Dimitar A. Chakarov

PhD student, Toyota Technological Institute at Chicago

Joined

September 2024

Names

Dimitar A. Chakarov (Preferred)

Dimitar Chakarov

Emails

****@ttic.edu (Confirmed)

****@princeton.edu (Confirmed)

****@uchicago.edu (Confirmed)

Personal Links

Career & Education History

PhD student

Toyota Technological Institute at Chicago (ttic.edu)

2024 – Present

Undergrad student

Department of Mathematics, Princeton University (princeton.edu)

2020 – 2024

Advisors, Relations & Conflicts

PhD Advisor

Nathan Srebro

2024 – Present

Coauthor

Nikola Konstantinov

2024 – Present

Coauthor

S. Matthew Weinberg

2022 – Present

Expertise

No areas of expertise listed

Publications

$\texttt{strategic-fl-sim}$: An Extensible Package for Simulating Strategic Behavior in Federated Learning
Dimitar A. Chakarov, Nikola Konstantinov
- NeurIPS 2025 - Reliable ML Workshop
- Readers: Everyone
$\texttt{strategic-fl-sim}$: An Extensible Package for Simulating Strategic Behavior in Federated Learning
Dimitar A. Chakarov, Nikola Konstantinov
- ACA-NeurIPS2025 Poster
- Readers: Everyone
A QPTAS For Up-To-ε Revenue Maximization With Multiple Constant-Demand Bidders Over Independent Items
Dimitar Chakarov, S. Matthew Weinberg, Eric Xue
- EC 2025
- Readers: Everyone
Incentivizing Truthful Collaboration in Heterogeneous Federated Learning
Dimitar Chakarov, Nikita Tsoy, Kristian Minchev, Nikola Konstantinov
- NeurIPS 2024 Workshop
- Readers: Everyone
Evaluation of the Complexity of Fully Homomorphic Encryption Schemes in Implementations of Programs
Dimitar Chakarov, Yavor Papazov
- CompSysTech 2019
- Readers: Everyone

Dimitar A. Chakarov

Names

Emails

Personal Links

Career & Education History

Advisors, Relations & Conflicts

Expertise

Publications

$\texttt{strategic-fl-sim}$: An Extensible Package for Simulating Strategic Behavior in Federated Learning

$\texttt{strategic-fl-sim}$: An Extensible Package for Simulating Strategic Behavior in Federated Learning

A QPTAS For Up-To-ε Revenue Maximization With Multiple Constant-Demand Bidders Over Independent Items

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning

Evaluation of the Complexity of Fully Homomorphic Encryption Schemes in Implementations of Programs

Co-Authors