Research
Our Research Focus
I direct the Cyber Identity & Behavior Research (CIBeR) Lab at the University of South Florida.
We study what it means for technology to “know” us—our identity, behavior, and intentions—and how to design systems that use that knowledge responsibly.
Our work naturally spans multiple fields (e.g., mental health, defense, medicine, education, cybersecurity) and requires collaborative partnerships.
We care most about why people behave the way they do in a digital world and how those behaviors reflect trust, risk, and decision-making.
When someone gives a child a smartphone, we ask why and what they believe the device will do for learning, safety, or connection — and whether they understand the risks.
When someone writes passwords on paper, we ask why that strategy feels safer or easier — and what it tells us about trust, memory, and everyday cybersecurity decisions.
The technology matters — but our work focuses on the human reasons behind actions.
Across domains—mental health and well-being, generative AI (e.g., ChatGPT), biometric and identity systems, and cybersecurity systems—our goal is always the same:
to understand human–AI interaction and use that understanding to guide the design of systems that people can trust, use, and live with.
How We Do This
We combine biometric intelligence, machine learning, and qualitative inquiry to understand human behavior in digital and intelligent systems.
- We collect signals from everyday devices (phones, smartwatches, keyboards, IMUs).
- We pair sensing with interviews, surveys, focus groups, and user studies.
- We build and evaluate models when needed to reveal or test behavioral patterns.
- We run longitudinal studies to observe behavior over time and under evolving risks.
- We explore emerging and speculative AI systems to anticipate future challenges.
Our goal is not just to measure behavior or build systems —
but to connect what technology detects with what people experience.
Core Research Areas
Human–AI Interaction & Behavior
How people interpret, trust, adopt, or reject intelligent systems; what drives digital behaviors and decision-making *(e.g., interactions with generative models like ChatGPT, trust and explainability in AI tools)*
Cybersecurity & Identity Systems
Human-centered authentication, access, identity management, and usable security practices (e.g., password habits, risk awareness in everyday security decisions)
Biometric Intelligence & Multimodal Sensing
Behavioral and physiological signals as markers of identity, intent, or state (e.g., keystrokes, gait, IMU motion, wearables for continuous authentication)
Behavioral Signals in Digital Environments
How human behavior and choices reveal identity, deception, trust, or vulnerability online (e.g., cyber decision-making, intent inference, deception in text or interaction patterns)
User Perspectives on Emerging and Speculative Technologies
Anticipating expectations, concerns, and equity considerations before systems exist or scale (e.g., future biometric infrastructures, defense applications, organizational AI readiness, educational AI impacts)
Lab Members
Active Members
Ph.D. Students
- Tyree Lewis — Ph.D. Candidate (Human-Centered Biometric Systems)
- Wilson Lozano — Ph.D. Candidate (Context in Continuous Authentication Systems)
- Meghna Chaudhary — Ph.D. Candidate (Natural Language Processing)
- Hoorad Abootaebi — Ph.D. Candidate (Human-Centered Cybersecurity)
Master’s Thesis Students
- Anil Mumbuc — M.S. Student (Cognitive Parallels in Generative AI)
Undergraduate Thesis Students
- Isadora Grasel — B.S. Student (Mis/Disinformation Detection Using Stylometry)
Lab Alumni
Ph.D. Alumni
- Parush Gera, Ph.D., 2025
- Sayde King, Ph.D., 2025
- Steven Diaz, Ph.D., 2022
Master’s Alumni
- Khadija Zanna, M.S., (2022)
Interested in Joining the CIBeR Lab?
Current funding is limited; we can only welcome new members with external fellowships.
Interested Ph.D. Students
Ph.D. students with experience in one or more of the following are encouraged to email Dr. Neal (include materials below): - Machine/deep learning, pattern recognition - Statistical analysis - Image processing / computer vision - Qualitative data analysis methods - NLP - Python, TensorFlow, Keras, Sci-kit Learn - Cloud storage - Data mining Ph.D. expectations: - Mentor undergraduate students - Communicate research effectively - Work collaboratively and productively Email materials: 1. Summary of two projects & your contributions 2. Your perspective on CIBeR lab work & how you'd expand it 3. Coursework + final grades 4. Degree + expected start date at USF 5. Updated CV + publications/slides if available 6. **Link to personal website**
Interested Master's Students
M.S. students seeking a thesis advisor should reach out no later than **mid-first semester**.
Interested Undergraduate Students
To join the lab, undergraduates should enroll in one of the following to gain structured research experience: - **Independent Study (CIS 4900 / COP 4900)** - **Supervised Research (CIS 4915)** > **Plan ahead.** Scheduling must align with course registration timelines. Funding is typically not available for undergraduates, but **students who continue into graduate research often secure long-term assistantships.**