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. Currently, I serve as a Graduate Technical Intern at Intel Corporation, where I work 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 designed and executed Design of Experiments (DOE) for exploratory thin film deposition projects, contributing to advanced technology node development. I investigated first-of-its-kind process integration tools and conducted comprehensive material characterization using DSIMS, XRR, stress analysis, and TEM image analysis. I also developed predictive analysis frameworks using AI and machine learning to assess thin film deposition impact on semiconductor process flows and device characteristics.

Technical Expertise

Programming & Simulation:

Python, MATLAB, C++, Verilog, Shell scripting
Machine learning frameworks and neuromorphic simulation tools

EDA Tools & Device Simulation:

Cadence Virtuoso, Spectre, HSPICE, TCAD Sentaurus
COMSOL Multiphysics, ModelSim, Synopsys Design Compiler

Device Characterization & Fabrication:

Atomic Force Microscopy (AFM), Scanning Electron Microscopy (SEM)
Probe station measurements, Hall effect characterization
X-ray Diffraction (XRD), Transmission Electron Microscopy (TEM)

AI & Advanced Computing:

Advanced proficiency in generative AI tools (Cursor, GitHub Copilot, VSCode, Cline) for research, teaching, and code development
Prompt engineering and AI-assisted development with integration into academic workflows
Edge AI optimization and hardware-software co-design

Process & Nanofabrication Skills:

Lithography: Optical Lithography (MLA150) and Electron Beam Lithography (EBPG5200)
Etching: Ion beam dry etching and wet chemical etching techniques
Deposition: Material deposition using Temescal FC-2000 Evaporator (CVD)
Advanced Characterization: Magnetic Probe Station (SemiProbe), Keithley/Keysight instruments with LabVIEW

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 26, 2025	Building an Academic Website: A PhD Student's Guide to Online Presence

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}, }
arXiv
Neuromorphic Cybersecurity with Semi-supervised Lifelong Learning

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

2025

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Inspired by the brain’s hierarchical processing and energy efficiency, this paper presents a Spiking Neural Network (SNN) architecture for lifelong Network Intrusion Detection System (NIDS). The proposed system first employs an efficient static SNN to identify potential intrusions, which then activates an adaptive dynamic SNN responsible for classifying the specific attack type. Mimicking biological adaptation, the dynamic classifier utilizes Grow When Required (GWR)-inspired structural plasticity and a novel Adaptive Spike-Timing-Dependent Plasticity (Ad-STDP) learning rule. These bio-plausible mechanisms enable the network to learn new threats incrementally while preserving existing knowledge. Tested on the UNSW-NB15 benchmark in a continual learning setting, the architecture demonstrates robust adaptation, reduced catastrophic forgetting, and achieves 85.3% overall accuracy. Furthermore, simulations using the Intel Lava framework confirm high operational sparsity, highlighting the potential for low-power deployment on neuromorphic hardware.
@misc{mia2025neuromorphic, title = {Neuromorphic Cybersecurity with Semi-supervised Lifelong Learning}, author = {Mia, Md Zesun Ahmed and Bal, Malyaban and Lu, Siyuan and Nishibuchi, Grace M. and Chelian, Srinivas and Vasan, Shantanu and Sengupta, Abhronil}, year = {2025}, eprint = {2508.04610}, archiveprefix = {arXiv}, primaryclass = {cs.CR}, url = {https://arxiv.org/abs/2508.04610}, }
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/}, }