Kumar Mallem | Energy | Best Researcher Award

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Dr. Kumar Mallem | Energy | Best Researcher Award

Dr. Kumar Mallem, Hong Kong University of Science and Technology, Hong Kong

Dr. Kumar Mallem is a researcher in electronic and computer engineering, specializing in quantum rod light-emitting diodes (QRLEDs) and advanced optoelectronic materials. He is pursuing a Ph.D. at the Hong Kong University of Science and Technology (HKUST), focusing on next-generation display and lighting applications. His research spans quantum dot and quantum rod LEDs, perovskite solar cells, and silicon-based photovoltaics. Dr. Mallem has extensive experience in device fabrication and characterization, with multiple high-impact publications and patents. Recognized with prestigious awards, including the Distinguished Paper Award at SID Display Week 2025, he continues to advance energy-efficient lighting technologies.

Publication Profile

Google Scholar

Education 🎓

Dr. Kumar Mallem is currently pursuing a Ph.D. in Electronic and Computer Engineering at HKUST (2020–2025) under Prof. Abhishek Kumar Srivastava, focusing on quantum rod light-emitting diodes for future display applications. He earned an M.Tech. in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, Kakinada (2012–2014), conducting research on HfO₂/Ge stacks for MOS devices at IIT Bombay under Prof. Saurabh Lodha. His B.Tech. in the same field was completed at AGCIT, JNTU Kakinada (2007–2011). His educational background integrates semiconductor device engineering, optoelectronics, and materials science, equipping him with expertise in advanced display and photovoltaic technologies.

Experience 🏆

Dr. Kumar Mallem has significant research experience in optoelectronics and semiconductor devices. Since 2020, he has been a Ph.D. researcher at HKUST, developing quantum rod LEDs and optimizing their efficiency. From 2015 to 2019, he worked as a research assistant at Sungkyunkwan University, South Korea, under Prof. Junsin Yi, specializing in silicon solar cells, thin-film transistors (TFTs), and MOS devices. His hands-on expertise includes device fabrication, nanomaterials assembly, and advanced characterization techniques. His work contributes to energy-efficient display technologies and next-generation solid-state lighting.

Awards & Honors 🏅

Dr. Kumar Mallem has received several prestigious recognitions, including the Distinguished Paper Award at SID Display Week 2025 for his work on highly efficient QRLEDs. He was awarded the Hong Kong PhD Fellowship Scheme (2020–2025) for academic excellence. He also won the Excellent Paper Award at the Asia-Pacific Forum on Renewable Energy in 2018. His contributions to optoelectronic devices and energy-efficient lighting technologies have earned him international recognition, reflecting his impact in advancing quantum dot and nanomaterial-based displays.

Research Focus 🔬

Dr. Kumar Mallem specializes in quantum rod and quantum dot light-emitting diodes, perovskite LEDs, silicon solar cells, and advanced optoelectronic materials. His research aims to enhance device efficiency, suppress charge leakage, and improve external quantum efficiency for high-performance display and lighting technologies. He works extensively with solution-processed nanomaterials, carrier injection engineering, and interface optimization to develop next-generation optoelectronic devices. His contributions to solid-state lighting and energy-efficient display technologies push the boundaries of modern photonic applications.

Publication Top Notes

  • Quantum‐Rod On‐Chip LEDs for Display Backlights with Efficacy of 149 lm W−1 |  Advanced Materials | 55 | 2021
  • Molybdenum oxide: A superior hole extraction layer for Si solar cells | Materials Research Bulletin | 53 | 2019
  • Influence of small size pyramid texturing on Ag-screen printed Si solar cells | Materials Science in Semiconductor Processing | 45 | 2018
  • Control of size and distribution of silicon quantum dots for solar cells | Renewable Energy | 33 | 2019
  • Ultralow roll‐off quantum dot LEDs using engineered carrier injection layer | Advanced Materials | 30 | 2023
  • Ambient annealing influence on MoOx layer for carrier-selective contact solar cells | Materials Science in Semiconductor Processing | 29 | 2019
  • MoOx work function & interface analysis for hole-selective Si heterojunction solar cells | Applied Surface Science | 24 | 2021
  • Solution-processed red, green, and blue quantum rod LEDs | ACS Applied Materials & Interfaces | 22 | 2022
  • High-efficiency crystalline silicon solar cells: A review  | 19 | 2019
  • Light scattering properties of multi-textured AZO films for Si thin film solar cells |  Applied Surface Science | 19 | 2018

Irfan Ali Channa | Power System Control | Excellence in Research

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Dr. Irfan Ali Channa | Power System Control | Excellence in Research

Ph.D Scholor, Institute of Automation, Beijing University of Chemical Technology, Beijing China

Irfan Ali Channa is a highly motivated PhD scholar at the Institute of Automation, Beijing University of Chemical Technology in China. He is passionate about innovation in the field of AI, particularly in power systems. Irfan has a strong background in experimental design, literature review, and scientific writing. His career includes significant experience as a lecturer at Bahria Engineering University in Karachi, Pakistan, where he contributed to both academic and engineering solutions.

Education 🎓

Irfan Ali Channa is currently pursuing his PhD at the Institute of Automation, Beijing University of Chemical Technology in Beijing, China, a program he began in 2019 and is expected to complete in 2024. His advanced studies focus on leveraging artificial intelligence to enhance power systems. Prior to this, he developed a solid foundation in engineering education and research during his tenure at Bahria Engineering University in Karachi, Pakistan.

Experience 🏫

From 2014 to 2017, Irfan Ali Channa served as a lecturer at Bahria Engineering University in Karachi, Pakistan. During this period, he was responsible for providing academic services to students, developing engineering solutions, and facilitating research innovations. His role involved extensive interaction with both students and faculty, promoting a collaborative and progressive educational environment.

Research Interests 🔬

Irfan’s research interests lie primarily in the application of artificial intelligence in power systems. He is particularly focused on developing innovative methodologies for the classification and detection of power quality disturbances. His work frequently involves the use of advanced techniques such as deep learning, evolutionary algorithms, and swarm-based optimization. Irfan is also interested in renewable energy sources, as evidenced by his studies on wind potential and photovoltaic modules.

Awards 🏆

Throughout his academic and professional career, Irfan Ali Channa has received recognition for his contributions to the field of electrical engineering. His innovative research and dedication to advancing power system technology have earned him accolades within the academic community, particularly for his work on power quality disturbances and renewable energy analysis.

Publications Top Notes 📚

  1. A new deep learning method for classification of power quality disturbances using DWT-MRA in utility smart grid
    • Published in: Computers and Electrical Engineering, Elsevier (2024)
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    • Cited by: Articles focused on power quality and smart grid technology.
  2. Evolutionary and Swarm Based Optimization of Fit k-Nearest Neighbor Classifier for Classification of Power Quality Disturbances
    • Published in: Electric Power Components and Systems (2023)
    • Cited by: Articles related to optimization algorithms in electrical systems.
  3. Detection and classification of power quality disturbances using STFT and deep neural Network
    • Published in: Proceedings of the 2023 7th International Conference on Computer Science and Artificial Intelligence (2023)
    • Cited by: Research on deep learning applications in power quality analysis.
  4. A comparative study to analyze wind potential of different wind corridors
    • Published in: Energy Reports, Volume 9 (2023)
    • Cited by: Studies on renewable energy and wind power assessment.
  5. Temperature and irradiance based analysis of specific variation of PV module
    • Published in: Jurnal Teknologi, Volume 83(6) (2021)
    • Cited by: Research on photovoltaic module performance under varying conditions.