression, Gradient Boosting, XGBoost, AdaBoost, KNN, ANN, along with Random Forest before and after fitting our proposed feature engineering techniques.
@inproceedings{mia2021irfd, title = {Irfd: A feature engineering based ensemble classification for detecting electricity fraud in traditional meters}, author = {Mia, Md Zesun Ahmed and Islam, Md Moinul and Haque, Monjurul and Islam, Saiful and Rahman, SMA Mohaiminur}, booktitle = {2021 24th International Conference on Computer and Information Technology (ICCIT)}, pages = {1--6}, year = {2021}, organization = {IEEE}, url = {https://ieeexplore.ieee.org/document/9689842/}, }

MWSCAS

Toward Variation-Tolerant Ferroelectric Neural Computing: Special Session Paper

Arnob Saha, Md Zesun Ahmed Mia, Jiahui Duan, and 2 more authors

In 2025 IEEE 68th International Midwest Symposium on Circuits and Systems (MWSCAS), 2025

Bib PDF

@inproceedings{saha2025toward,
  title = {Toward Variation-Tolerant Ferroelectric Neural Computing: Special Session Paper},
  author = {Saha, Arnob and Mia, Md Zesun Ahmed and Duan, Jiahui and Ni, Kai and Sengupta, Abhronil},
  booktitle = {2025 IEEE 68th International Midwest Symposium on Circuits and Systems (MWSCAS)},
  pages = {583--587},
  year = {2025},
  organization = {IEEE},
}