Robotics Engineer Perception I
Arcbest Technologies
April 2023 - Present
At ArcBest Technologies, my focus is on developing advanced perception systems in robotics, with a strong emphasis on the integration and optimization of sensor data processing, calibration techniques, and obstacle detection
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Migrated the point cloud processing pipeline from CPU to GPU, significantly enhancing performance and efficiency.
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Engineered calibration methods for aligning multi-sensor systems, including lidar-lidar, camera-lidar, and lidar-robot configurations, ensuring high spatial accuracy in complex environments.
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Developed a ground segmentation system utilizing LiDAR and camera data, combined with advanced algorithms, to detect low-lying obstacles that are challenging to identify with LiDAR data alone, thereby improving navigation.
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Designed and implemented a dimensioner system for determining pallet dimensions, achieving 1-2 inch accuracy in cluttered environments using LiDAR data.
Research Assistant
Machine Learning and AI Lab, Carnegie Mellon University
Feb 2023 - April 2023
During my time at MAIL CMU I worked on the intersection of large sequence modelling and chemistry. Some of my notable works are mentioned below.
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Developed GPCR-BERT, a protein language model, to interpret and design G protein-coupled receptors (GPCRs), which are key targets in drug design. Fine-tuned Prot-Bert to predict variations in GPCR motifs and analyzed 3D structures to understand higher-order interactions within receptor conformations. (publication link)
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Developed a self-supervised multimodal learning approach, combining graph neural networks (GNNs) and transformer-based language models, to improve the prediction of adsorption energy in catalysis. This method, called graph-assisted pretraining, reduces prediction errors by 10% and enhances model fine-tuning, showcasing the potential of language models in energy prediction without relying on atomic spatial coordinates. (publication link)
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Developed MOFGPT, a GPT-2 based model generating Metal-Organic Frameworks (MOFs) from SMILES strings with a perplexity of 1.2. Fine-tuned the base model for adsorption energy prediction and implemented the reinforce algorithm ensuring generated MOFs are fine-tuned to meet energy, validity, and novelty criteria.
Deep Learning Perception Engineer
Thordrive
May 2022 - Aug 2022, Jan 2023 - Feb 2023
During my time at Thordrive, I focused on advancing perception systems through the development of deep learning and image processing techniques.
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Built a deep learning-based semantic segmentation pipeline for LiDAR data, modifying the Cylinder 3D model to achieve high accuracy, specifically an IoU of 93.4% for the aircraft class, and integrated it into existing perception modules.
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Improved model processing speed by 66.6%, enabling real-time operation at 30 fps using the MinkowskiEngine library for fast sparse convolution, and integrated this optimized model with the robotics stack.
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Developed an image processing algorithm for lane detection, transforming front-center camera images into a bird’s eye view using geometric transformation and homography, to enhance navigation accuracy.
