Diba Rashidi

M.Sc. Information & Technology | dibra.rashidi@gmail.com

I'm Diba Rashidi, currently finishing my Master's in Information Technology at the University of Tehran. My academic journey started with a Bachelor's in Mathematics, shaping my analytical approach and love for rigorous problem-solving. Now, I combine that solid math foundation with practical skills in machine learning, IoT, and edge computing to build meaningful technology—like turning smartphones into early-warning earthquake detectors and designing wearable health devices for remote patient monitoring. As I look ahead to a PhD, my focus is on making AI systems more explainable, resilient, and trustworthy, especially in real-world applications. I thrive in projects where deep theory and hands-on experimentation meet, driven by curiosity and a genuine desire to create technology people can rely on.

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Education

University of Tehran

Master’s degree, Information Technology
Thesis: Using Machine Learning for Early Earthquake Warning with Smartphone Modeling.

GPA: 4/4

Sep 2022 - Feb 2025

Alzahra University

Bachelor of Science - Bachelor’s degree, Mathematics

GPA: 3.2/4

Sep 2018 - Sep 2022

Research Experience

Advanced Robotics and Intelligent Systems Lab

Earthquake Early Warning

• Bench-marked deep learning architectures (LSTM, CNN, ResNet) for denoising smartphone-acquired seismic time-series.

• Developed a generative model that synthesizes smartphone-grade seismic signals from high-quality datasets.

• Curated and structured a comprehensive seismic dataset collected from smartphone sensors.

• Implemented Gaussian-based model for epicenter estimation in smartphone-based EEWS, prioritizing cost-effectiveness and real-time deployment on edge devices; results published in peer-reviewed venues.

• Conducted a systematic evaluation of mechatronic platforms (CNC systems, robotic arms, hydraulic jacks) for physically simulating seismic activity in lab.

Machine Learning

• Explored and bench-marked interpretable machine learning techniques (Grad-CAM, LIME, SHAP, NAM) to improve the reliability and trustworthiness of deep vision models.

• Investigated model generalization under domain shift, utilizing hyper parameter optimization (normalization, augmentation, loss tuning) to improve robustness of Farsi handwriting recognition.

• Implemented and evaluated Fast Gradient Sign Method (FGSM) for robustness evaluation of image classifiers, analyzing model responses to adversarial attacks.

• Re-implementation of Neural Cleanse (Wang et. al.) for backdoor attack detection and trigger pattern analysis in Farsi handwriting recognition model.

Data Mining

• Worked on Data Warehousing and OLAP (Online Analytical Processing) to design and implement data storage solutions and enable complex queries and data analysis for business intelligence.

• Applied frequent pattern mining techniques to discover recurring patterns and associations in large datasets, enhancing data-driven decision-making and insights.

University of Tehran
Sep 2022 - Present

Wearable Sensors Lab

Wearable Sensors

• Designed a low-power wearable Holter monitor and digital stethoscope, enabling continuous remote monitoring of ECG signals.

• Developed a cloud-based healthcare architecture, comprising a BLE-enabled smartphone application for device connectivity, a web-based dashboard for clinicians, and backend server for real-time data collection, processing, and visualization.

• Developed an intermittent audio signal filter for denoising and up-sampling of heart and lung audio signals, optimized for edge devices like smart stethoscope.

• Developed an ensemble learning method based on decision tree and KNN algorithms for arrhythmia classification in ECG data.

Sharif University
Oct 2020 - Sep 2022

Iran IoT Research Center

IoT

• Integrated multi-range vibration sensors into an embedded predictive maintenance device by designing interface circuits and developing custom firmware for data acquisition.

• Developed a backend server and database architecture for reliable collection and storage of sensor hub data for industrial sites.

• Designed and implemented a decision-level fusion algorithm that combines outputs from multiple vibration sensors based on their performance under different operational conditions, improving detection accuracy over simple averaging or pooling methods.

Iran IoT Research Center
Feb 2020 - Sep 2022

Skills

  • Programming: Python, C, C++, Matlab, Java, Dart, Web Development(HTML, JavaScript,CSS), SQL
  • Libraries: Scikit, PyTorch, Numpy, Pandas, TensorFlow, Keras
  • Frameworks: Spring, Hibernate, flutter
  • Platforms: Linux, Arduino, Raspberry Pi, ESP32, STM32
  • Language: English (TOEFL iBT: 102, R=27, L=29, S=21, W=25), Farsi (Native)

Publication

Design and implementation of an ultralow-power ECG patch and smart cloud-based platform
IEEE Transactions on Instrumentation and Measurement

Bardia Baraeinejad, Masood Fallah Shayan, Amir Reza Vazifeh, Diba Rashidi, Mohammad Saberi Hamedani, Hamed Tavolinejad, Pouya Gorji, Parsa Razmara, Kiarash Vaziri, Daryoosh Vashaee, Mohammad Fakharzadeh

2022
Clinical IoT in Practice: A Novel Design and Implementation of a Multi-functional Digital Stethoscope for Remote Health Monitoring
Under Review

Bardia Baraeinejad ,Morteza Shams ,Mohammad Saberi Hamedani ,Amirhossein Nasiri-Valikboni ,Maryam Forouzesh ,Shayan Fakhraeelotfabadi ,Radmehr Karimian ,Saba Babaei ,Diba Rashidi ,Danesh Germchi ,Yasaman Torabi ,Pouya Gorji ,Daryoosh Vashaee

2023
A Low-cost Epicenter Estimation Scheme for Earthquake Early Warning Systems: A Preliminary Study
CSICC 2025

niousha baghaei araghi ,Diba Rashidi ,Bahar Baradaran Eftekhari ,Ali Moradi ,Mahmoud Reza Hashemi ,Hadi Moradi

2025
Smartphones for Science: Accuracy and Frequency Fidelity of Built-In Accelerometers
ICROM 2025

Diba Rashidi ,Ali Moradi ,Mahmoud Reza Hashemi ,Hadi Moradi

2025

Awards & Certifications

  • Certified Linux Administrator (LPIC-1) - Fanavaran Anisa 2021
  • IoT BootCamp 99 - IoTiran 2020
  • Java EE 8 Programming - MFT 2020
  • Java SE 8 Programming - MFT 2019