Labib Tazwar Rahman

Labib Tazwar Rahman has had a diverse and impressive work experience. In 2018, they were a Co-founder of Neubility. From 2015 to 2020, they were the Founder of InclusionX, an organization working on inclusion for people with disabilities and mental illness through online therapy and programs in mental health, reproductive health, inclusive computing and arts education, and trained 500k+ people. In 2020, they were the President of the Stanford University Physics Society and a Research Intern for the Stanford Computer Science Department - HCI Group. In 2019, they were a Research Intern (Human-Computer Interaction) for the Stanford University School of Medicine - Wall Lab, where they helped design the UI for the interactive game suitable for use in Bangladesh and led a team of 2 to make a geo-map of resources for people with disabilities in Dhaka. In 2018, they were a Research Intern - LZ Dark Matter Experiment (Computer Vision) for the SLAC National Accelerator Laboratory, where they built a framework that detects anomalies in the construction of grids in the dark matter detector and helped develop a computer vision model that automatically detects fibers on grids to improve LZ high-voltage performance. In 2017, they were the Captain of the National Team (Robotics) for FIRST, where they led their country (Bangladesh) at the Olympics-style robotics competition, FIRST Global Challenge 2017, in Washington D. C. and ranked above 100+ countries including top teams like the US, the UK, and Russia. Lastly, in 2015, they were a Research Intern for CERN, where they assisted in the testing of Gaseous Electron Multiplier (GEM) and authored the article titled "Fast Micro-pattern Gaseous Detectors with Applications Beyond Particle Physics" for a CERN publication for summer students.

Labib Tazwar Rahman holds a Bachelor of Science in Computer Science from Stanford University. Labib Tazwar also has several certifications from Coursera and Massachusetts Institute of Technology | edX, including Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization, Big Data and Social Physics, Convolutional Neural Networks, Deep Learning Specialization, Neural Networks and Deep Learning, Sequence Models, and Structuring Machine Learning Projects.