Armin Abdollahi

Mohammad Erfan Sadeghi, Arash Fayyazi, Suhas Somashekar et al.

Vision Transformers (ViTs) represent a groundbreaking shift in machine learning approaches to computer vision. Unlike traditional approaches, ViTs employ the self-attention mechanism, which has been widely used in natural language processing, to analyze image patches. Despite their advantages in modeling visual tasks, deploying ViTs on hardware platforms, notably Field-Programmable Gate Arrays (FPGAs), introduces considerable challenges. These challenges stem primarily from the non-linear calculations and high computational and memory demands of ViTs. This paper introduces CHOSEN, a software-hardware co-design framework to address these challenges and offer an automated framework for ViT deployment on the FPGAs in order to maximize performance. Our framework is built upon three fundamental contributions: multi-kernel design to maximize the bandwidth, mainly targeting benefits of multi DDR memory banks, approximate non-linear functions that exhibit minimal accuracy degradation, and efficient use of available logic blocks on the FPGA, and efficient compiler to maximize the performance and memory-efficiency of the computing kernels by presenting a novel algorithm for design space exploration to find optimal hardware configuration that achieves optimal throughput and latency. Compared to the state-of-the-art ViT accelerators, CHOSEN achieves a 1.5x and 1.42x improvement in the throughput on the DeiT-S and DeiT-B models.

Negin Ashrafi, Armin Abdollahi, Jiahong Zhang et al.

Heart failure affects millions of people worldwide, significantly reducing quality of life and leading to high mortality rates. Despite extensive research, the relationship between heart failure and mortality rates among ICU patients is not fully understood, indicating the need for more accurate prediction models. This study analyzed data from 1,177 patients over 18 years old from the MIMIC-III database, identified using ICD-9 codes. Preprocessing steps included handling missing data, removing duplicates, treating skewness, and using oversampling techniques to address data imbalances. Through rigorous feature selection using Variance Inflation Factor (VIF), expert clinical input, and ablation studies, 46 key features were identified to enhance model performance. Our analysis compared several machine learning models, including Logistic Regression, Support Vector Machine (SVM), Random Forest, LightGBM, and XGBoost. XGBoost emerged as the superior model, achieving a test AUC-ROC of 0.9228 (95\% CI 0.8748 - 0.9613), significantly outperforming our previous work (AUC-ROC of 0.8766) and the best results reported in existing literature (AUC-ROC of 0.824). The improved model's success is attributed to advanced feature selection methods, robust preprocessing techniques, and comprehensive hyperparameter optimization through Grid-Search. SHAP analysis and feature importance evaluations based on XGBoost highlighted key variables like leucocyte count and RDW, providing valuable insights into the clinical factors influencing mortality risk. This framework offers significant support for clinicians, enabling them to identify high-risk ICU heart failure patients and improve patient outcomes through timely and informed interventions.

Negin Ashrafi, Armin Abdollahi, Greg Placencia et al.

Predicting critical health outcomes such as patient mortality and hospital readmission is essential for improving survivability. However, healthcare datasets have many concurrences that create complexities, leading to poor predictions. Consequently, pre-processing the data is crucial to improve its quality. In this study, we use an existing pre-processing algorithm, concatenation, to improve data quality by decreasing the complexity of datasets. Sixteen healthcare datasets were extracted from two databases - MIMIC III and University of Illinois Hospital - converted to the event logs, they were then fed into the concatenation algorithm. The pre-processed event logs were then fed to the Split Miner (SM) algorithm to produce a process model. Process model quality was evaluated before and after concatenation using the following metrics: fitness, precision, F-Measure, and complexity. The pre-processed event logs were also used as inputs to the Decay Replay Mining (DREAM) algorithm to predict critical outcomes. We compared predicted results before and after applying the concatenation algorithm using Area Under the Curve (AUC) and Confidence Intervals (CI). Results indicated that the concatenation algorithm improved the quality of the process models and predictions of the critical health outcomes.

Armin Abdollahi, Negin Ashrafi, Maryam Pishgar

Background: Stroke is second-leading cause of disability and death among adults. Approximately 17 million people suffer from a stroke annually, with about 85% being ischemic strokes. Predicting mortality of ischemic stroke patients in intensive care unit (ICU) is crucial for optimizing treatment strategies, allocating resources, and improving survival rates. Methods: We acquired data on ICU ischemic stroke patients from MIMIC-IV database, including diagnoses, vital signs, laboratory tests, medications, procedures, treatments, and clinical notes. Stroke patients were randomly divided into training (70%, n=2441), test (15%, n=523), and validation (15%, n=523) sets. To address data imbalances, we applied Synthetic Minority Over-sampling Technique (SMOTE). We selected 30 features for model development, significantly reducing feature number from 1095 used in the best study. We developed a deep learning model to assess mortality risk and implemented several baseline machine learning models for comparison. Results: XGB-DL model, combining XGBoost for feature selection and deep learning, effectively minimized false positives. Model's AUROC improved from 0.865 (95% CI: 0.821 - 0.905) on first day to 0.903 (95% CI: 0.868 - 0.936) by fourth day using data from 3,646 ICU mortality patients in the MIMIC-IV database with 0.945 AUROC (95% CI: 0.944 - 0.947) during training. Although other ML models also performed well in terms of AUROC, we chose Deep Learning for its higher specificity. Conclusions: Through enhanced feature selection and data cleaning, proposed model demonstrates a 13% AUROC improvement compared to existing models while reducing feature number from 1095 in previous studies to 30.

Arseniy Shumilov, Yueting Zhu, Negin Ashrafi et al.

Sepsis is a severe condition responsible for many deaths in the United States and worldwide, making accurate prediction of outcomes crucial for timely and effective treatment. Previous studies employing machine learning faced limitations in feature selection and model interpretability, reducing their clinical applicability. This research aimed to develop an interpretable and accurate machine learning model to predict in-hospital sepsis mortality, addressing these gaps. Using ICU patient records from the MIMIC-III database, we extracted relevant data through a combination of literature review, clinical input refinement, and Random Forest-based feature selection, identifying the top 35 features. Data preprocessing included cleaning, imputation, standardization, and applying the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance, resulting in a dataset of 4,683 patients with 17,429 admissions. Five models-Random Forest, Gradient Boosting, Logistic Regression, Support Vector Machine, and K-Nearest Neighbor-were developed and evaluated. The Random Forest model demonstrated the best performance, achieving an accuracy of 0.90, AUROC of 0.97, precision of 0.93, recall of 0.91, and F1-score of 0.92. These findings underscore the potential of data-driven machine learning approaches to improve critical care, offering clinicians a powerful tool for predicting in-hospital sepsis mortality and enhancing patient outcomes.

Negin Ashrafi, Armin Abdollahi, Maryam Pishgar

Background: Ventilator-associated pneumonia (VAP) in traumatic brain injury (TBI) patients poses a significant mortality risk and imposes a considerable financial burden on patients and healthcare systems. Timely detection and prognostication of VAP in TBI patients are crucial to improve patient outcomes and alleviate the strain on healthcare resources. Methods: We implemented six machine learning models using the MIMIC-III database. Our methodology included preprocessing steps, such as feature selection with CatBoost and expert opinion, addressing class imbalance with the Synthetic Minority Oversampling Technique (SMOTE), and rigorous model tuning through 5-fold cross-validation to optimize hyperparameters. Key models evaluated included SVM, Logistic Regression, Random Forest, XGBoost, ANN, and AdaBoost. Additionally, we conducted SHAP analysis to determine feature importance and performed an ablation study to assess feature impacts on model performance. Results: XGBoost outperformed the baseline models and the best existing literature. We used metrics, including AUC, Accuracy, Specificity, Sensitivity, F1 Score, PPV, and NPV. XGBoost demonstrated the highest performance with an AUC of 0.940 and an Accuracy of 0.875, which are 23.4% and 23.5% higher than the best results in the existing literature, with an AUC of 0.706 and an Accuracy of 0.640, respectively. This enhanced performance underscores the models' effectiveness in clinical settings. Conclusions: This study enhances the predictive modeling of VAP in TBI patients, improving early detection and intervention potential. Refined feature selection and advanced ensemble techniques significantly boosted model accuracy and reliability, offering promising directions for future clinical applications and medical diagnostics research.

Armin Abdollahi, Negin Ashrafi, Mehdi Kamal et al.

Early, tool-free prediction of post-synthesis timing remains a key obstacle to rapid RTL iteration. We introduce TimingLLM, a two-stage retrieval-augmented LLM pipeline that estimates worst negative slack (WNS) and total negative slack (TNS) directly from Verilog. Stage 1 is a fine-tuned LLM that acts as a compact post-synthesis timing oracle, producing path-level arrivals/required times that are summarized into lightweight structural-timing cues (e.g., bag-of-gates counts, critical-path depth, gate-type patterns). Stage 2 is an LLM-based regressor that predicts WNS/TNS and applies a learned diagonal steering vector at the last transformer block, computed from the k nearest timing-labeled modules in a disjoint retrieval bank. On VerilogEval, TimingLLM attains R_WNS = 0.91 (MAPE 12%) and R_TNS=0.97 (MAPE 16%) while running 1.3-1.6 times faster than prior methods. Training uses a new 60k-module Verilog corpus with synthesis reports, which we will release. After training once, TimingLLM can be adapted to new technology libraries and PVT corners by refitting only a small regression head on 1000 labeled modules per setting, consistently outperforming state-of-the-art baselines.

Shuheng Chen, Junyi Fan, Armin Abdollahi et al.

Intracerebral hemorrhage (ICH) is a life-risking condition characterized by bleeding within the brain parenchyma. ICU readmission in ICH patients is a critical outcome, reflecting both clinical severity and resource utilization. Accurate prediction of ICU readmission risk is crucial for guiding clinical decision-making and optimizing healthcare resources. This study utilized the Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV) databases, which contain comprehensive clinical and demographic data on ICU patients. Patients with ICH were identified from both databases. Various clinical, laboratory, and demographic features were extracted for analysis based on both overview literature and experts' opinions. Preprocessing methods like imputing and sampling were applied to improve the performance of our models. Machine learning techniques, such as Artificial Neural Network (ANN), XGBoost, and Random Forest, were employed to develop predictive models for ICU readmission risk. Model performance was evaluated using metrics such as AUROC, accuracy, sensitivity, and specificity. The developed models demonstrated robust predictive accuracy for ICU readmission in ICH patients, with key predictors including demographic information, clinical parameters, and laboratory measurements. Our study provides a predictive framework for ICU readmission risk in ICH patients, which can aid in clinical decision-making and improve resource allocation in intensive care settings.

Houji Jin, Negin Ashrafi, Armin Abdollahi et al.

The rapid growth of large language models (LLMs) has heightened the demand for accurate detection of AI-generated text, particularly in languages like Chinese, where subtle linguistic nuances pose significant challenges to current methods. In this study, we conduct a systematic comparison of encoder-based Transformers (Chinese BERT-large and RoBERTa-wwm-ext-large), a decoder-only LLM (Alibaba's Qwen2.5-7B/DeepSeek-R1-Distill-Qwen-7B fine-tuned via Low-Rank Adaptation, LoRA), and a FastText baseline using the publicly available dataset from the NLPCC 2025 Chinese AI-Generated Text Detection Task. Encoder models were fine-tuned using a novel prompt-based masked language modeling approach, while Qwen2.5-7B was adapted for classification with an instruction-format input and a lightweight classification head trained via LoRA. Experiments reveal that although encoder models nearly memorize training data, they suffer significant performance degradation under distribution shifts (RoBERTa: 76.3% test accuracy; BERT: 79.3%). FastText demonstrates surprising lexical robustness (83.5% accuracy) yet lacks deeper semantic understanding. In contrast, the LoRA-adapted Qwen2.5-7B achieves 95.94% test accuracy with balanced precision-recall metrics, indicating superior generalization and resilience to dataset-specific artifacts. These findings underscore the efficacy of decoder-based LLMs with parameter-efficient fine-tuning for robust Chinese AI-generated text detection. Future work will explore next-generation Qwen3 models, distilled variants, and ensemble strategies to enhance cross-domain robustness further.

Armin Abdollahi, Mehdi Kamal, Massoud Pedram

This paper presents RocketPPA, a novel ultra-fast power, performance (delay), and area (PPA) estimator operating directly at the code-level abstraction using HDL code as input. The key technical innovation is its LLM-based regression model, which uniquely integrates a large language model (LLM) with a mixture-of-experts (MoE) architecture composed of multilayer perceptrons (MLPs). The LLM interprets the input HDL code and then utilizes its final hidden-layer representations to predict PPA metrics. Low-rank adaptation (LoRA) is used for parameter-efficient fine-tuning to enable efficient LLM training. Furthermore, the work includes the development of an LLM-based HDL code repair framework to generate a large and synthesizable training dataset. Experimental results on the VerilogEval benchmark demonstrate that RocketPPA achieves significant improvements in the accuracy of PPA estimation compared to previous state-of-the-art methods like Llama3-MetRex-8B. Specifically, at a 10% relative error threshold, RocketPPA enhances the pass rate for area prediction by 13.6%, delay by 9.4%, and power by 14.7%. At a 20% threshold, the improvements are 9.6% for area, 10.8% for delay, and 18.5% for power. Moreover, RocketPPA achieves a speedup of over 20x compared to MetRex and 30x over MasterRTL in processing the test set. The impact of RocketPPA is the potential to substantially accelerate the hardware design process by providing accurate PPA estimations early in the design cycle, thus avoiding the overhead of manual feature engineering and time-consuming synthesis flows.

Mehrshad Saadatinia, Minoo Ahmadi, Armin Abdollahi

Understanding emotions in videos is a challenging task. However, videos contain several modalities which make them a rich source of data for machine learning and deep learning tasks. In this work, we aim to improve video sentiment classification by focusing on two key aspects: the video itself, the accompanying text, and the acoustic features. To address the limitations of relying on large labeled datasets, we are developing a method that utilizes clustering-based semi-supervised pre-training to extract meaningful representations from the data. This pre-training step identifies patterns in the video and text data, allowing the model to learn underlying structures and relationships without requiring extensive labeled information at the outset. Once these patterns are established, we fine-tune the system in a supervised manner to classify the sentiment expressed in videos. We believe that this multi-modal approach, combining clustering with supervised fine-tuning, will lead to more accurate and insightful sentiment classification, especially in cases where labeled data is limited.