Sheila Alemany
AI Research Scientist - Technical Staff at MIT Lincoln Laboratory
Greater Boston United States
Summary
👤
Senior🎓
Top SchoolSheila Alemany is an AI research scientist and technical staff member at MIT Lincoln Laboratory with nine years of experience spanning adversarial machine learning, cybersecurity, and mobile wireless sensor networks. She holds a Ph.D. in Computer Science from FIU (NSF GRFP and GEM fellow) and has published work characterizing how data transformation techniques introduce vulnerabilities in ML models while developing frameworks to speed adversarial analysis. Her background blends rigorous theory (perfect 4.0 graduate GPA) with practical large-scale data work—spark and Hadoop graph-based feature extraction for terabytes of unlabeled network data. Sheila’s early research applied economic models to improve trust and data accuracy in resource-constrained sensor networks, a recurring theme of making systems both robust and efficient. Based in Miami, she pairs strong math instincts with hands-on engineering to bridge academic insights and operational cybersecurity needs.
9 years of coding experience
7 years of employment as a software developer
Bachelor of Science - BS, Computer Science, 3.78/4.00, Bachelor of Science - BS, Computer Science, 3.78/4.00 at Florida International University - College of Engineering & ComputingDoctor of Philosophy - PhD, Computer Science, 4.00/4.00, Doctor of Philosophy - PhD, Computer Science, 4.00/4.00 at FIU School of Computing and Information Sciences
English, Spanish
Github Skills (8)
keras10
recurrent-neural-networks10
python10
tensorflow9
neural-network9
deep-learning9
css5
javascript4
Programming languages (2)
Github contributions (5)
sheilaalemany/sheilaalemany.github.io
Jun 2017 - Mar 2025
Personal Academic Website
Contributions:9 PRs, 202 pushes in 7 years 10 months
cssjavascript
SparkDevAI/AI_2017
Oct 2017 - Dec 2017
Hurricane Trajectory using Recurrent Neural Networks. In our model, the inputs to the Recurrent Neural Network (RNN) are wind speed, pressure, and aggregated features of direction, distance, and grid identification. The distance and direction values were calculated from the latitude and longitude points given by the Unisys dataset. Our RNN is learning from all these features and it is predicting which grid location the hurricane will be moving to next.
Contributions:39 commits, 18 PRs, 35 pushes in 1 month
recurrent-neural-networks
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