Profile
I am an engineer and researcher with a Ph.D. in Information Technology, specializing in Cross-Domain Reasoning, graph-based learning, and artificial intelligence. My work explores how computational methods can reason across complex data, structures, and domains while translating research ideas into practical technology.
My background spans electrical engineering, bioengineering, software engineering, and AI research. Across these disciplines, I have worked on computational modeling, machine learning, graph-based methods, data-intensive applications, production software, and developer tools, giving me experience at the intersection of scientific investigation and engineering.
I am particularly interested in building intelligent systems that can generalize across domains and in transforming complex ideas into useful, reliable software. I value interdisciplinary problem solving, open research, and engineering approaches that connect technical innovation with real-world applications.
Contact
- tanmoysr2026@gmail.com
- Nacogdoches, Texas
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- GitHub
- Google Scholar
- ResearchGate
- ORCID
Skills
Industry Experience
Research Experience
Teaching Experience
Taught fundamentals of linear/non-linear data structures and algorithm analysis. Prepared students for complex algorithms used in AI and ML.
Covered core OOP concepts in Python (classes, inheritance, polymorphism) and OOD using UML.
Taught problem-solving skills using procedural programming (Python) including variables, conditionals, functions, and iteration.
Conducted recitation classes for signal conversion and circuit analysis methods.
Selected Publications
For a complete list of works, please visit my Google Scholar profile.
Education
Dissertation: Cross domain reasoning based on graph deep learning.
Presentation: Watch Defense
CGPA: 4.0/4.0
Concentration: Human-Computer Interaction.
CGPA: 3.7/4.0
Thesis: Development of a shooting training system using motion sensors and smartphone.
CGPA: 4.45/5.0
Thesis: Prospects of smart grid in Bangladesh.
CGPA: 3.05/4.0
Selected Projects & Research
For a complete list of technical implementations, please visit my GitHub profile & Projects page.
Alnoms
ProjectA Performance Intelligence Engine that analyzes and verifies algorithmic performance before code reaches production.
unixLiveResponseTools
ProjectAutomated forensic data collection toolkit for Unix systems. Arctic Code Vault Contributor ❄️.
Graph Learning & Network Dynamics
ResearchFocus: Influence maximization, source localization, and graph neural networks.
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Deep graph representation learning influence maximization with accelerated inference
Neural Networks (Elsevier), 2024 -
Source Localization for Cross Network Information Diffusion
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024 -
MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization
27th International Conference on Artificial Intelligence and Statistics (AISTATS), 2024 -
DeepGAR: Deep Graph Learning for Analogical Reasoning
IEEE International Conference on Data Mining (ICDM), 2022
Security, Hardware & Systems
ResearchFocus: Applied cryptography, VLSI timing analysis, and secure biometric sketches.
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RAPTA: A Hierarchical Representation Learning Solution For Real-Time Prediction of Path-Based Static Timing Analysis
Proceedings of the Great Lakes Symposium on VLSI (GLSVLSI), 2022 -
Multisketches: Practical secure sketches using off-the-shelf biometric matching algorithms
ACM SIGSAC Conference on Computer and Communications Security (CCS), 2019
Focus: Forecasting events in dynamic environments (Healthcare, Spatial data).
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Deep Multi-task Learning for Spatio-Temporal Incomplete Qualitative Event Forecasting
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024 -
Modeling Health Stage Development of Patients with Dynamic Attributed Graphs in Online Health Communities
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022 -
Effects of electrode position on spatiotemporal auditory nerve fiber responses: A 3D computational model study
Computational and Mathematical Methods in Medicine, 2015
State-of-the-Art AI Reviews
ResearchFocus: Major survey papers on LLMs and Neural Reasoning.
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Knowledge-enhanced Neural Machine Reasoning: A Review
arXiv preprint arXiv:2302.02093, 2023 -
Domain specialization as the key to make large language models disruptive: A comprehensive survey
ACM Computing Surveys, 2025
Grants, Awards & Honors
- Key researcher on projects funding top-tier publications (ACM SIGKDD, IEEE TKDE, ACM CCS).
- Award IDs: 1822094, 2113350, 2146726, 2318831, 2403312
- Supported Master's Thesis in Bioengineering at University of Ulsan.