Syam Sundar Nallamekala

Research

I publish what survives honest evaluation, including when that means retracting a result.

Two published papers, one more under review, one granted patent and one pending. The published pair are a hybrid architecture for unstained cell imagery and the undergraduate robotics work the patent came from; the paper under review is the osteoporosis work. What follows is a thesis in progress, and an audit that invalidated a prior result before it invalidated itself.

Publications

Two published, one under review

  • Neural Computing and Applications, Springer Nature

    Fuzzy deep LSTM for cancer cell death analysis

    Published — citation pending

  • 2022 6th International Conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, pp. 1153–1157

    FlowerBot: a deep learning aided robotic process to detect and pluck flowers

    Published 1 December 2022 — cited by 7

    Granted Indian patent IN 573184 (2025)

  • MS thesis research, University of Toledo

    Hip osteoporosis risk from CT imaging and clinical data

    Under review

    Patent pending; $50K NSF I-Corps grant; featured in UToledo News

  • University of Pennsylvania

    External validation

    The brain tumor classifier was validated externally, outside the group that built it.

Poster

Fuzzy deep neural network for brain tumor detection and classification

University of Toledo, Dept. of EECS — with Dr. Devinder Kaur

Presented at NCUR 2025, Pittsburgh

Research poster: fuzzy deep neural network for brain tumor detection and classification, showing the methodology, ROC curves and per-class performance metrics.
The hybrid reaches an ROC AUC of 0.926 against 0.871 for a CNN alone and 0.815 for traditional ML — the comparison matters more than the headline accuracy, because it measures the hybrid against what it replaces.

How I work

Method, and what I do when it fails

  • I audit inherited work before I extend it

    The osteoporosis pipeline came to me from a prior student. Auditing it end to end surfaced a data-leakage bug — the model was effectively seeing test data during training. After correction a previously reported headline result did not survive, and a simpler model proved the more trustworthy one.

  • I replace single splits with repeated cross-validation

    One train/test split made the initial osteoporosis results look strong. Repeated cross-validation showed most of that was noise, and confirmed genuine signal only under stricter testing.

  • I report conditions alongside numbers

    Every figure on this site carries what it was measured on. The traffic model reports 8.92% MAPE and, in the same breath, that it has 38,375,647 parameters trained on six examples and no baseline to beat.

  • 480+ documented research hours at ODOT

    Across three assignments, with three milestone presentations to stakeholders and a final technical report alongside the delivered prototype.