Rizwan Asif
Strategic Autonomous Systems Consultant
Helsinki, Finland
Summary
👤
Senior🎓
Top SchoolRizwan Asif is a Strategic Autonomous Systems Consultant and AI architect based in Helsinki with 11 years of experience building robotics, computer vision, cloud and space-related solutions. He combines hands-on engineering—ranging from DenseNet training optimizations for multi-GPU setups to manipulation planning for dual-arm robots—with product and project leadership as a repeat founder and consultant. Rizwan has led startups and technical teams, coached cross-functional delivery for cloud, IoT and functional safety projects, and now helps organizations translate strategic goals into deployable autonomous systems. He champions business-driven automation that shifts humans away from low-level tasks toward higher-value decision making. An unconventional strength is his knack for bridging academic research (IROS and Humanoids–recognized work) with pragmatic industry deployments.
11 years of coding experience
8 years of employment as a software developer
Master's degree, Autonomous Systems and Entrepreneurship, Master's degree, Autonomous Systems and Entrepreneurship at Aalto UniversityMaster's degree, Autonomous Systems and Entrepreneurship, Master's degree, Autonomous Systems and Entrepreneurship at KTH Royal Institute of TechnologyBachelor’s Degree, Electrical and Electronics Engineering, Bachelor’s Degree, Electrical and Electronics Engineering at National University of Sciences and Technology (NUST)
Urdu, English
Github Skills (6)
densenet10
tensorflow10
python9
gpu9
mle8
ml8
Programming languages (5)
Github contributions (5)
YixuanLi/densenet-tensorflow
Mar 2018 - Mar 2018
DenseNet Implementation in Tensorflow
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 1 PR, 5 comments in 1 day
Contributions summary:Rizwan focused on modifying and debugging the training configuration for a DenseNet implementation in Tensorflow. Their commits include adjustments to data augmentation using the `imgaug` library and addressing issues related to `tensorpack` integration. The user also modified the training configuration related to GPU usage, specifically adjusting the number of towers and their configuration, indicating a focus on performance optimization and multi-GPU training setup.
deep-learningdensenettensorflow
rizasif/kth-dd2410-assignment1
Aug 2018 - Sep 2018
Contributions:190 pushes, 1 branch in 6 days
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