Sogand Dehghan | Social Network Analysis | Best Researcher Award

Published on by Award and Honors

Ms. Sogand Dehghan | Social Network Analysis | Best Researcher Award

University Lecturer | K. N. Toosi University of Technology | Iran

Ms. Sogand Dehghan is a dedicated Data Analyst and IT Specialist with strong expertise in data mining, machine learning, text mining, social network analysis, and software development. She specializes in collecting, cleaning, and analyzing large-scale datasets to create insightful reports and dashboards that support organizational decision-making. Her work bridges academia and industry, with contributions spanning research, teaching, and practical implementation of data-driven solutions. She is particularly passionate about analyzing social media networks to align with organizational objectives and developing innovative tools that integrate analytics with business strategies.

Professional Profile

Scopus | Orcid | Googlescholar

Education

Ms. Dehghan earned her Master’s degree in Information Technology from K. N. Toosi University of Technology, where she conducted advanced research on social media user evaluation using machine learning techniques. She also holds a Bachelor’s degree in Information Technology from Payame Noor University. Her academic training has equipped her with a strong foundation in data visualization, machine learning, text mining, and database management, supported by proficiency in multiple programming and analytical tools.

Experience

Ms. Dehghan has served in both academic and industrial roles. In academia, she has worked as a Lecturer at the National Skills University, teaching advanced programming and mentoring students in data analytics. In the industrial sector, she has held positions as Data Analyst and Software Developer at GAM Arak Industry and Kherad Sanat Arvand, where she utilized tools such as Power BI, Python, SQL Server, and SSRS to develop interactive dashboards, predictive models, and enterprise applications. Her professional background demonstrates her ability to translate research concepts into scalable industry solutions.

Research Interests

Her research interests lie in credibility assessment of social network users, big data analytics, and text mining. She focuses on integrating heterogeneous data sources, such as social media profiles and scholarly databases, to assess user trustworthiness for organizational purposes. She also investigates emerging research trends in library and information science using text mining approaches, contributing to the identification of new and impactful academic domains.

Honors

Ms. Dehghan has been recognized for her innovative contributions to machine learning-based social media analysis and her ability to apply academic research in real-world industrial projects. Her research has been published in well-regarded, peer-reviewed journals and indexed in major databases such as Scopus, reflecting the impact and relevance of her work.

Top Noted Publications

The credibility assessment of Twitter/X users based on organizational objectives by heterogeneous resources in big data life cycle – 2 citations, Scopus index, 2025

The text mining approach to investigate active areas of library and information science and discover emerging topics – 1 citation, Scopus index, 2024

The main components of evaluating the credibility of users according to organizational goals in the life cycle of big data – 1 citation, Scopus index, 2023

Main components of user credibility assessment according to organizational goals in the big data – Scopus index

Conclusion

Ms. Sogand Dehghan exemplifies a professional who seamlessly integrates academic research excellence with industry-driven solutions. Her expertise in data analytics, software development, and social network analysis positions her as a valuable contributor to both research and practical innovation. Through her work, she has advanced the understanding of social media credibility assessment, big data life cycle management, and emerging trends in information science. With her continuous pursuit of impactful research and her ability to bridge theory with application, she is poised to make further significant contributions to the fields of data science and information technology.

Sarah Marzen | Data Science | Best Researcher Award

Published on by Award and Honors

Prof. Sarah Marzen | Data Science | Best Researcher Award

Associate Professor | Keck Graduate Institute, Claremont | United States

Professor Sarah Marzen is a prominent physicist and interdisciplinary researcher based at the W. M. Keck Science Department, representing Pitzer, Scripps, and Claremont McKenna Colleges in California. With a strong foundation in theoretical physics and complex systems, she is widely recognized for her research at the intersection of information theory, neuroscience, and machine learning. Her work explores how biological and artificial systems perceive, predict, and adapt to their environments. Through academic excellence and a commitment to scientific inquiry, she has established herself as a respected voice in computational neuroscience and resource-rational modeling

Professional Profile

Scopus | Google scholar

Education

Professor Marzen earned her Ph.D. in Physics from the University of California, Berkeley, where she conducted pioneering research on “Bio-inspired problems in rate-distortion theory” under the mentorship of Professor Michael R. DeWeese. Prior to her doctoral studies, she completed a Bachelor of Science degree in Physics at the California Institute of Technology (Caltech), reflecting an early and consistent commitment to scientific excellence. She has also participated in several prestigious summer schools and professional development programs, including the Santa Fe Institute’s Complex Systems School and the MIT Kauffman Teaching Certificate Program.

Experience

Dr. Marzen currently serves as Associate Professor of Physics at the W. M. Keck Science Department. Prior to this, she served as an Assistant Professor at the same institution . Her earlier career includes a postdoctoral fellowship at the Massachusetts Institute of Technology, where she collaborated with Professors Nikta Fakhri and Jeremy England. Her teaching experience is complemented by her role as a Seminar XL/LE Facilitator at MIT, underscoring her dedication to student engagement and mentorship.

Research Interests

Professor Marzen’s research focuses on sensory prediction, reinforcement learning, resource rationality, and the integration of information theory with biological systems. She investigates how both living and artificial neural systems process and respond to information in complex, dynamic environments. Her interdisciplinary approach spans computational modeling, machine learning theory, and theoretical neuroscience. She is currently involved in major research initiatives, including an Army Research Laboratory MURI project centered on hybrid biological-artificial neural networks and a series of workshops supported by the Sloan Foundation and Carnegie Institute

Honors

Dr. Marzen has received numerous recognitions for her academic contributions, including serving as Principal Investigator (PI) or Co-PI on several major research grants. Within her institution, she has held key service roles such as membership on the Executive Committee, DEI Committee, and Data Science Curriculum Coherence Committee, reflecting her leadership in fostering academic inclusivity and interdisciplinary learning.

Top Noted Publications

Title: Statistical mechanics of Monod–Wyman–Changeux (MWC) models
Citation: 128
Year of Publication: 2013

Title: On the role of theory and modeling in neuroscience
Citation: 100
Year of Publication: 2023

Title: The evolution of lossy compression
Citation: 65
Year of Publication: 2017

Title: Informational and causal architecture of discrete-time renewal processes
Citation: 46
Year of Publication: 2015

Title: Predictive rate-distortion for infinite-order Markov processes
Citation: 45
Year of Publication: 2016

Conclusion

Professor Sarah Marzen is a highly accomplished academic whose innovative research bridges physics, neuroscience, and artificial intelligence. Her work advances our understanding of how systems learn, adapt, and make decisions under constraints, with implications for both scientific theory and technological development. Through her leadership, mentorship, and scholarly impact, she continues to shape the future of interdisciplinary research and education. Her academic rigor, commitment to collaboration, and visionary research make her a key contributor to the global scientific community.

Prof. Dr Chih-Yung Tsai | Machine learning | Best Researcher Award |

Published on by Award and Honors

Prof. Dr Chih-Yung Tsai | Machine learning | Best Researcher Award

Professor, at University of Taipei, Taiwan.

Prof. Dr. Chih-Yung Tsai is a distinguished academic and researcher specializing in electrical engineering and nanotechnology. He is currently a professor at the Department of Electrical Engineering, National Taiwan University (NTU). Dr. Tsai’s work has significantly advanced the fields of nanolithography, integrated circuit design, and advanced manufacturing processes

Professional Profile

Scopus

ORCID

Google Scholar

🎓 Education 

Dr. Tsai earned his Bachelor of Science and Master of Science degrees from National Taiwan University. He then pursued his Ph.D. in Aeronautics and Astronautics, with a minor in Electrical Engineering, at Stanford University, completing it in 2002.

💼 Experience 

From 2002 to 2005, Dr. Tsai served as a Senior Process Engineer in lithography at Intel Corporation. During this time, he worked on performance monitoring and improvement of 193-nm microlithography scanners for Intel’s 90-nm process technology. In 2005, he joined the faculty of NTU’s Department of Electrical Engineering, where he established and directed several research laboratories, including the Nanoscale Design and Fabrication Systems Lab (NDFSL), Particle Beam Precision Patterning and Imaging Lab (PBPPIL), and High-Performance Servo Systems Lab (HPSSL).

🔬 Research Interests 

Dr. Tsai’s research centers on the design and application of advanced control, simulation, signal processing, optimization, and machine learning techniques to solve nanolithography and nanotechnology-related problems. His current focus includes the design and fabrication techniques of exploratory nanoscale integrated circuits at the IRDS 10 nm half-pitch node and beyond. He is also involved in aerospace-related research activities and has conducted research on control system design automation, automotive and aerospace electronics, consumer electronics, and biomedical equipment design.

🏆 Awards 

Dr. Tsai has been recognized for his contributions to the field with several honors. He has been invited by The Japan Society of Applied Physics to serve as Program Committee Section Sub Head in International Microprocesses and Nanotechnology since 2007. He has also been invited to present on innovative applications of helium ion beam nanofabrication and techniques for EUV mask defect detection technology research and development at various international workshops and seminars.

📚 Top Noted Publications 

Title: Applying the theory of planned behavior to explore the independent travelers’ behavior
Author(s): CY Tsai
Citations: 109
Index: African Journal of Business Management 4 (2), 221
Year of Publication: 2010

Title: Using the technology acceptance model to analyze ease of use of a mobile communication system
Author(s): CY Tsai, CC Wang, MT Lu
Citations: 79
Index: Social Behavior and Personality: an international journal 39 (1), 65-69
Year of Publication: 2011

Title: The impact of tour guides’ physical attractiveness, sense of humor, and seniority on guide attention and efficiency
Author(s): CY Tsai, MT Wang, HT Tseng
Citations: 55
Index: Journal of Travel & Tourism Marketing 33 (6), 824-836
Year of Publication: 2016

Title: An analysis of usage intentions for mobile travel guide systems
Author(s): CY Tsai
Citations: 48
Index: African Journal of Business Management 4 (14), 2962
Year of Publication: 2010

Title: Learning under time pressure: Learners who think positively achieve superior learning outcomes from creative teaching methods using picture books
Author(s): CY Tsai, YH Chang, CL Lo
Citations: 39
Index: Thinking Skills and Creativity 27, 55-63
Year of Publication: 2018

Conclusion

Prof. Dr. Chih-Yung Tsai’s illustrious career in electrical engineering and nanotechnology has significantly advanced the understanding and application of nanolithography and integrated circuit design. His dedication to research, education, and innovation continues to inspire and shape the future of technology.

Ms Lara Pörtner | Data Management | Best Researcher Award |

Published on by Award and Honors

Ms. Lara Pörtner | Data Management | Best Researcher Award | 

PhD Student , at Université Grenoble Alpes, Germany.

Lara Pörtner is a researcher and industry expert specializing in digital strategy, data analytics, and industrial engineering. With a strong academic foundation and professional experience in master data governance, she bridges the gap between research and real-world applications. Fluent in multiple languages, she has worked on international projects in data strategy, innovation management, and digital transformation.

Professional Profile

Scopus

Education 🎓

Lara is currently pursuing a PhD in Digital Strategy at Université Grenoble Alpes, France (2022–2025), where she focuses on developing customer-specific reference models for optimizing data strategies using maturity analysis methods. She holds a double master’s degree (M.Sc. Industrial Engineering and Management) from Karlsruhe Institute of Technology (KIT), Germany, and Institute National Polytechnique Grenoble (G-INP), France, with a specialization in innovation management, digitalization, and automotive engineering. Her academic journey began with a B.Sc. in Industrial Engineering and Management from KIT (2016–2020), where she built expertise in supply chain management, strategy, and organization.

Professional Experience 💼

Lara has held multiple roles in leading organizations across data & analytics, digital transformation, and strategy consulting. She started her career as an intern and working student in Master Data Management at cbs Corporate Business Solutions (2020–2021), where she supported SAP MDG system implementations. She later joined CAMELOT Management Consultants AG (2022–2024) as a Senior Consultant, contributing to SAP MDG BP implementation, process design, user training, and data strategy assessments. Currently, she is a Senior Associate in Data & Analytics at PricewaterhouseCoopers (PwC), Munich (2025–present), focusing on enterprise-wide digital strategy initiatives. Her industry experience is complemented by her early research work as a Student Assistant at wbk Institute of Production Tech., Karlsruhe Institute of Technology (2020), and an internship in Strategy & Innovation Management at Lufthansa Cargo AG (2018).

Research Interests 🌍

Her research primarily revolves around digital strategy, data analytics, and master data governance. She works on designing advanced methodologies for data maturity assessment, enabling organizations to optimize data-driven decision-making. Her work integrates elements of machine learning, enterprise data management, and business process optimization, contributing to the evolution of digital transformation frameworks.

Awards & Honors 🏆

Lara has received several prestigious recognitions, including the DLR Graduate Program (2022–2024), demonstrating her involvement in high-impact research. She was selected for the Accenture Female Talent Program (2021), highlighting her leadership potential in the tech and consulting space. Early in her academic career, she secured 2nd place in the Jugend Forscht competition (2016), showcasing her research abilities. She also holds professional certifications such as SCRUM Master, SAFe 6 Agilist, and LEAN Basic Certificate.

Top Noted Publications 📚

Title: Data Literacy Assessment – Measuring Data Literacy Competencies to Leverage Data-Driven Organizations
Authors: Lara Pörtner, Andreas Riel, Vivian Klaassen, Dilara Sezgin, Ysaline Kievits
Citations: 0

Conclusion

Lara Pörtner has an impressive profile with a strong academic foundation, industry research experience, and technical expertise in data strategy and analytics. If the award prioritizes applied research, digital strategy, and industry impact, she is a strong candidate. However, if the focus is on academic publications and fundamental research contributions, she may need to enhance her research output to improve her chances.

Mr Ernesto Diaz | Data Scientist | Best Researcher Award

Published on by Award and Honors

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

Orcid

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. 🚀

Dr Han Gao | Artificial Intellegence | Best Researcher Award |

Published on by Award and Honors

Dr. Han Gao | Artificial Intellegence | Best Researcher Award

postdoctoral fellow, at Harvard University, United States.

Dr. Han Gao is a dedicated researcher specializing in scientific deep learning, computational mechanics, and generative models for spatiotemporal physics. With a strong background in machine learning-driven physics simulations, he has contributed significantly to advancing numerical modeling and data-driven solutions for complex physical systems. His work bridges the gap between deep learning and traditional computational fluid dynamics, with applications in turbulence modeling, inverse problems, and reduced-order modeling.

Professional Profile

Scopus

Google Scholar

Education 🎓

Dr. Gao earned his Ph.D. in Aerospace and Mechanical Engineering from the University of Notre Dame (2018–2023), where he focused on scientific deep learning for forward and inverse modeling of spatiotemporal physics. He also holds a Master’s degree in Mechanical Engineering & Materials Science from Washington University in St. Louis (2016–2018), with research on numerical simulations of jet impingement and rotor blade effects. His academic journey began with a Bachelor’s degree in Mechanical Engineering from Shanghai University of Electric Power (2012–2016).

Professional Experience 💼

Dr. Gao is currently a Postdoctoral & Teaching Fellow at Harvard University (2023–present), where he continues his research in deep learning-driven physics simulations while mentoring students. Previously, he served as a Research & Teaching Assistant at the University of Notre Dame (2018–2023) and Washington University in St. Louis (2016–2018). Additionally, he gained industry experience as a Research Intern at Google Research (2022), where he worked on advanced AI-driven physics simulations.

Research Interests 🌍

Dr. Gao’s research revolves around the integration of deep learning techniques with physics-based modeling, particularly in solving partial differential equations (PDEs), turbulence modeling, generative models, and reduced-order modeling. He has developed novel physics-informed neural networks (PINNs), Bayesian generative models, and machine-learning frameworks for high-dimensional complex systems. His work is widely applicable in computational fluid dynamics (CFD), climate modeling, aerodynamics, and engineering simulations.

Awards & Honors 🏆

Dr. Gao has been recognized for his outstanding contributions to computational mechanics and machine learning applications in physics. His publications in top-tier journals, including Nature Communications, and prestigious machine learning conferences such as NeurIPS, ICML, and ICLR, reflect his impact in the field. He has also received competitive research opportunities, including a Google Research internship, showcasing his industry relevance.

Top Noted Publications 📚

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
L. Sun, H. Gao, S. Pan, J.-X. Wang – 916 citations – Computer Methods in Applied Mechanics and Engineering, 2020

PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
H. Gao, L. Sun, J.-X. Wang – 567 citations – Journal of Computational Physics, 2021

Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
H. Gao, M. J. Zahr, J.-X. Wang – 240 citations – Computer Methods in Applied Mechanics and Engineering, 2022

Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
H. Gao, L. Sun, J.-X. Wang – 214 citations – Physics of Fluids, 2021

Predicting physics in mesh-reduced space with temporal attention
X. Han, H. Gao, T. Pfaff, J.-X. Wang, L.-P. Liu – 106 citations – ICLR, 2022

Conclusion

Dr. Han Gao is a highly promising researcher with a strong publication record, interdisciplinary expertise, and experience at prestigious institutions. His contributions to scientific deep learning and computational mechanics make him a strong contender for the Best Researcher Award. To further solidify his case, he could focus on gaining more individual recognitions, expanding his leadership roles, and demonstrating the real-world impact of his research.