🧾 CV

📧 Email: danijy@tamu.edu
📍 Location: College Station, TX
🔗 LinkedIn · Google Scholar · GitHub · Website

🎓 Education

• Ph.D. in Computer Science, Texas A&M University (Jan 2022 – Present)
• Ph.D. in Computer Science, University of Cincinnati (Aug 2019 – 2022, transferred)
• M.S. in Computer Science, Texas A&M University – Corpus Christi (2017 – 2019)
• B.Tech in Information Technology, Charusat University, India (2013 – 2017)

🧠 Research Interests

Deep learning · foundation models · privacy‑preserving AI · trustworthy/explainable AI · cryptography · retrieval‑augmented generation (RAG) · reinforcement learning · natural language processing

🔬 Research Experience

Graduate Research Assistant — Texas A&M University (2022 – Present)

• Designed a DL and LLM-based framework that achieved 90% password-guess accuracy across nine datasets, uncovering vulnerabilities to guide stronger enterprise authentication policies.
• Developed an explainable AI framework leveraging computer vision to evaluate cryptographic indistinguishability and guide the design of next-generation encryption schemes.
• Developed a secure Multi-Party Computation protocol to support verifiable ML inference under adversarial conditions, increasing the trust and integrity of cloud-based AI systems.
• Co-developed FPTrace to demonstrate that user tracking persists despite GDPR and CCPA compliance, providing empirical evidence that influenced data privacy safeguard designs in online tracking systems.

Graduate Research Assistant — Texas A&M University–Corpus Christi (2017 – 2019)

• Integrated UAV-based imaging workflows into agricultural monitoring pipelines, reducing field-to-insight time by 40% and accelerating decision-making for precision crop management.
• Led cross-disciplinary efforts to deploy precision agriculture systems 30% faster, enabling earlier identification of crop stress and improving yield prediction accuracy.
• Applied computer vision to detect plant stress from multispectral UAV imagery, enabling early yield prediction that enhanced crop planning efficiency and resource allocation.
• Trained ML models on 3D point-cloud data to analyze plant health indicators, demonstrating the scalability of ML-based agricultural monitoring solutions.

💼 Industry Experience

Software Engineering Intern — Apollo Tyres Ltd., India (2017)

• Developed a Java-based tire and mold management system for three production lines, reducing manual data entry by 35% and improving production data traceability.
• Built quality-monitoring dashboards to visualize defect patterns and key performance indicators, reducing defect investigation time by 20% and enabling proactive quality improvement.
• Optimized database performance and implemented automated error tracking, increasing system uptime and ensuring consistent tire quality evaluation across inspection teams.

Embedded Systems Intern — Hewlett-Packard, India (2015)

• Developed a gesture-controlled vehicle prototype using tilt sensors and embedded microcontrollers, demonstrating the feasibility of real-time motion control for assistive robotics.
• Reduced control latency by 30% through optimized gesture recognition algorithms, improving responsiveness and stability of embedded robotic systems.
• Enhanced control precision and signal reliability using sensor fusion and calibration techniques, validating the robustness of motion control for industrial automation applications.

📄 Publications

Master’s Thesis

• Jimmy Dani, Jinha Jung, Mohammed Belkhouche, Kranthi Mandadi, Scott King, Detecting Plant Phenotypes From 3D Point Cloud Data.

Peer-reviewed

• [MDPI Cryptography 2026] Jimmy Dani, Kalyan Nakka, Nitesh Saxena, MIND-Crypt: A Machine Learning Framework for Assessing the Indistinguishability of Lightweight Block Ciphers Across Multiple Modes of Operation, MDPI Cryptography (Cryptography 2026)
• [IJCNLP AACL 2025] Kalyan Nakka, Jimmy Dani, and Nitesh Saxena, LiteLMGuard: Seamless and Lightweight On-Device Prompt Filtering for Safeguarding Small Language Models against Quantization-induced Risks and Vulnerabilities, The 14th International Joint Conference on Natural Language Processing & 4th Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP & AACL 2025).
• [CSCML 2026] Tzu-Shen Wang, Jimmy Dani, Juan Garay, Soamar Homsi, and Nitesh Saxena, Robust and Verifiable MPC with Applications to Linear Machine Learning Inference, The 9th International Symposium on Cyber Security, Cryptology, and Machine Learning (CSCML 2025)
• [PST 2025] Jimmy Dani, Kalyan Nakka, and Nitesh Saxena, A Machine Learning-Based Framework for Assessing Cryptographic Indistinguishability of Lightweight Block Ciphers, In the 22nd Annual International Conference on Privacy, Security, and Trust (PST 2025).
• [WWW 2025] Zengrui Liu, Jimmy Dani, Nitesh Saxena Yunzhi Cao, The First Early Evidence of the Use of Browser Fingerprinting for Online Tracking, In Proceedings of 2025 ACM Web Conference (WWW).
• [HOST 2023] Chenggang Wang, Jimmy Dani, Shane Reilly, Austen Brownfield, Boyang Wang, John M. Emmert, TripletPower: Deep-Learning Side-Channel Attacks over Few Traces, In Proceedings of 2023 IEEE Hardware Oriented Security and Trust (HOST).
• [IWQoS 2022] Hao Lui, Jimmy Dani, Hongkai Yu, Wenhai Sun, and Boyang Wang, AdvTraffic: Obfuscating Encrypted Traffic with Adversarial Examples, In Proceedings of 2022 IEEE/ACM Quality of Service (IWQoS).
• [IRI 2021] Jimmy Dani and Boyang Wang, HiddenText: Cross-Trace Website Fingerprinting over Encrypted Traffic. In Proceedings of 2022 Information Reuse and Integration for Data Science (IRI).
• [CODASPY 2021] Chenggang Wang, Jimmy Dani, Xiang Li, Xiaodong Jia, and Boyang Wang. Adaptive Fingerprinting: Website Fingerprinting over Few Encrypted Traffic, In Proceedings of ACM Conference on Data and Application Security and Privacy (CODASPY).

Pre-prints

• [ArXiv] Kalyan Nakka, Jimmy Dani, Nitesh Saxena, Is On-Device AI Broken and Exploitable? Assessing the Trust and Ethics in Small Language Models, arXiv preprint arXiv:2406.05364 (2024).
• [ArXiv] Jimmy Dani, Brandon McCulloh, and Nitesh Saxena, When AI Defeats Password Deception! A Deep Learning Framework to Distinguish Passwords and Honeywords, arXiv preprint arXiv:2407.16964 (2024).

🎓 Student Mentoring

• MS Students: Aayush Yadav, Anuska Pant, Veronika Maragulova
• UG Students: Mac Morrison, Mihir Cherukupalli, Aleksa Mićanović, Adnaan Yunus

🧑‍⚖️ Academic Service

• Reviewer: ACM TOPS (2024)
• Sub-reviewer: ACM CCS (2023, 2024, 2025), WWW (2024, 2025, 2026), ACSAC (2023)

🏆 Honors & Awards

• Awarded Best Performer in the Computer Organization & Microprocessor laboratory at Charusat University.
• Recipient of the Merit Scholarship in the Department of Information Technology at Charusat University.
• Ranked first in the Co-Digest (Coding) event on Charusat University’s 16th Annual Day.
• Recipient of UGS award at the University of Cincinnati.

📜 Certifications

• Fundamentals of Accelerated Computing with CUDA C/C++ from NVIDIA.
• Applications of AI for Anomaly Detection from NVIDIA.
• Big Data & Machine Learning by Pittsburgh Supercomputing Center.
• Advanced Professional Development Certificate in Career Exploration from Texas A&M University Graduate and Professional School.