Dr. Sthefanie Passo is an Assistant Professor in the Data Science Institute at Mississippi State University. She is a multidisciplinary data scientist whose work spans healthcare, supply chain, civil and electrical engineering, and cybersecurity — domains that share a common difficulty: extracting reliable signal from data that is noisy, high-dimensional, and expensive to collect.

Dr. Passo’s research is in quantum computing and quantum information, centered on quantum feature maps and kernel methods — the step that determines how classical data is encoded into a quantum system, and where most of the potential advantage is won or lost. She tests her methods on real quantum processors rather than idealized simulators. Recent projects include quantum kernels for detecting malware in supply chain networks, quantum generative models for EEG-based sleep staging, adaptive feature maps for breast cancer classification, and a characterization of IBM's quantum hardware identifying where noise degrades machine learning performance and where it does not. She also works on neural network architectures for arithmetic reasoning.

Over the past decade Dr. Passo has taught students on five continents, in STEAM subjects ranging from introductory to advanced. Her teaching covers the modern machine learning stack — deep learning, reinforcement learning, generative AI, and quantum machine learning — and she is especially interested in how quickly a motivated student can reach the research frontier in a field this young.

She welcomes students curious about quantum computing regardless of their physics background. The prerequisites are linear algebra and curiosity; the payoff is working on questions the field has not answered yet.

Kothavade V, Passo S, Prevost J. Quantum-Inspired Clustering Techniques for Malware Detection in Supply Chain Networks. In: Communications in Computer and Information Science [Internet] Cham: Springer Nature Switzerland; 2026. Chapter Chapter 12; 158-176p. Available from: https://link.springer.com/10.1007/978-3-032-22193-3_12.

Passo SJ, Haque M, Vishal KH, Prevost JJ. SARS-CoV-2 Classification Using Classical vs. Quantum Machine Learning: A Performance Comparison of SVM, QSVM, and Pegasos. In: 2026 IEEE 5rd International Conference on AI in Cybersecurity (ICAIC) USA: IEEE; 2026.

Passo SJ, Khan IU, Prevost JJ. Quantum Generative Adversarial Networks for EEG-Based Sleep Stage Classification in Noiseless and Noisy Scenarios. In: 2026 56th Annual IEEE International Conference on Dependable Systems and Networks Workshops (DSN-W) 2026: IEEE; 2026. 161--168p.

Passo SJ, Ho RJ. RL-Optimized Hybrid Quantum Convolutional Neural Network for Epileptic Seizure Detection in EEG Signals. In: International Conference on Computational Science and Computational Intelligence Chan: Springer Nature Switzerland; 2026.

Passo SJ, Kothavade VH, Prevost JJ. Quantum-PCA for Malware Detection in Supply Chains, Healthcare, and Smart Manufacturing Communications. In: International Conference on Computational Science and Computational Intelligence Cham: Springer Nature Switzerland; 2025.

Frey NT, Passo SJ, Prevost JJ. Quantum Adaptive Feature Map Architecture for Breast Cancer Classification. In: SC25: International Conference for High Performance Computing, Networking, Storage and Analysis USA: IEEE; 2025.

Passo SJ, Kothavade VH, Lin W, Walter C. Neural Arithmetic Logic Units with Two Transition Matrix and Independent Gates. Engineering Applications of Artificial Intelligence. 2025; 140:109663.

Passo SJ, Prevost JJ. Characterization of IBM quantum computers for quantum machine learning applications on brain tumor datasets. In: IET Conference Proceedings CP925 11 ed. London: IET; 2025. 17--26p.

Passo SJ, Kothavade VH, Prevost JJ. Supply Chain Malware Detection via Classical and Quantum Kernel Methods in Embedded Systems. In: 2025 55th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Italy: IEEE; 2025. 208--215p.

Passo S, Kothavade V, Lin W, Walton C. Neural Arithmetic Logic Units with Two Transition Matrix and Independent Gates. Engineering Applications of Artificial Intelligence. 2025 January; 140:109663-. doi: 10.1016/j.engappai.2024.109663.

Passo, Sthefanie Jofer Gomes, de Oliveira, Hygo Sousa, Pinto, Rafael Albuquerque, Quispe, Kevin Gustavo Montero, Gusti, Rafael, Souto, Eduardo James Pereira. Classificação de Arritmias com Paradigma Inter e Intra Paciente utilizando Aprendizagem Profunda. Journal of Health Informatics. 2021 March; 12.

Passo, Sthefanie Jofer Gomes, Stein, Mateus, de Souza, Jos\'e Edimar. Os primeiros tempos do telefone em Sapucaia do Sul (RS): memórias e uso social de uma tecnologia de comunicação. Revista Liberato. 2016 June; 17(27):105--116.