Andrew D'Angelo Email & Phone Number

New York, Ny, Us

Hoboken, New Jersey, United States

I am the head TA of ENGR-245 circuits lab. In this position, I teach multidisciplinary sections of undergraduate engineering students the basic principles of AC and DC circuitry. The typical class size ranges anywhere from 30-40 students with up to 10 sections a semester depending on the time of year. As the head TA, I am not only responsible for teaching, but I also manage a team of graduate and undergraduate TAs. Additionally, it is my responsibility to develop and administer all assignments and exams. As the head TA I am dedicated to ensuring my students have the best possible experience in the lab. No matter their major or career path it is my goal to make sure all students leave the class with a firm understanding of the material and most importantly a positive outlook on circuitry.

As a Graduate Student Researcher, I am deeply involved in cutting-edge research and teaching in the realm of AI and cybersecurity, with a particular focus on IoT security. My research centers around AI-driven intrusion detection systems for IoT networks, where I work on enhancing resilience against sophisticated cyber threats. I leverage deep neural networks to uncover hidden correlations among smart home devices, improving the accuracy of device fingerprinting and anomaly detection. Additionally, I have engineered a novel defense mechanism against IoT phantom delay attacks, utilizing a combination of TCP reset schemes, VPN tunneling, and firewall rule engineering, which has successfully reduced the attack window by 90%. My work also extends to developing large language model (LLM)-based security models for smart networks, using Retrieval-Augmented Generation (RAG) techniques to bolster threat detection and response capabilities.

Rome, New York, United States

I worked as a researcher with the AFRL for two consecutive summers. During my first summer with the Air Force, I researched semantic-based security solutions for smart home and smart building environments. I leveraged semantic information from API documentation, open-source code, and user activities to generate correlations between smart devices in IoT environments. This research was published in my paper, "Discovering Complex Correlations among Multiple IoT Devices in Smart Environments," presented at IEEE Globecomm. In the summer of 2023, I advanced this research by designing a novel transformer-based correlation miner using attention weights from a fine-tuned BERT transformer. I simulated real-world attacks and achieved a detection accuracy of 96.59% and recall of 97.38%. These results were published in "Transformer-based Compound Correlation Miner for Smart Home Anomaly Detection" at the IEEE CloudNET 2023 conference. Additionally, I developed a GRU-autoencoder attack detection system for IoT smart building systems, which attained 97.97% precision, 98.18% accuracy, and a 0.05% false alarm rate.

I co-founded SecureMeeting, a 501(c)(3) nonprofit, with the mission to provide free and secure video communication for people confined to their homes during the COVID-19 pandemic. Our goal was to ensure that everyone could connect confidently, without compromising their privacy or personal data. Since its launch, SecureMeeting has been used in over 65 countries, with an average of nearly 500 daily users. I led the engineering of a decentralized microservice architecture with custom APIs for video room management and additional features. We implemented Agile methodologies using JIRA, managing 2-week sprints with an average of 30 story points per sprint. Our efforts created a lasting, secure, and accessible communication platform for users worldwide.