Summary: I am passionate about fundamental applied research. I have the capacity to quickly grasp new concepts, come up with new ideas, and efficiently implement them. This has enabled me to smoothly change my field of work/research several times and constantly deliver. I have experienced different phases of maturity over the years, ranging from being a team member delving deep into pure research to serving as technical lead and project team lead roles driving innovation in product development. Currently, I am leading applied research in the area of deep learning and computer vision at Shell.

[Adjunct Assistant Professor @ Computer Vision Lab (CV Lab)]

  • Leading research on Deep Learning and Computer Vision

Shell

[Lead Research Scientist]:

  • Lead the technical and research track of deep learning/computer vision.

  • Initiated several R&D collaborations with top universities and research institutions. I keep on (co)supervising Masters, Ph.D. students, and PostDoc researchers from these institutions.

  • Made fundamental contributions to Shell's affinity to and adoption of deep learning based imaging and computer vision systems. Co-led the largest computer vision project in Shell focused on asset maintenance and integrity recognition, and therein a project team of data scientists/engineers.

[Senior Machine Learning Scientist]

  • Developed machine learning models for predicting seismicty in order to optimize production plans. Developed statistical models for analyzing seasonality therein.

  • Developed deep learning based semantic segmentation models to detect coherence energy is seismic inversion tomography systems.

[Research Scientist]

  • Pioneered the ideation and led the first implementation of wireless IoT (LoRa and NB-IoT) based monitoring and imaging systems in the Oil and Gas industry.

  • Developed novel methodologies for optimizing seismic acquisition and image reconstruction systems using deterministic non-uniform wavefield subsampling.

[Researcher][PhD student]

  • Developed novel distributed sparse regression algorithms using dual-subgradient optimization and consensus averaging with theoretical convergence analysis.

  • Developed dynamic multi-dimensional scaling (dynamic MDS) and dynamic principal component analysis (dynamic PCA) algorithms for mobile configuration tracking.

  • Developed novel compressive sensing (CS)-based blind multi-source time-difference-of-arrival (TDoA) and received signal strength (RSS) localization (blind parameter estimation) algorithms.

  • Developed several novel adaptive filters (modified EKF, UKF, particle filters) for motion prediction in partially connected multi-agent cooperative systems.

  • Developed multi-source localization and tracking algorithms in underwater acoustic sensor networks and subsurface monitoring systems.