Prisha Sanyal-Aich

B.Tech & M.Tech, Biotechnology · KIIT University, Bhubaneswar
Summer Research Intern · Institute of Life Sciences, Bhubaneswar

About

I am a Biotechnology student at KIIT University currently working at the Institute of Life Sciences (ILS), Bhubaneswar, under Dr. Shantibhusan Senapati. My current work focuses on computational pathology and tumour microenvironment biology, particularly in the context of oral and pancreatic cancer.

I am broadly interested in machine learning for biology, statistical genomics, and interpretable computational methods for biomedical data.

Previously, I interned at NISER, Bhubaneswar, where I worked on TRP ion channel biology, fluorescence image analysis, and statistical analysis of microscopy-derived data. I also contribute to open-source projects at events such as GSSoC and Hacktoberfest.

Research

Institute of Life Sciences (ILS), Bhubaneswar May 2026 – Present
Summer Research Intern · Dr. Shantibhusan Senapati

Studying tumor microenvironment (TME) dynamics in pancreatic and oral cancer.

Running qPCR experiments to assess gene expression across TME cell populations under varying experimental conditions.

Built a computational pathology pipeline for oral squamous cell carcinoma (OSCC) histopathology classification using handcrafted image descriptors and classical machine learning approaches for reproducible evaluation across multiple microscopic magnifications..

NISER, Bhubaneswar May – Jul 2025
Summer Research Intern · Prof. Chandan Goswami

Investigated expression and evolutionary conservation of TRP ion channels (TRPV4, TRPM8) through PCR primer design, molecular cloning into fluorescent reporter vectors, and multi-species phylogenetic analysis.

Quantified TRPM8 fluorescence intensity across 50+ confocal images and observed a 3.2× increase in calcium signalling under the tested condition (p < 0.01).

Conducted dose-response analysis of AMTB (a TRPM8 antagonist) on lipid droplet morphology across 4,878 observations, applying Kruskal-Wallis and Bonferroni-corrected Dunn's post-hoc testing for Area, Perimeter, and Integrated Density.

Selected Projects

Oral Cancer Histopathology Classification
@ILS · Python · scikit-learn · OpenCV

Computational pathology pipeline for classification of H&E-stained oral histopathology images (Normal vs. OSCC) using handcrafted image features and classical machine learning methods. Implemented Haralick texture descriptors, Local Binary Patterns (LBP), and colour-based features with Random Forest and SVM benchmarking across multiple magnifications and feature combinations.

The project includes reproducible cross-validation workflows, feature extraction pipelines, learning curve analysis, calibration analysis, explainability visualizations, and structured experiment tracking across ~250 experimental runs. Best-performing models achieved strong discrimination between normal and OSCC tissue on the evaluated dataset.

AMTB Dose-Response: Lipid Droplet Morphology
@ NISER · Python, SciPy, scikit-posthocs

Statistical analysis of AMTB (TRPM8 antagonist) effects on lipid droplet morphology across six concentration groups (Control–10 µM), 4,878 droplet observations. Non-normality and unequal variances confirmed (Shapiro-Wilk, Levene's); Kruskal-Wallis with Bonferroni-corrected Dunn's post-hoc applied to Area, Perimeter, and IntDen. 10 µM AMTB reduced all three measures consistently; lower doses were inconsistent. Analysis captures morphological effects of AMTB rather than direct TRPM8 activity.

Education

KIIT University, Bhubaneswar 2023 – 2028
B.Tech + M.Tech (Dual Degree) · Biotechnology

Activities: SPIC MACAY – KIIT Chapter (Violinist) · KORUS – Infinity Melody (Violinist) · Kreative Eye (Photographer)

SAI International School, Bhubaneswar 2009 – 2023
AISSE & AISSCE · Science (Physics, Chemistry, Biology)

Activities: Secretary, Symphony Club (2018–19) · Basketball