Mukhtar Sofi | Artificial Intelligence and Computational Biology | Proteomics Research Award

Dr. Mukhtar Sofi | Artificial Intelligence and Computational Biology | Proteomics Research Award

Assistant Professor at VIT Vellore, India

Dr. Mukhtar Ahmad Sofi is an accomplished academician and researcher specializing in the intersection of artificial intelligence and bioinformatics. He currently serves as an Assistant Professor at BVRIT Hyderabad, India. With a robust foundation in computer science, Dr. Sofi’s career reflects a dedication to scientific rigor, educational excellence, and interdisciplinary innovation. His key research efforts explore machine learning, deep learning, and computational biology, particularly in protein structure prediction and biomedical data analysis. Dr. Sofi’s scholarly contributions include impactful publications in high-ranking journals, patents, and participation in global conferences. His professional journey exemplifies a commitment to scientific advancement and collaborative research leadership.

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Education

Dr. Sofi earned his Ph.D. in Computer Science and Engineering from the University of Kashmir between 2019 and 2023, awarded officially in 2024. Prior to his doctoral work, he completed an M.Tech in Computer Science and Engineering (2015–2017) from Pondicherry Central University with a CGPA of 8.72. He also holds a Master’s in Computer Applications (2011–2014) from the same university, graduating with a CGPA of 8.51. His undergraduate degree, a BCA in Computer Science, was obtained from the University of Kashmir (2008–2011) with 62% marks. These academic milestones have equipped him with a rigorous theoretical and applied understanding of computer science and its emerging domains.

Experience

Dr. Sofi has been working as an Assistant Professor at BVRIT Hyderabad since February 2023. His experience bridges both teaching and research, providing students with advanced training in machine learning and deep learning while leading impactful research initiatives. Before his academic appointment, he gained experience as a research fellow during his doctoral studies under the University Grants Commission’s Senior and Junior Research Fellowship schemes. Additionally, Dr. Sofi has contributed to multiple workshops, training sessions, and has mentored students to win prestigious R&D showcases. His practical experience also includes leading projects in Docker environments and leveraging deep learning libraries on NVIDIA’s DGX A100 server infrastructure.

Research Interest

Dr. Sofi’s research interests lie at the intersection of machine learning, deep learning, computational biology, and bioinformatics. His primary focus is on protein secondary structure prediction using deep learning models. He explores data partitioning strategies, convolutional and recurrent architectures, and attention mechanisms to enhance prediction accuracy. His work also expands into broader applications such as reservoir water prediction, sentiment analysis in human-robot interactions, and medical diagnostics using digital twins. Through his research, Dr. Sofi aims to bridge the gap between computational modeling and real-world biological systems, contributing to personalized medicine and AI-driven biomedical innovations.

Awards

Among Dr. Sofi’s many recognitions are the UGC-NET and JK-SET qualifications in Computer Science, both achieved in 2018. He was awarded Senior and Junior Research Fellowships by UGC for his Ph.D. studies. He received a grant of ₹3.5 lakh from AICTE to conduct an ATAL Faculty Development Program on “Generative AI: Transforming Education and Research” in 2023. He was recently selected for a prestigious Post-Doctoral Fellowship at the National University of Singapore and Chinese Academy of Medical Sciences (2024). Additionally, he received the Best Paper Presentation Award at the IEEE ICDSNS-2024 and served as a supporting trainer for NVIDIA’s advanced deep learning workshop.

Publications

Dr. Sofi’s impactful scholarly work includes the following publications:

IRNN-SS: Deep learning for optimised protein secondary structure predictionInt. J. Bioinformatics Research and Applications, 2024. Cited by: 2.

RiRPSSP: A Unified Deep Learning method for Protein Secondary StructuresJournal of Bioinformatics and Computational Biology, 2023. Cited by: 8.

Protein secondary structure prediction using CNNs and GRUsInternational Journal of Information Technology (Springer), 2022. Cited by: 14.

Smart Toll Tax Collection using BLEInternational Journal of Advanced Research in Computer Science, 2017. Cited by: 5.

Bluetooth Protocol in IoT: Security ReviewInternational Journal of Engineering Research & Technology (IJERT), 2016. Cited by: 11.

Cheating Detection in Proctored Exams using Deep Neural NetworksIEEE Access (Accepted, minor revision).

Reservoir Water Prediction using LSTM and GRUWater Resource Management Journal (Springer) (Under review).

These publications highlight Dr. Sofi’s pioneering work in applying AI to bioinformatics and smart system solutions.

Conclusion

Dr. Mukhtar Ahmad Sofi exemplifies the future of proteomics research by merging computational intelligence with biological structure prediction. His innovative approaches, validated by high-impact publications and international recognition, significantly advance the field. Given his contributions to protein structure prediction and his visionary application of AI in bioinformatics, Dr. Sofi is an ideal recipient for the Proteomics Research Award, embodying the excellence, innovation, and interdisciplinary rigor the award stands for.

Eyachew Misganew Tegaw | AI in Personalized Medicine | Best Researcher Award

Assist. Prof. Dr. Eyachew Misganew Tegaw | AI in Personalized Medicine | Best Researcher Award

Assistant Professor of Medical Physics at  Debre Tabor University, Ethiopia.

Dr. Eyachew Misganew Tegaw is an Assistant Professor of Medical Physics at Debre Tabor University, Ethiopia. He earned his Ph.D. from Tehran University of Medical Sciences, specializing in intraoperative radiotherapy using nanoparticles for breast cancer treatment. His research spans medical imaging, radiation therapy, nanomedicine, and AI applications in oncology. Dr. Eyachew Misganew Tegaw has published extensively in peer-reviewed journals and actively contributes to scientific advancement through research, mentorship, and international collaboration.

👨‍🎓 Educational Background:

Eyachew Misganew Tegaw holds a Ph.D. in Medical Physics from the Tehran University of Medical Sciences (TUMS), Iran, where he specialized in intraoperative radiotherapy (IORT) using nanoparticles for breast cancer treatment. His academic journey began with a B.Sc. in Applied Physics from the University of Gondar, followed by an M.Sc. in Condensed Matter Physics from Mekelle University. Across these programs, he built a strong foundation in quantum mechanics, computational physics, and radiation therapy physics, gaining multidisciplinary expertise in both theoretical and applied domains.

Profile:

👨‍🏫 Professional Experience:

With over a decade of academic service, Dr. Eyachew Misganew Tegaw currently serves as an Assistant Professor in the Department of Physics at Debre Tabor University, Ethiopia. He previously held the position of Lecturer at the same institution, actively contributing to curriculum development and student mentorship. His leadership experience includes a two-year term as Department Head and three years as Chair of the Ethiopian Space Science Society (Debre Tabor Branch), showcasing his commitment to both academic and community development.

🧪 Research Interests:

Dr. Eyachew Misganew Tegaw is a highly driven researcher with diverse interests spanning medical physics, radiotherapy technologies, medical imaging, radiation dosimetry, nanomedicine, Monte Carlo simulations, and artificial intelligence. His work integrates cutting-edge approaches like deep learning and machine learning with traditional medical physics techniques, aiming to enhance cancer treatment planning and medical imaging. He is especially passionate about nanoparticle-enhanced therapies and computational modeling for improved patient outcomes in oncology.

🔍 Areas for Improvement:

While Dr. Eyachew Misganew Tegaw has made significant strides in academic research, increased visibility through invited talks, expanded international partnerships, and higher engagement in translational clinical projects could further enhance his research impact. Diversifying funding sources and mentoring emerging researchers may also solidify his standing in the global scientific community.

📚 Scientific Contributions

Dr. Eyachew has authored and co-authored numerous peer-reviewed journal articles in high-impact scientific journals. His publications cover a wide range of topics such as Monte Carlo simulations for radiotherapy, nanoparticle-enhanced imaging and treatment, and explainable AI in oncology. Notable among his recent works is a 2025 article in Scientific Reports comparing survival outcomes in breast cancer surgery, and another accepted paper focusing on AI for lung cancer survival prediction. His systematic reviews and meta-analyses have also significantly contributed to evidence-based practice in cancer diagnosis and therapy.

Publications:

  1. Diagnostic performance of mammography and ultrasound in breast cancer: A systematic review and meta-analysis
    Journal of Ultrasound, 2023

  2. Dosimetric characteristics of the INTRABEAM® system with spherical applicators in the presence of air gaps and tissue heterogeneities
    Radiation and Environmental Biophysics, 2020

  3. Techniques for generating attenuation map using cardiac SPECT emission data only: A systematic review
    Annals of Nuclear Medicine, 2019

  4. Molecular imaging approaches in the diagnosis of breast cancer: A systematic review and meta-analysis
    Iranian Journal of Nuclear Medicine, 2020

  5. Comparison of organs at risk doses between deep inspiration breath-hold and free-breathing techniques during radiotherapy of left-sided breast cancer: A meta-analysis
    Polish Journal of Medical Physics and Engineering, 2022

  6. Gold-nanoparticle-enriched breast tissue in breast cancer treatment using the INTRABEAM® system: A Monte Carlo study
    Radiation and Environmental Biophysics, 2022

  7. Dosimetric effect of nanoparticles in the breast cancer treatment using INTRABEAM® system with spherical applicators in the presence of tissue heterogeneities: A Monte Carlo study
    Biomedical Physics & Engineering Express, 2021

  8. A comparison between EGSnrc/Epp and MCNP in simulation of dosimetric parameters for radiotherapy applications
    Journal of Biomedical Physics & Engineering, 2021

  9. Explainable machine learning to compare the overall survival status between patients receiving mastectomy and breast conserving surgeries
    Scientific Reports, 2025

  10. Attenuation correction for dedicated cardiac SPECT imaging without using transmission data
    Molecular Imaging and Radionuclide Therapy, 2023

  11. Comparative study between EPID and CBCT for radiation treatment verifications
    Iranian Journal of Medical Physics, 2018