Robotics Researcher
Specializing in outdoor perception, visual–inertial odometry, and uncertainty‑aware planning. Building robust autonomous systems that work reliably in real-world environments.
Current Focus
Dependable Robotics & Autonomous Systems
Developing robust perception systems, visual-inertial odometry, and uncertainty-aware planning for autonomous vehicles and robotic systems operating in challenging real-world environments. Bridging the gap between academic research and practical deployment in outdoor robotics.
Industry Research
CrossWalk Illumination Project
Leading WisDOT-sponsored research on crosswalk illumination and safety, collaborating with TAPCO and UW-Madison. Applying robotics and computer vision expertise to develop practical solutions for pedestrian safety through advanced perception systems.
Academic Background
Graduate Student in Robotics at University of Wisconsin-Milwaukee (Rankings loading...) with a 3.94 GPA, focusing on computer vision, machine learning, and autonomous systems. Previously completed MS at Tsinghua University (QS Rank #17) in Mechanical Engineering with research on robotics and automotive systems, and BE from SUIT in Mechanical Engineering with research on robotics.
Research Philosophy
I believe in building systems that work reliably in real-world conditions - not just in controlled lab environments. My work emphasizes reproducible research, honest evaluation metrics, and practical deployment considerations.
Technical Approach
I focus on bridging the gap between theoretical advances and practical implementation, with particular emphasis on uncertainty quantification, robust perception, and safety-critical applications.
Industry Impact
My research has been featured in major media outlets and contributes to advancing the field of autonomous systems, with applications in transportation, safety, and urban planning.
Programming Languages
Robotics & Perception
Machine Learning
Systems & Tools
Mathematical Methods
Hardware Platforms
Graduate Teaching Assistant
Supporting undergraduate and graduate courses in robotics, computer vision, and autonomous systems. Developing practical lab exercises and mentoring students in hands-on robotics projects.
Research Mentoring
Mentoring undergraduate researchers and junior graduate students on reproducible research workflows, data collection methodologies, and scientific writing. Focus on practical skills in robotics and computer vision.
Conference Presentations
Active presenter at major robotics and transportation conferences including AVEC 2018 and upcoming TRB 2026. Sharing research findings with both academic and industry audiences.
Peer Review Activities
Contributing to the scientific community through peer review for journals and conferences in robotics, computer vision, and intelligent transportation systems.
Professional Memberships
Active member of professional organizations including IEEE, Transportation Research Board, and robotics research communities.
CrossTraj Dataset
Open-source bi-seasonal crosswalk trajectory dataset with panoramic video and RTK GNSS ground truth. Includes evaluation toolkit and benchmarking scripts for reproducible research.
VIO Uncertainty Framework
Lightweight C++ library for visual-inertial odometry with calibrated uncertainty quantification. Designed for integration with motion planners and risk-aware autonomous systems.
Robotics System Demos
Video demonstrations of 1/10 scale autonomous vehicle navigation, perception system performance, and real-world testing scenarios.
University of Wisconsin-Milwaukee
Current - Graduate Student in Robotics
Pursuing advanced studies in robotics and AI with a 3.94 GPA. Conducting cutting-edge research in computer vision, autonomous systems, and perception technologies.
Tsinghua University
Master's Degree - QS Rank #17
Completed Master's in Mechanical Engineering with focus on robotics and automotive systems. Recipient of prestigious CSC Scholarship and 2x Best Student of the Year awards.
SUIT University
Bachelor's Degree - Mechanical Engineering
Foundation in mechanical engineering with specialized focus on robotics. Built fundamental knowledge in control systems, mechanical design, and engineering principles.
Autonomous Vehicle Safety
Perception systems for urban environments, crosswalk safety research, and real-world deployment challenges in autonomous vehicles.
Visual-Inertial Odometry
Technical deep-dives into VIO systems, uncertainty quantification, and robust state estimation for robotic applications.
Reproducible Robotics Research
Best practices for dataset creation, evaluation methodologies, and open science in robotics research.
Academic Excellence
"Muhammad consistently demonstrates exceptional research capabilities and innovative thinking in robotics and autonomous systems. His work on crosswalk perception systems represents a significant contribution to transportation safety."
Industry Collaboration
"Working with Muhammad on the WisDOT project has been exceptional. His ability to translate complex research into practical solutions while managing multi-stakeholder collaborations is remarkable."
Research Impact
"His publications in IEEE Transactions and other top-tier journals demonstrate both technical depth and practical relevance. Muhammad's work bridges academic rigor with real-world applications."
Short-term Goals (1-2 years)
Complete CrossTraj dataset and make it widely available to the research community. Publish findings from WisDOT crosswalk illumination project. Develop advanced VIO systems with uncertainty quantification for real-world deployment.
Long-term Vision (3-5 years)
Establish a research program at the intersection of robotics, computer vision, and transportation safety. Develop next-generation autonomous systems that can reliably operate in complex urban environments with human-robot interaction.
Career Aspirations
Seeking opportunities in both industry R&D and academic research positions where I can continue advancing autonomous systems while mentoring the next generation of robotics researchers.
Impact Goals
Create robotics technologies that improve public safety, particularly in transportation systems. Build open-source tools and datasets that accelerate research in the broader robotics community.
Research Tutorials
Step-by-step guides on implementing VIO systems, dataset collection methodologies, and reproducible research practices in robotics.
Technical Blog Posts
In-depth technical discussions on robotics challenges, sensor fusion techniques, and lessons learned from real-world deployments.
Code & Tools
Open-source implementations, evaluation scripts, and research tools developed during various projects. All code includes comprehensive documentation and usage examples.
Languages
Research Methods
Project Management
CrossWalk Illumination Project
WisDOT Sponsored Research Initiative
Leading a collaborative research project focused on crosswalk illumination and safety, sponsored by the Wisconsin Department of Transportation (WisDOT). Working directly with industry partner TAPCO and academic collaborators at UW-Madison to develop practical solutions for pedestrian safety at crosswalks.
Transportation Safety Research
Government-Industry-Academia Collaboration
Bridging the gap between academic research and practical implementation through direct partnerships with state transportation agencies and industry leaders. Focus on translating research findings into deployable safety solutions for real-world traffic scenarios.
Industry Applications
From Lab to Field Deployment
Research directly applicable to autonomous vehicle development, traffic infrastructure improvement, and urban planning. Collaborating with transportation authorities and technology companies to implement research-backed solutions in real transportation systems.
Wisconsin Department of Transportation (WisDOT)
Leading sponsored research project on crosswalk illumination and pedestrian safety. Developing evidence-based recommendations for traffic infrastructure improvements across Wisconsin's transportation network.
TAPCO
Collaborating with leading traffic safety technology company on practical implementation of crosswalk illumination solutions. Bringing academic research insights to commercial traffic safety products and systems.
University of Wisconsin-Madison
Inter-institutional collaboration with UW-Madison researchers on crosswalk illumination project. Combining expertise across multiple disciplines and research groups to address complex transportation safety challenges.
Transportation Research Community
Active engagement with Transportation Research Board (TRB) and broader transportation research community. Contributing to the advancement of autonomous vehicle safety and traffic infrastructure research through publications and conference participation.
CrossWalk Illumination Project
WisDOT-Sponsored Research Initiative
Leading a comprehensive research project on crosswalk illumination and pedestrian safety, sponsored by the Wisconsin Department of Transportation. Collaborating with industry partner TAPCO and UW-Madison to develop evidence-based solutions that directly impact traffic safety policy and infrastructure design.
GNSS-Denied Navigation
Advancing visual-inertial odometry systems for robust navigation in GNSS-denied environments. Focus on self-supervised learning approaches and uncertainty quantification to provide reliable state estimation for autonomous vehicles in challenging scenarios.
Autonomous Vehicle Testing
Implementing and testing autonomous navigation systems on 1/10 scale vehicles with emphasis on real-time performance, safety validation, and systematic evaluation of perception-planning interfaces.
Safety-Critical Systems
Research on vehicle headlight effects on perception and safety, contributing to traffic safety studies with real-world implications for autonomous vehicle design and human-vehicle interaction.
CrossTraj Dataset
Comprehensive bi-seasonal dataset for urban crosswalk analysis featuring synchronized panoramic video and RTK GNSS data. Includes evaluation toolkit with standard metrics (IDF1, HOTA) and reproducible benchmarking scripts.
Perception-Planning Interface
Lightweight library designed to bridge perception systems and motion planners by providing calibrated uncertainty estimates. Enables risk-aware planning with quantified collision probabilities.
Outdoor Autonomy Benchmark
Standardized evaluation framework for outdoor robotic systems with weather-varied test scenarios and ground truth from RTK positioning. Includes automated evaluation scripts and visualization tools.
Multi-Modal Sensor Fusion
Advanced sensor fusion algorithms combining camera, IMU, and GNSS data for robust state estimation. Implemented with modern C++ and optimized for real-time performance on embedded systems.
CSC Scholarship Recipient
Awarded the prestigious Chinese Government Scholarship (CSC) for Master's studies at Tsinghua University. One of the most competitive international scholarships recognizing academic excellence and research potential.
Best Student of the Year (2x)
Recognized as "Best Student of the Year" twice during graduate studies at Tsinghua University, demonstrating consistent academic excellence and outstanding research contributions.
TRB Conference Submissions
Two manuscripts submitted to Transportation Research Board 2026 annual meeting, representing significant contributions to transportation safety and autonomous vehicle research.
Media Recognition
Research featured across multiple major news outlets including TMJ4 News, Yahoo! News, and industry publications, highlighting the real-world impact of autonomous vehicle research.
Open Source Contributions
Created widely-used datasets and evaluation tools that have been adopted by other researchers, contributing to the advancement of the robotics community.
Core Technical Areas
Applied Research Areas
Emerging Interests
Professional Inquiries
Interested in collaboration, research opportunities, or technical discussions? I'm always open to connecting with fellow researchers, industry professionals, and students.
Email: iamfahadusa@gmail.com
Academic & Research
For academic collaborations, dataset requests, or technical questions about my research, please include relevant details about your project or inquiry.
Response time: Typically within 48 hours
Social & Professional Networks
Connect with me on various platforms to stay updated on my latest research and professional activities.