About Me

Hi, I’m Mantej Singh, a Biomedical Engineering Phd Candidate at Johns Hopkins University School of Medicine. My interests are in the intersection of Medicine, Machines and Humanity. My research is in designing Brain Machine Interfaces and Computational Biophotonics. In my free time, I enjoy exploring coffeeshops, doing stand-up comedy, and rock climbing.

Resume | LinkedIn | Github

Education

Ph.D Candidate, Biomedical Engineering (Neuroengineering) - Johns Hopkins University School of Medicine 2022 - Present
Bachelor of Science, Computer Science - Rice University 2018 - 2022, Distinction in Research and Creative Works.

Publications

Research Event Location Tags Date
Parallel RRT Algorithm For Robotic Motion Planning Rice Undergraduate Research Conference Rice University, Houston, TX Poster Presentation 2022
Parallel RRT Algorithm For Robotic Motion Planning Ken Kennedy AI and Data Science Conference Biosciences Research Collaborative, Houston, TX Presentation 2021
Decoding Phonemes Using Brain Electrocorticography (ECoG) Data Tandon Lab McGovern Medical School, Houston, TX Research Paper (In Preparation) 2020 - 2021
Automatic Reconstruction of 3D Vascular model of Human brain from MRI Tandon Lab McGovern Medical School, Houston, TX Software 2020 - 2021
Parallel RRT Algorithm For Robotic Motion Planning Harvard Undergraduate National Collegiate Research Conference Harvard University, Boston, MA Plenary Speakership 2021
Open Source Innovation TEDx Talk GIIS, Singapore, SG Talk 2021
Parallel RRT Algorithm For Robotic Motion Planning Gulf Coast Undergraduate Research Conference Rice University, Houston, TX Presentation 2021

Work Experiences

Software Architecture Intern, Lutron Austin TX May 2021 — Aug 2021

Robotics Research Intern, Kavraki Lab Houston TX Jan 2021 — May 2021

Computational Neuroscience Intern, Tandon Lab Houston TX May 2020 — Aug 2020

Software Developer Intern, Concur Hipmunk San Francisco CA May 2019 — Aug 2019

Awards and Accomplishments

Distinction in Research and Creative Works Rice University, 2022

Outstanding Senior Award Rice University, 2022

Plenary Speaker – Harvard National Collegiate Research Conference Harvard University, 2022

Outstanding Junior Award Rice University, 2021

Presidents Honor Roll Rice University, 2021-2022

Hershel. M Rich Innovation Award Rice University, 2020

Lilie New Entrepreneurs Grant Rice University, 2018

Code and Software

Sophia - An Open-Source Computerized Maintenance Management System for Low-Resource Hospitals to track their inventory.

    // Coded in Kotlin with a SQL Server

    Currently in development   
}

Complete code can be found at this Github Repository.

Kinetica - A wearable glove that automatically converts American Sign Language to spoken English, empowering the deaf-mute to communicate with any members of society.

    // Coded in C++ and Python - Framework is based on a gesture library that classifies gesture to a predetermined word

    std::map<String, std::pair<double, double>> gestureMap () {
    /*Library that maps each tuple of data to the corresponding gesture*/
    std::map<String, std::pair<double, double>> gestureLibrary;
    gestureLibrary['Hello'] = std::pair<double, double>(0.0,0.0);
    gestureLibrary['Goodbye'] = std::pair<double, double>(0.0,0.0);
    gestureLibrary['Nice_to_meet_you'] = std::pair<double, double>(0.0,0.0);
    gestureLibrary['Yes'] = std::pair<double, double>(0.0,0.0);
    gestureLibrary['No'] = std::pair<double, double>(0.0,0.0);  
}

Complete code can be found at this Github Repository.

Nywton - A wearable sensor that can detect medically asymptomatic concussions during sports.

// Coded in C++ and Arduino - Based on the ADXL345 accelerometer sensor

    void ADXL_ISR() {
    if(adxl.triggered(interrupts, ADXL345_DOUBLE_TAP)){
      Serial.println("*** Possible Concussion ***");
      tone(piezoPin, 250, 1500);
    }
}

Complete code can be found at this Github Repository.

MAMx - A fully autonomous software that can analyse and diagnose abnormalities in breast mammograms.

// Coded in Python with the OpenCV image library

    // Calculating mean of non-black pixels, taking into account 1.2x for cancer markers

    average_color_per_row_L_CC = np.average(img_L_CC, axis=0) 
    average_color_per_row_L_CC = np.average(average_color_per_row_L_CC, axis=0)
    average_color_L_CC = np.uint8(average_color_per_row_L_CC)
    cancer_marker_intensity = average_color_L_CC * 1.2
    print(cancer_marker_intensity)
}

Complete code can be found at this Github Repository.