Andy Anderson Bery | Machine Learning in Physics | Best Researcher Award

Assoc. Prof. Dr. Andy Anderson Bery | Machine Learning in Physics | Best Researcher Award

University Lecturer, Universiti Sains Malaysia, Malaysia

Associate Professor Dr. Andy Anderson Anak Bery is an accomplished geophysicist and academic at Universiti Sains Malaysia (USM). With deep expertise in Geostatistics, Solid Earth Geophysics, and Machine Learning-based Predictive Analytics, Dr. Bery has authored over 70 indexed publications and contributed to multiple national and international research initiatives. Since joining academia in 2016, he has consistently merged scientific rigor with applied solutions, especially in environmental and subsurface characterization. His work not only pushes the boundaries of applied geophysics but also inspires a new generation of scholars in data-driven geoscience research.

👨‍🎓Profile

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📚 Early Academic Pursuits

Dr. Bery’s academic foundation was laid at Universiti Sains Malaysia (USM), where he earned his Bachelor’s, Master’s, and PhD degrees in Geophysics. His early focus was on exploration geophysics, particularly using seismic and resistivity techniques for subsurface imaging. These formative years cemented his interest in integrating mathematical models with geophysical datasets to address complex environmental and engineering challenges. His academic journey reflects a consistent trajectory of excellence, commitment, and specialization in earth sciences making him a strong contributor to Malaysia’s geoscience research capacity.

👨‍💼 Professional Endeavors

Since becoming a lecturer at the School of Physics, USM in 2016, Dr. Bery has been entrusted with teaching and mentoring at both undergraduate and postgraduate levels. His professional pursuits involve leading research teams, serving as a journal reviewer, and collaborating with industry partners on national and international site investigations. He has successfully secured 18 research grants (including 3 international), underscoring his leadership in applied research. His work frequently bridges academic theory with real-world utility, making him a sought-after expert in subsurface characterization and environmental geophysics.

🔬 Contributions and Research Focus

Dr. Bery’s primary contributions lie in geophysical modeling, machine learning for subsurface analysis, and site investigations. He explores both practical field applications and mathematical frameworks to improve environmental monitoring and hydrocarbon exploration. He is particularly noted for his work in seismic attribute transformation, multi-modal data integration, and probabilistic neural networks. Dr. Bery’s research not only contributes to geophysics but also intersects with data science, setting new standards for how geophysical data is interpreted using modern analytical tools.

🌍 Impact and Influence

With an h-index of 11 and 346 citations on Scopus, Dr. Bery’s research has made significant academic and practical impact in applied geosciences. His methodologies have been adopted in several national-scale environmental assessments and mineral exploration initiatives. He collaborates with researchers across Asia, Africa, and Oceania, enhancing international knowledge exchange. As a reviewer for top-tier journals, he influences scholarly directions in his field. His ability to bridge research, education, and industry continues to elevate his reputation within and beyond Malaysia.

đź“– Academic Citations

Dr. Bery’s work is widely cited in areas such as geotechnical investigations, subsurface mapping, and environmental risk assessment. His most cited work, “Correlation of seismic P-wave velocities with engineering parameters”, has received 126 citations, demonstrating its foundational role in linking seismic data with engineering applications. His citations stem from the relevance of his work in engineering geology, mineral exploration, and machine learning in geophysics. His publications serve as reference points for researchers working on resistivity imaging, seismic inversion, and hydrogeological surveys globally.

đź§Ş Research Skills

Dr. Bery possesses a diverse set of research competencies, including geostatistical modeling, seismic tomography, electrical resistivity tomography (ERT), and data-driven predictive analytics. He is proficient in applying regression modeling, machine learning algorithms, and probabilistic analysis to interpret complex geophysical data. His skill in multi-attribute integration allows for high-resolution analysis in both engineering and environmental geophysics. Dr. Bery’s ability to blend field-based methodologies with advanced computational models distinguishes him as a versatile and innovative geoscientist.

👨‍🏫 Teaching Experience

Dr. Bery has played a vital role in teaching Mathematics and Geophysics at USM since 2016. He is deeply involved in developing course materials, guiding postgraduate thesis supervision, and mentoring early-career researchers. His teaching emphasizes practical applications, often integrating fieldwork data and industry-standard software tools. Known for his structured approach and student-centered methods, Dr. Bery fosters critical thinking and research-driven learning. His role extends beyond classrooms, where he actively encourages students to participate in international conferences and publishing.

🚀 Legacy and Future Contributions

Dr. Bery’s legacy lies in his ability to unify traditional geophysics with modern computational tools to address pressing environmental and engineering challenges. He has laid a strong foundation in machine learning-driven geophysical modeling, and his work will likely inspire future frameworks in AI-assisted earth sciences. He continues to build capacity through cross-border collaborations and academic mentorship, ensuring a lasting impact. As environmental challenges grow more complex, Dr. Bery’s contributions will be critical in shaping sustainable geophysical solutions for the future.

Publications Top Notes

đź“„Magnetic-Assisted Radiometric, Speciation, and Environmental Studies of an Orogenic Gold Terrain: Okpella, Igarra Schist Belt, SW Nigeria
  • Authors: Adedibu Sunny Akingboye, Andy Anderson Bery, Abimbola Chris Ogunyele, Mbuotidem David Dick, Temitayo Olamide Ale, Emmanuel Adebayo Titus

  • Journal: Earth Systems and Environment

  • Year: 2025

đź“„Subsurface Lithological Characterization Via Machine Learning-assisted Electrical Resistivity and SPT-N Modeling: A Case Study from Sabah, Malaysia
  • Authors: Mbuotidem David Dick, Andy Anderson Bery, Adedibu Sunny Akingboye, Kufre Richard Ekanem, Erukaa Moses, Sanju Purohit

  • Journal: Earth Systems and Environment

  • Year: 2024

đź“„ Integrated Geophysical Investigation using Aero-radiometric and Electrical Methods for Potential Gold mineralization within Yauri/Zuru Schist Belts, Kebbi State NW Nigeria
  • Authors: Abdulrahaman Idris Augie, Kazeem Adeyinka Salako, Andy Anderson Bery, Adewuyi Abdulwaheed Rafiu, Mufutau Owolabi Jimoh

  • Journal: Earth Sciences Research Journal

  • Year: 2024

📄 Surface–Subsurface Characterization via Interfaced Geophysical–Geotechnical and Optimized Regression Modeling
  • Authors: Adedibu Sunny Akingboye, Andy Anderson Bery, Muslim Babatunde Aminu, Mbuotidem David Dick, Gabriel Abraham Bala, Temitayo Olamide Ale

  • Journal: Modeling Earth Systems and Environment

  • Year: 2024

đź“„A Novel Machine Learning Approach for Interpolating Seismic Velocity and Electrical Resistivity Models for Early-Stage Soil-Rock Assessment
  • Authors: Mbuotidem David Dick, Andy Anderson Bery, Nsidibe Ndarake Okonna, Kufre Richard Ekanem, Yasir Bashir, Adedibu Sunny Akingboye

  • Journal: Earth Science Informatics

  • Year: 2024

 

 

Arunima Singh | Computational Methods | Best Researcher Award

Prof. Arunima Singh | Computational Methods | Best Researcher Award

Assistant Professor at Arizona State University | United States

Dr. Arunima K. Singh is an Assistant Professor in the Department of Physics at Arizona State University (ASU) and a graduate faculty member in Materials Science and Engineering. Her research bridges computational materials science, applied physics, and machine learning, focusing on discovering novel materials for energy and electronic applications. She holds a Ph.D. from Cornell University and has conducted postdoctoral research at both NIST and Lawrence Berkeley National Lab. With over 57 publications, her work is highly regarded in the scientific community, earning prestigious awards, editorial roles, and invitations to speak globally on advanced materials research.

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🎓 Early Academic Pursuits

Dr. Singh’s academic journey began with a B.Tech. (Honors) in Metallurgical and Materials Engineering from IIT Kharagpur, where she earned multiple academic awards and graduated with a departmental silver medal. She pursued graduate studies at Cornell University, receiving both M.S. and Ph.D. degrees in Materials Science and Engineering, with a minor in Applied Physics. Under the guidance of Prof. Richard G. Hennig, her doctoral work focused on theoretical materials design. Her education was supported by prestigious fellowships including the McMullen Fellowship and Dow Chemical Fellowship, laying a strong foundation for her future research career.

đź’Ľ Professional Endeavors

Dr. Singh’s professional experience spans national labs and academia. Following her Ph.D., she held postdoctoral appointments at the National Institute of Standards and Technology (NIST) and Lawrence Berkeley National Lab (LBNL), collaborating with leaders like Dr. Francesca Tavazza and Prof. Kristin Persson. Since 2018, she has been a faculty member at ASU, where she also contributes as a graduate mentor and research leader. Beyond teaching and research, she serves on editorial boards, national committees, and plays an active role in shaping research programs in the DOE Energy Frontier Research Center and TMS divisions.

🔬 Contributions and Research Focus

Dr. Singh specializes in computational materials discovery, leveraging density functional theory (DFT), GW-BSE methods, and machine learning to uncover materials for photocatalysis, solar energy, and 2D electronics. She has developed high-throughput workflows like pyGWBSE, enabling scalable simulations for optoelectronic properties. Her notable contributions include predictive models for nanoscroll formation, ultra-wide band gap semiconductors, and surface film protectiveness. She is a pioneer in integrating AI techniques with first-principles simulations, pushing the boundaries of how materials are discovered and optimized for real-world applications, with her work often featured in high-impact journals like npj Computational Materials and Advanced Functional Materials.

🌍 Impact and Influence

With over 4,300 citations, an h-index of 25, and continuous recognition in global venues, Dr. Singh’s influence is widespread. Her research has made foundational contributions to photocatalytic energy materials, grain boundary physics, and 2D nanomaterials. She has mentored students who have gone on to win prestigious poster and research awards, reflecting her impact as an educator and scientist. Invited to give keynote speeches and colloquia across institutions, from Caltech to international webinars, she is recognized as a thought leader in her field. She plays a key role in shaping policy and research strategy through MaRDA, DOE, and TMS platforms.

📊 Academic Cites

Dr. Singh’s work has been published in top-tier journals like npj 2D Materials & Applications, Nano Letters, and Annual Review of Condensed Matter Physics. Her publications are frequently cited, reflecting both depth and breadth of research impact across fields including computational materials science, nanotechnology, and machine learning in physics. Her most cited works address CO₂ reduction photocatalysts, vibrational EELS theory, and strain-induced nanoscrolls. As of March 2025, her Google Scholar profile records 4,396 citations, a 25 h-index, and 35 i10-index, a clear testament to the lasting relevance and utility of her contributions in cutting-edge research.

đź§Ş Research Skills

Dr. Singh brings expertise in first-principles simulations, high-throughput computing, and machine learning for materials design. She has built custom computational workflows like pyGWBSE and developed data-driven algorithms for stability and performance prediction. Her skillset includes GW-BSE optical simulations, phonon and defect state analysis, and interface science. She collaborates with both theory and experiment teams, enhancing the real-world applicability of her computational models. Proficient in Python, VASP, Quantum ESPRESSO, and emerging AI frameworks, her skills position her at the frontier of materials informatics, enabling novel discoveries in photocatalysis, electronics, and energy storage.

👩‍🏫 Teaching Experience

As an Assistant Professor at ASU, Dr. Singh has taught and mentored students in Physics and Materials Science, often integrating cutting-edge research topics into her coursework. Her mentorship has led to student-led publications, poster awards, and graduate research accolades. She actively supervises Ph.D. students, guiding them through interdisciplinary research spanning condensed matter physics, AI in materials, and 2D materials design. Beyond classroom teaching, she regularly delivers technical workshops, participates in graduate admissions, and contributes to curriculum development. Her commitment to fostering the next generation of scientists is evident in her consistent student-centered approach.

🏆 Awards and Honors

Dr. Singh has earned numerous national and institutional accolades, including the 2023 DOE Early Career Research Award, the 2024 TMS Young Leaders Professional Development Award, and several graduate fellowships from Cornell and Dow Chemical. She has been recognized for her contributions to women in applied physics, being featured in special issues and highlighted by AIP. Her students have also received competitive honors, reflecting her impact as a mentor. These awards underscore her leadership, innovation, and dedication to excellence in research and education, solidifying her status as a standout researcher in materials physics and computational science.

đź”® Legacy and Future Contributions

Dr. Singh is on a trajectory to become a defining voice in AI-enabled materials design and computational physics. Her legacy will likely include tools and frameworks that democratize high-performance computing for materials discovery. As she continues to shape research agendas at DOE centers and through editorial influence, her work will foster sustainable energy solutions, new semiconductor technologies, and broader STEM participation. With a proven record of mentoring, publishing, and innovating, Dr. Singh is building a future where data, physics, and computation converge to revolutionize how materials power the world.

Top Noted Publications

Many-body physics and machine learning enabled discovery of promising solar materials
  • Authors: T. Biswas, A. Gupta, and A. K. Singh*
    Journal: RSC Advances
    Year: 2025
Predicting the structure and stability of oxide nanoscrolls from dichalcogenide precursors
  • Authors: A. Gupta, and A. K. Singh*
    Journal: APL Materials
    Year: 2025
Atomic-Resolution Mapping of Localized Phonon Modes at Grain Boundaries
  • Authors: B. Haas, T. M. Boland, C. Elsasser, A. K. Singh, K. March, J. Barthel, C. T. Koch, and P. Rez
    Journal: Nano Letters
    Year: 2023
Ab Initio-Based Metric for Predicting the Protectiveness of Surface Films in Aqueous Media
  • Authors: R. Gorelik, and A. K. Singh*
    Journal: npj Materials Degradation
    Year: 2023
pyGWBSE: A High Throughput Workflow Package for GW-BSE Calculations
  • Authors: T. Biswas, and A. K. Singh*
    Journal: npj Computational Materials
    Year: 2023