Gandhimathi (Mathi) Padmanaban
Ph.D., Industrial and Systems Engineering, University of Michigan-Dearborn
On the 2026–27 Academic Job Market · CV · gmathi@umich.edu
I am a researcher working on whether what an automated system reports can be relied on by the person who has to act on it. I hold a Ph.D. in Industrial and Systems Engineering and an M.S. in Human-Centered Design and Engineering, both from the University of Michigan-Dearborn, where I was advised by Dr. Fred Feng.
A system's own confidence is a poor guide to whether its output is right, because models are confidently wrong where they have not been before. My work checks outputs against something outside the model: the geometry of how an image was formed, a known standard, or the procedure used to certify the system. Longer term, I want reliability to be something an automated system can demonstrate to the people who depend on it, rather than something inferred from its own confidence.
Publications
Detection followed by a fine-tuned ViT over six classes defined by injury risk to cyclists. 89% accuracy at held-out sites with no retraining, and an "unknown" output under low confidence rather than a committed label. In preparation, CVPR 2027. Earlier preprint · Code
Speed-adjusted jerk thresholds jointly optimized with the classifier, because the same jerk magnitude means different things at 20 and 60 mph. 94% accuracy, AUC-ROC 0.971, across 556 trips and seven vehicle models. Submitted, SAE WCX 2027.
Perspective-geometry validation on top of RT-DETR detection and ByteTrack tracking, discarding tracks that are not physically consistent with an overtaking manoeuvre. 98.1% recall with one false positive across 315 events. Submitted, IEEE Transactions on Intelligent Transportation Systems. Poster, TRB Annual Meeting 2026. NSF PAR · Code
Estimates how closely a vehicle passes a cyclist from bounding-box geometry, with no camera calibration. 0.126 m MAE; R² = 0.812 re-fit on the Waymo Open Dataset. In preparation. Details
Transportation Research Board Annual Meeting, 2026. NSF PAR
Ph.D. dissertation, University of Michigan-Dearborn, 2026. Advisor: Dr. Fred Feng.
Whether the standardized cycles used to certify vehicle behaviour represent naturalistic driving. US06 approximates aggressive acceleration but overstates deceleration intensity, and the milder cycles overestimate it as well. WCX SAE World Congress Experience, 2025. SAE Technical Paper 2025-01-8605. DOI
AutomotiveUI '21 Adjunct, ACM, 2021, pp. 142-147. DOI
M.S. thesis, University of Michigan-Dearborn, 2021. Deep Blue