Dinesh Palli

• Reduced processing time by 40% by digitizing operational processes using Microsoft Power Apps and Excel. • Engineered an end-to-end data pipeline in python including ETL processes, automated regulatory reporting to French authorities, database management and internal ERP systems. • Created automated KPI dashboards with Power BI, data modeling and statistical analysis. • Collaborated in the healthcare aggregate spend disclosure process, ensuring compliance with EMEA regulations and GDPR legislation.

• Engineered a ML model for cell segmentation, implemented L2SP, elastic losses, multiple augmentations, and dataloaders for diverse data sources, setup Git hooks and utilized Weights & Biases (wandb) for run logging. • Conceptualized, devised a visualization tool for datasets with >10k rows of single-cell data resulting in improved data comprehension and interpretation. • Automated code quality checks, enforced coding standards, thereby enhancing codebase stability and maintainability.

• Increased cluster purity by 25% in high-dimensional cytometry data using Neighborhood Component Analysis. • Achieved feature extraction, embedding optimization, improving cluster purity by 10% over classical features, by training neural network scDINO. • Developed an analysis pipeline for high-dimensional biological image data, enabling cluster visualization across 3 datasets with over 22,000 labeled images, by integrating techniques - dimensionality reduction, data preprocessing, unsupervised clustering.

• Conceptualized and developed 3 functions in Python for open-source project to ETL, process >250k rows of data, used by >1000 biologists and biotechnology companies.

• Implemented reconstruction of phased haplotypes – using python, R. • Quantified bacteria using RNA and further data analysis using python, for plant immunity.

• Researched, evaluated start-ups, investors, and partners to identify potential business opportunities resulting in 4 partnerships. Managed and acted as PMO to collaborate both startups and other interns. • Analyzed, improved the GTM strategy leading to 7.4% increase in market reach. • Optimized, maintained the company databases and customer relationship management systems, data acquirement and analysis for marketing, advertising, and promotion activities. Reported the findings to the Founder.

• Optimized resource utilization to increase profitability for small & medium agribusinesses, achieving an +8.2% yield through effective risk management and performance evaluation. • Enhanced operational efficiency by 15%, reducing time, costs in agricultural processes, by designing field experiments aligned with local practices and monitoring metrics. • Improved forecast accuracy by 20% by developing financial models integrating economic indicators, financial statements, supporting investment decisions.

• Master thesis in deep learning, research in computational biology and bioinformatics. • Top 20%iler in the class of 153. • Awarded LMU SIST scholarship from DAAD, for academic excellence. • Orchestrated and hosted the 2022 Welcome Event, skillfully managing logistics and activities to engage and orient incoming students. • Tutored the courses ‘Machine Learning in Image Analysis’, ‘Introduction to single cell RNA sequence data analysis in ScanPy’.

Graduated with a grade of 27/30 (Distinction). Agricultural business management is the use of business fundamentals to improve the agricultural industry and farm production. Courses opted: • Project management • Strategic management • Business planning • Economics in the marketplace • Circular bio-economy • Supply chain management • Agro-pharmaceutical innovation • Basic accounting • International cooperation • Industry 4.0: Opportunity for forward-looking enterprises

Graduated with a grade of 8.45/10 (First Class). • Captained the Badminton team, runner-ups in inter-university.