Sara Tehsin | Deep learning | Best Researcher Award

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Ms. Sara Tehsin | Deep learning | Best Researcher Award

PhD Student, National University of Sciences and Technology, Islamabad, Pakistan

Sara Tehsin is a motivated and results-driven professional with over ten years of experience in Image Processing and Machine Learning. As an Engineering Lecturer at HITEC University in Taxila, Pakistan, she excels in delivering high-quality educational experiences and has a proven track record of producing outstanding results through her strong work ethic, adaptability, and effective communication skills. She is passionate about academic development and seeks opportunities to contribute her expertise while furthering her professional growth. 📚💻

Publication Profile

Google Scholar

Education

Sara Tehsin is currently pursuing a PhD in Computer Engineering at the National University of Sciences and Technology (NUST), Islamabad, where she has achieved a remarkable GPA of 3.83/4.00. Her research focuses on Digital Forensics, Deep Learning, and Digital Image Processing. She holds a Master’s degree in Computer Engineering from NUST, where she graduated with a GPA of 3.7/4.0, and a Bachelor’s degree from The Islamia University of Bahawalpur, with a GPA of 3.36/4.00. 🎓🌟

Experience

Sara has extensive teaching experience, currently serving as an Engineering Lecturer at HITEC University since September 2019, where she develops engaging curriculum and delivers lectures aligned with international standards. Previously, she was a Computer Science Lecturer at Sharif College of Engineering and Technology, and she also served as a Teaching Assistant at NUST and a Lab Engineer at Foundation University. Her roles have encompassed curriculum development, practical instruction, and student support in various computer science subjects. 👩‍🏫🔧

Research Interests

Sara’s research interests encompass Digital Forensics, Deep Learning, Digital Image Processing, and Machine Learning. She focuses on developing innovative solutions for image recognition and forgery detection, contributing significantly to the fields of computer vision and machine learning. Her work aims to enhance the accuracy and efficiency of image processing systems. 🧠🔍

Publications

Self-organizing hierarchical particle swarm optimization of correlation filters for object recognition
S. Tehsin, S. Rehman, M.O.B. Saeed, F. Riaz, A. Hassan, M. Abbas, R. Young, …
IEEE Access, 5, 24495-24502 (2017)
Cited by: 21

Improved maximum average correlation height filter with adaptive log base selection for object recognition
S. Tehsin, S. Rehman, A.B. Awan, Q. Chaudry, M. Abbas, R. Young, A. Asif
Optical Pattern Recognition XXVII, 9845, 29-41 (2016)
Cited by: 18

Fully invariant wavelet enhanced minimum average correlation energy filter for object recognition in cluttered and occluded environments
S. Tehsin, S. Rehman, F. Riaz, O. Saeed, A. Hassan, M. Khan, M.S. Alam
Pattern Recognition and Tracking XXVIII, 10203, 28-39 (2017)
Cited by: 12

Comparative analysis of zero aliasing logarithmic mapped optimal trade-off correlation filter
S. Tehsin, S. Rehman, A. Bilal, Q. Chaudry, O. Saeed, M. Abbas, R. Young
Pattern Recognition and Tracking XXVIII, 10203, 22-37 (2017)
Cited by: N/A

soheila nazari | neural network | Best Researcher Award

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Assist Prof Dr. soheila nazari | neural network | Best Researcher Award

university faculty, shahid beheshti university, Iran

🎓 Dr. Soheila Nazari is a dedicated researcher and expert in Digital Electronics and Neuromorphic Computing, with a particular focus on bio-inspired systems. With a PhD from Amirkabir University of Technology, she has contributed extensively to the fields of spiking neural networks and neuron-astrocyte interactions. Dr. Nazari’s research has been published in top scientific journals, making significant strides in the development of digital and bio-inspired neural systems.

Publication Profile

Google scholar

Strengths for the Award:

  1. Educational Background: Soheila Nazari has a strong academic foundation with a B.Sc., M.Sc., and Ph.D. in Digital Electronics from prestigious institutions like Amirkabir University of Technology, Tehran. Her high GPAs and excellent thesis scores (19.5, 20, and 20) demonstrate her commitment and expertise in her field.
  2. Innovative Research: Her Ph.D. thesis focuses on creating a mapping between two spiking neural networks to enable cognitive abilities, which is highly innovative and relevant in the field of neuromorphic computing and artificial intelligence.
  3. Publications in High-Impact Journals: She has several high-quality publications in respected journals, such as Neural Networks and Neuroscience Letters. Her research on neuron-astrocyte interactions and neuromorphic circuits is cutting-edge and aligns with current trends in neuro-inspired computational systems.
  4. Interdisciplinary Work: Soheila’s work spans across multiple fields including digital electronics, neuroscience, and biomedical engineering, showcasing her versatility and capability to work on interdisciplinary projects.
  5. Applications in Healthcare: Her involvement in the diagnostic value of impedance imaging systems in breast mass detection indicates that her research has real-world applications, particularly in healthcare, which enhances the societal impact of her work.

Areas for Improvement:

  1. Collaborations: While her research is strong, increasing her network through collaborations with international researchers or labs could enhance her visibility and broaden the impact of her work.
  2. Further Application of Research: While her publications are impressive, more practical applications or real-world implementations of her research could bolster her profile further, especially in translating neuromorphic computing models into usable technologies.
  3. Diversity of Research Topics: While she excels in neuromorphic computing, branching out into other emerging areas like quantum computing or deeper AI-related projects could further diversify her research portfolio.

Education

📚 Dr. Soheila Nazari holds a B.Sc. in Electrical Engineering (Electronics) from Razi University of Kermanshah, Iran (2008-2012), followed by an M.Sc. and Ph.D. in Digital Electronics from Amirkabir University of Technology, Tehran, Iran (2012-2014 and 2015-2018 respectively). Her academic performance has been outstanding, with a series of high-grade theses centered around neural networks and bio-inspired systems.

Experience

💻 Throughout her academic and professional career, Dr. Nazari has specialized in digital implementations of neuromorphic circuits and neuron-astrocyte interaction models. Her research experience spans numerous projects aimed at developing hardware-friendly solutions for neuromorphic applications, making her a pioneer in the digital neuromorphic circuit design field.

Research Focus

🧠 Dr. Nazari’s research primarily revolves around neuromorphic computing, bio-inspired stimulations, and digital implementations of spiking neural networks. Her work explores how neuron-astrocyte interactions can be used in hardware designs to model complex cognitive functions, and she has developed new methods for synaptic plasticity and signal processing in neural networks.

Awards and Honours

🏆 Dr. Nazari has earned recognition for her academic achievements, receiving top scores in her thesis work during her M.Sc. and Ph.D. studies. She continues to contribute to prestigious scientific conferences and journals, establishing herself as a leading voice in neuromorphic computing and digital electronics.

Publication Top Notes

📄 Dr. Nazari has published extensively in international journals, covering topics like digital neuron-astrocyte interactions, bio-inspired stimulators, and neuromorphic circuits. Her work is highly cited, reflecting its impact in the field.

A digital neuromorphic circuit for a simplified model of astrocyte dynamics (2014), Neuroscience Letters, cited by 85 articles.

A digital implementation of neuron–astrocyte interaction for neuromorphic applications (2015), Neural Networks, cited by 125 articles.

A novel digital implementation of neuron–astrocyte interactions (2015), Journal of Computational Electronics, cited by 70 articles.

Multiplier-less digital implementation of neuron–astrocyte signalling on FPGA (2015), Neurocomputing, cited by 95 articles.

A multiplier-less digital design of a bio-inspired stimulator to suppress synchronized regime in a large-scale, sparsely connected neural network (2015), Neural Computing and Applications, cited by 60 articles.

Conclusion:

Soheila Nazari is a strong candidate for the Research for Best Researcher Award. Her academic excellence, cutting-edge research, interdisciplinary work, and significant contributions to both neuromorphic computing and healthcare applications make her highly deserving of recognition. By focusing on international collaborations and translating her research into practical innovations, she could further solidify her standing as a leading researcher in her field.