Sara Tehsin | Deep learning | Best Researcher Award

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Ms. Sara Tehsin | Deep learning | Best Researcher Award

PhD Student, National University of Sciences and Technology, Islamabad, Pakistan

Sara Tehsin is a motivated and results-driven professional with over ten years of experience in Image Processing and Machine Learning. As an Engineering Lecturer at HITEC University in Taxila, Pakistan, she excels in delivering high-quality educational experiences and has a proven track record of producing outstanding results through her strong work ethic, adaptability, and effective communication skills. She is passionate about academic development and seeks opportunities to contribute her expertise while furthering her professional growth. 📚💻

Publication Profile

Google Scholar

Education

Sara Tehsin is currently pursuing a PhD in Computer Engineering at the National University of Sciences and Technology (NUST), Islamabad, where she has achieved a remarkable GPA of 3.83/4.00. Her research focuses on Digital Forensics, Deep Learning, and Digital Image Processing. She holds a Master’s degree in Computer Engineering from NUST, where she graduated with a GPA of 3.7/4.0, and a Bachelor’s degree from The Islamia University of Bahawalpur, with a GPA of 3.36/4.00. 🎓🌟

Experience

Sara has extensive teaching experience, currently serving as an Engineering Lecturer at HITEC University since September 2019, where she develops engaging curriculum and delivers lectures aligned with international standards. Previously, she was a Computer Science Lecturer at Sharif College of Engineering and Technology, and she also served as a Teaching Assistant at NUST and a Lab Engineer at Foundation University. Her roles have encompassed curriculum development, practical instruction, and student support in various computer science subjects. 👩‍🏫🔧

Research Interests

Sara’s research interests encompass Digital Forensics, Deep Learning, Digital Image Processing, and Machine Learning. She focuses on developing innovative solutions for image recognition and forgery detection, contributing significantly to the fields of computer vision and machine learning. Her work aims to enhance the accuracy and efficiency of image processing systems. 🧠🔍

Publications

Self-organizing hierarchical particle swarm optimization of correlation filters for object recognition
S. Tehsin, S. Rehman, M.O.B. Saeed, F. Riaz, A. Hassan, M. Abbas, R. Young, …
IEEE Access, 5, 24495-24502 (2017)
Cited by: 21

Improved maximum average correlation height filter with adaptive log base selection for object recognition
S. Tehsin, S. Rehman, A.B. Awan, Q. Chaudry, M. Abbas, R. Young, A. Asif
Optical Pattern Recognition XXVII, 9845, 29-41 (2016)
Cited by: 18

Fully invariant wavelet enhanced minimum average correlation energy filter for object recognition in cluttered and occluded environments
S. Tehsin, S. Rehman, F. Riaz, O. Saeed, A. Hassan, M. Khan, M.S. Alam
Pattern Recognition and Tracking XXVIII, 10203, 28-39 (2017)
Cited by: 12

Comparative analysis of zero aliasing logarithmic mapped optimal trade-off correlation filter
S. Tehsin, S. Rehman, A. Bilal, Q. Chaudry, O. Saeed, M. Abbas, R. Young
Pattern Recognition and Tracking XXVIII, 10203, 22-37 (2017)
Cited by: N/A

Dongbeom Kim | Artificial Intelligence | Best Researcher Award

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Mr. Dongbeom Kim | Artificial Intelligence | Best Researcher Award

Master’s Student, University of Seoul, South Korea

Dongbeom Kim is a dedicated Master’s student at the University of Seoul, specializing in Geoinformatics under the mentorship of Professor Chulmin Jun. With a robust academic background in Geography and a passion for innovative research, Dongbeom is actively engaged in developing smart systems for urban planning and vehicle safety. His work spans advanced studies in fire evacuation simulations, the application of artificial intelligence in urban growth modeling, and the development of safe driving systems for two-wheeled vehicles. 📊🛵

Publication Profile

Strengths for the Award:

  1. Academic Background: Dongbeom Kim has a solid educational foundation in Geography and Geoinformatics, with high GPAs in both his undergraduate and current Master’s studies. His ongoing education in Geoinformatics at the University of Seoul under the guidance of a reputed advisor further strengthens his research credentials.
  2. Research Publications: He has authored several papers published in reputable SCIE/ESCI journals like Sensors and Applied Sciences, along with multiple domestic publications. His research spans various topics, including fire evacuation simulations, vehicle safety, and urban growth modeling, indicating a diverse research portfolio.
  3. Conferences and Presentations: Dongbeom Kim has actively presented his research at several international and national conferences, such as the 18th International Conference on Location Based Services in Belgium and the Korean Society for Geospatial Information Science. These experiences highlight his engagement with the academic community and his ability to communicate his research effectively.
  4. Patents and Innovation: He is a co-inventor on four patents related to vehicle safety and route generation, demonstrating innovation and practical application of his research.
  5. Research Projects: Participation in multiple research projects, including those focused on greenhouse gas emission reduction and environmental big data analysis, shows his capability to contribute to significant scientific endeavors.

Areas for Improvement:

  1. Research Leadership: While Dongbeom Kim has collaborated on numerous projects and publications, there is limited evidence of him taking on a leading role in these efforts. Demonstrating more leadership in research projects or publications could strengthen his profile.
  2. Diversity in Research Impact: Although his research covers a range of topics, the majority are closely related to vehicle safety and geospatial data analysis. Expanding his research to cover other areas of geoinformatics or interdisciplinary applications could enhance the breadth of his research impact.
  3. Published Impact Factor: As some of his research is still under review and the impact factors of the journals in which he has published are not mentioned, highlighting the impact factor or citation index of his published work could further substantiate his research quality.

 

Education

Dongbeom holds a Bachelor’s degree in Geography from Kongju National University (2015-2021), achieving a GPA of 3.9/4.5. He is currently pursuing a Master’s degree in Geoinformatics at the University of Seoul, where he has achieved an impressive GPA of 4.33/4.5. 🎓🌍

Experience

Dongbeom’s experience includes multiple research projects, focusing on geospatial information science, urban growth modeling, and traffic safety. He has contributed to several conferences and published numerous peer-reviewed articles in international journals. His practical skills are reinforced by his active involvement in projects such as the development of a good driving evaluation system for two-wheeled vehicles and environmental big data analysis. 🌐📝

Research Focus

Dongbeom’s research primarily revolves around geoinformatics, fire evacuation simulations, urban growth modeling, and traffic safety. He is particularly interested in utilizing sensor-based approaches and artificial intelligence techniques to address urban challenges and enhance public safety. 🚒🌆

Awards and Honors

Dongbeom has presented his work at prestigious international and domestic conferences and has collaborated on innovative projects that have received national attention. He is also recognized for his contributions to patents related to traffic safety and environmental management. 🏆🔬

Publication Top Notes

Under Review: Dongbeom Kim, Hyemin Kim, Yuhan Han, Chulmin Jun, “Fire Evacuation Simulation with Agent-Based Fire Recognition Propagation” (Physica Scripta, 2024)

Dongbeom Kim, Hyemin Kim, Suyun Lee, Qyoung Lee, Minwoo Lee, Jooyoung Lee, Chulmin Jun, “Design and Implementation of a Two-Wheeled Vehicle Safe Driving Evaluation System” (Sensors, 2024) – Cited by 2 articles

Dongbeom Kim, Hyemin Kim, Chulmin Jun, “The Detection of Aggressive Driving Patterns in Two-Wheeled Vehicles Using Sensor-Based Approaches” (Applied Sciences, 2023) – Cited by 3 articles

Minjun Kim, Dongbeom Kim, Daeyoung Jin, Geunhan Kim, “Application of Explainable Artificial Intelligence (XAI) in Urban Growth Modeling: A Case Study of Seoul Metropolitan Area, Korea” (Land, 2023) – Cited by 5 articles

Suyun Lee, Dongbeom Kim, Chulmin Jun, “Calculation of Dangerous Driving Index for Two-Wheeled Vehicles Using the Analytic Hierarchy Process” (Applied Sciences, 2023) – Cited by 1 article

Minjun Kim, Dongbeom Kim, Geunhan Kim, “Examining the Relationship between Land Use/Land Cover (LULC) and Land Surface Temperature (LST) Using Explainable Artificial Intelligence (XAI) Models: A Case Study of Seoul, South Korea” (International Journal of Environmental Research and Public Health, 2022) – Cited by 4 articles 📖🔗

Conclusion:

Dongbeom Kim appears to be a promising candidate for the “Best Researcher Award” due to his solid academic background, active research publication record, involvement in innovative patents, and participation in impactful research projects. To further strengthen his candidacy, he could focus on assuming leadership roles in his research, diversifying his research impact, and emphasizing the citation metrics of his work. Overall, his contributions to the field of geoinformatics and vehicle safety suggest he is a strong contender for this award.

Rahma Mani | Artificial Intelligence | Women Researcher Award

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Ms. Rahma Mani | Artificial Intelligence | Women Researcher Award

PhD student, Escuela Técnica Superior de Ingeniería Informática, ETSII, Spain

Rahma Mani is a dedicated Ph.D. candidate in Electrical Engineering and Computer Science at the University of Seville, Spain, with a deep passion for wireless sensor networks, machine learning, and artificial intelligence. With a strong foundation in electrical engineering from the National Engineering School of Monastir, Tunisia, she has demonstrated her expertise through various academic and professional roles. Rahma has contributed to significant research projects and has a keen interest in innovative technologies.

Publication Profile

 

Strengths for the Award:

  1. Academic Excellence: Rahma is currently pursuing a Ph.D. in Electrical Engineering and Computer Science, focusing on cutting-edge fields such as wireless sensor networks, machine learning, and artificial intelligence. Her educational background is robust and well-aligned with emerging technological fields.
  2. Research Contributions: Rahma has multiple publications in reputable journals and conferences, including a submission to the prestigious Pervasive and Mobile Computing Journal by Elsevier. Her research in wireless sensor networks demonstrates innovation and contributes significantly to the field.
  3. Global Perspective: Rahma’s North African upbringing combined with her international academic and professional experiences in Spain, Italy, France, and Tunisia give her a unique global perspective. This diversity enhances her ability to approach problems from different angles, which is a valuable asset in research.
  4. Technical Skills: She possesses a wide range of digital and programming skills, including proficiency in languages like Java, C++, and MATLAB, as well as experience with technologies such as Vivado and Arduino. These skills are critical for her research and development work.
  5. Leadership and Innovation: Rahma demonstrated leadership in her role as the Electrical Committee leader in the ENIM TEAM, where she led the development of an electric car for an international competition. Her involvement in volunteer activities also highlights her leadership abilities and commitment to social causes.
  6. Language Proficiency: Fluent in English, Arabic, and French, with basic Spanish, Rahma’s multilingual capabilities are a significant asset in collaborative international research.

Areas for Improvement:

  1. Broader Research Exposure: While Rahma has a strong publication record, expanding her research impact by collaborating on interdisciplinary projects or participating in more international conferences could further enhance her profile.
  2. Advanced Certifications: Although Rahma has quality management certifications, pursuing advanced certifications related to her research areas (e.g., specialized AI or wireless communication certifications) could strengthen her expertise.
  3. Industry Collaboration: Increasing her engagement with industry partners, beyond internships, through joint research projects or consulting roles could provide practical applications for her research, enhancing its relevance and impact.

 

🎓 Education:

Rahma is currently pursuing her Ph.D. in Electrical Engineering and Computer Science at the University of Seville, Spain, specializing in wireless sensor networks, machine learning, and artificial intelligence. She earned her Electrical Engineering Diploma from the National Engineering School of Monastir, Tunisia, where she also led a team in designing and developing an electric car for an international competition. Rahma began her academic journey with preparatory engineering studies at the Preparatory Institute for Engineering Studies of Monastir, Tunisia.

💼 Experience:

Rahma has gained extensive experience as an adjunct professor at the Higher Institute of Applied Sciences and Technology of Mahdia, Tunisia, where she taught courses on digital signal processing, converters, and electrical machines. She also worked as a Junior Full Stack Engineer at HRDatabank Tunisia (WILL Group, Japan), contributing to the development of HR web applications. Additionally, Rahma has completed internships at Smart Sensors Systems (3S) in Nancy, France, and the Tunisian Electricity and Gas Company in Sousse, Tunisia.

🔬 Research Focus:

Rahma’s research focuses on wireless sensor networks, particularly in the areas of localization algorithms, edge computing, and FPGA-enhanced systems. She is passionate about applying machine learning and artificial intelligence techniques to improve the efficiency and reliability of sensor networks, especially in large-scale and industrial applications.

🏆 Awards and Honors:

Rahma received a merit-based fellowship to pursue her Ph.D. internship in Italy and Spain, recognizing her outstanding academic and research achievements.

📚 Publication Top Notes:

Localizing Unknown Nodes with an FPGA-Enhanced Edge Computing UAV in Wireless Sensor Networks: Implementation and Evaluation (2024)

Improved 3D localization algorithm for large-scale wireless sensor networks (2023).

Improved Distance vector-based Kalman Filter localization algorithm for wireless sensor network (2023) .

CRT-LoRa: An efficient and reliable MAC scheme for real-time industrial applications (2023).

Improved Least-Square DV-Hop Algorithm for Localization in Large Scale Wireless Sensor Network (2022) .

 

Conclusion:

Rahma Mani is a well-qualified candidate for the Research for Women Researcher Award. Her solid academic background, impressive research contributions, technical expertise, and leadership qualities make her a strong contender. With continued focus on expanding her research impact and industry collaborations, she is likely to make significant contributions to the field of Electrical Engineering and Computer Science, particularly in the areas of wireless sensor networks and AI. Her application for the award would be well-justified, showcasing both her achievements and potential for future advancements.

 

 

Avirup Roy | Machine Learning |Machine Learning Research Award

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Mr. Avirup Roy | Machine Learning |Machine Learning Research Award

PhD Student, Michigan State University, United States

Dr. Avirup Roy is a dedicated researcher and engineer specializing in networked embedded and wireless systems. Currently pursuing his PhD at Michigan State University, his work focuses on developing self-learning mechanisms for embedded hardware systems with limited computational resources. With a solid foundation in electronics and communication engineering, Avirup has gained extensive experience in both academia and industry, contributing to projects ranging from smart malaria detection to automated power management systems. His technical skills span machine learning, embedded systems, cloud computing, and web development. Beyond his professional life, Avirup is passionate about Indian classical music, photography, and swimming. 🌟📚🎵📷🏊‍♂️

Profile

ORCID

 

Education🎓

Michigan State University, East Lansing, MI, US PhD in Electrical and Computer Engineering (2020-Present). Dissertation: Self-learning mechanisms for Embedded hardware systems with limited computational resources. GPA: 3.75/4Maulana Abul Kalam Azad University of Technology, Kolkata, WB, India Bachelor of Technology (BTech) in Electronics and Communication Engineering (2013-2017)

Experience💼

Graduate Research Assistant, Michigan State University (Sep 2020 – Jul 2023),Developed an android and website application for smart malaria detection involving cloud database integration. Graduate Teaching Assistant, Michigan State University (Aug 2023 – Present), Instructed and graded labs for Embedded Cyber-physical Systems, VLSI Systems, and Digital Control courses. ICER Cloud Computing Fellow, Michigan State University (Sep 2023 – Present), Implemented Azure cloud resources in semi-supervised federated learning for embedded devices. Programmer Analyst, Cognizant Technology Solutions (Dec 2017 – Jul 2020), Developer and support analyst for ASP.NET based applications of MetLife Inc. Intern, Calcutta Electric Supply Corporation (CESC) Limited (Jul 2016 – Aug 2016), Worked on automated power management systems using SCADA communication. Intern, Bharat Sanchar Nigam Limited (BSNL) (Jun 2015 – Aug 2015), Explored general trends in wireless communication. Undergraduate Researcher, Maulana Abul Kalam Azad University of Technology (2015-2016), Presented research at various international conferences and served as the vice-president of SPIE Student Chapter.

Research Interests🔍

Embedded Machine Learning: Focused on developing efficient learning algorithms for resource-constrained devices.
Networked Embedded Systems: Exploring self-learning mechanisms and their applications in real-world scenarios.
Cloud Computing: Leveraging cloud resources for semi-supervised federated learning.
VLSI Systems: In-depth study and teaching of Very-Large-Scale Integration systems.
Cyber-Physical Systems: Research on embedded systems interacting with physical processes.

Awards🏆

National Social Entrepreneurship Programme (2014): Secured 2nd position for the ‘Hand-Made Paper Industry’ project.
SPIE Smart Structures and Non-destructive Evaluation Conference (2016): Presented research in Las Vegas, Nevada.
EAPE Conference (2015): Presented research on emerging areas of photonics and electronics.
Graduate Fellowships: Awarded multiple fellowships during PhD for research and teaching excellence.

Publications

Semi-Supervised Learning Using Sparsely Labelled Sip Events for Online Hydration Tracking Systems
A. Roy, H. Dutta, A. K. Bhuyan, and S. K. Biswas, 2023, International Conference on Machine Learning and Applications (ICMLA).
Cited by: 3 articles.

An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation
Roy, A., Dutta, H., Griffith, H., & Biswas, S., 2022, Sensors.
Cited by: 5 articles.

Hesham A. Sakr | Artificial Intelligence | Best researcher award

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Assist Prof Dr. Hesham A. Sakr | Artificial Intelligence | Best researcher award

Assistant professor, Assistant professor -Nile higher institute of engineering and technology -Mansoura -Egypt

📡 Hesham Ali Sakr is an Assistant Professor and Researcher specializing in Communication Networks and Cybersecurity. He earned his Ph.D. in Electrical, Electronics, and Communications Engineering from Mansoura University, Egypt. Dr. Sakr’s research focuses on optimizing wireless technologies for multimedia services, VoIP systems, and LTE-A networks. His contributions to the field are recognized through multiple publications in prestigious journals. He is actively involved in advancing the state-of-the-art in 5G and beyond communication technologies.

Profile

Google Scholar

 

Education

🎓 Ph.D. in Electrical, Electronics, and Communications Engineering
Mansoura University, Egypt (June 2016 – April 2020)
Thesis: Development of Accessing Multimedia Services over Wireless Technologies
GPA: 3.55/4

🎓 M.Sc. in Electrical, Electronics, and Communications Engineering
Mansoura University, Egypt (June 2010 – September 2014)
Thesis: Development of VoIP Systems using MPLS
GPA: 3.6/4

🎓 B.Sc. in Networks and Communications Engineering
Higher Technological Institute of Engineering, 10th of Ramadan, Egypt (September 2004 – August 2009)
Excellent with Honor Degree (84.9%)
Graduation Project Grade: Excellent

Experience

Specializing in Communication Networks and Cybersecurity, Dr. Sakr has significant academic and research experience. His work primarily focuses on enhancing wireless communication technologies, particularly in the realms of 5G and multimedia services. He has been affiliated with Mansoura University, contributing to various research projects and publications.

Research Interests

Dr. Sakr’s research interests encompass Communication Networks, Cybersecurity, and the development of efficient multimedia services over wireless technologies. His work includes performance evaluation of HARQ mechanisms, IPv6 multimedia management, and power-efficient mechanisms for LTE-A networks. He is particularly focused on optimizing handover management in LTE-A networks and evaluating VoIP versus VoMPLS performance.

Awards

Dr. Hesham Ali Sakr has been recognized for his outstanding contributions to the field of Communication Networks and Cybersecurity. His research achievements and academic excellence have earned him a commendable reputation among peers and colleagues in the industry.

Publications

📚 H.A. Sakr, and M.A. Mohamed, “Performance Evaluation Using Smart: HARQ Versus HARQ Mechanisms Beyond 5G Networks,” Wireless. Pers. Communication (Springer), June 2019. Cited by 26 articles

📚 Abeer Twakol Khalil, A. I. Abdel-Fatah and Hesham Ali Sakr, “Rapidly IPv6 multimedia management schemes based LTE-A wireless networks,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 4, 2018. Cited by 32 articles

📚 H. A. Sakr, A. I. Abdel-Fatah, A. T. Khalil, “Performance Evaluation of Power Efficient Mechanisms on Multimedia over LTE-A Networks,” International Journal on Advanced Science, Engineering and Information Technology (IJASEIT), vol. 9, no. 4, 2019. Cited by 18 articles

📚 H.A. Sakr and M.A. Mohamed, “Handover Management Optimization over LTE-A Network using S1 and X2 handover,” Proc. of The Seventh International Conference on Advances in Computing, Electronics and Communication – ACEC 2018, 2018. Cited by 15 articles

📚 M. Abdel-Azim, M., Awad, M. M., & Sakr, H. A., “VoIP versus VoMPLS Performance Evaluation,” International Journal of Computer Science Issues (IJCSI), 11(1), 2014. Cited by 20 articles

Gabriella d’Albenzio | Artificial Intelligence | Best Researcher Award

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Dr. Gabriella d’Albenzio | Artificial Intelligence | Best Researcher Award

Postdoc, Perk Lab Perk Lab Laboratory for Percutaneous Surgery, Canada

🎓 Gabriella d’Albenzio is a talented researcher with a focus on biomedical engineering and medical imaging. Currently pursuing a Ph.D. in Informatics at the University of Oslo, she has an impressive background in clinical engineering and biomedical engineering. Gabriella has worked on cutting-edge projects related to image-guided therapies and deep learning for medical applications, contributing significantly to her field through both research and development.

Profile

Scopus

 

Education

📚 Gabriella d’Albenzio holds a Ph.D. in Informatics from the University of Oslo (2021-2024). She completed her M.Sc. in Biomedical Engineering and B.Sc. in Clinical Engineering at Sapienza University of Rome, Italy, reflecting a solid foundation in both engineering and medical sciences.

Experience

💼 Gabriella d’Albenzio has extensive experience as a Scientific Software Developer at The Intervention Centre in Oslo, Norway, and as a Research Assistant at NTNU. She has also interned at the Rehabilitation Bioengineering Lab in Rome, contributing to various research projects involving advanced medical imaging and deep learning technologies.

Research Interests

🧠 Gabriella’s research interests are centered around enhancing surgical planning and medical imaging through deep learning and advanced computational techniques. Her work focuses on developing algorithms for medical image segmentation and predictive models for surgical outcomes, aiming to improve patient-specific treatment strategies.

Awards

🏅 Gabriella d’Albenzio has been recognized with the Globalink Research Internship by Mitacs, Canada, and a Grant Research Stay Abroad by The Research Council of Norway. These awards highlight her outstanding contributions to research and her commitment to advancing biomedical engineering.

Publications

Optimizing Surgical Plans for Parenchyma-Sparing Liver Resections through Contour-Guided Resection and Surface Approximation

Using NURBS for Virtual Resections in Liver Surgery Planning: A Comparative Usability Study

Patient-Specific Functional Liver Segments Based on Centerline Classification of the Hepatic and Portal Veins

ALive: Analytics for Computation and Visualization of Liver Resections

Laparoscopic Parenchyma-Sparing Liver Resection for Large (≥50 mm) Colorectal Metastases

Lourdes Swentek | Artificial Intelligence | Best Researcher Award

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Assist Prof Dr. Lourdes Swentek | Artificial Intelligence | Best Researcher Award

Academician/Research Scholar, UCI, United States

Dr. Lourdes Swentek is a highly accomplished trauma and critical care surgeon with extensive experience in surgical research and education. She completed her fellowship in Critical Care at the University of California, Irvine, and her residency in Surgery at Loma Linda University Health. Dr. Swentek has been recognized for her outstanding contributions to trauma and acute care surgery, earning numerous awards and accolades throughout her career. Her research interests focus on islet transplantation, oxidative stress in pancreatitis, and innovative surgical techniques.

Profile

Scopus

 

Education

🎓 Dr. Lourdes Swentek completed her Critical Care fellowship at the University of California, Irvine, and her Surgical Residency at Loma Linda University Health. She also served as a Research Resident in the Department of Surgery at the University of California, Irvine, where she focused on islet transplantation.

Experience

🔬 Dr. Lourdes Swentek’s professional journey includes a fellowship in Critical Care at the University of California, Irvine, and a surgical residency at Loma Linda University Health. She has significant research experience in islet transplantation and surgical innovation, having contributed to several impactful research projects and publications.

Research Interests

🧪 Dr. Lourdes Swentek’s research interests encompass islet transplantation, oxidative stress in pancreatitis, and the development of novel surgical techniques. Her work has contributed to advancing knowledge and improving practices in these areas, making a notable impact on the field of trauma and critical care surgery.

Awards

🏆 Dr. Lourdes Swentek has received numerous awards, including the East Oriens Award for her career in Trauma and Acute Care Surgery in 2018, the Highest Resident Absite Score at Loma Linda University Health in 2017, and the UCI School of Medicine Achievement Award for Clinical Science Lecturer in 2022. These accolades reflect her dedication and excellence in her field.

Publications

The Addition of a Nurse Practitioner to an Inpatient Surgical Team Results in Improved Utilization of Resources

Medium and Long-term Outcomes after Pneumatic Dilation or Laparoscopic Heller Myotomy for Achalasia: A Meta-analysis

Presentation, Diagnosis, and Treatment of Oesophageal Motility Disorders

Role of Oxidative Stress in the Pathogenesis of Pancreatitis: Effect of Antioxidant Therapy

Total Pancreatectomy and Islet Auto Transplantation for Chronic Pancreatitis

 

Xiaohui Huang | Artificial Intelligence| Best Researcher Award

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Assoc Prof Dr. Xiaohui Huang | Artificial Intelligence| Best Researcher Award

Dean, East China jiaotong university, Japan

👨‍🏫 Dr. Xiaohui Huang is an Associate Professor at the School of Information Engineering, East China Jiaotong University. He earned his PhD from the School of Computer Science, Harbin Institute of Technology in November 2014. He has been a visiting scholar at the German Cancer Research Center and Nanyang Technological University. Dr. Huang has been leading several high-impact research projects funded by national and provincial bodies. He is an expert reviewer for various prestigious journals and a member of notable academic associations.

Profile

Scopus

 

Education

🎓 PhD in Computer Science, Harbin Institute of Technology, November 2014, German Cancer Research Center, December 2010 – October 2011, School of Computer Science and Engineering, Nanyang Technological University, November 2017 – November 2018

Experience

💼 Associate Professor, School of Information Engineering, East China Jiaotong University, January 2018 – Present
Lecturer, School of Information Engineering, East China Jiaotong University, December 2014 – December 2017
Visiting Scholar, Nuclear Medicine Research Group, German Cancer Research Center, December 2010 – October 2011
Software Engineer, Yichun Branch, China Telecom, August 2008 – February 2010

🔬 Research Interests

Deep Learning. Remote Image Analysis. Intelligent Transportation

🏆 Awards

Principal Investigator for various prestigious research projects including the National Natural Science Foundation of China and Jiangxi Province Natural Science Foundation.

 Publications

Multi-view dynamic graph convolution neural network for traffic flow prediction. Expert Systems With Applications, 2023 (SCI Zone 1 top)
Cited by: 15 articles

MAPredRNN: Multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion. Applied Intelligence, 2023 (SCI Zone 2)
Cited by: 10 articles

SS-TMNet: Spatial–Spectral Transformer Network with Multi-Scale Convolution for Hyperspectral Image Classification. Remote Sensing, 2023 (SCI Zone 2, top)
Cited by: 8 articles

Multi-mode dynamic residual graph convolution network for traffic flow prediction. Information Sciences, 2022 (SCI Zone 1 top)
Cited by: 20 articles

A time-dependent attention convolutional LSTM method for traffic flow prediction.