Tomasz Arodz, Ph.D.

Associate Professor

  • Engineering East Hall, Room E4252, Richmond VA UNITED STATES

Dr. Arodz's research focuses on machine learning and bioinformatics



Tomasz Arodz is an associate professor in the Department of Computer Science at VCU. His research has been funded by NSF, NIH, and CDC. Dr. Arodz holds a Ph.D. in computer science from AGH University of Science and Technology in Krakow, Poland. He is a laureate of the Prime Minister of Poland Award for his Ph.D. dissertation. Dr. Arodz also holds a M.Sc. in biotechnology from Jagiellonian University in Krakow.

Industry Expertise


Areas of Expertise

Biomedical Data Science
Foundational Models
Machine Learning & Deep Learning
Quantum Machine Learning


Researcher of the Year 2020


VCU Computer Science Department

NSF CAREER Grant Award


National Science Foundation

Prime Minister of Poland Award


Laureate of the Award for Ph.D. Dissertation

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AGH University of Science and Technology


Computer Science


AGH University of Science and Technology


Computer Science


Jagiellonian University




Media Appearances

To Relieve Holiday Stress, Techies Trot Out Artificial Intelligence

Style Weekly  print


By now the journey to 2018 can feel more like a crawl than a mad dash.

There's pressure to entertain family, reconnect with old friends and take that special someone on a memorable date. But a Richmond startup says artificial intelligence can solve the indecision over where to go and what to do. While some experts caution against placing exaggerated faith in artificial intelligence, early adopters are hoping for a more perfect holiday experience.

. . . Just remember, if you're struggling to plan a not-so-silent night, don't give up on your gut, says Tom Arodz, another VCU professor who studies machine learning.

"AI may learn to never recommend a symphony to heavy-metal lovers," Arodz says. "But just like with human instinct, it is often difficult to say why any particular recommendation is made."

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Selected Articles

Real-valued group testing for quantitative molecular assays

Conference on Research in Computational Molecular Biology RECOMB'2022

We proposed a new group testing approach tailored for scenarios where quantitative measurements are available (e.g. Ct values in PCR tests). It allows using much fewer tests than there are samples to be tested.

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Shapeshifter: a parameter-efficient Transformer using factorized reshaped matrices

Conference on Neural Information Processing Systems NeurIPS'2021

We designed a technique for reducing the size of embedding matrices and self-attention weight matrices in deep Transformer-based language models using a much more compact yet expressive representation based on Kronecker/tensor products.

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Quantum semi-supervised kernel learning

Quantum Machine Intelligence 3:1-11, 2021

We formulated and analyzed a quantum machine learning algorithm for training semi-supervised SVM based on quantum HHL/LMR protocol.

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