Dr. Ali Yener Mutlu

Roles: - Developing AI frameworks using machine learning algorithms and time-series analysis techniques - Teaming up with stakeholders to integrate data driven algorithms and the business needs - Managing and coordinating projects in development Projects: - Artificial Intelligence based pricing, which accounts for dynamic and personalized pricing - Price Elasticity of Demand estimation using machine learning - Demand forecasting for using trend, seasonality and residual estimation

Research focused on: - smart phone based artificial intelligence and machine learning techniques to automatically identify colorimetric tests - development of Artificial Intelligence based methods for estimating energy products of biomass gasification - efficient signal representation, detection and machine learning applications to identify epileptic seizures in the human brain

Courses Taught: - Digital Logic Design - Telecommunications II (Digital Communication) - Introduction to Programming

- Received Horizon 2020 Proposal Development Certificate in Brussels, April 2015. - Brought Innovative Ideas - Prepared project proposals and acted as the Principal Investigator for Horizon 2020 and EUREKA (Celtic Plus and ITEA clusters) Incentive Programs - Performed Research and Development of Data Analysis Algorithms - Designed Project Architecture - Performed Agile Project Management

Research Interests: The theory and applications of statistical signal processing, in particular non-stationary signal analysis using Time-Frequency Distributions and graph theoretical approaches. Research focused on the applications of statistical signal processing to biological signals, in particularly Electroencephalogram (EEG), by developing a comprehensive signal processing framework to study the time-varying functional connectivity of the human brain.

Worked on modeling and analysis of physiological systems with applications to alarm management algorithms for physiological patient monitoring devices in Intensive Care Unit (ICU). Developed a new framework using statistical signal processing skills which significantly reduced the false alarms, eliminated the artifacts, increased the detection rate and performance of electrocardiogram (ECG) and impedance respiration patient monitoring / health care devices in ICUs.

Improved customer satisfaction and productivity of the organization by ensuring fast and accurate quote and order handling. Investigated a new business management software, which is compatible with the infrastructure of the organization and is planned to be used instead of SAP.

Completed an automated data structure conversion software project, from DICOM (The Digital Imaging and Communications in Medicine) data structure into NRRD (Nearly Raw Raster Data). Pursued a research on DTI (Diffusion Tensor Imaging) for the visualization of 3D images of biological tissues.

- Recipient of the College of Engineering Outstanding Graduate Research Award, Feb. 2012 - Ranked first among the 27 participants of PhD qualifying exam, 2009 - Proposed Dissertation Title: “A Multivariate Time-Frequency Based Approach to Phase Synchrony Estimation Across Frequency”

- Graduated with honors degree in Electrical and Electronics Engineering.