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WORKING WITH THE ACI

The ACI Summer Internship Program (SIP)


The Summer Internship Program is an 8-week course designed to provide interns with real world experience in futuristic cyber research at it relates to the security of the nation. Interns will be assigned to different research teams supporting a variety of cyber-related projects from policy development to hands on technical development. The interns will be exposed to the entire research cycle, from idea development to briefing a product, and how each piece of a research project integrates with a larger effort.


Is the program really 8 weeks?


Not necessarily. The ACI offers internship slots from 3 weeks to 12 weeks with a focus on an 8 week block allowing for the most immersive experience.


How do I get involved?


If you are part of the ROTC Cadet Leader Development Program, contact ROTC.Internships@usma.edu or visit our CLDP page for more information. If you are a college student and not part of ROTC, please contact cyber.internships@usma.edu for more information.

Previous Summer Internship Projects
Cyber Threats & Physiological Responses
Cyber Threats & Physiological Responses

Cyber Threats & Physiological Responses
As traditional authentication mechanisms become outdated and compromised easily, many researchers believe the keys to more reliable authentication mechanisms lie in biometrics. In addition, there is a significant desire to determine when computer operators are working at peak efficiency and when they begin to function with less precision. The intersection of these two concepts is human physiology. During this project, cadets assisted in conducting a variety of computer and visual bases tests to determine what physiological responses could be observed from different stimuli and whether or not the reactions to those different types of stimuli presented in similar or different measures depending on the stimulus. Cadets then analyzed the results and determined next steps to be taken to continue the research.

Sentiment Analysis of Social Media
Sentiment Analysis of Social Media

Sentiment Analysis of Social Media
With the ubiquity of social media, the ability to determine the feeling of a large population of anonymous users toward a certain idea has become easier. This analysis of constant feedback is crucial to understanding how the public perceives components of the United States Army. During this project, ACI researcher instructed cadets about different mechanisms for performing sentiment analysis, analyzed anonymous data, and determined the public perception of the USMA Band and USMA Band concerts with the intent of making the performances more enjoyable for future audiences

Countering Unmanned Vehicles
Countering Unmanned Vehicles

Countering Unmanned Vehicles
Unmanned vehicles pose a large threat to transportation safety, firefighting, law enforcement, and the Department of Defense. Because drones and other unmanned vehicles are so inexpensive, they have become readily accessible to almost anyone from the hobbyist to military forces. This project was aimed at creating an extremely inexpensive, mobile, solution to disable any kind of unmanned vehicle within range. Cadets worked with ACI researchers to learn the basics of wireless communications using a variety of technologies and researched different techniques for degrading those signals. The cadet them implemented their chosen solution using hardware components computer languages. This solution was then tested against different types of unmanned vehicles to determine its effectiveness.

Applying Ethics to Artificial Intelligence
Applying Ethics to Artificial Intelligence

Applying Ethics to Artificial Intelligence
As the Department of Defense and commercial sectors become more dependent on Artificial Intelligence for enabling rapid decisions, the integration of ethical principles into AI algorithms is a topic of debate. What impacts would occur with these additions? Would AI algorithm efficiencies suffer? Would decision recommendations suffer? The cadet team working on this project investigated current Artificial Intelligence algorithms and determined whether or not it would be possible to integrate ethical decision making into these algorithms in a manner that would be transparent and auditable while still producing the best possible recommendation