Dr. Bhavana Kaushik | Image Processing | Best Researcher Award |

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Dr. Bhavana Kaushik | Image Processing | Best Researcher Award

Assistant Professor at University of Petroleum and Energy Studies, India.

Dr. Bhavana Kaushik is a dynamic academician, researcher, and technology leader with over a decade of experience in teaching, research, and innovation. She currently serves as an Assistant Professor at the University of Petroleum and Energy Studies (UPES), Dehradun. With a deep commitment to blending technology with societal transformation, Dr. Kaushik is actively involved in projects that promote digital inclusion, women’s empowerment, and entrepreneurship. Her interdisciplinary expertise spans computer vision, artificial intelligence, data science, and sustainable development. In addition to her academic accomplishments, she also holds leadership roles such as the State President (Uttarakhand) for the Information Technology Council under WICCI, where she champions women in technology across the state.

Professional Profile

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🎓 Education 

Dr. Kaushik is currently pursuing her Ph.D. in Computer Vision and Image Processing at UPES, Dehradun, where her research explores the intersection of artificial intelligence and visual computing. She holds a Master of Technology (M.Tech) in Computer Science from GLA University, Mathura, where she graduated with a Silver Medal and an impressive CGPA of 9.34. Her foundational education includes a Bachelor of Technology (B.Tech) in Computer Science and Engineering from Uttar Pradesh Technical University, graduating with distinction. She also excelled in her secondary and higher secondary schooling under the ICSE and CBSE boards.

💼 Experience 

Dr. Kaushik brings over 10 years of diverse experience across academia and industry. She has served as an Assistant Professor at UPES since 2018, where she teaches core computer science subjects and mentors student projects. Prior to this, she worked as a Systems Engineer at Infosys Limited, Pune, where she gained hands-on experience in Python programming, mainframe technologies, and application development. She also contributed to academia as a Teaching Assistant at GLA University. Her roles have included curriculum development, lab modernization, academic administration, and leadership of student societies and hackathons. Additionally, she leads women-in-tech initiatives as the WICCI State President (Uttarakhand) for the IT Council.

🔬 Research Interests

Dr. Kaushik’s research primarily centers around Computer Vision, Image Processing, and the application of Artificial Intelligence in Medicine, Surveillance, and Socioeconomic Development. Her work includes medical image compression, object tracking in videos, solar flare classification, and deepfake detection. She has also contributed to impactful research in rural development and digital empowerment through ICT tools. Her current pursuits explore the integration of AI technologies within healthcare imaging and metaverse environments, reflecting her commitment to high-impact, interdisciplinary research.

🏆 Honors & Awards

Dr. Kaushik’s academic journey is marked by notable accolades. She has been awarded a Silver Medal for her M.Tech performance and consistently topped her class in B.Tech. She is a qualified NET and GATE candidate (multiple years), which reflects her academic rigor. As an International Speaker at the Women Economic Forum – ASEAN 2025 and a regular contributor to national development programs funded by DST, she continues to receive recognition for both scholarly and social innovation contributions.

Top Noted Publications:

Title: Computational Intelligence‐Based Method for Automated Identification of COVID‐19 and Pneumonia by Utilizing CXR Scans
Authors: B. Kaushik, D. Koundal, N. Goel, A. Zaguia, A. Belay, H. Turabieh
Citations: 8
Index: Computational Intelligence and Neuroscience
Year of Publication: 2022

Title: Investigation of Solar Flare Classification to Identify Optimal Performance
Authors: A. Kakde, D. Sharma, B. Kaushik, N. Arora
Citations: 6
Index: ELCVIA Electronic Letters on Computer Vision and Image Analysis
Year of Publication: 2021

Title: A Context Based Tracking for Similar and Deformable Objects
Authors: B. Kaushik, M. Kumar, C. Bhatanagar, A.S. Jalal
Citations: 5
Index: International Journal of Computer Vision and Image Processing (IJCVIP)
Year of Publication: 2018

Title: Intelligent Interactions: Exploring Human–Computer Interaction in the Metaverse Through Artificial Intelligence
Author: B. Kaushik
Citations: 3
Index: Understanding the Metaverse (Springer Book Chapter)
Year of Publication: 2024

Conclusion:

Dr. Bhavana Kaushik exemplifies the modern academic researcher — technically proficient, socially responsible, and future-focused. Her balanced contributions to both scholarly research and community development make her a valuable asset to the academic and innovation ecosystem. With her ongoing Ph.D., growing list of high-impact publications, and active role in promoting women in STEM, she stands out as an ideal candidate for recognition such as the Best Researcher Award. Her journey reflects a perfect harmony between academic depth, leadership, innovation, and empowerment.

Mr Ernesto Diaz | Data Scientist | Best Researcher Award

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Mr Ernesto Diaz |  Data Scientist | Best Researcher Award

Assistant Specialist at University of California, San Francisco – Radiology & Biomedical Imaging , United States.

Ernesto Diaz is an accomplished researcher and data scientist specializing in biomedical imaging and artificial intelligence applications in healthcare. With a strong background in medical imaging, deep learning, and data science, he has contributed significantly to Hyperpolarized Carbon-13 MRI research, cancer imaging, and radiation oncology. His work has been recognized through prestigious NIH awards, peer-reviewed publications, and multiple conference presentations. Passionate about advancing healthcare technology, Ernesto combines technical expertise with a commitment to mentorship and diversity in STEM.

Professional Profile

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Education 🎓

Ernesto earned his Bachelor of Science in Computer Science from San Francisco State University in 2022, graduating with Dean’s List honors (2020-2022). His education provided a strong foundation in programming, data analysis, and computational research, which he has applied extensively in biomedical imaging and artificial intelligence projects.

Professional Experience 💼

  • As a Data Scientist at UCSF’s Department of Radiology and Biomedical Imaging, Ernesto leads software development for medical imaging analysis, enhancing data processing and visualization tools. His previous research experience includes working on automated radiation treatment planning and bioinformatics coding for population health studies. His contributions have improved efficiency in clinical workflows and advanced AI applications in medical imaging.

Research Interests 🌍

His research revolves around Hyperpolarized Carbon-13 MRI, deep learning for medical image segmentation, and automation in radiation oncology. At UCSF, he developed a DICOM standardization tool for metabolic imaging and co-developed a U-Net deep learning model for prostate cancer segmentation. Additionally, he has explored health disparities in underserved communities, analyzing COVID-19’s impact on marginalized populations.

Awards & Honors 🏆

  • NIH Diversity Supplement Award (2022-2024) – Recognized for contributions to Hyperpolarized 13C MRI research.
  • NIH-SF BUILD Scholar (2021-2022) – Selected for leadership potential and commitment to diversity in research.
  • Dean’s List (2020-2022) – Awarded for academic excellence at San Francisco State University.

Top Noted Publications 📚

Title: Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts

Authors: Satvik Nayak, Henry Salkever, Ernesto Diaz, Avantika Sinha, Nikhil Deveshwar, Madeline Hess, Matthew Gibbons, Sule Sahin, Abhejit Rajagopal, Peder E. Z. Larson, et al.

Journal: Tomography

Publication Year: 2025

DOI: 10.3390/tomography11030021

Indexing: Indexed in major scientific databases.

Conclusion

Ernesto Diaz is a rising leader in medical imaging research, blending AI, data science, and biomedical imaging to drive innovation. With his technical skills, research excellence, and dedication to mentorship, he continues to push the boundaries of healthcare technology and scientific discovery. 🚀

Mr Siraj Khan | Computer Vision | Best Researcher Award |

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Mr. Siraj Khan | Computer Vision | Best Researcher Award

PhD Scholar, at Islamia College Peshawar,Pakistan

Mr. Siraj Khan is a dedicated researcher and educator specializing in digital image processing and medical image analysis. He is currently affiliated with the Digital Image Processing Lab at Islamia College, Peshawar, Pakistan. Mr. Khan holds a Ph.D. in Computer Science, focusing on the detection and classification of cells in blood smear images using Convolutional Neural Networks (CNNs). His research interests span deep learning applications in medical imaging, IoT, bioinformatics, and smart healthcare services. With expertise in Python, MATLAB, and various deep learning frameworks like TensorFlow and PyTorch, Mr. Khan is passionate about deploying research to improve healthcare technologies in smart cities. His work has been published in several high-impact journals, contributing significantly to the fields of medical diagnostics and artificial intelligence.

Professional Profile

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Education 🎓

Mr. Siraj Khan is a highly skilled researcher and educator, currently affiliated with the Digital Image Processing Lab at Islamia College, Peshawar, Pakistan. He holds a Ph.D. in Computer Science, specializing in medical image processing, from Islamia College, Peshawar (2018–2021). His research for his Ph.D. focused on “Detection and Classification of Cells in Blood Smear Images using Convolutional Neural Networks.” He also holds a Master’s degree in Computer Vision (2014–2016) from the same institution, where he developed a resource-aware framework for leucocyte segmentation in blood smear images. Earlier, Mr. Khan completed his MCS (Computer Science) at the Federal Urdu University of Arts Science & Technology, Karachi (2008–2010), focusing on computer vision.

Experience 💼

Mr. Khan is currently a full-time researcher at the Digital Image Processing Laboratory (DIP Lab) at Islamia College, Peshawar. He has led numerous research projects related to medical image segmentation, classification, and detection, applying deep learning tools like TensorFlow, Keras, and PyTorch. In addition, Mr. Khan has collaborated with various research groups internationally, enhancing the scope and impact of his work. His work on healthcare services for smart cities reflects his ability to bridge the gap between academia and practical, deployable technology.

Research Interest 🔬

Mr. Khan’s research primarily revolves around deep learning applications in medical image analysis, focusing on the detection, segmentation, and classification of cells in blood smear images. His work employs advanced techniques such as Convolutional Neural Networks (CNNs) and dual attention networks for efficient leukocyte detection. His research also extends to smart healthcare services in smart cities, leveraging IoT technologies and bioinformatics to enhance healthcare systems. Additionally, Mr. Khan is passionate about deploying his research in real-world applications, using frameworks like TensorFlow, PyTorch, and MATLAB.

Award 🏅

Mr. Khan’s research excellence is evident in his contributions to the fields of medical image processing and smart healthcare. Although specific honors are not listed, his work has received significant academic recognition through publications in high-impact journals and conference proceedings. His contributions to the development of healthcare frameworks, including the Raspberry Pi-assisted face recognition system for law enforcement in smart cities, have been acknowledged by peers in the academic community.

Top Noted Publication 📑

Raspberry Pi Assisted Face Recognition Framework for Enhanced Law-Enforcement Services in Smart Cities

Authors: M Sajjad, M Nasir, K Muhammad, S Khan, Z Jan, AK Sangaiah, …

Citations: 241

Index: Future Generation Computer Systems

Year of Publication: 2020

 

Brain Tumor Segmentation Using K-means Clustering and Deep Learning with Synthetic Data Augmentation for Classification

Authors: AR Khan, S Khan, M Harouni, R Abbasi, S Iqbal, Z Mehmood

Citations: 218

Index: Microscopy Research and Technique

Year of Publication: 2021

 

Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities

Authors: M Sajjad, S Khan, Z Jan, K Muhammad, H Moon, JT Kwak, S Rho, …

Citations: 131

Index: IEEE Access

Year of Publication: 2016

 

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

Authors: S Khan, M Sajjad, T Hussain, A Ullah, AS Imran

Citations: 107

Index: IEEE Access

Year of Publication: 2020

 

Computer Aided System for Leukocytes Classification and Segmentation in Blood Smear Images

Authors: M Sajjad, S Khan, M Shoaib, H Ali, Z Jan, K Muhammad, I Mehmood

Citations: 24

Index: 2016 International Conference on Frontiers of Information Technology (FIT)

Year of Publication: 2016

Conclusion

Mr. Siraj Khan exhibits the qualities of a strong contender for the Best Researcher Award. His exceptional contributions to medical image processing, particularly in the use of deep learning for blood smear analysis, are groundbreaking and highly relevant in the healthcare sector. His technical abilities, combined with a proven track record of high-impact publications, make him an ideal candidate for this award. Expanding his research into other cutting-edge areas of AI and increasing his research visibility through international collaborations and media could elevate his career further.