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Melissa Tessa
Observed names:
TESSA, Melissa,
Tessa, Melissa
Institutions:
University of Luxembourg,
Interdisciplinary Centre for Security, Reliability and Trust
Observed institutions:
University of Luxembourg,
École Nationale Supérieure d'Informatique,
Interdisciplinary Centre for Security, Reliability and Trust
ORCID:
0009-0008-7525-5522
Observed ORCID:
0009-0008-7525-5522
OpenAlex ID:
A5092356431
Towards interactive explanation-based nutrition virtual coaching systems
2024
·
Berk Buzcu
, Melissa Tessa
, et al.
·
Autonomous Agents and Multi-Agent Systems
·
13
PDF
Enhancing Explanaibility in AI: Food Recommender System Use Case
2023
·
Melissa Tessa
, Sarah Abchiche
, et al.
·
International Conference on Human-Agent Interaction (HAI)
·
8
PDF
Evaluating Inference-Time Defenses against Package Hallucination in LLM-Generated Code
2026
·
Alberick Euraste Djire
, Iyiola E. Olatunji
, et al.
·
IEEE/ACM International Conference on Automated Software Engineering (ASE)
RiskyPy: Toward Automated Prompt Generation Tasks Calibrated to Model Proficiency for Joint Security and Functionality Evaluation of Generated Code
2026
·
Chenail-Larcher, Zacharie
, Lunghi, Daniele
, et al.
·
Zenodo (CERN European Organization for Nuclear Research)
Trajectory-Level Security Debt in LLM Coding Agents
2026
·
Rajput, Prateek Kumar
, Kabore, Abdoul Kader
, et al.
·
arXiv (Cornell University)
PDF
Position: The Iceberg of Pitfalls in LLM-Based Secure Code Generation
2026
·
Melissa Tessa
, Iyiola E. Olatunji
, et al.
·
Proceedings of the AAAI Symposium Series
PDF
Guided Decoding for Package Hallucination Mitigation
2026
·
Djire, Alberick Euraste
, TESSA, Melissa
·
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Camouflage
2026
·
Borsukiewicz, Paweł
, Lunghi, Daniele
, et al.
·
arXiv (Cornell University)
PDF
How Secure is Secure Code Generation? Adversarial Prompts Put LLM Defenses to the Test
2026
·
Tessa, Melissa
, Olatunji, Iyiola E.
, et al.
·
arXiv (Cornell University)
PDF
A Lay User Explainable Food Recommendation System Based on Hybrid Feature Importance Extraction and Large Language Models
2026
·
Melissa Tessa
, Diderot D. Cidjeu
, et al.
·
Procedia Computer Science
A Lay User Explainable Food Recommendation System Based on Hybrid Feature Importance Extraction and Large Language Models
2025
·
Tessa, Melissa
, Cidjeu, Diderot D.
, et al.
·
arXiv (Cornell University)
PDF
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Works count:
11
Citation count:
21
H-index:
2
I10-index:
1
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