Welcome to my research journey!
I'm fascinated by the elusive nature of success—what makes some individuals, teams, and organizations flourish while others falter? I also love science and data, I can spend endless hours devising novel experiments or analyzing massive datasets. These interests drive me to conduct research aiming to break down barriers and empower people and organizations to achieve their highest potential.
A particular fascination of mine is discovering what influences whether a creative idea is accepted and whether someone will have a long, prolific creative career. To explore these questions, I employ diverse methodologies, ranging from big data analytics, network analysis, natural language processing (NLP), to controlled experiments and surveys.
Below you'll find the projects I've been working on, they are my efforts towards using science to improving how we understand and interact with the world around us. You can also use the navigation bar to explore each project in detail.
Is Your Network Boosting or Blocking Your Career? Insights from Big Data on Social Networks
Objective: In this project, I investigate how professional networks influence career longevity and productivity, with a focus on differences between men and women. Understanding how each gender benefits from professional connections is key to designing inclusive network interventions that enhance career success for everyone.
Methods: Using a comprehensive IMDb dataset spanning from 2000 to 2023, I applied advanced big data analytics techniques to examine how the same types of professional connections affect men and women's careers differently. For a closer look at how I did this, check out this interactive notebook. In it, I analyzed professional networks over 21 years, each year the network comprises approximately 100,000 individuals and facilitates around 5 billion potential connections.
Findings: My research reveals distinct gender differences in the benefits derived from network types. While men often benefit more from "open" networks—where their contacts do not know each other—such networks may disadvantage women. However, both men and women experience significant career boosts when they connect with individuals who themselves have open networks, with women reaping greater benefits from such connections.
For a detailed overview of the project, click here. For all analysis codes and results, visit my GitHub repository.
Are Positive Stereotypes Holding Back Your Business? What Experiments Tell Us About Hidden Bias in Startup Evaluation
Collaboration and Recognition: For this project, I led an international, interdisciplinary team of distinguished experts, including Dr. Ivona Hideg (Oxford University); Dr. Yuval Engel (the University of Amsterdam); Dr. Frédéric Godart (INSEAD Paris). Our research was published in Entrepreneurship: Theory and Practice (a leading scientific journal recognized among the Financial Times' Top 50 most influential business journals) and was also featured in The Conversation Canada.
Objective: We aimed to uncover how benevolent sexism—attitudes that seemingly praise women yet subtly depict them as fragile and in need of protection—affects how decision-makers evaluate and fund women-led versus men-led startups. This knowledge is crucial for developing strategies to eliminate biases and promote fairness in the startup ecosystem.
Methods: We conducted a series of lab and online experiments where decision-makers evaluated identical startups that differed only by the gender of the founder. We also measured the evaluators’ levels of benevolent and hostile sexism through surveys to examine their impact on decision-making.
Findings: Our study revealed that benevolent sexism does not disadvantage women-led startups directly. Rather, it inflates the perceived potential of men-led startups without altering that of women-led startups. This bias grants men unwarranted advantages, while women receive a standard treatment, perpetuating a less visible but deeply harmful inequality in the startup space.
For a detailed overview of the project, click here. For all analysis codes and results, visit my GitHub repository.
What Are Scientific Papers About? Using Natural Language Processing (NLP) to Find Major Topics Scientists Are Discussing
Goal: To identify influential ideas in scientific fields and understand how they evolve over time, everaging advanced Natural Language Processing (NLP) techniques. This demonstrates the power of NLP to help us efficiently decode and synthesize vast amounts of academic literature.
Methods: Utilized a range of NLP techniques to analyze over one hundred research paper abstracts, including TF-IDF (Term Frequency-Inverse Document Frequency) and weighted log odds for identifying distinctive terms that distinguish one set of documents from another., and Latent Dirichlet Allocation (LDA) for topic modeling to discover the underlying thematic structures within scientific abstracts.
Key Findings: Revealed significant thematic shifts and dominant topics driving discourse in the fields of novelty reception and gender dynamics in professional networks. These findings highlight how certain themes have come to drive scholarly conversations, reflecting broader shifts in research focus and academic interest.
For a detailed overview of the project, click here. For all analysis codes and results, visit my GitHub repository.