Octav Chipara, PhD

Professor

Research interests (keywords)

Embedded Systems; Intelligent Hearing Technology; Machine Learning

Octav Chipara's research develops artificial intelligence and machine-learning systems for intelligent hearing technologies operating in complex real-world environments. His work combines efficient machine learning, embedded systems, auditory scene analysis, and neural signal processing to create low-latency, closed-loop systems capable of inferring listener intent and enabling adaptive hearing technologies. His work increasingly emphasizes machine-learning methods for auditory attention decoding (AAD), neural decoding from electroencephalography (EEG), and confidence-aware inference that support robust, personalized hearing assistance in dynamic listening environments. These efforts build on a sustained research program in AI-enabled hearing technologies and neuro-steered hearing systems.

Dr. Chipara has led multidisciplinary research programs supported by the National Science Foundation's Smart and Connected Health (SCH) program and has served as a key investigator on projects funded through the Rehabilitation Engineering Research Centers (RERC) program, bringing together expertise in artificial intelligence, computer systems, audiology, neuroscience, and clinical research to develop deployable hearing-health technologies. His research has produced novel machine-learning methods, scalable software platforms, reproducible computational pipelines, and large-scale human-subject studies that translate advances in AI into clinically relevant auditory systems.

Selected publications:

[1] J. Ham, I. Pope, J. Kim, I. Choi, and O. Chipara, "Linking attentional modulation to auditory attention decoding: Using colocated stimuli with a fixed temporal structure," Trends Hear., 2026.

[2] E. Jorgensen, J. Xu, O. Chipara, and Y.-H. Wu, "Auditory environment diversity quantified using entropy from real-world hearing aid data," Front. Digit. Health, vol. 5, p. 1141917, 2023.

[3] I. Pope, J. Ham, I. Choi, and O. Chipara, "End-to-end EEG-based auditory attention decoding for cochlear implant users," 2026. ICHI 2026

[4] C. C. Dunn, E. Stangl, J. Oleson, M. Smith, O. Chipara, and Y.-H. Wu, "The influence of forced social isolation on the auditory ecology and psychosocial functions of listeners with cochlear implants during COVID-19 mitigation efforts," Ear Hear., vol. 42, no. 1, pp. 20–28, 2020.

[5] D. Vyas, E. Jorgensen, Y.-H. Wu, and O. Chipara, "Evaluating and optimizing hearing-aid self-fitting methods using population coverage," Front. Audiol. Otol., vol. 1, p. 1223209, 2023.

[6] Y.-H. Wu, E. Stangl, O. Chipara, S. S. Hasan, S. DeVries, and J. Oleson, "Efficacy and effectiveness of advanced hearing aid directional and noise reduction technologies for older adults with mild to moderate hearing loss," Ear Hear., vol. 40, no. 4, pp. 805–822, 2018.

[7] Y.-H. Wu, E. Stangl, S. Smith, J. Oleson, C. Miller, and O. Chipara, "Psychometric characteristics and feasibility of microinteraction-based Ecological Momentary Assessment in audiology research," Front. Audiol. Otol., vol. 2, p. 1506306, 2025.

[8] O. Chipara, C. Lu, T. C. Bailey, and G.-C. Roman, "Reliable clinical monitoring using wireless sensor networks: Experiences in a step-down hospital unit," in Proc. 8th ACM Conf. Embedded Networked Sensor Systems (SenSys), pp. 155–168, 2010.

Research areas

Health- and Human-Centric Computing

octav-chipara@uiowa.edu

319-335-0561

Personal website,

Mobile Systems Laboratory website,

Google Scholar

Office

University of Iowa
201D MacLean Hall (MLH)
Iowa City, IA 52242
United States