Dehong Gao | Artificial Intelligence | Outstanding Scientist Award

Posted on by sciencefather

Assoc. Prof. Dr. Dehong Gao | Artificial Intelligence | Outstanding Scientist Award

Associate Professor at Northwestern Polytechnical University, China

Dehong Gao is a distinguished expert in the fields of natural language processing, machine learning, and large language models. With over a decade of research and practical experience, he has made significant contributions to advertising technologies, multimodal AI models, and AI-driven e-commerce solutions. Gao currently leads groundbreaking work on multimodal pre-training models, including the award-winning 70B-FashionGPT model. As an associate professor at Northwestern Polytechnical University and a distinguished researcher at Zhejiang University of Technology, Gao continues to advance AI research, particularly in the areas of large language models, multimodal learning, and cross-lingual search. 🚀📚💡

Publication Profile : 

Scopus

Educational Background 🎓

  • Ph.D. in Computer Science from Hong Kong Polytechnic University (2014-2022), under the supervision of Li Wenjie.
  • Master’s in Automation from Northwestern Polytechnical University (2010).
  • Bachelor’s in Automation from Northwestern Polytechnical University (2007).

Professional Experience 💼

Dehong Gao’s career spans both academia and industry. He is currently an Associate Professor at the School of Cyberspace Security at Northwestern Polytechnical University. He also serves as a Distinguished Researcher at the Zhejiang University of Technology Artificial Intelligence Innovation Institute. Gao previously held the position of Senior Algorithm Expert (P8) at Alibaba Group, where he led a team of over 20 full-time algorithm engineers and contributed to the development of large-scale machine translation and AI-driven e-commerce solutions. As an expert in Alibaba AIR Project, Gao has been instrumental in technical breakthroughs related to large model technologies, fine-tuning multimodal models, and advancing AI-based search and advertising systems. 💻📈

Research Interests 🔬

Gao’s research interests are focused on:

  • Large Language Models (LLMs) and Multimodal Learning 🌐🤖
  • Natural Language Processing: Information retrieval, recommendation systems, sentiment analysis, and automated summarization 📑🔍
  • E-commerce AI: Developing search algorithms and multilingual representation learning for cross-border e-commerce applications 🌏🛒
  • Federated Learning and AI-driven personalization in business settings 🔒🤖

He has authored and co-authored several influential papers and has been a leading figure in the development of multimodal AI models for industries such as fashion, e-commerce, and healthcare. His work continues to push the boundaries of AI application in real-world environments. 🏆📚

Publications 📚

  1. Gao, D., Chen, K., Chen, B., et al. (2024). LLMs-based Machine Translation for E-commerce. Expert Systems with Applications, Volume 258 (SCI Zone 1, Top Journal).
  2. Chen, K., Chen, B., Gao, D., Dai, H., et al. (2024). General2Specialized LLMs Translation for E-commerce. The Web Conference (WWW), short paper (CCF-A).
  3. Shen, G., Sun, S., Gao, D., Yang, L., et al. (2023). EdgeNet: Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising. The 32nd ACM International Conference on Information & Knowledge Management (CIKM), (CCF-B).
  4. Gao, D., Ma, Y., Liu, S., Song, M., Jin, L., et al. (2024). FashionGPT: LLM Instruction Fine-tuning with Multiple LoRA-adapter Fusion. Knowledge-Based Systems, Volume 299 (SCI, Top Journal).
  5. Chen, B., Jin, L., Wang, X., Gao, D., et al. (2023). Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval. Industry Track of The Web Conference (WWW), (CCF-A).
  6. Mei, X., Yang, L., Jiang, Z., Cai, X., Gao, D., et al. (2024). An Inductive Reasoning Model Based on Interpretable Logical Rules Over Temporal Knowledge Graphs. Neural Networks, Volume 174, Pages (SCI Zone 1, Top Journal).
  7. Liang, Z., Chen, B., Ran, Z., Wang, Z., Gao, D., et al. (2024). Self-Renewal Prompt Optimizing with Implicit Reasoning. The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) findings (CCF-A).
  8. Yang, Z., Gao, H., Gao, D., Yang, L., et al. (2024). MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction. The 18th ACM Conference on Recommender Systems (RecSys), (CCF-B).
  9. Zhang, X., Wang, D., Gao, D., Jiang, W., et al. (2022). Revisiting Cold-Start Problem in CTR Prediction: Augmenting Embedding via GAN. The 31st ACM International Conference on Information & Knowledge Management (CIKM), (CCF-B).
  10. Zhang, F., Zhang, Z., Gao, D., Zhuang, F., et al. (2022). Mind the Gap: Cross-lingual Information Retrieval with Hierarchical Knowledge Enhancement. The 36th AAAI Conference on Artificial Intelligence (AAAI), (CCF-A).

 

 

 

Swati Jaiswal | Deep Learning | Women Researcher Award

Posted on by sciencefather

Mrs. Swati Jaiswal | Deep Learning | Women Researcher Award

Assistant Professor at DES Pune University, Pune, India

Swati Jaiswal, Ph.D. candidate at VIT Vellore, is an experienced Assistant Professor in Computer Engineering with over 14 years of academic and research expertise. Currently, she is serving at the School of Computer Engineering & Technology, DES Pune University. She has held various teaching and administrative roles across esteemed institutions like PCCOE, ZCOER, and SKNSITS, contributing significantly to academic development and research. Swati’s contributions span diverse fields like Machine Learning, Cybersecurity, Autonomous Vehicles, AI, and IoT, reflected in her numerous publications, patents, and book chapters 📚🔍. Swati’s dedication to research and teaching is complemented by a passion for developing innovative solutions to real-world problems 🤖💡.

Publication Profile : 

Google Scholar

Education🎓

Swati holds a Master’s in Computer Science & Engineering with 86% from RGPV, Bhopal (2012), and a BE in the same discipline with 80% (2009). She is currently pursuing a Ph.D. in the field of AI and Machine Learning at VIT Vellore, under the guidance of Dr. Chandra Mohan B. Her academic journey also includes certifications in various fields like Data Science, Machine Learning, and Software Testing 🎓📜.

Professional Experience💼

Swati began her career as an Assistant Professor at SAMCET Bhopal in 2009, where she coordinated seminars and workshops. Over the years, she worked at several prestigious institutions, including SKNSITS, ZCOER, and PCCOE, contributing to curriculum development, departmental coordination, and research activities. Since June 2024, she has been with DES Pune University, where she continues her academic journey while nurturing the next generation of engineers and researchers. Along with teaching, she has overseen various academic and administrative responsibilities, including time-table coordination, research guidance, and university exams 🏫📊.

Research Interests🔬

Her research primarily focuses on Machine Learning, Artificial Intelligence, Cybersecurity, Autonomous Systems, and Internet of Things (IoT). She has explored deep learning models for real-time systems, especially in autonomous driving, vehicle communication systems, and intelligent robotics. Additionally, Swati is passionate about the application of AI and ML in solving complex real-world problems such as fraud detection, data security, and predictive analytics 💻🔐🚗.

Publications Top Notes📚

  1. Jha, R. K., Kumar, A., Prakash, S., Jaiswal, S., Bertoluzzo, M., Kumar, A., Joshi, B. P., & … (2022). Modeling of the resonant inverter for wireless power transfer systems using the novel MVLT method. Vehicles, 4(4), 1277-1287. [34 citations]
  2. Kachhoria, R., Jaiswal, S., Khairnar, S., Rajeswari, K., Pede, S., Kharat, R., … (2023). Lie group deep learning technique to identify the precision errors by map geometry functions in smart manufacturing. The International Journal of Advanced Manufacturing Technology, 1-12. [12 citations]
  3. Kachhoria, R., Jaiswal, S., Lokhande, M., & Rodge, J. (2023). Lane detection and path prediction in autonomous vehicle using deep learning. In Intelligent edge computing for cyber physical applications (pp. 111-127). [11 citations]
  4. Swati Jaiswal, D. C. M. B. (2017). A survey: Privacy and security to Internet of Things with cloud computing. International Journal of Control Theory and Applications, 10(1), 487-500. [7 citations]
  5. Jaiswal, S., & Rodge, J. (2019). Comprehensive overview of neural networks and its applications in autonomous vehicles. In Computational Intelligence in the Internet of Things (pp. 159-173). [6 citations]
  6. Kati, S., Ove, A., Gotipamul, B., Kodche, M., & Jaiswal, S. (2022). Comprehensive overview of DDOS attack in cloud computing environment using different machine learning techniques. In Proceedings of the International Conference on Innovative Computing. [5 citations]
  7. Raut, R., Jadhav, A., Jaiswal, S., & Pathak, P. (2022). IoT-assisted smart device for blind people. In Intelligent Systems for Rehabilitation Engineering (pp. 129-150). [4 citations]
  8. Jaiswal, S., & Desai, M. (2019). Importance of information security and strategies to prevent data breaches in mobile devices. In Improving Business Performance Through Innovation in Digital Economy (pp. 215-225). [4 citations]
  9. Jaiswal, S., & Chandra, M. B. (2023). An efficient real-time decision-making system for autonomous vehicle using timber chased wolf optimization-based ensemble classifier. Journal of Engineering Science and Technology Review, 16(1), 75-84. [3 citations]
  10. Jaiswal, S., & Balasubramanian, C. M. (2023). An advanced deep learning model for maneuver prediction in real-time systems using alarming-based hunting optimization. International Journal of Advances in Intelligent Informatics, 9(2). [2 citations]
  11. Sorde, C., Jadhav, A., Jaiswal, S., Padwad, H., & Raut, R. (2023). Generative adversarial networks and its use cases. In Generative Adversarial Networks and Deep Learning (pp. 1-11). [2 citations]
  12. Rajeswari, K., Vispute, S., Maitre, A., Kharat, R., Aher, N., Vivekanandan, N., … (2023). Time series analysis with systematic survey on COVID-19 based predictive studies during pandemic period using enhanced machine learning techniques. iJOE, 19(07), 161. [2 citations]
  13. Jadhav, A., Raut, R., Jhaveri, R., Patil, S., Jaiswal, S., Katole, A., … (2021). A device for child safety and security. [2 citations]
  14. Jaiswal, S., Prakash, S., Gupta, N., & Rewadikar, D. (n.d.). Performance optimization in ad-hoc networks. International Journal of Computer Technology and Electronics Engineering. [2 citations]
  15. Jaiswal, S., & Mohan, B. C. (2024). Deep learning-based path tracking control using lane detection and traffic sign detection for autonomous driving. Web Intelligence, 22(2), 185-207. [1 citation]
  16. Raut, R., Jadhav, A., Jaiswal, S., Kathole, A., & Patil, S. (2023). Intelligent information system for detection of COVID-19 based on AI. In Proceedings of 3rd International Conference on Recent Trends in Machine Learning and Artificial Intelligence. [1 citation]
  17. Jaiswal, S., Sarkar, S., & Mohan, C. (2017). COT: Evaluation and analysis of various applications with security for cloud and IoT. In Examining Cloud Computing Technologies through Internet of Things (pp. 251-263). [1 citation]
  18. Prakash, S., Saxena, V., & Jaiswal, S. (2016). Smart grid: Optimized power sharing and energy storage system framework with recent trends and future ahead. In Handbook of Research on Emerging Technologies for Electrical Power Planning and Analysis (pp. 1-12). [1 citation]
  19. Jaiswal, S., Gupta, N., & Shrivastava, H. (2012). Enhancing the features of intrusion detection system by using machine learning approaches. International Journal of Scientific and Research Publications, 166. [1 citation]
  20. Kharat, R. S., Kalos, P. S., Kachhoria, R., Kadam, V. E., Jaiswal, S., Birari, D., … (2023). Thermal analysis of fuel cells in renewable energy systems using generative adversarial networks (GANs) and reinforcement learning. [No citation count]

 

 

 

Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

Posted on by sciencefather

Mr. Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

PhD Student at Politecnico di Torino, Italy

Rafael Natalio Fontana Crespo is a dedicated and sociable Ph.D. student specializing in Computer and Control Engineering at Politecnico di Torino. With a strong academic background in mechatronics and practical experience in electrical energy analysis, he is passionate about tackling complex challenges through innovative solutions. 🌐💡

Publication Profile : 

Orcid

 

🎓 Educational Background :

Rafael is currently pursuing a Ph.D. in Computer and Control Engineering at Politecnico di Torino, Italy, since May 2022. He previously obtained a Master’s Degree in Mechatronic Engineering from the same institution, graduating with 110/110 cum laude in July 2022. His master’s thesis focused on designing and developing a distributed software platform for additive manufacturing. Rafael studied Electromechanical Engineering at the Universidad Nacional de Córdoba, Argentina, where he also completed a double degree program.

💼 Professional Experience :

Rafael gained practical experience during his internship at EPEC (Empresa Provincial de Energía de Córdoba) in Argentina, where he worked in the Statistics and Technical Department from May 2020 to May 2021. He was involved in analyzing thermal images of electrical components to prevent failures, contributing to the overall safety and efficiency of electrical systems.

📚 Research Interests : 

Rafael’s research interests lie at the intersection of computer engineering, control systems, and mechatronics, particularly focusing on additive manufacturing, machine learning applications in energy systems, and the optimization of neural networks.

📝 Publication Top Notes :

      1. Fontana Crespo, R.N., E. Patti, S. Di Cataldo, D. Cannizzaro. (2022). Design and Development of a Distributed Software Platform for Additive Manufacturing. Master’s Thesis, Politecnico di Torino.
      2. Fontana Crespo, R.N. (2023). Machine Learning in Energy Applications. Course Exam Paper, Politecnico di Torino.
      3. Fontana Crespo, R.N. (2023). IoT Platforms for Spatial Analytics in Smart Energy Systems. Course Exam Paper, Politecnico di Torino.
      4. Fontana Crespo, R.N. (2023). Optimized Execution of Neural Networks at the Edge. Course Exam Paper, Politecnico di Torino.
      5. Fontana Crespo, R.N. (2023). Adversarial Training of Neural Networks. Course Exam Paper, Politecnico di Torino.