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Applied Artificial Intelligence and Sensing Laboratory (AIS-Lab) is primarily focused on research at the intersection of sensing technologies, artificial intelligence, and infrastructure systems, with applications in intelligent infrastructure, multimodal data fusion, digital twins, and human–technology interaction. The lab actively supports research by graduate students at the Master’s and PhD levels. Motivated undergraduate students with relevant interests are also welcome to get involved.
More information can be found at:
Why Do Research at AIS-Lab?
Why Do Research at AIS-Lab?
We aim high and work hard — our goal is to publish in top-tier journals and conferences in sensing, AI, and infrastructure.
Collaborative, dynamic environment — you’ll join a group of enthusiastic and driven students who also know how to enjoy the journey.
Hands-on learning — the lab is equipped with a variety of sensing devices, edge computing platforms, and immersive tools that support both experimentation and simulation.
Mentorship and teamwork — Dr. Wang considers every AIS-Lab member a collaborator. You’ll work closely with faculty and peers throughout the week.
Supportive leadership — Dr. Wang is an active part of the lab community, offering guidance and collaboration rather than just supervision.
Inclusive and empowering — we are deeply committed to supporting students from all backgrounds, and strongly encourage women and underrepresented groups to pursue leadership roles in research and engineering.
Cross-disciplinary opportunities — our projects often span civil engineering, computer science, data analytics, and human-sensing technologies, providing a broad platform to grow.
What Kind of Background Fits AIS-Lab?
What Kind of Background Fits AIS-Lab?
Students majoring in Civil Engineering, Electrical Engineering, Computer Engineering, Computer Science, Mechanical Engineering, Data Science, or other related fields.
Strong interest in sensing technologies, multimodal data fusion (e.g., acoustic, vision, thermal), artificial intelligence, digital twins, human–infrastructure interaction, or infrastructure resilience and safety.
Preferred technical background in Python or MATLAB programming. Familiarity with machine learning frameworks (e.g., PyTorch, TensorFlow), signal/image processing, or simulation modeling tools (e.g., COMSOL, Unity, OpenCV) is a strong plus.
Bonus skills include experience with embedded systems, real-time data collection, GIS/remote sensing, ROS, or edge computing platforms (e.g., Jetson Nano, Raspberry Pi).
Are You an Undergraduate at UD? Here’s How to Join AIS-Lab :
Are You an Undergraduate at UD? Here’s How to Join AIS-Lab :
We look for strong performance in relevant courses such as MATLAB programming, sensing systems, data science, civil engineering labs, or AI-related electives. If you’ve done well in these, let’s talk!
You are encouraged to join AIS-Lab through an independent study or undergraduate research project (for credit).
If you are a U.S. citizen, you may consider applying for DAGSI (Dayton Area Graduate Studies Institute) opportunities for advanced research.
Consider applying for summer undergraduate research funding through programs like SURE (Summer Undergraduate Research Experience) or GSSF (Graduate Student Summer Fellowship, if transitioning).
Students interested in infrastructure, sensing, or AI applications in engineering are especially welcome.
Are You Starting Graduate School? Here’s How to Join AIS-Lab :
Are You Starting Graduate School? Here’s How to Join AIS-Lab :
You must first be admitted to the University of Dayton’s Master’s or PhD program. Recommended majors include Civil Engineering, Computer Engineering, Electrical Engineering, Mechanical Engineering, or Data Science.
It is strongly recommended that you take courses in data analytics, sensing systems, machine learning, or numerical modeling during your first semester. If you excel in these, we should talk.
Master’s students will typically complete 6 credits of thesis research as part of the degree. Research-based master's students are preferred in AIS-Lab.
If you are an incoming PhD student, we will review your Master’s thesis to assess your technical depth and research potential, even if your previous topic is not identical to ours.
We will also evaluate your prior publications, especially those demonstrating strong analytical, modeling, or programming skills.
Thinking of Joining AIS-Lab? Start Here
Thinking of Joining AIS-Lab? Start Here
Please send an email to hwang12@udayton.edu.
In your email, briefly introduce yourself, describe your research interests, and explain why you want to join AIS-Lab.
Attach your resume, including GPA, universities you’ve attended, and the professors or research groups you've worked with.
PhD applicants: please also include copies of your recent publications, if available.
Recommendation letters from professors or mentors are always appreciated.
Coding & Technical Requirements:
Coding & Technical Requirements:
Familiarity with Python or MATLAB is expected. Experience with tools like COMSOL, Unity, OpenCV, ROS, or machine learning libraries (e.g., PyTorch/TensorFlow) is a plus.
You may be asked to complete a brief coding test or modeling assignment to assess your proficiency.
A short interview will be conducted, either in person or via video conference.
If you are currently enrolled at UD:
If you are currently enrolled at UD:
Consider taking courses related to sensing systems, data analytics, or intelligent infrastructure.
We’d be happy to arrange a lab tour and meet to discuss potential research involvement.
Funding: What You Should Know Before You Apply
Funding: What You Should Know Before You Apply
Teaching assistantships in our department are limited and competitive.
Research assistantships (RAs) are available on a project-by-project basis, with priority given to those whose skills best match current funded projects.
Well-trained PhD students typically have an advantage over Master’s students in RA positions due to the long-term nature of the work.
While we can’t guarantee funding upfront, many Master’s students in AIS-Lab have been funded through research grants in the past.
Students are encouraged to apply for external fellowships, such as DAGSI (for U.S. citizens), and internal programs like SURE and GSSF for summer research funding. We are happy to support strong applicants through this process.
Please understand that funding is performance-based—students who do not meet expectations may lose their RA support.
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