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Adjunct Professor (Quantum Error correction, Quantum computing), Department of Physics and Engineering Physics, Tulane University, USA
Working in the field of quantum machine learning and quantum computing, with a particular focus on quantum error correction, where the output of the work carries quantum information rather than a classical summary. The goal is to evaluate the real advantage of quantum ML over classical ML by combining AI algorithms with quantum circuit design. With over a decade of experience spanning statistical modeling, deep learning, computer vision, SVR kernels, neural networks, and emerging architectures such as KANs, I now apply this foundation to advance research in quantum information processing.
IU International University of Applied Sciences
Certificate of Study, Artificial Intelligence
January 1, 2020 – January 1, 2021
University of Manitoba
Reseach Fellow, Molecular Dynamics Simulations
January 1, 2017 – January 1, 2017
Indian Institute of Technology, Roorkee
Doctor of Philosophy (Ph.D.), Optical data analysis, Physics
January 1, 2011 – January 1, 2017
Guru Gobind Singh Indraprastha University
Master of Technology (M.Tech.), Computational Physics, Nanotechnology
January 1, 2009 – January 1, 2011
Tulane University
Adjunct Professor
November 1, 2025 – Present
New Orleans, LA
Amrita Vishwa Vidyapeetham
Assistant Professor (School of AI)
April 1, 2023 – January 1, 2024
On-site
Presidency Global
AI trainer
January 1, 2023 – March 1, 2023
SunMoon university
Visiting Researcher for AI assisted medical imaging
September 1, 2022 – December 1, 2022
Asan, South Chungcheong, South Korea
UNIKUL SOLUTIONS PVT LTD
Artificial Intelligence Scientist
December 1, 2021 – September 1, 2022
Bengaluru
Hamburg University of Technology
Postdoctoral Scientist: Optical data analysis
January 1, 2019 – June 1, 2020
Hamburg, Germany
On the Robustness of KAN network: extrapolation
January 1, 2026 – Present
As the dataset we took following problem to start with: Designing experiments for Au nanoparticles using the data of Ag. This research article showed in-depth analysis how with very limited experimental data we can design experiments for unseen inputs.
Feature engineering is not dead: reviving classical machine learning with entropy, hog, and LBP feature fusion for image classification
January 1, 2026 – January 1, 2026
In this study, we revisit classical machine learning based image classification through a novel approach centered on Permutation Entropy (PE), a robust and computationally lightweight measure traditionally used in time series analysis but rarely applied to image data.
How Quantum circuits performs better than classical ML if output has quantum information
January 1, 2026 – Present
As a toy-dataset, we took optical data of nanoparticles in the form of density matrix where diagonal elements contain information about quantum entanglement, and we test how a quantum circuit is better at picking it up as it has inherently quantum entanglement feature unavailable in classical ML.
From Classical to Quantum Reinforcement Learning and Its Applications in Quantum Control: A Beginner's Tutorial
January 1, 2025 – January 1, 2025
This tutorial is designed to make reinforcement learning (RL) more accessible to undergraduate students by offering clear, example-driven explanations. It focuses on bridging the gap between RL theory and practical coding applications, addressing common challenges that students face when transitioning from conceptual understanding to implementation.
Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN
January 1, 2025 – January 1, 2025
In this work, we implement the Physics-Informed Kolmogorov-Arnold Neural Networks (PIKAN) through efficient-KAN and WAV-KAN, which utilize the Kolmogorov-Arnold representation theorem.
Deep learning based face recognition system.
March 1, 2022 – March 1, 2023
Feature extraction using CNN, and support vector regression-based face classification for polytechnic college.
Machine learning based prediction of IIT-JEE score with FIIT-JEE
January 1, 2022 – December 1, 2022
This data science/ML based project was done at Unikul Solutions, Banglore.
Certificate of Study
IUBH Internationale Hochschule - Berlin New Campus
June 25, 2026 – Present
Cultural Fit Analysis
The candidate's background is heavily academic and research-oriented, with a focus on AI and Quantum Computing. While this demonstrates intellectual curiosity and a drive for innovation, the projects and experience descriptions do not clearly align with typical software engineering development cycles, product delivery, or collaborative team environments often found in industry. The absence of specific software development technologies (e.g., specific programming languages beyond Python, frameworks, cloud platforms, version control systems) in project descriptions makes it difficult to assess cultural fit for a standard software engineering role. The candidate seems more aligned with an R&D or AI research scientist role rather than a general software engineer.
Soft Skills & Operational Fit
The candidate's experience as an Assistant Professor and AI trainer suggests strong communication and mentorship skills. Their research background implies a methodical and problem-solving approach. However, the resume lacks explicit details on collaboration within a software engineering team context or experience with agile methodologies, which are crucial for operational fit in many software engineering roles.