Tomasz Kuśmierczyk
Scholar

Tomasz Kuśmierczyk

Google Scholar ID: kpDCshMAAAAJ
University of Helsinki
Applied Machine LearningData ScienceSocial Media Mining
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Citations & Impact
All-time
Citations
219
 
H-index
10
 
i10-index
10
 
Publications
20
 
Co-authors
0
 
Contact
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Publications
5 items
Bayesian Fine-tuning in Projected Subspaces
2026
Cited
0
TACTIC for Navigating the Unknown: Tabular Anomaly deteCTion via In-Context inference
2026
Cited
0
Hi-fi functional priors by learning activations
2025
Cited
0
Efficient Uncertainty in LLMs through Evidential Knowledge Distillation
2025
Cited
0
High-Fidelity Transfer of Functional Priors for Wide Bayesian Neural Networks by Learning Activations
arXiv.org · 2024
Cited
0
Resume
Academic Achievements
  • Published numerous papers in prestigious international journals and conferences such as JMLR, ACML, AAAI, NeurIPS, WWW, covering a wide range of topics including but not limited to:
  • - Prior specification for Bayesian matrix factorization
  • - Reliable categorical variational inference
  • - Uplift modeling
  • - Correcting predictions for approximate Bayesian inference
  • - Variational Bayesian decision-making for continuous utilities
  • - Online food recipe title semantics
  • - Computational approach to dendritic spine taxonomy and shape transition analysis
  • - Gender differences in online cooking
  • - And more.
Research Experience
  • Involved in multiple research projects, including but not limited to:
  • - Investigating online food recipe upload behavior
  • - Validating and predicting the influence of digital badges on individual users
  • - Mining correlations on massive bursty time series collections
  • - Analyzing temporality in online food recipe consumption and production, etc.
Background
  • A computer scientist interested in applied machine learning. Currently, working on a project focused on balancing priors and learning biases to improve Bayesian Neural Networks.
Miscellany
  • Contact: tomasz [dot] kusmierczyk [at] gmail.com; Active on platforms like GitHub, LinkedIn, Google Scholar, DBLP; Conducted several presentations on topics related to variational inference, causal effects, etc.