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

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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.

profile

google scholar

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 prediction – Int. J. Bioinformatics Research and Applications, 2024. Cited by: 2.

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

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

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

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

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

Reservoir Water Prediction using LSTM and GRU – Water 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

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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

Hua Zhu | AI in Drug Discovery | Best Researcher Award

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Prof. Hua Zhu | AI in Drug Discovery | Best Researcher Award   

Researcher, Ph.D. Supervisor at  Beijing Cancer Hospital & Institute, Peking University, China.
Prof. Hua Zhu is a distinguished researcher and full professor at the Department of Nuclear Medicine, Beijing Cancer Hospital & Institute, Peking University. With a focused career in molecular imaging and nuclear medicine, Prof. Zhu has made pioneering contributions to the clinical translation of cancer theranostics. His extensive work in radiopharmaceuticals, multimodality molecular probes, and targeted delivery systems places him among the leading scientists in his field.

🎓 Academic Background:

Prof. Zhu earned his Bachelor of Science degree in Applied Chemistry from Nanjing Normal University in 2006, followed by a Ph.D. in Inorganic Chemistry (specializing in Radiopharmaceuticals) from the Shanghai Institute of Applied Physics, Chinese Academy of Sciences in 2011. He also enriched his academic perspective through a visiting assistant professorship at Stanford University from 2014 to 2015.

Profile:

Professional Experience:

Prof. Hua Zhu is a full professor at the Department of Nuclear Medicine, Beijing Cancer Hospital & Institute, Peking University. He has over a decade of professional experience in the development of molecular imaging agents and targeted radiopharmaceuticals for cancer diagnosis and therapy. He has led multiple national-level research projects and previously served as a visiting assistant professor at Stanford University, contributing to international research collaboration. His expertise spans from radionuclide production to clinical translation, and he actively holds leadership roles in key academic societies related to nuclear medicine and imaging science.

🧪 Research Focus:

Prof. Zhu’s research is dedicated to the development of molecular imaging tools and targeted delivery systems for cancer diagnosis and treatment. His focus areas include the production and application of solid-target based radionuclides such as ⁶⁴Cu, ⁸⁹Zr, and ¹²⁴I, and the design of multimodal imaging probes. His efforts are instrumental in bridging the gap between bench research and clinical oncology.

🏆 Academic Honors:

Prof. Zhu has received numerous prestigious honors that recognize his research excellence and leadership in the field:

  • 🌟 National-level Young Talent (2023)

  • 🧠 Young Beijing Scholar (2024)

  • 📘 Boya Young Scholar (2025)

  • 🌆 “Bai Qian Wan” Talent of Beijing (2019)

  • 💫 Beijing Young Top-notch Talent (2018)

  • ✨ Beijing Nova Program (2017)

🔬 Research Leadership:

As a principal investigator, Prof. Zhu leads four National Natural Science Foundation of China (NSFC) projects and over ten national/provincial initiatives. His work exemplifies effective leadership in interdisciplinary and cross-institutional collaboration.

Publications:

  1. Detectability of Al¹⁸F-NOTA-HER2-BCH PET for Nodal Metastases in Patients With HER2-Positive Breast Cancer
    Clinical Nuclear Medicine, 2025

  2. Construction of Bispecific T-Cell Engager Radiotracer and Its Micro-PET Evaluation in Pancreatic Cancer
    Molecular Pharmaceutics, 2025

  3. Radiolabelled Anti-PD-L1 Peptide PET/CT in Predicting the Efficacy of Neoadjuvant Immunotherapy Combined With Chemotherapy in Resectable Non-Small Cell Lung Cancer
    Annals of Nuclear Medicine, 2025

  4. [¹⁷⁷Lu]Lu-XYIMSR-01: A Novel CAIX-Targeted Radiotherapeutic for Enhanced Treatment of Malignant Glioma
    Bioconjugate Chemistry, 2025

  5. A Whole-Body Imaging Technique for Tumor-Specific Diagnostics and Screening of B7H3-Targeted Therapies
    Journal of Clinical Investigation, 2025 (Open Access)

  6. Development and First-in-Human Evaluation of a Site-Specific [¹⁸F]-Labeled PD-L1 Nanobody PET Radiotracer for Noninvasive Imaging in NSCLC
    Bioorganic Chemistry, 2025

  7. Radioiodinated Nanobody ImmunoPET Probe for In Vivo Detection of CD147 in Pan-Cancer
    European Journal of Nuclear Medicine and Molecular Imaging, 2025

  8. Strategies for Specific Multimodal Imaging of Cancer-Associated Fibroblasts and Applications in Theranostics of Cancer
    Open Access Review

  9. Efficacy of Radiolabelled PD-L1-Targeted Nanobody in Predicting and Evaluating the Combined Immunotherapy and Chemotherapy for Resectable Non-Small Cell Lung Cancer
    European Journal of Nuclear Medicine and Molecular Imaging, 2025

  10. Recent Advances in Emerging Radiopharmaceuticals and the Challenges in Radiochemistry and Analytical Chemistry
    Review Article, 2025

Nazanin Zounemat-Kermani | Computational Systems Medicine | Best Researcher Award

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Dr Nazanin Zounemat-Kermani | Computational Systems Medicine | Best Researcher Award

Dr. Nazanin Zounemat-Kermani is an accomplished biomedical data scientist whose research stands at the forefront of precision medicine, respiratory disease, and artificial intelligence. Currently serving as a Postdoctoral Research Associate at both the National Heart and Lung Institute (NHLI) and the Data Science Institute (DSI) at Imperial College London, she has earned international recognition for her leadership in multi-omics data integration and machine learning applications in health sciences.

Profile:

🎓 Educational Background:

Dr. Kermani’s academic journey reflects a rare interdisciplinary blend of computer science and biomedical research. She holds a PhD in Biomedicine from Imperial College London (2020), following dual master’s degrees in Artificial Intelligence from the University of Amsterdam (2011) and Machine Learning and Robotics from the University of Tehran (2004). Her academic roots trace back to a Bachelor of Engineering in Software Engineering from Ferdowsi University of Mashhad (2002).

💼 Professional Experience:

Since 2020, Dr. Kermani has worked as a Postdoctoral Research Associate at Imperial College London, after several years as a Research Assistant and PhD candidate in Zoltan Takats’ Lab. Her academic trajectory within one of the world’s top institutions reflects both her scientific rigor and leadership capabilities.

🎓🤝 Academic Mentorship:

Dr. Nazanin Zounemat-Kermani is widely respected not only for her scientific contributions but also for her unwavering commitment to mentorship and academic leadership. Throughout her career, she has actively nurtured the growth of early-career scientists, postdoctoral researchers, and graduate students. As a senior member of interdisciplinary consortia such as AI-RESPIRE, PRISM, and U-BIOPRED, she has led cross-functional teams spanning clinical science, data engineering, and machine learning—mentoring junior colleagues in both technical development and scientific publishing. Her leadership style is inclusive and empowering, emphasizing hands-on guidance in areas such as statistical modeling, research communication, and responsible data science.

🏆 Awards and Honors:

Dr. Kermani has received numerous accolades, including the MSACL Young Investigator Award (2016), Scholar Awards from the American Thoracic Society (2022, 2023), and multiple research grants from prestigious bodies such as the UKRI, MRC, and EPSRC. She has also been invited to present her research by eminent scholars at institutions like Oxford University and Amsterdam UMC.

🌍 Global Projects and Leadership

Dr. Kermani has led and contributed to numerous high-impact international projects, including:

  • RASP-UK: She developed a robust data management platform supporting clinical and omics data integration for asthma research.

  • PIONEER: Co-led machine learning initiatives in prostate cancer, resulting in a key senior-author publication.

  • U-BIOPRED: Coordinated Europe’s largest severe asthma cohort study, publishing over 20 peer-reviewed articles and acting as corresponding author on influential multi-omics studies.

  • UK-Korea PRISM: Leads cross-national efforts in asthma pathophysiology using advanced analytics and single-cell data.

  • AI-RESPIRE: Heads AI model development for environmental and physiological time-series data, mentoring early-career researchers.

  • DeVENT and PROmics: Oversees analysis in critical care and deep learning integration in patient-reported outcomes.

📚 Publications:

  1. CC16 Confers Protection Against Influenza A Virus Infection in Human Airway Epithelium
    H. Kimura, N.Z. Kermani, N. Kimura, M.M. Siddiq, D. Francisco, I.M. Adcock, et al.
    American Journal of Respiratory and Critical Care Medicine, Vol. 211 (Abstracts), 2025.

  2. Distinct Single-Cell Transcriptional Profile in CD4⁺ T-Lymphocytes Among Obese Children With Asthma
    V. Tejwani, R. Wang, A. Villabona-Rueda, K. Suresh, T.D. Wu, I.M. Adcock, et al.
    American Journal of Physiology – Lung Cellular and Molecular Physiology, Vol. 328(3), 2025.

  3. Female Sex Hormones and the Oral Contraceptive Pill Modulate Asthma Severity Through GLUT-1
    A.C. Brown, O.R. Carroll, J.R. Mayall, N. Zounemat-Kermani, S.L.E. Vinzenz, et al.
    Mucosal Immunology, 2025.

  4. Neutrophilic Inflammation in Sputum or Blood Does Not Define a Clinically Distinct Asthma Phenotype in ATLANTIS
    P.J.M. Kuks, T.M. Kole, M. Kraft, S. Siddiqui, L.M. Fabbri, K.F. Rabe, A. Papi, et al.
    ERJ Open Research, Vol. 11(1), 2025.

  5. The Role of WNT5a and TGF‐β1 in Airway Remodelling and Severe Asthma
    T. Daud, S. Roberts, N. Zounemat-Kermani, M. Richardson, L.G. Heaney, et al.
    Allergy, 2025.

  6. Clinical Importance of Patient-Reported Outcome Measures in Severe Asthma: Results from U-BIOPRED
    R. Meys, F.M.E. Franssen, A.J. Van ‘t Hul, P.S. Bakke, M. Caruso, B. Dahlén, et al.
    Health and Quality of Life Outcomes, Vol. 22(1), Article 109, 2024.

  7. Radiomultiomics: Quantitative CT Clusters of Severe Asthma Associated With Multiomics
    N.Z. Kermani, K.F. Chung, G. Macis, G. Santini, F.A.A. Clemeno, A. Versi, K. Sun, et al.
    European Respiratory Journal, Vol. 64(5), 2024.

  8. S12 Association Between Disease Duration and FEV1 in Severe Asthma Phenotypes and Endotypes
    F. Yang, N. Zounemat-Kermani, P. Dixey, I.M. Adcock, C.I. Bloom, K.F. Chung
    Thorax, Vol. 79(Suppl 2), A15–A16, 2024.

  9. S120 Post-Hoc Analysis of Transcriptomic and Clinical Predictors of Remission in the ATLANTIS Cohort
    A.A. Kumar, T.M. Kole, M.C. Nawijn, K.F. Rabe, A. Papi, C. Brightling, D. Singh, et al.
    Thorax, Vol. 79(Suppl 2), A83–A84, 2024.

  10. Discovery and Validation of a Volatile Signature of Eosinophilic Airway Inflammation in Asthma
    R. Peltrini, R.L. Cordell, M. Wilde, S. Abuhelal, E. Quek, N. Zounemat-Kermani, et al.
    American Journal of Respiratory and Critical Care Medicine, Vol. 210(9), pp. 1101–1112, 2024.

  11. Cardiovascular Events in CML Patients Treated With Nilotinib: Validation of the HFA-ICOS Baseline Risk Score
    M. Andres, F. Fernando, S. Claudiani, N. Kermani, G. Ceccarelli, J. Apperley, et al.
    European Heart Journal, Vol. 45(Suppl 1), ehae666.3169, 2024.

  12. Scientific Business Abstracts
    F. Cooles, G. Vidal-Pedrola, N. Naamane, A. Pratt, B. Barron-Millar, et al.
    QJM: An International Journal of Medicine, Article hcae157, 2024.

  13. A Severe Asthma Phenotype of Excessive Airway Haemophilus influenzae Relative Abundance Associated With Sputum Neutrophilia
    A. Versi, A. Azim, F.X. Ivan, M.I. Abdel-Aziz, S. Bates, J. Riley, M. Uddin, et al.
    Clinical and Translational Medicine, Vol. 14(9), e70007, 2024.

  14. Host-Microbial Interactions Differ With Age of Asthma Onset
    A. Versi, A. Azim, F.X. Ivan, M.I. Abdel-Aziz, S. Bates, J. Riley, et al.
    European Respiratory Journal, 2024.

  15. Cardiovascular Events in CML Patients Treated With Nilotinib: Validation of the HFA-ICOS Baseline Risk Score
    F. Fernando, M.S. Andres, S. Claudiani, N.Z. Kermani, G. Ceccarelli, A.J. Innes, et al.
    Cardio-Oncology, Vol. 10(1), Article 42, 2024.

  16. Endotypes of Severe Neutrophilic and Eosinophilic Asthma From Multi-Omics Integration of U-BIOPRED Sputum Samples
    N.Z. Kermani, C.X. Li, A. Versi, Y. Badi, K. Sun, M.I. Abdel-Aziz, M. Bonatti, et al.
    Clinical and Translational Medicine, Vol. 14(7), e1771, 2024.

  17. IL-33 Induced Gene Expression in Activated Th2 Effector Cells Is Dependent on IL-1RL1 Haplotype and Asthma Status
    A.K.S. Jayalatha, M.E. Ketelaar, L. Hesse, Y.E. Badi, N. Zounemat-Kermani, et al.
    European Respiratory Journal, Vol. 63(6), 2024.

  18. Comparison of Asthma Phenotypes in Severe Asthma Cohorts (SARP, U-BIOPRED, ProAR and COREA) From Four Continents
    S.Y. Park, S. Fowler, D.E. Shaw, I.M. Adcock, A.R. Sousa, R. Djukanovic, et al.
    Allergy, Asthma & Immunology Research, Vol. 16(4), p. 338, 2024.

  19. Enose-Derived Response Clusters in Severe Asthmatics Treated With Anti-IL5/5R Biologics
    P. Dixey, N. Zounemat-Kermani, K. Raby, P.K. Bhavsar, K.F. Chung
    B16. Novel Insights Into Asthma Pathogenesis, A3013, 2024.

  20. Association of CC16 Expression in the Airways With Signature Expression of Multi-Omics Data
    H. Kimura, N.Z. Kermani, I.M. Adcock, K.F. Chung, M. Kraft
    D91. Bridging the Gap: Translational Studies in ARDS, Pneumonia, and Sepsis, 2024.

Nuno Domingues | Algorithm Development for Bioinformatics | Excellence in Research Award

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Prof. Dr. Nuno Domingues |Algorithm Development for Bioinformatics|Excellence in Research Award  

Prof . Dr at ISEL, Portugal.

Dr. Nuno A. S. Domingues is a distinguished researcher and educator in the fields of Electrical Engineering, Energy Systems, and Science Communication, currently serving as a Professor at the Instituto Superior de Engenharia de Lisboa (ISEL) in Portugal. With a rare blend of technical depth and interdisciplinary expertise, Dr. Domingues has pursued multiple postdoctoral fellowships and has been at the forefront of innovation in areas such as smart grids, sustainable technologies, machine learning, and engineering education. His career reflects a strong commitment to advancing clean and intelligent technologies, bridging science and society, and inspiring the next generation of engineers and researchers.

Profile:

🎓 Academic Background

Dr. Nuno A. S. Domingues is a highly accomplished academic with a deeply interdisciplinary foundation in engineering and science communication. He holds three postdoctoral degrees—in Mechanical Engineering (2022) and Electrical Engineering & Computer Science (2020) from IST, and in Science Communication (2021) from FCSH-UNL. He earned his Ph.D. in Electrical Engineering and Computer Science from FCT-UNL in 2015, following his Master’s degree from IST in 2008 and an Undergraduate (5-year) program in Electrical Engineering from ISEL in 2005. His academic journey reflects a rare blend of technical depth and communicative versatility.

🔬 Research Interests

Dr. Domingues’ research explores the cutting-edge intersection of technology, sustainability, and human decision-making. His core areas include modeling and simulation, energy systems, SCADA, decision support systems (DSS), intelligent optimization, and machine learning. He also delves into clean technologies, mobility and transport, sustainable consumption, regulatory frameworks, e-learning, and science communication. This wide-ranging expertise equips him to tackle real-world problems from both technical and societal perspectives.

📜 Certifications & Honors

Beyond academia, Dr. Domingues is a certified science and engineering trainer by the Conselho Científico-Pedagógico da Formação Contínua of Minho University. He holds the European Computer Driving License (ECDL) certification, demonstrating his commitment to digital literacy and technology proficiency. Additionally, his Leadership Certification from the Military Academy showcases his ability to lead and inspire in high-pressure, strategic environments.

Publication 

  1. Ferreira, J. C., Elvas, L. B., Martins, A. L., & Domingues, N. (Year unavailable). Blockchain, IoT, and Smart Grids Challenges for Energy Systems.