Principal Venture Builder

• May 2023 - Present

Associate Venture Builder

• May 2022 - May 2023

NLP Data Scientist

• Jul 2021 - May 2022

I was responsible developing core NLP capabilities and supporting NLP Products in Healthcare domain: Specifically, algorithms that can classify, extract and connect biomedical entities from several different documents including insurance claims, grant data, conferences, press releases, scientific articles, clinical trials, FDA approvals, etc. My role also included deployment of ML models on AWS platform using services like AWS Lambda, Sagemaker, EC2, ECS, Cloud9 and S3. I was responsible developing core NLP capabilities and supporting NLP Products in Healthcare domain: Specifically, algorithms that can classify, extract and connect biomedical entities from several different documents including insurance claims, grant data, conferences, press releases, scientific articles, clinical trials, FDA approvals, etc. My role also included deployment of ML models on AWS platform using services like AWS Lambda, Sagemaker, EC2, ECS, Cloud9 and S3.

Data Scientist and Product Development

• Aug 2019 - Jul 2021

My role involved developing algorithms for extraction, analysis and visualisation of data from different text sources and databases. My responsibilities included extraction of material and biological data from different data sources, creating data analysis and machine learning pipelines, and creating meaningful features for Patsnap products. I was also involved in product development and white space analysis. My role involved developing algorithms for extraction, analysis and visualisation of data from different text sources and databases. My responsibilities included extraction of material and biological data from different data sources, creating data analysis and machine learning pipelines, and creating meaningful features for Patsnap products. I was also involved in product development and white space analysis.

PhD Researcher

• Jan 2016 - Nov 2019

Project description: In my Ph.D. project, I engineered thermostable exoshells (tES) as a novel cage-confinement chaperone that can encapsulate and fold inclusion bodies. When internalized within the exoshell, the proteins were protected against non-optimal conditions including organic, proteolytic, thermal and chemical denaturation. Using tES exoshells, I successfully encapsulated and refolded nine monomeric target proteins including human serum albumin (HSA), firefly luciferase (FFL), Horseradish peroxidase isozyme-C (HRPc), Renilla luciferase (rLuc), Green Fluorescent Protein (GFPuv), rFasxiator, phospholipase A2 (PLA2), α- and λ- conotoxins and 3 multimeric target proteins including alkaline phosphatase, Omp2a, and p53 with diverse physicochemical properties. I observed that charge complementary tES significantly improved the folding efficiency of all target proteins (5x – 1000x). Through this study, I reported the use of my novel refolding technique as a robust and systematic protein folding approach. For HRPc, The refolded protein and tES was then used as a prodrug and delivery vehicle respectively for targeted cancer therapy.Skills acquired: Analytical Ultracentrifuge (AUC), Dynamic Light Scattering (DLS), Protein expression, Protein engineering, Chromatographic techniques like Size Exclusion, Hydrophobic interaction, Western blot and Gel Imaging, Transmission Electron Microscopy (TEM), Mass Spectrometry (QTOF 6550), Thermal shift studies, Scientific writing, and differential scanning calorimetry (DSC), Machine LearningNotable accomplishments: 1. This project is now being co-developed with Merck-Sigma as a protein production kit for distribution across all laboratories.2. This project has led to 2 scientific publications in Nature Communications and a successful patent application.3. This project has led to 3 successful research grants 4. This project was featured in Strait times, Science Daily and Genetic Engineering and Biotechnology News (GENS) Show less

Research Assistant

• Jun 2015 - Jan 2016

I worked on 3D printing of artificial skin and co-culture membranes using the e-jetting technique. I also worked on material characterization and biological aspects of various 3D printed scaffolds. This project gave me a good insight into 3D printing materials and processes.Skills acquired: 3D printing using E-Jetting, E-spinning and Drop on Demand setup, Scanning Electron Microscope(FE-SEM), Computer Aided Designing and Drafting in Solidworks

Research Intern

• Dec 2013 - May 2014

Project title: Development of PTFE/PEEK coatings to reduce gear tip friction. In this project, I developed PTFE and PEEK coatings using emulsion technique and characterized them for adhesion strength, impact strength and other important physical properties. I created a new PTFE coating formulation with high thermal and wear resistance for surface protection of gear tips which is currently being sold under the tradename 'Teflub'. This project provided me with good insight into polymer coating technology. Skills acquired: Impact testing (Impact test), conical mandrel testing(Bend test), Crosshatch testing(Adhesion testing), Polymer Science and Polymer Chemistry Show less

Research Intern

• Jun 2013 - Oct 2013

I synthesized pure nanostructured Sodium Tantalate (NaTaO3) and Sodium Niobate (NaNbO3) using Sodium Hydroxide (hydrothermal method). The prepared nanomaterials were characterized using X-ray diffraction, diffuse reflectance UV-Visible and photoluminescence spectroscopy, FE-SEM and TEM techniques. Then the properties of both were compared and material suitable for photocatalytic activity was proposed. Photocatalytic activity was evaluated by observing the aqueous R6G and MB degradation under a 400 W mercury vapor lamp. It was noted that the photo-catalytic activity of NaNbO3 (20 h) was better than all other samples making it a possible candidate for photocatalytic degradation. Skills acquired: Scanning Electron Microscope(FE-SEM), Transmission Electron Microscope (TEM), X-ray Diffraction (XRD) instrument, UV-Visible Spectrophotometer, Spectrofluorophotometer Show less