Vesela Nikolova

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Hello, I am Vesela!

Senior student at High School of Natural Sciences and Mathematics N. Popovich, Shumen, Bulgaria, with a focus on Maths, Informatics, and English. Beyond the classroom, I’m deeply passionate about programming, volunteering, and folklore dancing — activities that let me explore creativity, community, and culture.

My peers and mentors often describe me as an outgoing and responsible individual who brings enthusiasm to every event. I’m driven by curiosity and a desire to make a positive impact through my skills and passions.

Folklore dacing

Rhythm of Heritage

I’ve been dancing since I can remember — 14 years and counting! Folklore dancing connects me with Bulgaria’s traditions, culture, and past. During the years I’ve performed with youth ensembles and since 2022 with the "Zadyavka" dance club under choreographer Zhechko Kolev.

Together, we’ve earned prizes like a first place at the International Folklore Festival "Sea of Rhythms". I’ve also proudly represented Bulgaria’s dances at international olympiads in Informatics. About them, you can read below.

Programming

Code. Sleep. Repeat.

I discovered programming the summer before 5th grade and began my journey at A&B Private School under the mentorship of Biserka Yovcheva. Together, we’ve attended countless competitions both national and international, creating memories that I’ll cherish forever.

As I grew, I started teaching younger students, which sharpened my presentation skills. Technology inspires me because it holds the power to solve real-world problems and impact thousands of lives—a future I’m excited to be part of!

Volunteering

Compassion in Action

Since 2022, I’ve been part of the huge Bulgarian Red Cross Youth family, certified in First Aid, Health program, Humanitarian education, and Active inclusion in the social environment. I’ve led school presentations for hundreds of students, participated in charity bazaars, and contributed to numerous campaigns of the organization.

In collaboration with local NGOs, we worked on developing Shumen’s Youth Strategy for 2025-2030. Knowing I’m making a positive impact in my community during my spare time, volunteering gives me a sense of purpose and joy!

Projects

Research projects

  • Adaptive urban traffic analysis

    The Adaptive Urban Analysis Project aims to eliminate traffic congestion and optimize traffic light timings through advanced simulations. By integrating data on demographics, building characteristics, traffic signals, and road regulations, it models scenarios like new construction or street closures. This predictive approach helps prevent traffic disruptions, improving urban mobility and easing city traffic flow.

    Mentor: Ivan Dimitrov, NVNA "Nikola Y. Vaptsarov"

  • Comparative analysis of microbiomes using De Bruijn graphs

    De Bruijn graphs, named after mathematician Nicolaas Govert de Bruijn, are vital in genome assembly and microbiome research. They compress sequence data, overcome assembly challenges, and reconstruct microbial genomes. Our project uses these graphs to compare microbiomes across hosts and environments, uncovering shared and unique microbial traits and insights into co-evolution and environmental impacts.

    Mentor: Vanesa Getseva, a Ph.D. candidate at Columbia University

  • Association between genetic variants and human diseases

    Although humans share 99.9% of their DNA, the 0.1% variation influences genetic predispositions to diseases like diabetes and cystic fibrosis. This project uses genome data, algorithms, and machine learning to study disease-associated variants and predict their effects. Our analysis found no significant link between genotype and sex but identified key mutations in the ninth and tenth chromosomes related to type 2 diabetes.

    In collaboration with: Maria Kirilova, student at AUBG

    Mentor: Vanesa Getseva, Ph.D. candidate at Columbia University

  • Comparison of classification algorithms on histological images

    Cancer refers to diseases involving uncontrolled cell growth that can invade or spread to other body parts, affecting over 10 million people globally each year. This project evaluates modern object classification algorithms on histological images, testing six approaches. The Bernoulli Naive Bayes classifier achieved the highest accuracy at 64%, highlighting current limitations in using these algorithms for breast cancer diagnosis.

    Mentor: Vanesa Getseva, Ph.D. candidate at Columbia University

Awards