Mar 2021 – Apr 2021 · 1 mo

• Co-mentored two undergraduate research teams on their spam classification projects for Google's eCSR (explore Computer Science Research) workshop.

Jan 2021 – Jun 2021 · 5 mos · New York, New York, United States

• Developed camp curriculum material, lesson plans, and lab activities on several cybersecurity subjects
• Led camp facilitation and instruction; taught cybersecurity lessons to ~30 students throughout a week-long camp
• Hired graduate student assistants to assist with camp planning and facilitation
• Coordinated other faculty members and industry experts to give guest lectures
• Reviewed student applications and led the application review process
• Attended meetings with NSA GenCyber representatives and site-visitors; justified our curriculum and agendas, incorporated feedback into the rest of the camp week's agenda
• Adapted an originally-proposed in-person student camp to be completely virtual, operated from Fordham's Center for Cybersecurity
• GenCyber is a national initiative supported by the National Science Foundation (NSF) and the National Security Agency (NSA) with the goals of encouraging interest and diversity in cybersecurity education and workforce opportunities, improving teaching methods for delivering cybersecurity education, and strengthening security knowledge and practices among the next generation of cybersecurity professionals. Fordham's Center for Cybersecurity held its first GenCyber summer camp for high school students in 2021.

Sep 2020 – Jan 2022 · 1 yr 4 mos · New York, New York, United States

Oct 2018 – Apr 2019 · 6 mos · New York, New York

• More and more public open data is coming online every day. This information represents a layer of unique and meaningful intelligence, helping us better understand and navigate our world — to avoid risk, drive insight, and unlock opportunity. Vigilant provides solutions to access this data, and tools to help understand it.

Apr 2018 – Sep 2019 · 1 yr 5 mos · New York City Metropolitan Area

• Class Imbalance Research. Use of scikit-learn toolkit to evaluate relative impact of class imbalance on 13 different classification algorithms as applied to 29 data sets with varying levels of class imbalance. See publication, Identifying Classification Algorithms Most Suitable for Imbalanced Data
• Educational Data Mining Research, contributor
• Identifying Extremism in Text Using Deep Learning, contributor

Sep 2016 – Sep 2019 · 3 yrs