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 cancer, as well as oral cancer.

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

Building and validating a classical ML pipeline for histopathology image classification of oral squamous cell carcinoma (OSCC) vs. normal tissue using handcrafted image features; exploring deep learning extensions as a next step.

→ OSCC Classification (GitHub)
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.

→ AMTB Dose-Response Analysis (GitHub)

Selected Projects

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

Classical ML pipeline classifying H&E-stained oral histopathology slides (Normal vs. OSCC, 230 patients, open-source dataset). Handcrafted features: colour statistics, LBP, and Haralick/GLCM descriptors. Random Forest and SVM benchmarked across magnifications, dataset sizes, and feature sets under 5-fold stratified cross-validation. Best result: SVM + Haralick at 100× (AUC 0.988 on balanced set). Pipeline pending validation on lab-generated data; deep learning extensions in exploration.

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