Daniel Mutia Mwendwa | Energy Systems | Best Researcher Award

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Mr. Daniel Mutia Mwendwa | Energy Systems | Best Researcher Award

Daniel Mutia Mwendwa at University of Oxford, United Kingdom

Summary:

Daniel Mutia Mwendwa is a Rhodes Scholar currently pursuing a DPhil in Engineering Science at the University of Oxford, where his research focuses on geospatial analysis of solar-powered irrigation systems in Sub-Saharan Africa. He holds an MSc in Energy Systems (Distinction) from Oxford and a BEng (First Class Honours) in Electronics and Electrical Engineering from the University of Edinburgh. Co-founder of BuniTek, he is dedicated to introducing technology to African youth. Daniel also works as a Research Assistant in the Climate Compatible Growth Programme, contributing to energy planning in Kenya and global green hydrogen initiatives.

Professional Profile:

👩‍🎓Education:

University of Oxford, UK (2023 – Present)
DPhil in Engineering Science (Rhodes Scholar)

  • Thesis Topic: Geospatial analysis of solar-powered irrigation in Sub-Saharan Africa, focusing on the water, energy, and food nexus.

University of Oxford, UK (2021 – 2022)
MSc in Energy Systems (Distinction, Rhodes Scholar)

  • Thesis: Developed a geospatial methodology for sizing and costing solar-powered irrigation systems for different crops.

University of Edinburgh, UK (2017 – 2021)
BEng (Hons) in Electronics and Electrical Engineering (First Class Honours, MasterCard Foundation Scholar)

  • Thesis: Developed an optimized hybrid energy storage system (HESS) combining battery storage, hydrogen fuel cells, and supercapacitors using MATLAB Simulink.

🏢 Professional Experience:

Rhodes Trust, Oxford, UK (Mar 2024 – Present)
Rhodes Scholarship Ambassador for East Africa

  • Engages with universities across East Africa to introduce students to the Rhodes Scholarship and supports their application process.

University of Oxford, Oxford, UK (Mar 2022 – Present)
Research Assistant, Climate Compatible Growth Programme

  • Developed least-cost electrification pathways for Kenyan counties using the Open-Source Spatial Electrification Tool (OnSSET). Conducted capacity-building workshops on energy planning models like OSeMOSYS and IRENA’s Flextool. Also involved in geospatial modeling of green hydrogen production potential globally.

Loughborough University, Nairobi, Kenya (Nov 2022 – Present)
Research Consultant

  • Designed off-grid electrification solutions for poultry farming and health facilities. Authored academic papers on energy planning and developed funding proposals.

BuniTek, Nairobi, Kenya (Jun 2020 – Present)
Co-Founder

  • Co-founded BuniTek, an initiative that introduces technology concepts to African high school students in an engaging and hands-on manner. Led a team of 15 volunteers to develop 14 new courses.

Research Interests:

Daniel Mutia Mwendwa’s research interests lie in renewable energy systems, particularly in optimizing solar-powered irrigation and hybrid energy storage solutions. He is passionate about integrating geospatial modeling and energy planning to address sustainability challenges in Sub-Saharan Africa. His work also focuses on green hydrogen production, off-grid electrification, and the energy-water-food nexus.

Author Metrics:

Mwendwa has co-authored several research papers and reports, including:

  1. “Spatial Data Starter Kit for OnSSET Energy Planning in Kitui County, Kenya” – Published in Data in Brief (2022).
  2. “Mapping the Energy Planning Ecosystem in Kenya” – Published on GOV.UK (2023).
  3. “County Energy Planning Data Flows in Kenya: Practitioner Perspectives” – Published on GOV.UK (2023).

Top Noted Publication:

GIS-Based Method for Assessing the Viability of Solar-Powered Irrigation

  • Journal: Applied Energy
  • Publication Date: January 2025
  • DOI: 10.1016/j.apenergy.2024.124461
  • Contributors: Daniel Mutia Mwendwa, Alycia Leonard, Stephanie Hirmer
  • Summary: This paper presents a Geographic Information System (GIS)-based methodology for evaluating the feasibility of solar-powered irrigation systems. The method integrates spatial data with factors such as water availability, crop water demand, and solar energy potential to determine where solar-powered irrigation systems can be most effectively deployed in Sub-Saharan Africa.

Spatial Data Starter Kit for OnSSET Energy Planning in Kitui County, Kenya

  • Journal: Data in Brief
  • Publication Date: December 2022
  • DOI: 10.1016/j.dib.2022.108691
  • Contributors: Daniel Mutia Mwendwa, Jeffrey Tchouambe, Emily Hu, Micaela Flores Lanza, Andrea Babic Brener, Gyubin Hwang, Layla Khanfar, Alycia Leonard, Stephanie Hirmer, Malcolm McCulloch
  • Summary: This article provides a comprehensive spatial data kit designed for energy planning in Kitui County, Kenya. It is part of the Open-Source Spatial Electrification Tool (OnSSET) framework, which assists planners in developing least-cost electrification pathways. The dataset includes geographic and energy-related data to facilitate more efficient and accurate energy planning.

Minseok Ryu | Power Systems Computation | Best Researcher Award

Posted on by Best Paper Award

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

Assistant Professor at Arizona State University, United States

Summary:

Dr. Minseok Ryu is an Assistant Professor at the School of Computing and Augmented Intelligence at Arizona State University. He completed his Ph.D. in Industrial and Operations Engineering from the University of Michigan and holds a master’s and bachelor’s degree in Aerospace Engineering from KAIST. His postdoctoral research at Argonne National Laboratory has been recognized by the Department of Energy for its contributions to advanced scientific computing. Dr. Ryu’s research interests lie in developing scalable and privacy-preserving algorithms for complex systems, with a strong emphasis on interdisciplinary collaboration. He has received numerous accolades, including the Alliance Fellowship from Mayo Clinic and ASU and highlighted research recognitions from the DOE-ASCR.

Professional Profile:

👩‍🎓Education:

Ph.D. in Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI; May 2020

M.S. in Aerospace Engineering, KAIST, Daejeon, Korea; February 2014

B.S. in Aerospace Engineering, KAIST, Daejeon, Korea; February 2012

🏢 Professional Experience:

  • Assistant Professor, School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ; August 2023 – Present
    • Responsibilities: Leading research initiatives, teaching, and mentoring students. Participating in committee roles and overseeing various academic and administrative duties.
  • Postdoctoral Appointee, Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL; August 2020 – July 2023
    • Responsibilities: Conducting advanced research in applied mathematics, computer science, and developing scalable algorithms for various applications.
  • Research Assistant, Applied Mathematics and Plasma Physics Group, Los Alamos National Laboratory, Los Alamos, NM; May 2019 – August 2019
    • Responsibilities: Engaged in research projects focusing on applied mathematics and plasma physics.
  • Post Baccalaureate Research Fellow, Kellogg School of Management, Northwestern University, Evanston, IL; November 2014 – April 2015
    • Responsibilities: Conducted research in operations management and contributed to academic publications.

Honors & Award:

2024 Alliance Fellow, Mayo Clinic and ASU Alliance for Health Care

Highlighted Research, Department of Energy, Advanced Scientific Computing Research (DOE-ASCR) in 2022 and 2023

Rackham Graduate Student Research Grant, University of Michigan, 2016

MICDE Fellowship, University of Michigan, 2015

National Science Foundation Student Award, INFORMS Computing Society, 2015

National Scholarship, Government of Korea, 2010-2013

Department Honor, KAIST, 2010

Best Technical Poster Award, KAIST, 2010

Research Interests:

Dr. Minseok Ryu’s research focuses on the development of scalable algorithms and machine learning models for applications in industrial engineering, operations research, and augmented intelligence. His work encompasses privacy-preserving federated learning, optimization of power systems, and computational methods for enhancing system resilience against extreme threats. He is particularly interested in interdisciplinary collaborations that integrate advanced computational techniques with practical applications in engineering and healthcare.

Top Noted Publication:

Data-Driven Distributionally Robust Appointment Scheduling over Wasserstein Balls

  • Authors: R. Jiang, M. Ryu, G. Xu
  • Published in: arXiv preprint arXiv:1907.03219, 2019
  • Citations: 28

APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning

  • Authors: M. Ryu, Y. Kim, K. Kim, R. K. Madduri
  • Published in: IEEE International Parallel and Distributed Processing Symposium Workshops, 2022
  • Citations: 19

A Privacy-Preserving Distributed Control of Optimal Power Flow

  • Authors: M. Ryu, K. Kim
  • Published in: IEEE Transactions on Power Systems, 2022, 37(3), 2042-2051
  • Citations: 18

An Extended Formulation of the Convex Recoloring Problem on a Tree

  • Authors: S. Chopra, B. Filipecki, K. Lee, M. Ryu, S. Shim, M. Van Vyve
  • Published in: Mathematical Programming, 2017, 165, 529-548
  • Citations: 18

Nurse Staffing under Absenteeism: A Distributionally Robust Optimization Approach

  • Authors: M. Ryu, R. Jiang
  • Published in: arXiv preprint arXiv:1909.09875, 2019
  • Citations: 14