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​​In her third appearance on the DataTalks.Club Podcast, Tatiana Habruseva returns to talk about what has changed since her last interview, from becoming a mother and relocating to Silicon Valley to continuing her work in ML research, evaluation, and responsible AI.
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We’ll cover two main themes:

  • ​Career growth through competitions: How platforms like Kaggle, AIcrowd, and Topcoder can help build a portfolio, create visibility, and open doors to research, consulting, and job opportunities, if used strategically.
  • ​Evaluation, benchmarks, and responsible AI: Why evaluating modern AI systems and agents is getting harder, why benchmarks need to evolve, and what responsible AI looks like in practice.

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We’ll also discuss:

  • ​Why winning competitions is not enough on its own
  • ​How to turn technical work into papers, open source, and career opportunities
  • ​The pros and cons of ML competitions
  • ​Benchmark saturation, contamination, and alignment challenges

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About the Speaker
​Tatiana Habruseva, Ph.D. is a Staff Software Engineer, Machine Learning at LinkedIn, where she leads applied R&D on recommendation systems, multimodal alignment, and AI systems benchmarking. Before joining LinkedIn, she developed AI-based decision support systems for labor monitoring at Cork University Maternity Hospital. Her work on Weighted Boxes Fusion, a method for combining predictions from object detection models, has been cited over 700 times and ranks in the top 0.1% cited publications in computer science. Tatiana is a Kaggle Competitions Master and the 1st prize winner of the international Sound Demixing Challenge 2023. She holds a Ph.D. in Applied Physics from Cork Institute of Technology, is a Senior IEEE Member, and has authored over 27 peer-reviewed publications in computer science and physics.

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