ELECTRICAL & ELECTRONICS ENGINEERING · BITS PILANI, HYDERABAD CAMPUS
I am an undergraduate researcher in the Department of Electrical & Electronics Engineering at BITS Pilani, Hyderabad Campus, working broadly on AI Hardware and intelligent physical systems. My current active research sits in the applications layer of neuromorphic computing at the Nanoscale Devices Lab (NSDL), under the Centre for Research Excellence in Semiconductor Technologies (CREST), advised by Dr. Parikshit Sahatiya.
Prior to this, I completed an eight-week research internship at the Solid State Physics Laboratory (SSPL), DRDO, under Dr. Lalit Kumar (Scientist F) and Dr. Vikhen Kumar (Scientist E), where I built a real-time ML anomaly-detection pipeline for free-space BBM92 Quantum Key Distribution — work that led to an accepted paper at IEEE INDISCON 2026.
On campus, I contribute to the Quantum Computing & ML Team at the IEEE Student Branch, and the Machine Learning Team at the ACM BPHC Chapter.
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IEEE INDISCON 2026 — Accepted, Track 5: Communication Systems & Network Technologies ▸ Temporal Cross-Channel Features for Eavesdropping Detection in Free-Space BBM92 QKD under Hierarchical Threat Models Saksham Gupta · BITS Pilani, in association with SSPL, DRDO · Paper ID 2445 7th IEEE India Council Subsections Conference · MNIT Jaipur, September 2026 Machine-learning-based eavesdropping detection for free-space BBM92 QKD under realistic atmospheric noise, moving beyond static QBER thresholding by exploiting temporal and cross-channel relationships between quantum observables to detect stealthy, sub-threshold attacks. |
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IACR ePrint 2026/1282 — June 2026 ▸ Physics-Aware Temporal Feature Engineering for Eavesdropping Detection in BBM92 Quantum Key Distribution Saksham Gupta · International Association for Cryptologic Research A 24-dimensional physics-aware feature space — QBER, Bell S parameter, photon coincidence rates, detector activity, HOM visibility, optical path loss over a 30-second sliding window — for detecting eavesdropping in entanglement-based BBM92 QKD over noisy free-space channels.
Evaluated across 5 random seeds on a simulated 24-hour free-space optical telemetry dataset under blended sub-threshold eavesdropping attacks. |
Applied Neuromorphic Computing & Event-Driven AI Hardware Investigating application-layer implementations of neuromorphic AI hardware — event-based data representations, low-latency streaming inference, and temporal processing models — at the Nanoscale Devices Lab under Dr. Parikshit Sahatiya.
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▸ AI Hardware & Compute Architectures ▸ Applied Neuromorphic Systems ▸ Event-Driven Sensing & Edge Intelligence ▸ Quantum Key Distribution (BBM92) Telemetry |
▸ Physics-Informed Machine Learning ▸ Temporal Spiking Dynamics & Sparsity ▸ Hardware-Aware Deep Learning Pipelines ▸ High-Dimensional Physical Sensor Fusion |
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Entanglement-Based Free-Space QKD Eavesdropping Monitor · core research project, DRDO SSPL github.com/SakshamDev/bbm92-qkd-anomaly-detection ↗ Physics-aware temporal feature engineering and anomaly-detection pipeline for free-space BBM92 QKD links, developed during the SSPL internship. Includes an optical channel physics simulator, four attack models, a 22-dimensional temporal feature vector over 30-second sliding windows, an XGBoost classifier with SHAP interpretability, and a live Streamlit dashboard.
Recall (unseen attacks) 91.3% · Precision 94.2% · F2 0.914 ± 0.113 |
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Indian Equity Portfolio Optimizer · live production system portfolio-optimizer.sakshamdev.tech ↗ Full-stack quantitative finance tool implementing Modern Portfolio Theory to optimize Indian equity portfolios — live NSE ticker data, efficient-frontier computation via quadratic programming, target-return constrained optimization.
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Cardiovascular Disease Risk Prediction Model · verified repository github.com/SakshamDev/heart-disease-prediction ↗ Supervised ML classification pipeline achieving 86.67% test accuracy on patient cardiovascular risk, using feature engineering and stratified k-fold cross-validation.
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| 2026 — PRESENT | Undergraduate Researcher, NSDL @ CREST — Advisor: Dr. Parikshit Sahatiya, BITS Pilani |
| MAY — JUL 2026 | Research Intern, Solid State Physics Laboratory (SSPL), DRDO, New Delhi |
| 2026 — PRESENT | Quantum Computing & Machine Learning Team, IEEE Student Branch, BITS Pilani |
| 2026 — PRESENT | Machine Learning Team, ACM BPHC Chapter |
| 2025 — 2029 | B.E. Electrical & Electronics Engineering, BITS Pilani, Hyderabad Campus |
| AI HARDWARE & ARCHITECTURES | Neuromorphic Applications · Spiking Dynamics · Edge AI |
| ML & TELEMETRY MODELING | XGBoost · Scikit-Learn · SHAP · NumPy · Pandas · SciPy |
| LANGUAGES & SYSTEMS | Python · C++ · C · MATLAB · Streamlit · JavaScript |
| RESEARCH FOUNDATIONS | BBM92 QKD Simulation · Free-Space Telemetry · Temporal Features |
I welcome academic inquiries regarding the accepted IEEE INDISCON 2026 paper, collaborative research in AI hardware architectures, applied neuromorphic systems, or visiting student appointments.
| INSTITUTIONAL | f20250785@hyderabad.bits-pilani.ac.in |
| DIRECT | visit.sakshamx@gmail.com |
| GITHUB | github.com/SakshamDev |
| linkedin.com/in/sakshamgupta19 | |
| SCHOLAR | Saksham Gupta — Google Scholar |