Welcome to Cooperative Human-AI Teaming (CHAT) Lab With advances in Artificial Intelligence (AI) and Machine learning-driven applications, there is a dire need to interact and cooperate with AI/ML algorithms to develop robust, scalable solutions that prevent performance degradation and adapt to the deployed environment while meeting expectations. CHAT lab proposes, designs, and develops novel AI/ML algorithms by incorporating human-in-the-loop and human-on-the-loop to solve real-world problems. The research pursues the junction of natural language processing, human-computer interaction (HCI), and pervasive computing.
Here is the overview of the key research focus of our lab, which aims to build applied machine learning systems for the betterment of human life.
Understanding Context and Problem: Fundamental understanding of people and the environment through smart devices and user interaction warrants specific research problems. Environment and context are the determinants of people’s involvement in the now and future. How we can understand and predict activities in a complex environment with limited information.
Health and wellness Research and Design: Health and wellness Research and Design: To improve the quality of life - human health and wellness, we envision integrating innovative sensing paradigm technology with the key healthcare settings. Our goal is to design, develop, and deliver smart systems adaptable to people of different needs and connect with clinicians and community members to assist people.
Long term Learning: Any data-driven application depends on the user data. Sensed data changes over time. These changes may be due to environmental settings, adaptation, people preferences, etc. How can we adapt our system to such changes? Can we infer possible changes and learn these parameters. How can we engage and improve user interaction over time?
Our research themes combine multiple disciplines - such as sensor technology, human-computer interaction, healthcare engineering, machine learning system design, and natural language understanding. In short, our lab engages in algorithm design, novel application building, prototype development in real-world settings.
We are always looking for motivated students to join our CHAT lab.
If you are interested, please drop me an email.
PhD, Graduate and Undergraduate Students:
PhD Students
- Abm. Adnan Azmee (CS PhD)
- Francis Nweke (CS PhD)
- Haige Zhu (MS CS)
- Sanjay Potluri (MS CS)
- Mason Pederson (Undergrad CS)
- Ryan Tran (Undergrad CS)
- Kris Prasad (Undergrad CS)
Past Students:
- Martin Brown (DS PhD Graduated)
- Manohar Murikipudi (Grad)
- Ryann William (Grad)
- Vamsi Krishna Dhulipalla (Grad)
- Naga Sai Krishna Adatrao (Grad),
- Akula Sai Shashank (Grad)
- Sreekanth Gopi (Grad)
- Donovan McGregor (Undergad)
- Cesar Lucena Trujillo (Undergad)
- Megan Vo (Undergrad)