About

Tamer Kahveci is a Professor and Associate Chair of Academic Affairs in the Department of Computer and Information Science and Engineering at the University of Florida. He is the director of the Bioinformatics Lab, where his research focuses on indexing, storing, accessing, and the use of bioinformatics data. His work contributes to advancing the computational methods and infrastructure necessary for managing complex biological information. Professor Kahveci has been actively involved in teaching bioinformatics courses, including CAP 5510 (Bioinformatics) since Fall 2004. Beyond his research and teaching, he has served in numerous professional service roles, including as PC Co-Chair of the ACM-BCB Conference in 2012 and 2016, and the CNB-MAC Workshop since 2015. He has also held positions such as tutorials chair for ACM-BCB 2015 and tutorials co-chair for IEEE BIBM conferences. He is a member of program committees for many prestigious conferences and serves as a referee for various journals. Additionally, he is an associate editor of ACM/IEEE TCBB, a member of the editorial review board for the International Journal of Knowledge Discovery in Bioinformatics, and the lead guest editor of a special issue on computational analysis of biological networks for the Journal of Advances in Bioinformatics.

Research topics

• Computer Science
• Machine Learning
• Biology
• Artificial Intelligence
• Mathematics
• Data Mining
• Computer network
• Data science
• Computational biology
• Theoretical computer science
• Ecology
• Botany
• Econometrics
• Statistics

Selected publications

• Guaranteeing Privacy in Hybrid Quantum Learning through Theoretical Mechanisms

  ArXiv.org · 2026-02-02

  articleOpen access

  Quantum Machine Learning (QML) is becoming increasingly prevalent due to its potential to enhance classical machine learning (ML) tasks, such as classification. Although quantum noise is often viewed as a major challenge in quantum computing, it also offers a unique opportunity to enhance privacy. In particular, intrinsic quantum noise provides a natural stochastic resource that, when rigorously analyzed within the differential privacy (DP) framework and composed with classical mechanisms, can satisfy formal $(\varepsilon, δ)$-DP guarantees. This enables a reduction in the required classical perturbation without compromising the privacy budget, potentially improving model utility. However, the integration of classical and quantum noise for privacy preservation remains unexplored. In this work, we propose a hybrid noise-added mechanism, HYPER-Q, that combines classical and quantum noise to protect the privacy of QML models. We provide a comprehensive analysis of its privacy guarantees and establish theoretical bounds on its utility. Empirically, we demonstrate that HYPER-Q outperforms existing classical noise-based mechanisms in terms of adversarial robustness across multiple real-world datasets.

  PublisherOA PDF

• SPACT: A clustering-driven multi-modal framework for survival prediction using genomic and histopathology data

  Medical Image Analysis · 2026-04-17

  article
  PublisherDOI

• Guaranteeing Privacy in Hybrid Quantum Learning through Theoretical Mechanisms

  Open MIND · 2026-02-02

  preprint

  Quantum Machine Learning (QML) is becoming increasingly prevalent due to its potential to enhance classical machine learning (ML) tasks, such as classification. Although quantum noise is often viewed as a major challenge in quantum computing, it also offers a unique opportunity to enhance privacy. In particular, intrinsic quantum noise provides a natural stochastic resource that, when rigorously analyzed within the differential privacy (DP) framework and composed with classical mechanisms, can satisfy formal $(\varepsilon, δ)$-DP guarantees. This enables a reduction in the required classical perturbation without compromising the privacy budget, potentially improving model utility. However, the integration of classical and quantum noise for privacy preservation remains unexplored. In this work, we propose a hybrid noise-added mechanism, HYPER-Q, that combines classical and quantum noise to protect the privacy of QML models. We provide a comprehensive analysis of its privacy guarantees and establish theoretical bounds on its utility. Empirically, we demonstrate that HYPER-Q outperforms existing classical noise-based mechanisms in terms of adversarial robustness across multiple real-world datasets.

  DOI

• FIDDLE: Reinforcement Learning for Quantum Fidelity Enhancement

  ACM Transactions on Quantum Computing · 2025-10-29

  article

  Quantum computing has the potential to revolutionize fields like quantum optimization and quantum machine learning. However, current quantum devices are hindered by noise, reducing their reliability. A key challenge in gate-based quantum computing is improving the reliability of quantum circuits, measured by process fidelity, during the transpilation process, particularly in the routing stage. In this article, we address the Fidelity Maximization in Routing Stage (FMRS) problem by introducing FIDDLE, a novel learning framework comprising two modules: a Gaussian Process-based surrogate model to estimate process fidelity with limited training samples and a reinforcement learning module to optimize routing. Our approach is the first to directly maximize process fidelity, outperforming traditional methods that rely on indirect metrics such as circuit depth or gate count. We rigorously evaluate FIDDLE by comparing it with state-of-the-art fidelity estimation techniques and routing optimization methods. The results demonstrate that our proposed surrogate model is able to provide a better estimation on the process fidelity compared to existing learning techniques, and our end-to-end framework significantly improves the process fidelity of quantum circuits across various noise models.

  PublisherDOI

• Quantification and comparison of prediction uncertainty associated with different practices of crop modeling

  Agricultural and Forest Meteorology · 2025-05-21 · 4 citations

  article
  PublisherDOI

• FIDDLE: Reinforcement Learning for Quantum Fidelity Enhancement

  ArXiv.org · 2025-10-17

  preprintOpen access

  Quantum computing has the potential to revolutionize fields like quantum optimization and quantum machine learning. However, current quantum devices are hindered by noise, reducing their reliability. A key challenge in gate-based quantum computing is improving the reliability of quantum circuits, measured by process fidelity, during the transpilation process, particularly in the routing stage. In this paper, we address the Fidelity Maximization in Routing Stage (FMRS) problem by introducing FIDDLE, a novel learning framework comprising two modules: a Gaussian Process-based surrogate model to estimate process fidelity with limited training samples and a reinforcement learning module to optimize routing. Our approach is the first to directly maximize process fidelity, outperforming traditional methods that rely on indirect metrics such as circuit depth or gate count. We rigorously evaluate FIDDLE by comparing it with state-of-the-art fidelity estimation techniques and routing optimization methods. The results demonstrate that our proposed surrogate model is able to provide a better estimation on the process fidelity compared to existing learning techniques, and our end-to-end framework significantly improves the process fidelity of quantum circuits across various noise models.

  PublisherOA PDFDOI

• Vision transformer supported Kolmogorov-Arnold networks for survival prediction in lung cancer

  2025-10-12

  articleOpen access

  Lung cancer is one of the most common and deadly cancers worldwide. Accurate survival prediction is critical for guiding treatment, yet existing deep learning approaches often struggle with capturing the complexity of histological and tabular features and fusing them effectively. We address these challenges by introducing a novel Kolmogorov-Arnold Network for tabular and fusion tasks, combined with advanced vision models for histology image processing. Experiments show that our method achieves superior survival prediction accuracy compared to unimodal predictors. Furthermore, it provides explainable predictions, as 10 of the top 20 genes identified as most influential are known to play roles in cancer survival and progression.

  PublisherOA PDFDOI

• Spatio-Temporal Migration of Gene Behavior in Pathologies Through Aging

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Differential causal networks highlight sex-based differences in human tissues

  Briefings in Bioinformatics · 2025-07-01

  articleOpen access

  Sex differences appear in healthy and pathological conditions and may influence sex-specific therapeutic responses. Understanding such differences is a key activity for developing precision medicine strategies. This study investigates sex differences in gene expression across 40 human tissues by applying a Differential Causal Network (DCN) analysis using data from the Genotype-Tissue Expression project. We identified sex-based DCNs that highlight distinct molecular mechanisms influencing both health and disease in men and women. For example, in pancreas tissue, genes associated with immune system show significant differences in their regulatory patterns between sexes, demonstrating a possible different response to diseases such as diabetes mellitus and cancer. Our findings provide valuable information on the biological underpinnings of sex differences, offering potential pathways for the development of precision medicine strategies.

  PublisherDOI

• PartialFibers: An Efficient Method for Predicting Drug-Drug Interactions

  Lecture notes in computer science · 2025-01-01 · 1 citations

  book-chapterSenior author
  PublisherDOI

Recent grants

• EMT/BSSE: Biological networks as a communication model for entities with complex interactions

  NSF · $300k · 2008–2012

• CIF: Small: Novel biologically inspired methods for analyzing multilayer networks

  NSF · $331k · 2021–2025

• CAREER: New Technologies for Querying Pathway Databases

  NSF · $400k · 2009–2015

• ABI Innovation: Querying Massive Dynamic Biological Network Databases

  NSF · $493k · 2013–2018

• CIF: EAGER: Modeling and Querying of Probabilistic Biological Networks

  NSF · $175k · 2013–2016

Frequent coauthors

• Ferhat Ay

  University of California, San Diego

  25 shared

• Sanjay Ranka

  University of Florida

  23 shared

• Alin Dobra

  University of Florida

  20 shared

• Ambuj K. Singh

  University of California, Santa Barbara

  16 shared

• Aisharjya Sarkar

  University of Florida

  16 shared

• Ahmet Ay

  Colgate University

  15 shared

• Nirmalya Bandyopadhyay

  Jadavpur University

  14 shared

• Haitham Gabr

  14 shared

Education

• Ph.D., Computer Science

  University of California at Santa Barbara

  2004

Awards & honors

• Ralph E. Powe Junior Faculty Enhancement award (2006)
• CSB best paper award (2008)
• NSF Career award (2009)
• ACM-BCB best student paper award (2010)
• ACM-BCB honorary best paper award (2011)