Qualifications:• Data analysis• Python programming (Numpy, Pandas, Matplotlilb, ...)• Matlab programming• SQLExamples of projects:• Analyzed textual and speech data to identify the accuracy of people’s understanding of speech and to extract features with a fundamental role in speech perception (text mining, natural language processing, signal processing, and classification)• Studied whether Baidu’s speech-to-text recurrent neural network processes speech like humans (training, testing, manipulation, and evaluation of the models)• Automatized complex data source separation of cognitive processes (unsupervised learning, multivariate timeseries analysis, data reduction, statistical analysis)• Defined behavioral performance metrics in speech perception experiments with English/non-English participants (project scoping and translating the hypothesis into measurable meaningful metrics, cohort analysis and segmentation)• Developed preprocessing pipelines for big datasets (sanity check, missing data, outliers, filtering, and resampling)• Mentored several data projects at the graduate level, educated the team, and tracked their progress (Brainstorming, prioritization, experimentation, and resource allocation)Examples of computational methods:• Regression models• Classifications• Machine Learning• Deep Learning• Multivariate analysis (GPFA, PCA, ACA, ...)• Statistical analysis

Qualifications:• Data analysis• Advance mathematical skills• Python programming (Numpy, Pandas, Matplotlilb, ...)• Matlab programming• SQLExamples of projects:• Defined behavioral metrics to understand subject’s motivation in decision-making (hypothesis development and testing, causal inference, applied statistics and power analysis)• Discovered the root cause of a paradoxical behavior in choosing between the options in an A/B test experiment (metric selection, causal inference, power analysis, regression, classification, Gaussian Process Factor Analysis)• Developed ML algorithms to extract patterns and trends in high-dimensional timeseries of brain activity and behavioral data (statistical analysis, regression modeling, GLMs, Gaussian Processes, and multivariate analysis)• Modeled subjects’ locations from time series of their brain activity with a significant higher accuracy than traditional Bayesian approach (deep neural networks, Bayesian modeling, supervised learning, predictive modeling, optimization, cross-validation)• Developed preprocessing pipeline for large datasets (compressed data using PCA and clustered data using their features)• Ran exploratory analysis and pilot studies to derive data-driven insights and design experimentsExamples of computational methods:• Regression models• Classifications• Machine Learning• Deep Learning• Multivariate analysis (GPFA, PCA, ACA, ...)• Statistical analysis

Teaching assistant for four semesters in a hands-on crash-course on computational methods in data analysis.• Assisted students in an active hands-on computer lab.• One-by-one assistance to students at project times on their questions about computational methods, algorithms, programming, and implementation of methods by Matlab.• Cooperated with the group of instructors to understand their needs and assist in their teaching. Also, prepared course materials for for one of them (Including time and frequency domains, FFT, PSD, Bode diagrams, and an introduction to filters and their design by Matlab).• Achievements: The dynamic nature of the class enhanced my multi-tasking skills as I had to simultaneously listen to the teacher, be prepared for the questions, and answer students accordingly. This interactive environment has also given a boost to my mentoring and communication skills.