Ph.D. in Electrical Engineering, State University of New York at Binghamton, Binghamton, NY, USA.
M.Sc. In Electrical Enginieering, Shiraz University, Shiraz, Iran.
B.Sc. In Electrical Engineering, Isfahan University of Technology, Isfaha, Isfahan, Iran.
After graduation in electrical engineering (control) field, Dr. Sadeghian worked for 7 years in several industries as a senior industrial automation engineer. He decided to continue his education in signal processing field which gave him the motivation of starting a Ph.D. During his Ph.D. research, he began to get familiar with Machine learning with the focus of diagnosing human health disorders.
Dr. Sadeghian worked on speech features as the baseline to diagnose the speech delay of children and Alzheimer's disease in elderly people. To address the issues properly, he have worked with several tools such as DNN and RNN networks and have published several papers that proved his hypothesis of using speech recognition in diagnosing health disorders in early stages.
Teaching and Research Interests:
Teaching interests iniclude Graduate courses such as Analytics, Data Visualization, Signal Processing, Automatic speech recognition, Pattern recognition, Machine learning, Power systems, Convex optimization, Modern Control.
Undergraduate courses include any undergraduate course related to signal processing (signals and systems, digital signal processing) and applied mathematics (linear algebra, probability theory).
Dr. Sadeghian's research is founded at the nexus of science and engineering of clinical diagnosis and early notice of assessments and also analytics and data science. Specific topics include:
– Analytics, Big data and data science, Data visualization
– Modeling, detection and tracking of paralinguistic information, such as health state, and traits from human speech signal
– Machine learning applications in speech processing
– Acoustic and Language Modeling and analyzing the speech signals with applications to recognition and clinical assessments of speech
– Developing new and improving the current speech features to improve ASR accuracy.
Courses Taught at HU:
Analytical Methods II
Machine Learning I
Principles and Applications