About

Thomas G. Robertazzi is a professor at the Department of Electrical and Computer Engineering at Stony Brook University. His research focuses on big data performance evaluation, exa- and peta-scale computing, scheduling, quantum computing, and machine learning performance evaluation. His work involves analyzing and optimizing large-scale computational systems, with an emphasis on advancing understanding and capabilities in high-performance computing environments. As a distinguished faculty member, he contributes to the development of innovative methodologies and frameworks that address the challenges of modern data-intensive and computationally demanding applications.

Research topics

• Computer Science
• Social Science
• Machine Learning
• Sociology
• Computer network
• Artificial Intelligence
• Algorithm
• Parallel computing
• Mathematical optimization
• Distributed computing
• Theoretical computer science

Selected publications

• Capacity Constraints in Ball and Urn Distribution Problems

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Capacity constraints in ball and urn distribution problems

  Results in Applied Mathematics · 2025-05-01

  articleOpen accessSenior authorCorresponding

  This paper explores the distribution of indistinguishable balls into distinct urns with varying capacity constraints, a foundational issue in combinatorial mathematics with applications across various disciplines. We present a comprehensive theoretical framework that addresses both upper and lower capacity constraints under different distribution conditions, elaborating on the combinatorial implications of such variations. Through rigorous analysis, we derive analytical solutions that cater to different constrained environments, providing a robust theoretical basis for future empirical and theoretical investigations. These solutions are pivotal for advancing research in fields that rely on precise distribution strategies, such as physics and parallel processing. The paper not only generalizes classical distribution problems but also introduces novel methodologies for tackling capacity variations, thereby broadening the utility and applicability of distribution theory in practical and theoretical contexts. • Developed a framework for distributing identical balls into urns with capacity limits • Developed novel analytical solutions for constrained ball-urn distribution systems • Introduced methodologies for analyzing upper and lower capacity constraints in urn models • Expanded distribution theory to complex real-world scenarios

  PublisherDOI

• A DLT-Aware Performance Evaluation Framework for Virtual-Core Speedup Modeling

  Future Internet · 2025-11-14

  articleOpen accessSenior authorCorresponding

  Scheduling computing is a well-studied area focused on improving task execution by reducing processing time and increasing system efficiency. Divisible Load Theory (DLT) provides a structured analytical framework for distributing partitionable computational and communicational loads across processors, and its adaptability has allowed researchers to integrate it with other models and modern technologies. Building on this foundation, previous studies have shown that Amdahl-like laws can be effectively combined with DLT to produce more realistic performance models. This paper further develops analytical models that further extend such integration by incorporating Gustafson’s Law and Juurlink’s Law into DLT to capture broader scaling behaviors. It also extends the analysis to workload distribution in virtual multicore systems, providing a more structured basis for evaluating parallel performance. Methods include analytically computing speedup as a function of the number of cores and the parallelizable fraction under different scheduling strategies, with comparisons across workload conditions. Results show that combining DLT with speedup laws and virtual core design offers a deeper and more structured approach for analytical parallel system evaluation. While the analysis remains theoretical, the proposed framework establishes a mathematical foundation for future empirical validation, heterogeneous workload modeling, and sensitivity analysis.

  PublisherDOI

• Capacity Constraints in Ball and Urn Distribution Problems

  ArXiv.org · 2025-02-05

  preprintOpen accessSenior author

  This paper explores the distribution of indistinguishable balls into distinct urns with varying capacity constraints, a foundational issue in combinatorial mathematics with applications across various disciplines. We present a comprehensive theoretical framework that addresses both upper and lower capacity constraints under different distribution conditions, elaborating on the combinatorial implications of such variations. Through rigorous analysis, we derive analytical solutions that cater to different constrained environments, providing a robust theoretical basis for future empirical and theoretical investigations. These solutions are pivotal for advancing research in fields that rely on precise distribution strategies, such as physics and parallel processing. The paper not only generalizes classical distribution problems but also introduces novel methodologies for tackling capacity variations, thereby broadening the utility and applicability of distribution theory in practical and theoretical contexts.

  PublisherOA PDFDOI

• Advanced Shuttle Strategies for Parallel QCCD Architectures

  IEEE Transactions on Quantum Engineering · 2024-01-01 · 1 citations

  articleOpen accessSenior author

  Trapped ions (TI) are at the forefront of quantum computing implementation, offering unparalleled coherence, fidelity, and connectivity. However, the scalability of TI systems is hampered by the limited capacity of individual ion traps, necessitating intricate ion shuttling for advanced computational tasks. The Quantum Charge-Coupled Device (QCCD) framework has emerged as a promising solution, facilitating ion mobility for universal quantum computation. Current QCCD architectures predominantly feature a linear topology, which is increasingly recognized as inefficient for complex quantum operations. Anticipating the shift towards more efficacious designs, this paper introduces an innovative quantum scheduling strategy optimized for parallel QCCD topologies. Our strategy proposes a probabilistic formula for ion movement, alongside ingenious methods for local layer generation and layer compression, yielding a significant reduction in ion shuttle times. Through simulations, we demonstrate that our strategy not only substantially outstrips the linear model but also exhibits better performance over other parallel strategies that employ greedy algorithms. This is achieved through our nuanced resolution of complexities such as traffic blocks and trap capacity limitations. The consequent reduction in shuttle operations leads to lower energy consumption and an enhancement in the quantum computer's fidelity, ultimately accelerating program execution times.

  PublisherDOI

• Apparatus and method for optimal phase balancing using dynamic programming with spatial consideration

  OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2023-01-23

  articleOpen access1st authorCorresponding

  Provided are an apparatus and method for load-balancing of a three-phase electric power distribution system having a multi-phase feeder, including obtaining topology information of the feeder identifying supply points for customer loads and feeder sections between the supply points, obtaining customer information that includes peak customer load at each of the points between each of the feeder sections, performing a phase balancing analysis, and recommending phase assignment at the customer load supply points.

  PublisherOA PDF

• Optimum Large Sensor Data Filtering, Networking and Computing

  Annals of Computer Science and Information Systems · 2023-09-26

  articleOpen accessSenior author

  In this paper we consider filtering and processing large data streams in intelligent data acquisition systems.It is assumed that raw data arrives in discrete events from a single expensive sensor.Not all raw data, however, comprises records of interesting events and hence some part of the input must be filtered out.The intensity of filtering is an important design choice because it determines the complexity of filtering hardware and software and the amount of data that must be transferred to the following processing stages for further analysis.This, in turn, dictates needs for the following stages communication and computational capacity.In this paper we analyze the optimum intensity of filtering and its relationship with the capacity of the following processing stages.A set of generic filtering intensity, data transfer, and processing archetypes are modeled and evaluated.

  PublisherOA PDFDOI

• Analyzing Data Intensive Networks on Chips

  2022-12-01

  articleSenior author

  A novel framework [2] [3] is proposed to find efficient data-intensive flow distributions on Networks on Chip (NoC). In [3], the authors discussed the virtual cut-through switching, and we extend the flow matrix and analysis in a new switching mechanism, a modified store-and-forward switching mechanism. We explore the various workload distribution applications compared to the previous data load evenly distribution scenario. The new algorithms lead to an efficient makespan and a significant saving in the number of cores used.

  PublisherDOI

• Optimal Signal Selection for Sensors

  arXiv (Cornell University) · 2021-07-04

  preprintOpen accessSenior author

  The focus of this research is sensor applications including radar and sonar. Optimal sensing means achieving the best signal quality with the least time and energy cost, which allows processing more data. This paper presents novel work by using an integer linear programming "algorithm" to achieve optimal sensing by selecting the best possible number of signals of a type or a combination of multiple types of signals to ensure the best sensing quality considering all given constraints. A solution based on a heuristic algorithm is implemented to improve the computing time performance. What is novel in this solution is synthesis of an optimized signal mix using information such as but not limited to signal quality, energy and computing time.

  PublisherOA PDFDOI

• Integrating Amdahl-like Laws and Divisible Load Theory

  Parallel Processing Letters · 2021-06-01 · 5 citations

  articleSenior author

  A simple means of integrating the characteristics of networked processors under divisible loads into Amdahl’s Law is presented. Amdahl’s Law serves as an upper bound to these speedup results. Amdahl’s Law with divisible load processing characteristics included serves as an upper bound to speedup for any model taking into consideration more detailed peculiarities of real systems such as the overhead of task creation, synchronization, resource contention and memory issues.

  PublisherDOI

Frequent coauthors

• Dantong Yu

  New Jersey Institute of Technology

  15 shared

• Aurel A. Lazar

  Columbia University

  14 shared

• Kevin Brown

  Stony Brook University

  11 shared

• Serge Luryi

  Stony Brook University

  10 shared

• Yuan-Chieh Cheng

  Beijing Jiaotong University

  9 shared

• Mequanint Moges

  University of Houston

  8 shared

• Jeeho Sohn

  Stony Brook University

  7 shared

• Yufei Ren

  Aerospace Information Research Institute

  7 shared

Labs

• Electrical and Computer EngineeringPI

Education

• Ph.D., Electrical Engineering

  University of California, Los Angeles

  1986

• M.S., Electrical Engineering

  University of California, Los Angeles

  1982

• B.S., Electrical Engineering

  University of California, Los Angeles

  1980