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Bengisu Ozbay, Ph.D. LinkedIn: LINKEDIN LINK AVAILABLEEmail : EMAIL AVAILABLE, Mobile : PHONE NUMBER AVAILABLE SummaryExperienced AI and Machine Learning Ph.D. skilled in developing, optimizing, and deploying advanced models and algorithms. Proficient in Python, Java, and MATLAB with a strong background in collaborative, cross-functional teams. Skills Programming languages: Python (expert), C++, MATLAB, Java, Bash Scripting Machine Learning Libraries: PyTorch, numpy, pandas, matplotlib, scikit-learn, OpenCV Software & Tools: CUDA, GPU computing, Jupyter, Git/Github, ROS, Linux, Visual Studio Code, JIRA, Confluence Research ExperienceNortheastern University, Electrical and Computer Engineering Department Boston, MA Research Assistant Sept 2015 - May 2024Fast Two-View Motion Segmentation: Defined research goals and developed a high-speed segmentation algorithm using 3D view geometry. Managed datasets and benchmarked against 8 methods. Published in ECCV2022.Unsupervised model development: Innovated a subspace clustering algorithm to facilitate identification and clustering within large-scale datasets, focusing on dynamical systems and linear subspaces. Applied mathematical principles of linear algebra and statistics. Validated the model across 10 machine learning datasets. Published at CDC2019 and IFAC 2020.Cross-view Object Segmentation: Contributed to asynchronous non-calibrated multi-camera segmentation algorithm based on dynamics and semantic information. Created novel multi-camera dataset. Published in ICCV2017.Real-time Human Tracking System: Utilized RGB-D data for human tracking using OpenCV in Python and motion control in ROS. Contributed to 2018 RoboCup At Home Challenge.Sparse Linear Constrained Controller Design Through a Convex Optimization Approach: Contributed to designing a decentralized controller with information constraints for cooperative robotic vehicles. Published in CDC 2016.Bilkent University, Electrical and Electronics Engineering Department Ankara, TURKEY Senior Project Sept 2014 - June 20153D Sensor System Development for Autonomous Vehicles: Led a team of 6 students to prototype a high-speed 3D Lidar system. Applied sensor fusion to data from IMU, 2D Lidar, and odometry sensors. Explored applications for SLAM and obstacle avoidance. Published in IEEE ICAR 2015 and SAGE TIMC, 2018. Work ExperienceMathWorks Inc. Natick, MAEngineering Development Group Intern May 2021  Aug 2021MATLAB Toolbox Enhancement: Developed algorithms for dynamic behavior analysis, utilizing piecewise linear and nonlinear mathematical models. Conducted simulations to refine model accuracy and robustness in real-world applications.Motorola Solutions Inc. Somerville, MAMachine Learning Engineer Intern May 2020  Sept 2020Real-World AI Solutions: Developed mask detection model for public safety during the COVID-19 pandemic. Customized and optimized an off-the-shelf PyTorch-based multi-stage CNN, training it on GPU infrastructure with human face dataset. Leadership and Teaching Experience Python Course Assistant: Led workshops and offered hands-on support for projects, code reviews and unit tests via GitHub. Signals Processing Assistant: Provided expert support in circuit simulation, design, and testing, along with signal analysis using MATLAB for various modalities, including sound recordings and ECG signals. Created demos on digital filter applications, diagnostics, and lab device functionalities. Earned Best TA Award for outstanding teaching and dedicated students support. Leaders Program: Engaged in professional development, acquiring insights into project management and leadership concepts. EducationNortheastern University Boston, MassachusettsDoctor of Philosophy (Ph.D.) in Electrical and Computer Engineering May 2024Northeastern University Boston, MassachusettsMaster of Science in Electrical and Computer Engineering May 2017Bilkent University Bilkent, Ankara, TURKEYBachelor of Science (B.S.) in Electrical and Electronics Engineering June 2015 Selected Publications ECCV 2022: Ozbay, B., Camps, O., and Sznaier, M. Fast Two-View Motion Segmentation Using Christoffel Polynomials. In European Conference on Computer Vision. ICCV 2017: Zhang, X., Ozbay, B., Sznaier, M. and Camps, O., 2017. Dynamics Enhanced Multi-camera Motion Segmentation from Unsynchronized Videos. In Proceedings of the IEEE International Conference on Computer Vision (pp. 4668-4676).