Interests: Fire Clearance, Brain Computer Interfaces, Artificial Intelligence
My research interests lie at the intersection of computer science,
applied artificial intelligence, and community resilience. I am
particularly focused on biometric security, deep learning, and
brain–computer interface (BCI) technologies, as well as the ethical
and practical challenges of deploying these systems in real-world
contexts. I am drawn to projects that combine pattern recognition,
machine learning, and signal processing—for example, advancing
algorithms such as PCA, LDA, and convolutional neural networks to
improve accuracy, efficiency, and interpretability.
Beyond technical innovation, I am motivated by the societal applications
of computing. I am especially interested in how computational tools and
decision-support systems can be used to address large-scale public
safety challenges, such as wildfire prevention and disaster management,
while ensuring equity, privacy, and accessibility. This includes
exploring how data-driven insights and cybersecurity frameworks can
strengthen nonprofit initiatives, support vulnerable populations, and
improve coordination between community organizations and private
enterprises.
Looking ahead, my goal is to pursue graduate-level research that bridges
computer science and business administration, enabling me to develop
both the technical depth and the strategic leadership skills necessary
to guide innovative projects. I aim to contribute both to academic
knowledge and to practical solutions that have a tangible, positive
impact on communities.
Research Projects
Research Project One
Virtual reality (VR) technology can be used for multiple purposes, for
simulating or enhancing real-life situations. VR-based interfaces can
provide new operational advantages and reduce hardware requirements,
i.e., weight, by offering various enhanced input modalities that
otherwise could not be used in a physical cockpit. This study
presents a comparative analysis of three input modalities for a
virtual keyboard in a fully immersive VR environment. Each input
modality has two steps: 1) pointing to the desired button, and 2)
the selection of the item. We compare the hand laser pointer with
trigger selection, the head laser pointer with trigger selection,
and the head laser pointer with the dwell time selection
(no need to use the hands). In addition, we propose new types of
visual feedback: for all the input modalities, we display in the
field of view of the user the last 5 letters. For the modality with
the dwell time, we add a circular progress bar around the field of
view of the user to provide a constant awareness of the possible
button selection. The proposed virtual keyboard input modalities
were assessed by 32 participants. The results support the conclusion
that the hand pointer with the trigger control provides the best
speed compared to other modalities.
A great challenge for brain-computer interface (BCI) systems is their
deployment in clinical settings or at home, where a BCI system can be
used with limited calibration sessions. BCI should be ideally
self-trained and take advantage of unlabeled data. When performing a
task, the EEG signals change over time, hence the recorded signals
have non-stationary properties. It is necessary to provide
machine-learning approaches that can deal with self-training and or
use semi-supervised learning methods for signal classification.
A key problem in graph-based semi-supervised learning is determining
the characteristics of the affinity matrix that defines the
relationships between examples, including the size of the neighborhood
of each example. In this paper, we propose two approaches for building
the affinity matrix using the distance between examples and the number
of neighbors, with a limited number of hyper-parameters, making it easy
to reuse. We also compare the Euclidean distance and Riemannian geometry
distances to construct the affinity matrix. We assess the classification
performance with motor imagery data with two classes from a publicly
available dataset of 14 participants. The results show the interest of
the proposed semi-supervised approaches with the use of distances to
define the neighborhood using Riemannian geometry-based distances with
an average accuracy of 73.75%.
The applications of virtual reality extend beyond en tertainment and
video games. They can be used to re place real interfaces such as
keyboards. Virtual keyboards can offer tree-menu capabilities and,
more importantly, re duce the amount of necessary hardware. In this
paper, we propose and compare the performance of two virtual keyboards
in fully immersive virtual reality. This study is based on the
NASA MINDS project where the goal was to create a cockpit in virtual
reality. The interaction with but tons and switches can be achieved
through natural controls (virtual hands with laser pointer) and/or a
laser pointer from the head that would work in conjunction with a key
press on a controller. The results show a significant dif ference
between the two virtual keyboards (VK1: standard qwerty layout, VK2:
tree-based menu layout), with VK1 taking less time per command ratio
than VK2, but found no significant difference between the keyboards
regarding workload and usability.
