Education

Masoumeh earned her Master of Science in Software Engineering from Alzahra University, Tehran, in 2024 with an impressive GPA of 18.57/20. Her thesis focused on collaborative filtering recommender systems using deep learning, supervised by Dr. Hasheminejad. She also holds an M.Sc. in Electronic Engineering from Shahid Beheshti University, where her thesis explored nanoscale spintronic technology for three-valued memory. She completed her Bachelor’s in Electronic Engineering from Technical and Vocational University with a GPA of 18.87/20. Throughout her academic journey, she excelled in both practical and theoretical fields.

Work Experience

Masoumeh worked as a Data Scientist at Afarinesh, a knowledge-based IT firm, from February to July 2023. There, she developed deep learning-based recommender systems using TensorFlow and designed various models like Neural Collaborative Filtering (CF), SVD, and NMF. She also managed relational databases, processed large datasets, and conducted A/B tests for performance evaluation. Masoumeh played a key role in enhancing data-driven decision-making processes within the company’s ecosystem of startups, contributing to projects like Sayeh platform and Boxel. Her experience spans technical model development and business-focused data analysis.

Skills & Competencies

Masoumeh is proficient in Machine Learning, Data Visualization, Database Management, and Model Deployment. She is skilled in programming languages such as Python, C#, C, VHDL, and Verilog, and works comfortably with frameworks like TensorFlow, PyTorch, Scikit-learn, and Keras. Her key soft skills include Critical Thinking, Adaptability, and a strong Openness to Feedback. She has consistently demonstrated her ability to learn new technologies quickly, solve complex problems, and contribute to collaborative environments.

Awards & Honors

Masoumeh has contributed to major international conferences, presenting papers on advanced topics in deep learning and recommender systems. Her research, co-authored with Dr. Hasheminejad, was presented at the 13th International Conference on Computer and Knowledge Engineering, focusing on link prediction for recommendation systems. She also co-authored another paper on location-based deep collaborative filtering for IoT service quality prediction, which was presented at the 7th International IoT Conference. These recognitions highlight her innovative contributions to both academia and industry.

Membership & Affiliations

Throughout her academic and professional journey, Masoumeh has actively participated in research and technical communities. Her involvement in collaborative research groups led to valuable insights in areas such as deep learning, IoT, and complex dynamical networks. While no specific memberships are listed, her participation in international conferences and collaborations with academic advisors and peers indicates strong engagement with the scientific and technical community. Her work is grounded in research excellence and practical application.

Teaching Experience

Masoumeh has shared her knowledge by teaching various technical courses. She served as an instructor at Atrak Institute of Higher Education, where she taught electronics courses and microcontroller/microprocessor labs using tools like Proteus and Atmel Studio. She also worked as a teaching assistant for Electric Circuits and Logic Circuit courses under Dr. Ramin Rajaee at Shahid Beheshti University. Her passion for teaching has allowed her to guide and mentor students in understanding complex concepts, fostering a collaborative learning environment.

Research Focus

Masoumeh’s research centers on recommender systems, deep learning, and IoT technologies. Her thesis explored collaborative filtering using deep learning techniques, aiming to enhance recommendation accuracy. She has also worked on link prediction for recommender systems, leveraging machine learning algorithms such as GCN-GNNs for better user experience. Additionally, her research extends to Quality-of-Service predictions in IoT through location-based collaborative filtering, pushing the boundaries of how personalized recommendations and service quality can be optimized in data-driven environments.

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