Sarah Marzen | Data Science | Best Researcher Award

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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.

Mr Farshad Sadeghpour | Engineering | Best Researcher Award |

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Mr.Farshad Sadeghpour | Engineering | Best Researcher Award | 

Researcher, at Petroleum University of Technology (PUT) ,Iran

Mr. Farshad Sadeghpour is a highly motivated and multidisciplinary Petroleum Engineer and Data Scientist with a strong academic foundation and hands-on industry experience. He specializes in applying artificial intelligence, machine learning, and geomechanical modeling to tackle real-world challenges in petroleum exploration and reservoir engineering. With an impressive track record of publications, research collaborations, and award-winning projects, Mr. Sadeghpour stands out as an innovative young professional pushing the frontiers of modern petroleum engineering.

Professional Profile

Orcid

Education 🎓

Mr. Sadeghpour holds a Master of Science in Petroleum Engineering (Exploration) from the Petroleum University of Technology, Abadan, Iran (2019–2022), where he graduated with a stellar GPA of 18.82/20. He earned his Bachelor of Science in Petroleum Engineering (Exploration) from the Islamic Azad University, Science and Research Branch, Tehran, Iran (2015–2019), achieving an exceptional GPA of 19.14/20. His academic journey reflects deep technical knowledge, diligence, and consistent excellence.

Experience 👩‍

Mr. Sadeghpour has worked across leading organizations in Iran’s energy sector. His roles include Petroleum Engineer, Petrophysicist, and Data Scientist at institutions such as the Research Institute of Petroleum Industry (RIPI), Petro Vision Pasargad (PVP), Computer Aided Process Engineering (CAPE), and the National Iranian South Oil Company (NISOC). His work involved RCAL, SCAL, EOR laboratory operations, geomechanical modeling, machine learning implementation, and reservoir data analysis—reflecting a strong blend of fieldwork, laboratory experience, and data-driven insights.

Research Interests 🔬

Farshad’s research is at the intersection of petroleum engineering, data science, and geomechanics. He focuses on using machine learning, deep learning, and AI-based models to solve complex reservoir problems such as mud loss prediction, permeability estimation, and CO₂ storage assessment. His work emphasizes both theoretical modeling and practical industry applications, often conducted in collaboration with organizations such as NISOC, RIPI, and the National Iranian Oil Company. His master’s thesis and several projects revolve around neural networks, genetic algorithms, and petrophysical characterization, showing his innovative edge.

Awards 🏆

Farshad’s dedication to excellence was recognized internationally when he secured Third Prize in the EAGE Laurie Dake Challenge 2022 held in Madrid, Spain—a highly competitive event for petroleum engineering students worldwide. He has also been involved in significant national-level research projects with reputed institutions, showcasing his contributions to both academic and industrial progress.

Top Noted Publications 📚

Farshad has co-authored and led multiple high-impact publications in Q1 journals, including:

Energy (2025): Machine learning for CO₂ storage feasibility.

Journal of Petroleum Exploration and Production Technology (2025): New petrophysical-mathematical approach for RQI and FZI.

Geoenergy Science and Engineering (2024): Upscaling methods for elastic modulus prediction.

Journal of Rock Mechanics and Geotechnical Engineering (2024): Stress effects on fracture development in the Asmari reservoir.

Multiple papers under review in Marine and Petroleum Geology, International Journal of Coal Geology, and others.

Conclusion

In conclusion, Farshad Sadeghpour exemplifies the profile of a next-generation energy researcher—technically brilliant, research-oriented, and industry-relevant. His interdisciplinary expertise, publication record, award-winning work, and innovative mindset make him an outstanding candidate for prestigious recognitions such as the Best Researcher Award. His contributions are not only academically significant but also strategically aligned with the global shift toward smart and sustainable energy solutions.

Ms Lara Pörtner | Data Management | Best Researcher Award |

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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.