• Development of IoT, cloud and AI/ML-based tech & solution for water, oil & gas industrial applications and smart infrastructures.• Real-time monitoring systems and software solutions for autonomous anomaly/failure detection and predictive maintenance.• Prototyping of frameworks, algorithms, modules and tools for real-time data fusion, visualization and end-to-end analytics.• Feature engineering and model training for varieties of complex signals sensed from fluid transport networks.• Design and instrumentation: energy harvesting devices, RTUs, control systems, smart fittings, sensor-integrated structures, etc.• Multisource heterogeneous sensing and signal processing: pressure, flow, acoustic, temperature, in-pipe robotics, etc.• R&D on fiber optics and electro-optics hybrid sensing tech for nerve-like distribution on next-generation oil & gas pipeline.• Heavily involved in product development lifecycle from prototyping, pilot projects, mock tests to real-world deployment.• Design, prototyping and algorithm development for a variety of IoT, AI and SaaS products for clients and partners.• Built and led a multidisciplinary R&D team. Served a team leader role to drive and support product development and innovations.• Volunteer IT administrator in the startup company, managing and maintaining multiple servers, clusters, clouds and databases.

• Researched on cutting-edge products and feasible technologies applied to complex piping mechanical system with respect to monitoring, failure detection, failure pinpointing, and pre-failure early assessment.• Worked with a multi-disciplined team on design and development of data analytics products for intelligent pipeline systems.• Performed validation tests, designs and analyses for DAQ, WSN, wired/wireless configurations, self-powering, and data exchange via interfaces.• Participated in the design, calibration, test and instrumentation of data acquisition units and piping system components.• Designed and conducted lab-scale / reduced-scale experiments to mimic transient signals due to pipe failure events.• Analyzed analog & digital signals in case studies and processed time series data for validation, calibration and evaluation.• Assisted CTO of the Intelligent Pipenet Technology (IPT) branch on various development activities.• Collaborated with software engineers on the development of client front-end portals with visualization of live data and results.

• Developed an automated real-time adaptive prediction system for monitoring and analyzing interplays between physical and biological processes in gulf coasts. Established the nowcast-forecast capabilities through ensemble datasets.• Provided modeling support to produce daily coastal weather conditions to assist the adaptive sampling and the understanding of interactions between physical, thermodynamical, biological, and ecological processes.• Developed, validated, and optimized numerical modeling strategies and algorithms to facilitate coastal modeling activities.• Developed robust visualization tools for displaying live image loads, signature patterns and animations on Google Earth geospatial platform. • Integrated remote sensing imagery data of satellites and LiDAR, in-situ data (e.g., underwater sea glider) and simulation data to improve system performance by adapting multisource ensemble features.• Heavily worked with ocean optics, remote sensing and hydrographic data streams, data exchange, data synthetics, and data quality control.• Processed pixel-based images for various tasks such as band-sharpening, feature extraction, pattern/object detections, etc.• Functioned as IC and a technical lead in the Ocean Weather Laboratory (OWX) in the marine science department.

• Developed HPC numerical procedures to retrieve surface currents by processing large loads of sequential images of geostationary satellite (GOCI), polar-orbiting satellite (NPP-VIIRS), and Doppler HF radar systems. • Invented algorithms for quantifying sea surface advections from the overlapped sensor swaths of polar orbiting satellites.• Processed large amount of images using multiple strategies (MCC, optical flow, convolution, segmentation, etc.) to detect pattern formations, variations and migrations in coastal currents.• Performed Bio-Optical Forecasting (BioCast) virtual simulations with synthetic velocities to assess models and schemes.• Implemented image-based flow decomposition and reconstruction aiming to improve vector coverage and quality in flow fields, and developed innovative algorithms for characterizing upper ocean 3D structures in both coastal and open waters.

• Performed numerical simulations for Eulerian-vs-Lagrangian evaluation of various 3D products yielded from multitypes of operational models.• Statistically analyzed model system performances on predicting the transport and dispersion of oil during the DWH oil spill.• Applied Lagrangian stochastic trajectory models to the dynamic fields of host model to guide ocean debris search and rescue.• Developed prediction capabilities for estimating the dynamic trajectories of objects freely released in sea surface currents.• Quantified errors and biases in the deterministic field of mesoscale numerical models and the virtual Lagrangian trajectories.• Worked at NOAA's AOML as a CIMAS contractor (non-federal employee).

• Conducted fraternal “twin-model” virtual simulations (OSSEs) in multiple domains using hybrid-coordinate ocean models.• Modeled and analyzed the impacts of various existing and next-generation observing system components on the NWP system performance.• Developed a state-of-the-art ocean adaptive prediction system with parallelization schemes, data assimilation and vigorous OSSE capabilities.• Quantified AMOC uncertainty & variability across a large ensemble of operational models, and analyzed the thermodynamical & dynamical processes responsible for varieties of classified AMOC states.• Characterized model responses to air-sea interactions, atmospheric forcing, freshwater transport, and overturning circulations.• Routinely worked with large amounts of multisource real-world observation data and model-generated data.• Worked at NOAA's AOML as a CIMAS contractor (non-federal employee).

• Developed data mining methods to derive composite friction factor correlations for flow in smooth and artificial rough pipes. • Invented logistic function based analytical procedures for extracting patterns & regressions from fluids data with self-similar regions.• Simulated the 2D and 3D motions of particles in pressure-driven pipe and channel Poiseuille flows using Direct Numerical Simulations (DNS) and Finite Element Analysis (FEA).• Applied ALE moving mesh scheme and DLM fictitious domain scheme to the CFD simulations of complex solid-liquid flow.• Modeled and analyzed friction and viscosity variabilities in pressure-driven turbulent pipe flows laden with particles.• Modeled and characterized the features of high-viscosity multiphase polymeric flow through perforation under various flow conditions.• Conducted simulation and analytics of heat transfer in motor oil and polymeric liquid seeded with carbon nanotubes.• Quantified and evaluated the flow response enhancements of non-Newtonian solutions laden with nanoparticles and sub-millimeter particles.• Researched particle motion regimes in non-Newtonian liquid films rimming the inside of a partially filled rotating cylinder.

• Performed numerical simulations and hydrodynamic loading experiments for wave-induced oscillatory flows around pipeline• Analyzed structural vibrations, vortex dynamics and the scour of sandy seabed with dynamic interface in oscillatory flow• Processed large loads of digital images using PIV & PTV velocimetry methods for flow visualization and pattern detection• Participated in R&D related to the optimal design and safety analyses for marine/offshore oil & gas pipeline systems