Sheila Alemany

Hi, I'm Sheila!

I am a member of the technical staff in the Artificial Intelligence Technology and Systems Group at MIT Lincoln Laboratory, where I have worked since 2024. My work focuses on building robust natural language processing and computer vision systems, and my research interests include the robustness, explainability, and interpretability of machine learning. I also help organize the Synthetic & Adversarial ForEnsics (SAFE) workshop series on synthetic media detection, adversarial robustness, and AI forensics.

I earned my Ph.D. in computer science from Florida International University (FIU) in 2023, where I was advised by Dr. Niki Pissinou and supported by an NSF Graduate Research Fellowship and the GEM Consortium. My doctoral research focused on defenses against adversarial attacks on machine learning models [Ph.D. research]. Before that, I earned a B.S. in computer science and a B.S. in mathematics (2019) and an M.S. in telecommunications and networking (2022) from FIU, and did my first research on reliable data management for mobile wireless sensor networks [undergraduate research].

News

Oct 2026Two papers accepted at the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026) at EMNLP 2026 in Budapest, Hungary. Our zero-shot relation classification paper is nominated for best paper and will be presented as a featured paper on October 28.
Oct 2026Serving as Vision Area Chair for SAFE at AAAI 2027 in Montréal, Québec, Canada. Submissions are due November 2, 2026.
Sep 2026Our paper on cross-aircraft flight phase classification, the outcome of mentoring an Air Force captain through the data science and machine learning research process, received an Outstanding Paper Award at IEEE HPEC 2026.
Jun 2026Co-organized SAFE at CVPR 2026 in Denver, Colorado.
Apr 2026Chaired the Intelligence and Special Operations track at the MORS 2026 Artificial Intelligence Workshop, hosted by RAND and Carnegie Mellon University in Pittsburgh, Pennsylvania.
Mar 2026Co-organized SAFE at WACV 2026 in Tucson, Arizona.
Jan 2024Joined MIT Lincoln Laboratory's Artificial Intelligence Technology and Systems Group.
Dec 2023Earned my Ph.D. in computer science from FIU.
Oct 2023Defended my dissertation, Mitigating Adversarial Perturbations in Machine Learning Models in the Presence of Uncertainty.

Recent Publications

Zero-Shot Relation Classification with Large Language Models: Binary Prompting and Relevance Elicitation featured paper
Workshop on Automated Knowledge Base Construction at EMNLP (AKBC 2026)
Sheila Alemany, Danielle Sullivan-Pao, Olga Simek
Distill-RE: Few-Shot Relation Extraction via Teacher LLM Reasoning Distillation
Workshop on Automated Knowledge Base Construction at EMNLP (AKBC 2026)
Danielle Sullivan-Pao, Sheila Alemany, Olga Simek
Scalable Cross-Aircraft Flight Phase Classification Using Transfer Learning and ADS-B Data outstanding paper award
IEEE High Performance Extreme Computing Conference (HPEC 2026)
Jacob Kiefer, Sheila Alemany

Service

2027Area Chair (Vision), Synthetic & Adversarial ForEnsics (SAFE) Workshop at AAAI 2027
2026Organizer, SAFE Workshop at CVPR 2026
2026Track Chair (Intelligence and Special Operations), MORS Artificial Intelligence Workshop
2026Organizer, SAFE Workshop at WACV 2026

Earlier Research

2019–2023 · FIU Ph.D. research My doctoral research developed defenses that make machine learning models more robust to imperceptible adversarial perturbations, resulting in five papers.

2016–2019 · FIU Undergraduate research As an undergraduate, I developed reliable data management techniques for mobile wireless sensor networks and worked on hurricane trajectory prediction and IoT location privacy, resulting in six papers.