San Diego, California, United States
Extensive interdisciplinary experience in both theoretical and technical aspects of AI and ML, with expertise in neuropsychological applications such as affective computing/interactions, in healthy and clinical populations. * Technical Skills: SQL, Python (Tensorflow, Keras, Pandas, Numpy) * Research Skills: AI (ANN, CNN, Pooling, Pruning, Batch Normalization), ML (Randomforest, AdaBoost, GradientBoosting, GridSearchCV) * Experimental Skills: Experiment Design, Multi-modal Data Collection, Database Design, Hypothesis testing, Statistical Analysis, Regression * Qualitative Skills: MAXQDA qualitative analysis, Story telling from data, Mentorship, Higher education teaching, Academic editing, E-mental health content management, Ethical board application procedures * Organizational Skills: Lab/Grant Management, Curriculum Design, International/National Conference Organization, Fundraising * Interdisciplinary knowledge: Cognitive and Affective Neuroscience, Human Computer Interaction, STEM * Application areas: Stress detection, Sentiment analysis, Wearable devices (using pupil dilation, GSR, temperature, HR), Human brain mapping with MRI, fMRI, fNIRS * Clinical populations: PD, MDD * Accomplishments: -Improved automatic classification performance more than 20% using fusion techniques on multi-modal data across several domains -Improved data collection, pre-processing and data analysis procedures for healthy aging and clinical populations -Published more than 20 journal papers -Published and presented more than 100 conference papers -Finished 3 national (Tubitak) and 1 international (European Commission) academic research projects as a PI or co-PI -Supervised more than 30 MS/PhD students as academic advisor (3 of them received best thesis awards) -Opened a new neuroscience track and designed its curriculum under Medical Informatics -Developed more than 5 new courses in Medical Informatics, HCI, Cognitive and Affective Neuroscience * Education: Electrical and Electronics Engineering (BS, MS), Computer Science (PhD), AI/ML in Business (PGP)
Developed a new course in Cognitive Neuroscience enhanced with relationships to deep learning and AI. Skills: PsychoPy
Developed machine learning algorithms and modeling for tumor progression in metastatic breast cancer to predict objective response. Improved the existing models by adding capability to accommodate missing data. Tools used: Sql, python.
Stress prediction by developing novel hardware and software to collect psychophysiological data from the facial area. The psychophysiological signals are classified through 2 methods: 1. GoogleNet 2. Classifier level fusion after classifying each signal separately. Prediction performance (accuracy) is around 88%.
Taught hypothesis testing, linear regression and optimization to the undergraduate students of the business school. Class was conducted with hands-on applications in Excel.