I am a Senior ML Research Engineer at the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University. Prior to this, I have served as a lecturer in Computer Science at University of California Riverside as well as the San Diego State University.

I received my PhD in Computer Science from University of California Riverside under supervision of Dr. Rajiv Gupta where I was a member of the RIPLE research group and was working on GRASP projects. I received two master’s degrees one in Computer Science from University of California Riverside and one in Computer Architecture from University of Tehran.

🔥 News

• 2025.05: My title has been updated to “Senior ML Research Engineer”.
• 2025.04: Will be instructing Data Parallelism Workshop at the NeuroAI Symposium at Harvard University on June 4th, 2025.
• 2025.01: I’ve been officially granted NVIDIA Deep Learning Institute (DLI) University Ambassadorship from NVIDIA.
• 2024.10: Co-instructed Large Language Model Distributed Training Workshop at Kempner Institute at Harvard University.

📊 Research

My research interests lie in distributed high-performance parallel computing, and in the distributed training and inference of deep learning and large language model workflows on state-of-the-art computing clusters, both on premises and in the cloud.

During my Ph.D. at UCR, my research has been focusing on developing scalable high-performance solutions for graph processing by employing resources available on a heterogeneous computing cluster. More specifically, we developed the distributed MultiLyra [BigData’19] system whose scalability enables simultaneous evaluation of batches of hundreds of iterative graph queries. then, BEAD [BigData’20] extends MultiLyra to consider scenarios in which a batch of queries needs to be continuously reevaluated due to changes to the graph (for growing graphs). In the shared-memory setting, we have developed SimGQ [HiPC’20] online system that optimizes the evaluation of a batch of queries by sharing results of common sub-computations among them which won the best paper award in HiPC’20 and extended in SimGQ+ [JPDC’22].

I am also interested in Computer Architecture in general. During my master study I worked on High Performance Chip Multiprocessors by focusing on their Interconnection Networks; my thesis was titled “Using hybrid packet-circuit switching to improve memory access in NoC-based CMPs”.

Research Interests

• Distributed Training and Inference of DL and LL Models
• Distributed Graph Analytics & AI
• High Performance & Parallel Computing
• Computer Architecture
• GPU Micro-Architecture and Programming

📝 Publications

• BigGraph'23 ExpressWay: Prioritizing Edges for Distributed Evaluation of Graph Queries
  Abbas Mazloumi, Mahbod Afarin, and Rajiv Gupta.
  IEEE 10th International Workshop on High Performance Big Graph Data Management, Analysis, and Mining - IEEE BigData, Dec. 2023.

• JPDC'22 SimGQ+: Simultaneously Evaluating Iterative Point-to-All and Point-to-Point Graph Queries
  Chengshuo Xu, Abbas Mazloumi, Xiaolin Jiang, and Rajiv Gupta.
  Journal of Parallel and Distributed Computing, Feb. 2022.

• BigData'20 BEAD: Batched Evaluation of Iterative Graph Queries with Evolving Analytics Demands
  Abbas Mazloumi, Chengshuo Xu, Zhijia Zhao, and Rajiv Gupta.
  IEEE International Conference on Big Data, Dec. 2020.

• HiPC'20 SimGQ: Simultaneously Evaluating Iterative Graph Queries
  Chengshuo Xu, Abbas Mazloumi, Xiaolin Jiang, and Rajiv Gupta.
  IEEE International Conference on High-Performance Computing, Data & Analytics, Dec. 2020.

• BigData'19 MultiLyra: Scalable Distributed Evaluation of Batches of Iterative Graph Queries
  Abbas Mazloumi, Xiaolin Jiang, and Rajiv Gupta.
  IEEE International Conference on Big Data, Dec. 2019.

• BigData'19 (Poster) Enabling Faster Convergence in Distributed Irregular Graph Processing
  Abbas Mazloumi and Rajiv Gupta.
  IEEE International Conference on Big Data, Dec. 2019.

• BigData'19 (Poster) Border Gateway Protocol Anomaly Detection Using Neural Network
  Mohsen Karimi, Ali Jahanshahi, Abbas Mazloumi, and Hadi Zamani Sabzi.
  IEEE International Conference on Big Data, Dec. 2019.

• TC'18 Fast Data Delivery for Many-Core Processors
  Mohammad Bakhshalipour, Pejman Lotfi-Kamran, Abbas Mazloumi, et al.
  IEEE Transactions on Computers, Volume: 67, Issue: 10, Pages: 1416-1429, Oct. 2018.

• ARCS'17 Parallel Forwarding for Efficient Bandwidth Utilization in Networks-on-Chip
  Elham Momenzadeh, Mehdi Modarressi, Abbas Mazloumi, and Masoud Daneshtalab.
  30th International Conference on Architecture of Computing Systems, Apr. 2017.

• CAL'16 Dynamic resource sharing for high-performance 3-d networks-on-chip
  S. Hossein Seyyedaghaei R., Abbas Mazloumi, Mehdi Modarressi, and Pejman Lotfi-Kamran.
  IEEE Computer Architecture Letters, Volume: 15, Issue: 1, Pages: 5-8, Jun. 2016.

• ICEE'16 High performance hybrid-switched network-on-chip using shortcut paths
  Sina Sayardoost Tabrizi, Iman Soltani Mohammadi, Abbas Mazloumi, and Mehdi Modarressi.
  24th Iranian Conference on Electrical Engineering, May 2016.

• SC'15 Integrated circuit-packet switching NoC with efficient circuit setup mechanism
  Farhad Pakdaman, Abbas Mazloumi, and Mehdi Modarressi.
  The Journal of Supercomputing, Volume: 71, Issue: 8, Pages: 2787-2807, Aug. 2015.

• DATE'15 A hybrid packet/circuit-switched router to accelerate memory access in NoC-based chip multiprocessors
  Abbas Mazloumi, and Mehdi Modarressi.
  18th Design, Automation & Test in Europe Conference & Exhibition, Mar. 2015.

🤝 Service

Program Committee

• Data Analytics 2022-2024, DAC 2020, IETE 2020, JOC 2020

Sub-reviewer

• MICRO’23, ICS’23, CGO’21, PACT’20, CGO’20, ISPASS’19, IA3 at SC’19, ASPLOS’18, PACT’17, IPDPS’14, HPIN’14

📖 Teaching

Workshops

• Summer 2025 - Data Parallelism Workshop at the NeuroAI Symposium
• Fall 2024 - Large Language Model Distributed Training Workshop

Lecturer

• Spring 2024 - CS 005: Introduction to Computer Programming
• Winter 2024 - CS 005: Introduction to Computer Programming
• Fall 2023 - CS 005: Introduction to Computer Programming
• Fall 2023 - CS 635: Advanced Object-Oriented Programming
• Summer 2021 - CS 153: Design of Operating Systems
• Spring 2021 - CS/EE 147: GPU Computing and Programming