Research Summary | Mobina Kashaniyan

How LLM-Driven AutoML Can Improve Cross-Lingual Handwritten OCR

Thu, 02 Jul 2026 00:00:00 +0000

Handwritten text recognition remains a challenging problem in machine learning, especially when models must work across multiple languages and writing systems. Arabic, English, and Persian scripts each introduce different visual structures, character shapes, stroke patterns, and recognition challenges. These differences make cross-lingual handwritten OCR difficult to scale using traditional manually designed models.

In our paper, “LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4,” we study how large language models can support fully automated model design for handwritten optical character recognition.

The main idea is to use LLMs as AutoML agents. Instead of relying on manual neural architecture design, domain-specific preprocessing, or human model selection, the proposed pipeline allows large language models to generate, evaluate, and refine OCR architectures in a closed-loop neural architecture search process.

The framework uses GPT-5, GPT-4o, and Claude Sonnet 4 to independently propose and improve neural network architectures for handwritten OCR. The approach is evaluated on Arabic, English, and Persian handwritten text recognition, making it relevant to multilingual OCR, low-resource language processing, document intelligence, and automated machine learning.

This work contributes to research on LLM-driven AutoML, cross-lingual OCR, handwritten text recognition, neural architecture search, multilingual AI, and automated model design.

Researchers working on OCR, AutoML, LLM agents, computer vision, Persian handwriting recognition, Arabic handwriting recognition, or low-resource language technologies may find this work useful.

Paper

LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4
Mobina Kashaniyan, Amirhossein Ghassemi, Nasser Mozayani
Published in 2025 15th International Conference on Computer and Knowledge Engineering (ICCKE), IEEE.

DOI: https://doi.org/10.1109/ICCKE68588.2025.11273810

PDF : https://mobinakashaniyan.github.io/papers/llm-driven-automl-cross-lingual-handwritten-ocr.pdf

IEEE Xplore: https://ieeexplore.ieee.org/abstract/document/11273810

Keywords

LLM-driven AutoML, handwritten OCR, cross-lingual OCR, multilingual OCR, optical character recognition, neural architecture search, GPT-5, GPT-4o, Claude Sonnet 4, LLM agents, Persian OCR, Arabic OCR, English handwriting recognition, document intelligence, low-resource language processing, automated model design, computer vision.

PerfMamba: Performance Analysis and Pruning of Selective State Space Models

Thu, 02 Jul 2026 00:00:00 +0000

Modern sequence models are becoming increasingly powerful, but their computational cost, memory behavior, and deployment efficiency remain important challenges. This is especially relevant for emerging architectures such as Mamba and selective state space models, which have been proposed as efficient alternatives to Transformer-based sequence models.

Our paper, “PerfMamba: Performance Analysis and Pruning of Selective State Space Models,” presents an empirical study of Mamba-1 and Mamba-2. The work analyzes runtime behavior, resource utilization, computation patterns, memory access, I/O characteristics, and scaling properties across sequence lengths from 64 to 16,384 tokens.

The study shows how different components of selective state space models contribute to performance and efficiency. In particular, the paper examines the SSM component, which plays a central role in Mamba-style architectures, and identifies opportunities for optimization through pruning.

Based on the profiling results, PerfMamba explores pruning low-activity states in the SSM component to improve throughput and reduce memory usage while maintaining model accuracy under moderate pruning.

This work contributes to research on Mamba, selective state space models, performance analysis, runtime profiling, model pruning, model compression, efficient AI, high-performance computing, and hardware-aware optimization.

Researchers working on large language models, sequence modeling, AI systems, neural network acceleration, scalable machine learning, and efficient deep learning may find this work useful.

Paper

PerfMamba: Performance Analysis and Pruning of Selective State Space Models
Abdullah Al Asif, Mobina Kashaniyan, Sixing Yu, Juan Pablo Muñoz, Ali Jannesari

Published in: International Symposium on Benchmarking, Measuring and Optimization
Publisher: Springer, Singapore
Year: 2026
Pages: 27–44

Paper page: https://mobinakashaniyan.github.io/publications/perfmamba/

PDF: https://mobinakashaniyan.github.io/papers/perfmamba-performance-analysis-pruning-selective-state-space-models.pdf

Keywords

PerfMamba, Mamba, Mamba-1, Mamba-2, selective state space models, state space models, SSM, sequence modeling, Transformer alternatives, performance analysis, runtime profiling, benchmarking, resource utilization, memory access patterns, I/O characteristics, scaling analysis, model pruning, state pruning, model compression, efficient AI, high-performance computing, hardware-aware optimization, large language models, AI systems, neural network acceleration.

Citation

Al Asif, A., Kashaniyan, M., Yu, S., Muñoz, J. P., & Jannesari, A. (2026). PerfMamba: Performance analysis and pruning of selective state space models. In International Symposium on Benchmarking, Measuring and Optimization (pp. 27–44). Springer, Singapore.

Why Dependency-Aware Auto-Scaling Matters in Serverless Computing

Thu, 02 Jul 2026 00:00:00 +0000

Serverless computing provides automatic resource management and pay-per-use execution, but efficient auto-scaling remains challenging. Dynamic workloads, cold-start latency, and inter-function dependencies can make scaling decisions unreliable when each function is treated independently.

In my paper, “An Auto-Scaling Approach for Serverless Environments Based on a Multi-Expert Consensus Mechanism,” we propose a dependency-aware auto-scaling framework for Function-as-a-Service environments.

The framework models serverless applications as directed dependency graphs, identifies high-impact bottleneck functions using degree centrality, and applies short-horizon workload forecasting. Instead of relying on one predictor, the system combines multiple lightweight predictive models using a multi-expert consensus mechanism inspired by Bayesian model averaging.

This research contributes to serverless computing, cloud resource management, workload forecasting, dependency-aware scaling, ensemble learning, and cost-aware optimization.

Paper

An Auto-Scaling Approach for Serverless Environments Based on a Multi-Expert Consensus Mechanism
Mobina Kashaniyan, Mehrdad Ashtiani, Amirhossein Ghassemi
Published in Journal of Ambient Intelligence and Smart Environments, SAGE Publications.

DOI: https://doi.org/10.1177/18761364261459585

Paper page: https://mobinakashaniyan.github.io/publications/consensus-autoscaling-serverless/

PDF: https://mobinakashaniyan.github.io/papers/an-auto-scaling-approach-serverless-environments-multi-expert-consensus.pdf