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Aqeel Anwar

Work Experience

Senior Machine Learning Engineer — Autonomous Vehicles
NVIDIA Corporation, Santa Clara, CA
Jun 2021 - Present

Working on improving perception DNN performance for autonomous vehicles using computer vision, machine learning and Generative AI based synthetic data generation. Responsibilities include generating synthetic data with Omniverse Replicator, leveraging realistic augmentations, developing multi-camera vision systems, and applying transfer learning to enhance perception. The work directly contributes to advancing safe and reliable autonomous driving.

Graduate Research Assistant
Georgia Institute of Technology, Atlanta, GA
Aug 2017 – Jun 2021

Conducted research on enabling edge intelligence in resource-constrained autonomous systems, with a focus on reinforcement learning applications. Developed energy-efficient ML algorithms (e.g., domain randomization), proposed novel hardware-aware architectures, and addressed scalability challenges in multitask federated reinforcement learning systems. Work focused on drone navigation and on-device learning.

Machine Learning System Intern
Samsung Semiconductor, San Jose, CA
May 2019 – August 2019

Worked in the Advanced Logic Lab on a novel STT-MRAM-based analog Processing-In-Memory (PIM) DNN accelerator. Developed an end-to-end simulation framework to optimize power-performance trade-offs for various DNN topologies. Achieved notable improvements in energy efficiency and latency over digital DNN accelerators.

Machine Learning Engineer — Autonomous Vehicles
Aivsol, Germany (Remote)
Jun 2017 – Aug 2017

Developed deep learning models for object detection and classification in self-driving car applications using TensorFlow. Focused on pedestrian, vehicle, and traffic sign recognition. Adapted state-of-the-art algorithms, trained models, and provided recommendations for optimal deployment strategies.

Publications
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[1] Aqeel Anwar, T. E. Choe, Z. Wang, S. Fidler, and M. Park, Augmented reality based simulated data (arsim) with multi-view consistency for AV perception networks, 2024. arXiv:2403.15370 [cs.CV]
[2] A. S. Lele, Y. Fang, Anwar, Aqeel, and A. Raychowdhury, “Bio-mimetic high-speed target localization with fused frame and event vision for edge application,” Frontiers in Neuroscience, vol. 16, 2022.
[3] Aqeel Anwar and A. Raychowdhury, Multi-task federated reinforcement learning with adversaries, 2021. arXiv:2103.06473 [cs.LG]
[4] A. K. Kosta, Anwar, Malik Aqeel, P. Panda, A. Raychowdhury, and K. Roy, “Rapid-RL: A reconfigurable architecture with preemptive-exits for efficient deep-reinforcement learning,” arXiv preprint arXiv:2109.08231, 2021.
[5] Z. Wan, Anwar, Aqeel, Y.-S. Hsiao, T. Jia, V. Janapa Reddi, and A. Raychowdhury, “Analyzing and improving fault tolerance of learning-based navigation system,” ACM/EDAC/IEEE DAC, 2021.
[6] A. Anwar and A. Raychowdhury, “Autonomous navigation via deep reinforcement learning for resource constraint edge nodes using transfer learning,” IEEE Access, vol. 8, pp. 26549–26560, 2020.
[7] A. Anwar, A. Raychowdhury, R. Hatcher, and T. Rakshit, “XbarOpt – enabling ultra-pipelined, novel STT MRAM based Processing-in-Memory DNN accelerator,” in IEEE AICAS, 2020, pp. 36–40.
[8] Aqeel Anwar and A. Raychowdhury, Masked face recognition for secure authentication, 2020. arXiv:2008.11104 [cs.CV]
[9] S. Zeng, Anwar, Aqeel, T. Doan, A. Raychowdhury, and J. Romberg, “A decentralized policy gradient approach to multi-task reinforcement learning,” arXiv preprint arXiv:2006.04338, 2020.
[10] I. Yoon, Anwar, Aqeel, T. Rakshit, and A. Raychowdhury, “Transfer and online reinforcement learning in STT-MRAM based embedded systems for autonomous drones,” in IEEE DATE, 2019, pp. 1489–1494.
[11] I. Yoon, Anwar, Malik Aqeel, R. V. Joshi, T. Rakshit, and A. Raychowdhury, “Hierarchical memory system with STT-MRAM and SRAM to support transfer and real-time reinforcement learning in autonomous drones,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2019.
[12] Anwar, Malik Aqeel and A. Raychowdhury, “Navren-RL: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular images,” in IEEE M2VIP, 2018, pp. 1–6.
[13] Anwar, Malik Aqeel, A. B. Siddique, and M. Tahir, “Relative self-calibration of wireless acoustic sensor networks using dual positioning mobile beacon,” IEEE Systems Journal, vol. 12, no. 1, pp. 862–870, 2016.
Dissertation: Anwar, Malik Aqeel. "Enabling edge-intelligence in resource-constrained autonomous systems." Georgia Institute of Technology, 2021.
[P1] Anwar, Malik Aqeel, T. E. Choe, Z. Wang, S. Fidler, and M. Park, “Data augmentation for model training in autonomous systems and applications,” US Patent App. 18/592,025, Sep. 2024.
[P2] T. Rakshit, Anwar, Malik Aqeel, and R. Hatcher, “Batch size pipelined PIM accelerator for vision inference on multiple images,” US Patent 11,769,043, Sep. 2023.
[P3] T. Rakshit, Anwar, Aqeel, and R. Hatcher, “Ultra pipelined accelerator for machine learning inference,” US Patent App. 16/838,971, Apr. 2021.