Materials (Ph.D.) and chemical (B.S.) engineer with 10+ years of R&D experience in interfacial engineering, advanced materials development, and manufacturing process development across wet-lab and fab environments.

Author of 19 first- or corresponding-author publications, with deep expertise in controlling organic/inorganic, metal/substrate, and material/liquid interfaces through materials synthesis, formulation, surface functionalization, coating, deposition, printing, and device fabrication.

Expertise spans semiconductor manufacturing, metal interconnects, displays, sensors, and biomedical devices. Recently expanding expertise into computer vision and machine learning-driven automated materials development and process optimization. 

Interfacial Engineering, Materials Synthesis & Process Development, Device Fabrication

• Organic/inorganic, metal/substrate, and material/liquid interface engineering

• Surface chemistry, surface functionalization, wettability, adhesion, and interfacial interactions

• Polymeric, metallic, semiconductor, colloidal, and 2D material synthesis and formulation

• Conductive, optical, and semiconducting materials for metal interconnects, transistors, displays, and sensors

• Silicon/wafer-based device fabrication with hands-on cleanroom and fab experience

• Wet coating, PVD/evaporation, CVD, sputtering, printing, lithography, etching, and metal plating processes

• Materials characterization: SEM, TEM, AFM, XPS, DSC, TGA, FT-IR, UV-Vis, Raman, SAXS, XRD, HPLC, and mechanical testing

Functional Materials & Biomedical Applications

• Near-infrared- and ultrasound-responsive functional materials

• Biocompatible materials, surface coatings, and cytotoxicity evaluation

• Functional particles and materials for medical imaging and therapeutic delivery

• Translation of materials and interfacial engineering concepts into in vitro and in vivo applications

• 3+ years of collaborative biomedical materials research involving cell and animal studies

Computer Vision, Machine Learning & Automated Experimentation

• Computer vision and machine learning for materials characterization and process optimization

• Development of image-based monitoring and automated experimental workflows

• Python, OpenCV, YOLO, PyTorch, and MATLAB

• SolidWorks for experimental apparatus and prototype design