Anton Loskutov | Energy and Sustainability | Research Excellence Award

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Dr. Anton Loskutov | Energy and Sustainability | Research Excellence Award

Dr. Anton Loskutov | Energy and Sustainability | Research Excellence Award | Associate Professor | Nizhny Novgorod State Technical University | Russia

Dr. Anton Loskutov is an accomplished researcher in intelligent electrical networks, power system automation, and high-voltage fault-location technologies, recognized for his extensive contributions to smart-grid development and advanced diagnostic algorithms. He has established himself as a leading specialist through his strong academic background, having completed his doctoral education in electrical engineering with research focused on automation devices, distributed generation systems, and sequential decision-making algorithms for network reliability. Dr. Anton Loskutov’s professional experience spans multiple collaborative engineering projects where he has worked closely with multidisciplinary teams to design, model, and optimize high-voltage systems, develop machine learning–based recognition methods for emergency modes, and implement simulation-driven approaches for improving relay protection and network automation. His research interests include power-line fault detection, traveling-wave pattern recognition, machine learning applications in electrical networks, data-compression techniques for emergency-mode analysis, and algorithmic control strategies for distributed generation environments. He is proficient in high-level research skills such as advanced modeling, ANSYS-based structural simulations, sequential analysis, algorithm development, signal-processing methods, and machine learning techniques applied to real-time power-system automation. Throughout his academic and professional journey, he has demonstrated strong analytical ability, technical precision, and a solution-oriented mindset that supports cutting-edge innovations in the energy sector. Dr. Anton Loskutov has earned recognition from peers and institutions for his publications in reputable journals, contributions to IEEE-indexed conferences, and collaborative advancements in next-generation intelligent grid technologies. His honors include repeated acknowledgements for impactful research studies, participation in international engineering conferences, and contributions to the development of advanced control methods for distributed generation networks. With a robust research portfolio, strong interdisciplinary expertise, and continued engagement in scientific collaborations, he continues to strengthen global understanding of smart-grid stability, automation algorithms, and data-driven fault-recognition systems. In conclusion, Dr. Anton Loskutov stands out as a highly dedicated and innovative researcher whose work significantly enhances the reliability, efficiency, and intelligence of modern electrical networks, making him an influential contributor to the advancing field of power-system engineering.

Profile: ORCID 

Featured Publications

  1. Loskutov, A. (2025). High-voltage overhead power line fault location through sequential determination of faulted section. 2025. Citations: 5

  2. Loskutov, A. (2023). Fault location method for overhead power line based on a multi-hypothetical sequential analysis using the Armitage algorithm. 2023. Citations: 18

  3. Loskutov, A. (2023). Decision tree models and machine learning algorithms in the fault recognition on power lines with branches. 2023. Citations: 22

  4. Loskutov, A. (2023). Enhanced readability of electrical network complex emergency modes provided by data compression methods. 2023. Citations: 12

  5. Loskutov, A. (2023). Application of search algorithms in determining fault location on overhead power lines according to the emergency mode parameters. 2023. Citations: 15

  6. Loskutov, A. (2022). WSPRT methods for improving power system automation devices in the conditions of distributed generation sources operation. 2022. Citations: 20

  7. Loskutov, A. (2022). Relay protection and automation algorithms of electrical networks based on simulation and machine learning methods. 2022. Citations: 30

 

Divyanee Garg | Mathematics | Research Excellence Award

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Ms. Divyanee Garg | Mathematics | Research Excellence Award

Ms. Divyanee Garg | Mathematics | Research Excellence Award | PhD Scholar | Indian Institute of Technology in Delhi | India

Ms. Divyanee Garg is an emerging researcher in quantitative finance and mathematical optimization, currently pursuing her Ph.D. in Mathematics at IIT Delhi, where she works on portfolio optimization, behavioural finance, robust allocation models, and data-driven decision techniques. Her academic journey reflects exceptional consistency, beginning with a strong foundation in Mathematics through her B.Sc. from S. S. Jain Subodh College, Jaipur, followed by an M.Sc. in Mathematics from IIT Roorkee, and culminating in her doctoral research supported by prestigious recognitions. Ms. Divyanee Garg has demonstrated outstanding academic excellence through multiple national-level achievements, including selection under the Prime Minister’s Research Fellows (PMRF) scheme and securing AIR 119 in CSIR-UGC NET (JRF), AIR 210 in GATE Mathematics, AIR 155 in JAM, and receiving the INSPIRE Scholarship from DST for five consecutive years. Professionally, she has contributed significantly as a teaching assistant in diverse mathematical domains such as Financial Mathematics, Fuzzy Sets, Optimization Methods, Econometrics, and Machine Learning, handling both undergraduate and postgraduate teaching responsibilities at IIT Delhi. Her research interests include portfolio optimization under risk measures like Expectile VaR and CVaR, cumulative prospect theory, robust optimization with neural networks, numerical optimization, and large-scale computational methods. Research skills demonstrated by Ms. Divyanee Garg include expertise in Python, R, MATLAB, LaTeX, MS Excel, and the formulation of optimization models using advanced mathematical programming techniques. She has published impactful research in reputed international journals such as Computational and Applied Mathematics and Omega, with additional manuscripts under revision. Her work has been showcased at major academic platforms, including the International Symposium at ISI Delhi, the International Conference on Computations and Data Science at IIT Roorkee, the Annual Convention of ORSI at IIT Bombay, and the EURO Conference in the UK. She has also engaged in summer schools and workshops related to large-scale optimization, strengthening her methodological foundations and collaborative experience. Her academic distinctions include district-level awards and formal recognition for academic excellence. In conclusion, Ms. Divyanee Garg exemplifies a strong blend of analytical capability, high-quality research output, and dedicated academic service, making her a promising researcher in quantitative finance and optimization. Her continuous contributions through publications, teaching, international presentations, and interdisciplinary problem-solving reflect her commitment to advancing scientific knowledge, while her growing expertise positions her for impactful leadership roles in research, innovation, and academic communities.

Profile: ORCID | Scopus | Google Scholar

Featured Publications 

  1. Garg, D., & Mehra, A. (2026). Portfolio optimization with expectile value at risk and conditional value at risk: Deviation measure and robust allocation. Computational and Applied Mathematics.

  2. Garg, D., Khan, A. Z., & Mehra, A. (2026). Enhanced indexing using cumulative prospect theory utility function with expectile risk.

  3. Garg, D., Sehgal, R., & Mehra, A. (n.d.). Data-driven approach to robust portfolio optimization using deep neural networks. Manuscript under revision.

  4. Garg, D., & Swaminathan, A. (n.d.). Numerical improvement of Gauss–Chebyshev quadrature rule. Unpublished research study.

  5. Garg, D., & Gupta, S. K. (n.d.). Optimality and duality conditions for semi-infinite programming problems. Project report.

  6. Garg, D. (n.d.). Robust allocation models using behaviour-driven portfolio optimization. Working paper.

  7. Garg, D. (n.d.). Machine learning-assisted optimisation frameworks for financial decision making. Working paper.