Participating Lab: Air Force Research Laboratory Human Performance (AFRL RH)

Understand the biosensor landscape and overcome the technical hurdles leading to the development and performance evaluation of biosensors in the lab. Formulate plans for mass manufacturing and determine constraints, evaluate sterilization effects on biosensor performance in animal/human testing, and explore/develop sensor data processing in real time or for rapid point-of-care measurement.

Combat systems operators simultaneously perform several demanding tactical tasks (e.g., managing communications, making fast-paced calculations and decisions, tracking mission schedules) during long-endurance missions. In an ongoing study, participants complete 40-minute simulated scenarios once per hour across two 6-hour sessions: one with operational acoustic noise exposure and one in a quiet control environment. The intern will help collect and analyze behavioral and neurophysiological data, informing a mathematical framework that maps performance across a 5-task cognitive battery and subjective survey metrics to dynamic, resource-specific latent factors.

The group’s research focuses on engineering molecular tools to create systems that sense Airmen’s and Guardians’ (A/G’s) biosignatures, which we define as the combination of biomarkers related to a specific condition like stress or cognitive function, and provide information to assess and improve A/G‘s mental and physical performance.

The main technical research areas that interns joining the group can participate on are:

1) In silico/in vitro selection of biorecognition elements (BREs) responsive to biomarkers of interest,

2) Integration of BREs in different sensing platforms including nano-, electrochemical- and biological, 3) Selection and design of regulatory elements (REs) to control gene expression in different organisms in response to specific biomarkers,

4) Integration of REs in biological circuits to engineer bacteria and their cell-free transcription/translation systems to design sensing platforms with complex data analysis/decision-making capabilities (sense/assess/respond),

5) development of matrices to deploy biological sensors in- and on-the body. Student interns will perform lab experiments in the “wet” labs or using computational tools to aid sensor design.

Understanding an individual’s cognitive state is of critical importance to the Air Force to effectively train, plan missions, and design relevant interventions if performance goes awry. To characterize an individual’s state, a combination of neural (e.g., EEG), peripheral (e.g., eye tracking, pupillometry, EKG), and behavioral (e.g., task performance) data are combined. These rich multimodal data—capturing the interaction of the brain and the body—are modeled alongside task performance to understand which signals provide the most value, in which settings, and derive actionable insights using sophisticated machine learning techniques.

With access to data from cognitive neuroscience laboratory settings as well as in-flight physiological recordings, there will be opportunities for students to develop their statistical skills and coding abilities (experience with MATLAB, R, or Python is required), data visualization, multi-sensor data fusion, algorithm development, and expand their conceptual and theoretical knowledge of physiologically measured cognitive states. Students will gain experience in critical thinking, public speaking, and working on multidisciplinary teams.