Minseok Ryu | Power Systems Computation | Best Researcher Award

Posted on by ScienceFather

Assist Prof Dr. Minseok Ryu | Power Systems Computation | Best Researcher Award

PHD at the University of Michigan, United States

Minseok Ryu is an Assistant Professor at Arizona State University’s School of Computing and Augmented Intelligence. He earned his Ph.D. in Industrial and Operations Engineering from the University of Michigan in 2020. Prior to his current role, he held a postdoctoral position at Argonne National Laboratory and conducted research at Los Alamos National Laboratory. Ryu’s expertise spans privacy-preserving federated learning, optimization, and power system resilience. He has secured significant funding, including from DOE-ASCR and NSF, and developed the open-source APPFL package for federated learning. Ryu is actively involved in professional societies like INFORMS and IEEE, contributing to conferences and serving as a panelist for NSF reviews.

Professional Profiles

Education

Ph.D. Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI; May 2020 M.S. Aerospace Engineering, KAIST, Daejeon, Korea; Feb 2014 B.S. Aerospace Engineering, KAIST, Daejeon, Korea; Feb 2012

Work Experience

Assistant Professor, School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ Aug 2023–present Postdoctoral Appointee, Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL Aug 2020–Jul 2023 Research Assistant, Applied Mathematics and Plasma Physics Group, Los Alamos National Laboratory, Los Alamos, NM May 2019–Aug 2019 Post Baccalaureate Research Fellow, Kellog School of Management, Northwestern University, Evanston, IL Nov 2014–Apr 201

Honors & Awards

2024 Alliance Fellow, Mayo Clinic and ASU Alliance for Health Care 2023 Highlighted Research, Department of Energy, Advanced Scientific Computing Research (DOE-ASCR) 2022 Highlighted Research, DOE-ASCR

Professional Activities

Membership in Professional Societies: INFORMS, IEEE, IISE, SIAM Proposal Review: Panelist for National Science Foundation (NSF) Journal/Conference Review: Numerous journals and conferences including IEEE Transactions on Power Systems, Management Science, and others.

Research Focus

Minseok Ryu’s research primarily focuses on advanced optimization techniques and privacy-preserving federated learning systems. His work spans several key areas including privacy-preserving distributed control in power systems, data-driven distributionally robust optimization for scheduling, and mitigating uncertain disturbances in electric grids. Ryu has also contributed significantly to the development of algorithms for differentially private federated learning, enhancing security and robustness in biomedical research and heterogeneous computing environments. His expertise extends to heuristic algorithms for geomagnetically induced current blocking devices, showcasing a deep commitment to advancing resilient infrastructure and secure data handling in complex operational environments.

Publications

  1. A GPU-based Distributed Algorithm for Linearized Optimal Power Flow in Distribution Systems, Publication date: 2023.
  2. Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx (preprint), Publication date: 2023.
  3. Efficient Heuristic Approaches to Binary Optimization: a Sensor Placement Application, Publication date: 2023.
  4. APPFLX: Providing privacy-preserving cross-silo federated learning as a service, Publication date: 2023.
  5. Heuristic Algorithms for Placing Geomagnetically Induced Current Blocking Devices, Publication date: 2023.
  6. Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx, Publication date: 2022.
  7. APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning, Publication date: 2022.
  8. A Privacy-Preserving Distributed Control of Optimal Power Flow, Publication date: 2022.
  9. Differentially private federated learning via inexact ADMM with multiple local updates, Publication date: 2022.
  10. Mitigating the Impacts of Uncertain Geomagnetic Disturbances on Electric Grids: A Distributionally Robust Optimization Approach,  Publication date: 2022.
.

Ferdinando di martino | Computational Methods | Computational Science Excellence Award

Posted on by ScienceFather

Prof. Ferdinando di martino | Computational Methods | Computational Science Excellence Award

Associate professor at Federico II University of Naples, Italy

Ferdinando Di Martino is an associate professor of computer science at Federico II University of Naples, specializing in fuzzy-based models and computational intelligence for data and image analysis. He has authored over one hundred research papers published in international journals and holds editorial roles in leading academic publications. Di Martino is a member of EUSFLAT and actively contributes to its Research Working Group on soft computing in image processing. He directs a specialization course on advanced spatial analysis in GIS for urban and complex systems and manages research projects funded by Italian and European bodies, focusing on AI applications in managing environmental and climate risks.

Professional Profiles

Associate Professor of Computer Science

Ferdinando Di Martino serves as an associate professor of computer science at the Department of Architecture of the Federico II University of Naples.

Academic Leadership

Di Martino contributes significantly to academic programs and research initiatives: Member of the Italian national doctoral college in Artificial Intelligence within the agricultural-environmental sector. Director of the specialization course on advanced spatial analysis technologies in Geographical Information Systems (GIS) for urban systems and complex systems management.

Project Management

He leads research groups and tasks in both Italian and European funded projects. His projects focus on applying artificial intelligence and computational intelligence techniques to manage urban and complex systems, particularly in addressing environmental and climate-related risks.

Research Focuse

Ferdinando Di Martino’s research focuses on the application of fuzzy transforms and computational intelligence in image and data analysis. He has contributed extensively to methods for image coding and decoding using fuzzy transforms, as well as compression techniques and fragile watermarking for tamper detection. His work spans diverse applications including climate vulnerability assessment models for urban systems, segmentation methods, and predictive data analysis. Di Martino’s expertise also extends to spatial GIS applications, particularly in developing tools for vulnerability modeling and analysis in complex systems like aquifers. His research is pivotal in advancing the intersection of fuzzy logic, computational intelligence, and practical applications in various domains.

Publications

  1. A novel quantum inspired genetic algorithm to initialize cluster centers in fuzzy C-means, Publication date: 2022.
  2. Improving the emotion‐based classification by exploiting the fuzzy entropy in FCM clustering, Publication date: 2021.
  3. Graphical views of intuitionistic fuzzy double-controlled metric-like spaces and certain fixed-point results with application, Publication date: 2022.
  4. D-nisq: a reference model for distributed noisy intermediate-scale quantum computers, Publication date: 2022.
  5. A fuzzy partition-based method to classify social messages assessing their emotional relevance, Publication date: 2022.
  6. Fuzzy-Based Spatiotemporal Hot Spot Intensity and Propagation—An Application in Crime Analysis, Publication date: 2022.
  7. A theoretical development of cubic pythagorean fuzzy soft set with its application in multi-attribute decision making, Publication date: 2022.
  8. A GIS-based fuzzy multiclassification framework applied for spatiotemporal analysis of phenomena in urban contexts, Publication date: 2022.
  9. A GIS-Based Hot and Cold Spots Detection Method by Extracting Emotions from Social Streams, Publication date: 2022.
  10. A GIS-based framework to assess heatwave vulnerability and impact scenarios in urban systems, Publication date: 2023.
.

Radha Somaiya | Computational Methods | Best Researcher Award

Posted on by ScienceFather

Dr. Radha Somaiya | Computational Methods | Best Researcher Award

 PHD at S.V. National Institute of Technology, Surat, India

Dr. Radha N. Somaiya is an Institute Post-Doctoral Fellow at IIT Bombay, specializing in computational condensed matter physics. Her research focuses on the electronic, optical, and thermoelectric properties of 2D materials, particularly silicon-based compounds. She investigates their applications in photo(electro)catalysis for water splitting and reduction of CO2 and nitrogen. Dr. Somaiya holds a Ph.D. from S.V. National Institute of Technology, Surat, where she studied silicon-based 2D materials for energy and sensing applications. With over 11 journal publications and extensive experience in first-principles calculations, she contributes significantly to the fields of renewable energy and nanotechnology.

Professional Profiles

Education

M.Sc. in Applied Physics (2013 – 2015) The Maharaja Sayajirao University of Baroda, Gujarat, India B.Sc. in Physics (Main), Chemistry, and Mathematics (2010 – 2013) M. B. Patel Science College, Sardar Patel University, Anand, Gujarat, India Research Profile Post-Doctoral Fellow (June 2021 – December 2023) Area of Research: Photo(electro)catalytic water splitting, Carbon dioxide reduction, Nitrogen reduction Institution: Indian Institute of Technology Bombay (IIT-Bombay) Mentor: Prof. Aftab Alam Ph.D. in Computational Condensed Matter Physics (2017 – 2021) Thesis Title: Ab initio Study of Some Silicon-based 2D Materials for Energy and Sensing Applications Institution: S.V. National Institute of Technology Surat, Gujarat Mentor: Asso. Prof. Yogesh Sonvane

Experience

Physics Instructor at Rameshwar School of Science, Vadodara (Aug 2016 – July 2017) Physics Laboratory Assistant at Mahatma Gandhi High School, Vadodara (June 2014 – April 2016) Physics Laboratory Assistant at Navjeevan High School, Vadodara (June 2013 – April 2014). First-principles calculations for electronic structure, optical, elastic, strain, thermodynamic, and photo(electro)catalytic properties. Proficient in Python programming and beginner in Machine Learning. Experience in B. Tech first-year laboratory duties, semester exam duties, and PhD exam duties at IIT-Bombay. Mentored a PhD student at S.V. National Institute of Technology Surat.

Research Interests

Investigating the structural, electronic, and photo(electro)catalytic properties of low-dimensional systems. Carrier transport and carrier recombination rates. Optical properties of solar light harvesting materials. Molecular dynamics, density functional perturbation theory, and transition state nudge elastic band calculations. Water splitting (Hydrogen and Oxygen evolution reactions). Reduction of CO2 and N2 to useful hydrocarbons and NH3. Efficient HER catalyst development. Hydrogen storage and Li-ion or Na-ion batteries.

Honors/Fellowships

Shortlisted with provisional offer letter for the INSPIRE PhD Programme (2015) Qualified the PhD Entrance Test (PET) for the Faculty of Science at The Maharaja Sayajirao University of Baroda (2015) Awarded Prof. (Dr.) Padmini Agarwal Gold Medal for excellence in M.Sc. (Applied Physics) (2015) Silver Medal for highest CGPA in B.Sc. (Physics) at M.B. Patel Science College (2013) Silver Medal for second position in B.Sc. (Physics) at M.B. Patel Science College (2013) Selected for the Summer Programme-Advanced B.Sc. (Physics) at St. Xavier’s College, Ahmedabad (2012)

Research Focuse

Dr. Radha N. Somaiya’s research is centered on computational condensed matter physics, with a strong focus on the properties and applications of two-dimensional (2D) materials. Her work involves exploring the electronic, optical, and thermoelectric properties of various 2D materials such as silicon-based compounds and other low-dimensional systems. Key areas include photo(electro)catalytic water splitting, hydrogen evolution reaction (HER), CO2 reduction, and nitrogen reduction. Utilizing techniques like density functional theory (DFT), Dr. Somaiya investigates the structural and electronic properties of materials for applications in energy conversion, storage, and environmental remediation.

Publications

  1. Palladium-decorated SiX (X= N, P, As, Sb, Bi) catalysts for hydrogen evolution, Publication date: 2024.
  2. Strain modulated optical properties of MoSi2P4 monolayer–insights from DFT, Publication date: 2024.
  3. Biphenylene nanoribbon as a promising electrocatalyst for hydrogen evolution, Publication date: 2024.
  4. Efficient Electrochemical CO2 Reduction Reaction over Cu-decorated Biphenylene, Publication date: 2024.
  5. The activity of Pd supported Pd supported SiX (X= Group-V) Single-atom catalysts for hydrogen evolution reaction., Publication date: 2024.
  6. Biphenylene Nanoribbon as Promising Electrocatalyst for Hydrogen Evolution, Publication date: 2024.
  7. Biphenylene for efficient electrochemical renewable energy conversion., Publication date: 2023.
  8. Quasi-2D PdSi2–xGexN4 (x = 0, 1, 2): Promising Candidates for Spontaneous Overall Water Splitting, Publication date: 2023.
  9. Exploring the transport and optoelectronic properties of silicon diselenide monolayer, Publication date: 2021.
  10. Van der waals SiSe2 homo-bilayers for optoelectronics applications, Publication date: 2021.
.