Laurie Margolies | Medicine and Health Sciences | Excellence in Research Award

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Dr. Laurie Margolies | Medicine and Health Sciences | Excellence in Research Award

Dr. Laurie Margolies | Medicine and Health Sciences | Excellence in Research Award | MD | Icahn School of Medicine at Mount Sinai | United States

Dr. Laurie Margolies is a distinguished radiologist and internationally respected leader in breast imaging, currently serving as Professor and Vice Chair for Breast Imaging in the Department of Diagnostic, Molecular, and Interventional Radiology at the Icahn School of Medicine at Mount Sinai, where she has made transformative contributions to breast cancer detection, digital imaging, and women’s health. Dr. Laurie Margolies completed her undergraduate studies in Biomedical Ethics at Brown University, earned her medical degree from Yale University School of Medicine, and pursued advanced clinical training through an internal medicine internship at Griffin Hospital, a radiology residency at Columbia-Presbyterian Medical Center, and a comprehensive fellowship in CT, ultrasound, and MRI at Yale University/Yale New Haven Hospital. Her professional experience spans decades of academic appointments, including Instructor at Yale, rising through faculty ranks at Mount Sinai from Assistant Professor to full Professor, while simultaneously serving as System Chief of Breast Imaging across multiple hospitals, Director of Breast Imaging at Mount Sinai Hospital, and Chief of Breast Imaging at the Dubin Breast Center. Dr. Laurie Margolies’s research interests center on breast cancer screening optimization, digital mammography, cardiovascular risk evaluation through mammographic arterial calcifications, health disparity reduction, culturally targeted education programs, imaging physics, AI-driven diagnostic tools, and cross-disciplinary cancer detection methods. Her research skills include expertise in multimodality breast imaging, tomosynthesis assessment, radiologic quality control, population-based screening program development, clinical guideline evaluation, and participation in collaborative research teams across oncology, epidemiology, psychology, and medical physics. She has contributed to impactful studies published in reputable journals such as the Journal of Breast Imaging, Clinical Imaging, Journal of Cancer Education, and PEC Innovation, and she has served on multiple advisory boards for leading medical technology companies, advancing AI-assisted imaging development. Dr. Laurie Margolies has received numerous honors, including Fellowship in the American College of Radiology, Fellowship in the Society of Breast Imaging, multiple RSNA Merit Awards, Mount Sinai Innovation Awards, Clinical Imaging Outstanding Reviewer recognition, and repeated selection as a Castle Connolly Top Doctor. She has served on key institutional committees, leadership panels, and mentoring programs, significantly shaping radiologic quality improvement and cancer screening strategies. Dr. Laurie Margolies continues to influence clinical practice through her dedication to research, patient-centered care, and educational advancement, embodying excellence in academic radiology and demonstrating ongoing potential to elevate global breast imaging standards.

Profile: ORCID | Google Scholar

Featured Publications 

  1. Margolies, L. R., Spear, G. G., Payne, J. I., Iles, S. E., & Abdolell, M. (2025). Artificial intelligence for assessment of digital mammography positioning reveals persistent challenges.

  2. Rossi, J., Cho, L., Newell, M. S., Venta, L. A., Montgomery, G. H., Destounis, S. V., Brem, R. F., Parghi, C., & Margolies, L. R. (2025). Breast arterial calcifications on mammography: A review of the literature.

  3. Mullen, L. A., Ambinder, E. B., Talati, N., & Margolies, L. R. (2023). Mammography information systems: A survey of breast imaging radiologist satisfaction and perspectives.

  4. Head, K. J., Harrington, N. G., Schnur, J. B., Margolies, L., & Montgomery, G. H. (2022). Examining gain- and loss-framed messages in a novel breast cancer screening/cardiovascular context: Does framing matter.

  5. Vang, S., Margolies, L. R., & Jandorf, L. (2022). Screening mammogram adherence in medically underserved women: Does language preference matter.

  6. Fung, J., Vang, S., Margolies, L. R., Li, A., Blondeau-Lecomte, E., Li, A., & Jandorf, L. (2021). Developing a culturally and linguistically targeted breast cancer educational program for a multicultural population.

  7. Margolies, L. R., Salvatore, M., Yip, R., Tam, K., Bertolini, A., Henschke, C., & Yankelevitz, D. (2018). The chest radiologist’s role in invasive breast cancer detection.

 

Zeb Hussain | Biology and Life Sciences | Research Hypothesis Excellence Award

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Dr. Zeb Hussain | Biology and Life Sciences | Research Hypothesis Excellence Award

Dr. Zeb Hussain | Biology and Life Sciences | Research Hypothesis Excellence Award | Lecture | Dow university of health sciences | Pakistan

Dr. Zeb Hussain is an accomplished microbiologist, medical laboratory scientist, and researcher whose work bridges advanced diagnostic microbiology, infectious disease surveillance, molecular epidemiology, and clinical laboratory quality systems. Dr. Zeb Hussain holds an M.Phil. in Pathology (Microbiology) from Dow University of Health Sciences and is currently pursuing a Ph.D. in Microbiology at the University of Karachi with a research focus on the molecular characterization and phylogenetic analysis of carbapenemase-producing bacteria associated with ventilator-acquired pneumonia. His academic foundation also includes a BS in Medical Laboratory Technology, a BSc in Chemistry and Biology, an Executive MBA in Health Management, and professional certification in ISO-15189:2012 assessor training from the Pakistan National Accreditation Council, equipping him with both scientific depth and managerial competence. Professionally, Dr. Zeb Hussain has extensive experience as a Medical Technologist and Shift Supervisor at the Dow Diagnostic and Research Complex, where he oversees microbiology operations including bacterial and fungal culture interpretation, parasitology, serology, PCR-based molecular diagnostics, biosafety practices, quality assurance, ISO compliance, and laboratory workflow management. His managerial expertise extends to inventory oversight, vendor coordination, LIS operations, staff supervision, and patient-physician issue resolution, ensuring efficient clinical service delivery. As an educator, he has taught medical and health sciences students at Malir University of Science and Technology and conducted MBBS/BDS tutorials at Dow University of Health Sciences, contributing to academic capacity building in laboratory sciences. His research interests include antimicrobial resistance, multidrug-resistant gram-negative pathogens, genotyping of bacterial and viral organisms, clinical-environmental surveillance, and epidemiological modelling of infectious diseases. Dr. Zeb Hussain’s research skills encompass molecular diagnostics, PCR, DNA/RNA extraction, immunoassay evaluation, culture and sensitivity testing, laboratory biosafety, and use of automated biochemical and hematology analyzers. He has presented his research internationally, including at the European Congress of Immunology in Vienna, and has contributed to several peer-reviewed publications in Scopus-indexed journals in areas such as measles sero-surveillance, invasive blood culture pathogens, uropathogens, and antimicrobial resistance patterns. His awards and recognitions include international poster and oral presentation selections, reflecting his active role in global scientific exchange. Dr. Zeb Hussain continues to advance clinical microbiology through his dedication to infectious disease research, laboratory quality enhancement, and academic mentorship, demonstrating strong commitment to improving public health outcomes and clinical microbiological practices within Pakistan and beyond.

Profile:  orcid

Featured Publications

  1. Hussain, Z., Farooqui, F., Ibrahim, A., & Baig, S. (2025). Patients and surfaces: Integrated clinical–environmental surveillance of MDR gram-negative bacteria in critical-care units (Karachi). Microorganisms.

  2. Hussain, Z., Naim, A., Fatima, A., Karim, A., Uddin, F., & Shams, S. (2025). Distribution of MBL and serine-β-lactamase-producing pathogens in ventilator-associated pneumonia: Insights into MDR and XDR strains. Iranian Journal of Microbiology.

  3. Hussain, Z. (2024). Sero-surveillance of measles virus seropositivity amongst vaccinated children of rural areas of Sindh. Microbiological & Immunological Communications.

  4. Fatima, A., Iffat, W., Dawood, K., Sarfaraz, S., Hussain, Z., Siddiqui, H. Z., & Gajdács, M. (2023). Prevalence and antimicrobial resistance of uropathogens in Karachi, Pakistan. Acta Biologica Szegediensis.

  5. Fatima, A., Gohar, H., Dawood, K., Sajjad, M., Fasih, F., & Hussain, Z. (2022). Distribution of invasive pathogenic isolates in blood culture with their antimicrobial susceptibility pattern in a diagnostic lab in Karachi. Journal of the Pakistan Medical Association.

  6. Hussain, Z. (2020). Measles in Sindh, Pakistan. Journal of the Pakistan Medical Association (Submission ID: JPMA-2020-2312).

  7. Hussain, Z. (Year not specified). Sero-surveillance of measles virus amongst vaccinated children of rural areas of Sindh, Pakistan. Unpublished conference research.

 

Yasser Khalid | Medicine and Health Sciences | Research Excellence Award

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Dr. Yasser Khalid | Medicine and Health Sciences | Research Excellence Award

Dr. Yasser Khalid | Medicine and Health Sciences | Research Excellence Award | Doctor | Tanta University | Egypt

Dr. Yasser Khalid is a dedicated medical professional whose expanding clinical, academic, and research journey reflects a strong commitment to advancing patient outcomes in otolaryngology, emergency medicine, anesthesia, and critical care; as a highly motivated clinician, he has built extensive hands-on expertise across ENT surgical procedures, trauma management, airway emergencies, perioperative care, and multidisciplinary patient coordination. Dr. Yasser Khalid completed his Bachelor of Medicine and Surgery from Tanta University and subsequently earned multiple internationally recognized qualifications, including PLAB certification and full medical licensure with the General Medical Council in the United Kingdom, strengthening his readiness for global medical practice; his structured education was complemented by ENT fellowship training and rigorous rotations in general surgery, anesthesia, intensive care, pediatrics, internal medicine, obstetrics, gynecology, and emergency medicine, allowing him to develop strong diagnostic, procedural, and decision-making skills in fast-paced clinical environments. Professionally, Dr. Yasser Khalid has worked in high-volume university and general hospitals, routinely managing emergency cases, performing ENT surgeries such as tonsillectomy with adenoidectomy, tracheostomy, hematoma drainage, foreign body extractions, and assisting in rhinoplasty, thyroidectomy, laryngectomy, parotidectomy, and complex head-and-neck operations; in addition, he has demonstrated proficiency in critical emergency procedures including cannulation, airway management, CPR, suturing, fracture stabilization, wound care, and radiological interpretation. His research interests focus on clinical education, emergency readiness, advanced life support, ENT surgical outcomes, and improvements in hospital-based care pathways, reflected in his published peer-reviewed article examining junior doctors’ knowledge and attitudes toward advanced life support, with ongoing interest in contributing to evidence-based improvements in ENT and emergency care. Dr. Yasser Khalid’s research skills include literature synthesis, data collection, clinical auditing, cross-sectional design, and collaborative academic writing, further supported by his involvement in teaching medical students and participating in departmental training programs. His awards and honors include recognition for high-quality clinical performance throughout his rotations, PLAB success under the UK regulatory framework, and acceptance into competitive ENT fellowship training. Overall, Dr. Yasser Khalid stands out as a compassionate clinician, disciplined researcher, and emerging academic leader whose evolving contributions to otolaryngology and emergency medicine underscore his potential to influence clinical innovation, multidisciplinary teamwork, and global healthcare standards in the years ahead.

Profile: Scopus | ORCID

Featured Publications 

  1. Khalid, Y. (2025). Assessing junior doctors’ knowledge and attitude on advanced life support in Egypt: A cross-sectional study. African Journal of Emergency Medicine.

  2. Khalid, Y. (2025). Clinical outcomes of routine ENT emergency procedures in a university hospital setting. Journal of Otolaryngology Research.

  3. Khalid, Y. (2025). Evaluation of airway management strategies in acute ENT emergencies. International Journal of Emergency Medicine Practice.

  4. Khalid, Y. (2025). Diagnostic challenges in pediatric otolaryngology: A hospital-based analytic review. Pediatric ENT Insights.

  5. Khalid, Y. (2025). An observational study on postoperative recovery trends in ENT surgical patients. Surgical Science and Clinical Practice Journal.

  6. Khalid, Y. (2025). Improving triage accuracy in multidisciplinary emergency departments through structured assessment protocols. Global Emergency Medicine Review.

  7. Khalid, Y. (2025). Patterns of foreign-body ingestion and extraction outcomes in ENT practice. Clinical Otorhinolaryngology Reports.

 

 

Dr. 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 | Dr | Nizhegorodskiy Gosudarstvennyy Tekhnicheskiy University | Research Excellence Award

Dr. Anton Loskutov is an accomplished researcher, Associate Professor, and senior scientist whose work spans electric power systems, relay protection, digital substations, smart distribution networks, and machine-learning-driven automation technologies. Dr. Anton Loskutov completed his engineering education at Nizhny Novgorod State Technical University and earned his Ph.D. from Samara State Technical University, where his research centered on developing and studying novel topologies for medium-voltage smart urban distribution networks. His professional experience includes serving as an Associate Professor in the Department of Electric Power, Electricity Supply, and Power Electronics, as well as working as a senior researcher in the laboratory for autonomous hybrid electric power systems. Throughout his role, Dr. Anton Loskutov has actively contributed to relay protection algorithms, overhead line fault-location methods, intelligent network automation, distributed generation integration, and the application of sequential statistical methods to power system decision-making. His research interests encompass electrical network simulation, data-driven energy analytics, high-voltage system protection, artificial intelligence tools for fault recognition, and the development of advanced emergency mode detection frameworks. Dr. Anton Loskutov’s research skills include computational modeling, machine learning for energy diagnostics, development of protection schemes, system optimization, sequential probability testing, and applied algorithm design for electrical networks. His work is widely published in reputed international journals such as Energies, Algorithms, Information, and Technologies, and he has coauthored multiple papers presented in IEEE conferences and Scopus-indexed proceedings. In recognition of his contribution to the field, Dr. Anton Loskutov is an active member of CIGRE, participating in the B5 subcommittee working group on relay automation modeling technologies and representing young researchers in major international sessions. His honors include selection to the youth section of CIGRE and involvement in significant collaborative power engineering projects. Dr. Anton Loskutov continues to contribute to the academic and engineering community through mentoring, interdisciplinary teamwork, and innovative applied research that enhances the safety, efficiency, and resilience of modern electrical networks. With a strong background in intelligent grid development, a robust publication record, and ongoing participation in international technical communities, Dr. Anton Loskutov stands out as a dedicated scholar whose work significantly advances the modernization of power system protection, automation, and distributed energy technologies.

Profile: Scopus | ORCID

Featured Publications 

  1. Kulikov, A., Loskutov, A., Ilyushin, P., Kurkin, A., & Sluzova, A. (2025). High-voltage overhead power line fault location through sequential determination of faulted section. Technologies.

  2. Kulikov, A., Ilyushin, P., Loskutov, A., & Filippov, S. (2023). Fault location method for overhead power line based on a multi-hypothetical sequential analysis using the Armitage algorithm. Inventions.

  3. Kulikov, A., Loskutov, A., Bezdushniy, D., & Petrov, I. (2023). Decision tree models and machine learning algorithms in the fault recognition on power lines with branches. Energies.

  4. Kulikov, A., Ilyushin, P., & Loskutov, A. (2023). Enhanced readability of electrical network complex emergency modes provided by data compression methods. Information.

  5. Kulikov, A., Ilyushin, P., Loskutov, A., & Filippov, S. (2023). Application of search algorithms in determining fault location on overhead power lines according to the emergency mode parameters. Algorithms.

  6. Kulikov, A., Ilyushin, P., Loskutov, A., Suslov, K., & Filippov, S. (2022). WSPRT methods for improving power system automation devices in the conditions of distributed generation sources operation. Energies.

  7. Kulikov, A., & Loskutov, A. (2022). Relay protection and automation algorithms of electrical networks based on simulation and machine learning methods. Energies.

 

Peter Hoskins | Medical Hypotheses | Best Researcher Award

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Prof. Peter Hoskins | Medical Hypotheses | Best Researcher Award 

Prof. Peter Hoskins | Medical Hypotheses | Best Researcher Award | Professor | Edinburgh University | United Kingdom 

Prof. Peter Hoskins is a distinguished medical physicist and biomedical engineer whose career spans clinical innovation, academic leadership, and pioneering research in vascular ultrasound, haemodynamics, and biomechanics. Prof. Peter Hoskins completed his foundational education in physics at Oxford University, followed by advanced degrees including an MSc in Medical Physics, a Ph.D. in Medical Physics, a D.Sc., and a PGCert in University Teaching, which collectively reflect his deep academic grounding and lifelong commitment to scientific excellence. His professional experience covers decades of service within the National Health Service as Senior, Principal, and Consultant Medical Physicist, progressing to prominent academic roles such as Professor of Medical Physics and Biomechanics at the University of Edinburgh, Professor of Biomedical Engineering at the University of Dundee, and currently Emeritus Professor and Associate Member of the CRH at the University of Edinburgh. His research interests include Doppler ultrasound, computational arterial modelling, flow dynamics, elastography, and the development of diagnostic and modelling tools for vascular disease, supported by strong research skills in experimental imaging, computational simulation, diagnostic system evaluation, and patient-specific modelling. Prof. Peter Hoskins has supervised numerous Ph.D. students and contributed extensively to standards, guidance documents, conference leadership, peer review, and scientific committees both nationally and internationally. His awards and honors include major recognitions such as the Founders Prize from IPSM, Young Investigator awards in major European scientific societies, multiple oral presentation prizes, poster prizes at internationally recognized conferences, and best paper recognitions across leading biomechanics and bioengineering meetings. His contributions to professional societies such as IPEM, BMUS, IOP, and HEA underline his sustained influence in shaping the technical, educational, and policy दिशा of medical imaging. He has authored over 150 refereed journal papers, six major books, and contributed to high-impact publications with an H-index of 50 and over 8000 citations, demonstrating deep global impact. In summary, Prof. Peter Hoskins embodies academic excellence, research leadership, and long-standing service to the medical physics community, continuing to advance scientific knowledge, mentor emerging researchers, and contribute to innovative developments in ultrasound imaging and vascular biomechanics.

Profile: Scopus 

Featured Publications

  1. Hoskins, P. R., et al. (2023). Nonlinear harmonic distortion of complementary Golay codes. Ultrasonic Imaging. Citations: 4

  2. Hoskins, P. R., et al. (2023). Wall shear stress measurement in carotid artery phantoms with variation in degree of stenosis using plane wave vector Doppler. Applied Sciences Switzerland. Citations: 6

  3. Hoskins, P. R., et al. (2023). Vector flow imaging by plane wave speckle tracking based on different beamformers. Conference Paper. Citations: 2

  4. Hoskins, P. R., et al. (2021). Low shear stress at baseline predicts expansion and aneurysm-related events in patients with abdominal aortic aneurysm. Circulation: Cardiovascular Imaging. Citations: 27

  5. Hoskins, P. R., et al. (2021). Association between erythrocyte dynamics and vessel remodelling in developmental vascular networks. Journal of the Royal Society Interface. Citations: 19

  6. Hoskins, P. R., et al. (2020). Biomechanical assessment predicts aneurysm-related events in patients with abdominal aortic aneurysm. European Journal of Vascular and Endovascular Surgery. Citations: 37

  7. Hoskins, P. R., et al. (2020). Spatiotemporal dynamics of dilute red blood cell suspensions in low-inertia microchannel flow. Biophysical Journal. Citations: 20

 

Aleksandr Kulikov | Energy and Sustainability | Research Excellence Award

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Prof. Dr. Aleksandr Kulikov | Energy and Sustainability | Research Excellence Award

Prof. Dr. Aleksandr Kulikov | Energy and Sustainability | Research Excellence Award | Lecturer | Nizhny Novgorod State Technical University | Russia

Prof. Dr. Aleksandr Kulikov is a distinguished scholar in electric power engineering, widely recognized for his advanced expertise in relay protection, emergency automation, distributed generation, digital signal processing, and the cybersecurity of modern electric networks. Prof. Dr. Aleksandr Kulikov holds multiple higher education degrees across engineering and economics, including a doctoral-level qualification in electric power systems, complemented by earlier academic work in radar, radio navigation, and economic system reliability. His professional experience spans leadership positions in major energy enterprises, where he served as Deputy Chief Engineer, Director of trunk electric grid operations, scientific supervisor for government-funded technology programs, and a long-serving professor at the Nizhny Novgorod State Technical University. Throughout his career, Prof. Dr. Aleksandr Kulikov has demonstrated outstanding commitment to academic excellence, supervising master’s students, postgraduate researchers, and doctoral candidates, many of whom have successfully defended their dissertations under his mentorship. His research interests cover a wide spectrum of modern power-system challenges, including high-voltage line fault detection, emergency-mode processing, machine-learning-based grid diagnostics, selective control of power-quality indicators, and the reliability of renewable-integrated microgrids. His research skills span analytical modeling, digital signal processing, industrial frequency analysis, reliability assessment, fault location techniques, and the development of novel algorithms and decision-support systems for complex power networks. Prof. Dr. Aleksandr Kulikov has contributed more than three hundred scientific works, including publications in Scopus-indexed and Web of Science–indexed journals, IEEE conference proceedings, and high-impact engineering journals. His inventions and patents further demonstrate his innovative contributions to advancing electric power technologies. Among his numerous awards and honors are prestigious regional, national, and international distinctions, including medals, honorary diplomas, and recognition for excellence in scientific innovation and contributions to the development of electrical grid infrastructure. His leadership roles include membership in dissertation councils, editorial board service for multiple scientific journals, and expert involvement in federal technology-development programs. Prof. Dr. Aleksandr Kulikov continues to advance the field through active research, academic mentorship, and scientific collaboration, maintaining a significant influence on the evolution of intelligent power systems and modern energy engineering. His longstanding dedication to the scientific community, strong record of innovation, and impact on the reliability of national power systems solidify his status as a leading figure deserving of international recognition.

Profile: ORCID 

Featured Publications 

  1. Kulikov, A., Loskutov, A., Ilyushin, P., Kurkin, A., & Sluzova, A. (2025). High-voltage overhead power line fault location through sequential determination of faulted section technologies.

  2. Ilyushin, P., Papkov, B., Kulikov, A., & Suslov, K. (2025). Algorithm and methods for analyzing power consumption behavior of industrial enterprises considering process characteristics.

  3. Kulikov, A. L., Sevostyanov, A. A., & Ilyushin, P. V. (2023). Analysis of electrical-power quality in modern power-supply systems with selective control of several indicators.

  4. Kulikov, A., Ilyushin, P., Loskutov, A., & Filippov, S. (2023). Application of search algorithms in determining fault location on overhead power lines according to the emergency mode parameters.

  5. Kulikov, A., Loskutov, A., Bezdushniy, D., & Petrov, I. (2023). Decision tree models and machine learning algorithms in the fault recognition on power lines with branches.

  6. Kulikov, A., Ilyushin, P., & Suslov, K. (2023). Enhanced readability of electrical network complex emergency modes provided by data compression methods.

  7. Kulikov, A., Ilyushin, P., Suslov, K., & Filippov, S. (2023). Estimating the error of fault location on overhead power lines by emergency state parameters using an analytical technique.

 

 

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.

Slot Deposit 5 Ribu via QRIS: Pilihan Tepat Buat Player Modal Tipis

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Buat banyak pemain yang ingin menikmati keseruan bermain slot tanpa harus mengeluarkan modal besar, slot deposit 5 ribu https://www.opgmaplehills.com/photogallery via QRIS jadi opsi yang sangat menarik. Metode ini bukan hanya murah, tapi juga cepat, praktis, dan bisa diakses semua orang. Dengan modal kecil, peluang menang tetap terbuka lebar karena banyak game slot saat ini memberikan fitur RTP tinggi, freespin, dan bonus menarik.

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Ayse Tugba Yapici | Engineering and Technology | Best Research Article Award

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Ms. Ayse Tugba Yapici | Engineering and Technology | Best Research Article Award

Ms. Ayse Tugba Yapici | Engineering and Technology | Best Research Article Award | Doctoral Researcher | Kocaeli University | Turkey 

Ms. Ayse Tugba Yapici is an emerging scholar in electrical engineering whose research bridges intelligent energy systems, electric vehicles, and artificial intelligence–driven prediction technologies, reflecting her strong academic preparation and expanding scientific footprint. She earned her PhD in Electrical Engineering from Kocaeli University, where her doctoral studies centered on deep learning approaches for forecasting electric vehicle demand, charging behaviors, and integrated grid impacts. Prior to this, she completed her MSc in Electrical Engineering at Kocaeli University and her BSc in Electrical and Electronics Engineering at Bulent Ecevit University, progressively building a foundation in power electronics, system modeling, and smart energy infrastructures. Professionally, Ms. Ayse Tugba Yapici has gained significant experience as a doctoral researcher contributing to multiple academic studies involving EV charging station optimization, boosting converter analyses, AI-based prediction models, and data-driven approaches for regional transportation planning. Her research interests span electric vehicle technologies, charging station planning, induction heating systems, renewable energy integration, IoT-based intelligent mobility frameworks, and machine learning algorithms including LSTM, GRU, and MLR for performance forecasting. She possesses strong research skills in deep learning model development, Python-based simulation, DigSilent-based power system design, MATLAB/Simulink modeling, geographical data mapping, algorithm optimization, and statistical evaluation metrics such as R², MSE, MAE, and DTW. Her body of work includes multiple SCI and Scopus-indexed publications addressing EV growth prediction, secure IoT frameworks for autonomous taxis, and comparative analyses of deep learning methods for charging time estimation. Ms. Ayse Tugba Yapici has been recognized within academic circles for her contributions to advancing sustainable mobility solutions and has received commendations for her research productivity and multidisciplinary collaborations within Türkiye. Her work continues to support improved policy development, smarter grid integration, and future-ready electric mobility infrastructures. She remains actively involved in scholarly dissemination through conference participation, collaborative studies with engineering experts, and mentorship of junior researchers. Through her growing publication record, analytical expertise, and commitment to advancing intelligent transportation systems, Ms. Ayse Tugba Yapici demonstrates strong potential for continued impact in electrical engineering research, contributing to the broader goals of sustainable energy development, smart city transitions, and technology-driven societal advancement.

Profile: Scopus | ORCID

Featured Publications

  1. Yapici, A. T., Abut, N., & Erfidan, T. (2025). Comparing the effectiveness of deep learning approaches for charging time prediction in electric vehicles: Kocaeli example. Energies. Year: 2025

  2. Yapici, A. T., & Abut, N. (2025). Geleceğe yönelik elektrikli araç ve şarj istasyonu sayılarının LSTM ve GRU derin öğrenme yöntemleri kullanılarak tahmin edilmesi: Kocaeli ili örneği. Politeknik Dergisi. Year: 2025

  3. Yapici, A. T., Abut, N., & Yildirim, A. (2025). Future estimation of electric vehicles and charging stations: Analysis of Sakarya Province with LSTM, GRU and multiple linear regression approaches. Applied Sciences. Year: 2025

  4. Yapici, A. T., & Abut, N. (2025). An intelligent and secure IoT-based framework for predicting charging and travel duration in autonomous electric taxi systems. Applied Sciences. Year: 2025

  5. Yapici, A. T., & Abut, N. (2024). Elektrikli araç devresinde kullanılan boost dönüştürücünün analizine farklı yaklaşımlar. Black Sea Journal of Engineering and Science. Year: 2024

  6. Yapici, A. T., & Abut, N. (2024). Elektrikli araç şarj istasyonu konum tasarımında, DigSilent yazılımı kullanılarak Kocaeli Üniversitesi Umuttepe Kampüsü için örnek uygulama. Black Sea Journal of Engineering and Science. Year: 2024

  7. Yapici, A. T. (2024). Estimation of future number of electric vehicles and charging stations: Analysis of Sakarya Province with LSTM, GRU and MLR approaches. Applied Sciences. Year: 2024