Experience

Pelican
• Belgium
• Sports and Recreation Instruction
• Machine Learning Engineer Intern

  • Aug 2023 - Present


Planar (YC W23)

• Machine Learning Engineer Intern

  • May 2023 - Aug 2023

  I experimented with a range of both open-source and closed-source Language Model (LLM) solutions, aiming to optimize the code review workflow. These models include GPT-3.5/4, Falcon, Llama, Mosaic's MPT, etc. Through this experience, I developed a series of demonstrations aimed at discerning subtle low-context code modifications. This encompassed tasks such as bug detection, rectifying formatting discrepancies, and enhancing overall code elegance and efficiency. I gained insight into the capabilities/limitations of open-source and closed-source LLMs. This experience allowed me to discern their unique strengths and weaknesses, enabling informed decision-making in selecting the most suitable solution for specific tasks. Through experimentation, I developed a nuanced understanding of how these LLMs can be harnessed to enhance user productivity. Show less



Brown University

• United States
• Higher Education
• 700 & Above Employee

• Research Engineer

  • Jan 2021 - Jan 2023

  Currently, electrode-based retinal implants are severely limited in the restoration of vision loss. These implants are highly-invasive, limited in resolution, and degrade in utility overtime. My lab is developing a novel, minimally-invasive retinal prosthesis to overcome such limitations and stimulate retinal neurons to restore vision in blindness. This system uses gold nanorods (AuNRs) and near-infrared (NIR) light to activate retinal neurons by causing temperature changes in the neuronal membranes. My lab is using a custom experimental setup to validate the photothermal activation of retinal neurons ex-vivo. An important aim in the ex-vivo validation is to determine the number of RGCs activated per unit time by analyzing the images taken by the camera. Our imaging system captures ~1000 retinal neurons, and it is very challenging, if not impossible, to manually identify individual cells and obtain their time traces, especially when we have tens of retinal samples and data to analyze. My work focused on implementing various image segmentation algorithms that automatically identify individual cells from a grayscale fluorescence image of a retinal explant and comparing their performance. Show less


Systems Neuroscience Institute
• Norway
• Research Services
• 1 - 100 Employee
• Research Assistant

  • Oct 2019 - Mar 2020

Visual Neuroscience Laboratory
• Research Assistant

  • Sep 2018 - Oct 2019

Rehabilitation and Neural Engineering Laboratory
• Research Intern

  • May 2018 - Aug 2018


University of Delaware

• United States
• Higher Education
• 700 & Above Employee

• Neuroscience Lab Research Assistant

  • Jun 2013 - Aug 2014

  Prepared rat brain tissue onto microscope slides and analyzed images of gene expression in rat brains. Worked with a graduate student on a project to develop a brain processing and image protocol in a joint project with University of Delaware's Material Science and Engineering Department and Behavioral Neuroscience Lab. Prepared rat brain tissue onto microscope slides and analyzed images of gene expression in rat brains. Worked with a graduate student on a project to develop a brain processing and image protocol in a joint project with University of Delaware's Material Science and Engineering Department and Behavioral Neuroscience Lab.