Deepali Hirolikar | Machine Learning Award | Best Researcher Award

Posted on by ScienceFather

Dr. Deepali Hirolikar | Machine Learning Award | Best Researcher Award

Dr. Deepali Hirolikar, PDEA,s College of Engineering, Manjari(Bk.), Pune, India

Dr. Deepali S. Hirolikar is the Head of the Department of Information Technology at PDEA’s College of Engineering, Pune, with 18 years of experience in academia. She holds a PhD in Information Technology from Shri JJT University, Rajasthan. Dr. Hirolikar has published numerous papers in national and international journals, focusing on topics such as IoT, cloud computing, and machine learning. She has also published a book on IoT security paradigms. As an active contributor to various workshops and conferences, she has received multiple accolades for her work. 🖥️📚🎓

Publication Profile

Orcid

Experience 🏆

Prof. Dr. Deepali S. Hirolikar has amassed over 18 years of experience in academia. She currently serves as the Head of the Information Technology Department and Assistant Professor at PDEA’s College of Engineering, Manjari, Pune, a position she has held since September 6, 2005. Before this, she was a Lecturer in the Computer Engineering Department at SRGSIOT, Hadapsar.

Education 📚

She completed her SSC at Keshavraj Vidyalaya, Latur in 1995 with distinction, and her HSC at Dayanand Science Junior College, Latur in 1997 with first class. She earned her Diploma in Computer Science Engineering from PLGP, Latur in 2000 with first class, and her BE in Computer Science and Engineering from Dr. BAMU, Aurangabad in 2004 with distinction. Prof. Dr. Hirolikar obtained her ME in Information Technology from UOP Pune, MIT College of Engineering, Pune in 2011 with first class, and her PhD in Information Technology from Shri JJT University, Rajasthan in 2021.

 

Research Focus

Deepali Hirolikar’s research primarily focuses on using metaheuristic methods and machine learning for efficiently predicting and classifying heart disease data. Her work includes the development and application of advanced algorithms to enhance the accuracy and efficiency of heart disease prediction models. By leveraging mathematical and engineering principles, she contributes to the field of medical data analysis, particularly in identifying patterns and improving diagnostic processes. Her research also spans the integration of machine learning techniques with medical datasets to facilitate better health outcomes.

Publication Top Notes

Metaheuristic Methods for Efficiently Predicting and Classifying Real Life Heart Disease Data Using Machine Learning

Qibin Zhao | Machine Learning Award | Best Researcher Award

Posted on by ScienceFather

Prof Dr. Qibin Zhao | Machine Learning Award | Best Researcher Award

Prof Dr. Qibin Zhao, RIKEN, Japan

👨‍💼 Dr. Qibin Zhao is a prominent figure in the field of machine learning and deep learning, serving as the Team Leader at RIKEN Center for Advanced Intelligence Project in Tokyo, Japan. With a Ph.D. in Computer Science and Engineering from Shanghai Jiao Tong University, China, his expertise spans across tensor networks, computer vision, and brain imaging/signal processing. Dr. Zhao has received numerous research grants and awards, including the ICASSP Best Student Paper Award in 2019. He actively contributes to academic activities as an area chair and organizer in prestigious conferences like NeurIPS and ICML, while also serving as a reviewer for leading journals.

 

Publication Profile:

Scopus

Education

📚 Dr. Qibin Zhao’s academic journey is marked by excellence and dedication. He earned his Ph.D. in Computer Science and Engineering from Shanghai Jiao Tong University, China, from 2004 to 2009, laying the foundation for his future contributions to the field. Prior to this, he obtained his M.S. in Computer Science at Guangxi University, China, from 2001 to 2004, and his B.S. in Computer Science at Henan University of Science and Technology, China, from 1996 to 2000. This comprehensive educational background equipped him with the necessary skills and knowledge to excel in his career in research and academia. 🎓

 

Working Experience

👨‍💼 Dr. Qibin Zhao’s professional journey reflects a commitment to advancing the fields of artificial intelligence and computer science. Since 2020, he has held the position of Team Leader at the Tensor Learning Team within the RIKEN Center for Advanced Intelligence Project in Tokyo, Japan, guiding cutting-edge research initiatives. Concurrently, he serves as a Visiting Professor at Tokyo University of Agriculture and Technology and was a Part-time Lecturer at Waseda University, both in Tokyo. His leadership roles include being the Unit Leader of the Tensor Learning Unit at RIKEN from 2017 to 2020. Dr. Zhao’s international influence extends to his visiting professorships in China and Japan, alongside his impactful research scientist roles at RIKEN. 🌐

 

Awards and Honors:

🏆 Dr. Qibin Zhao’s contributions to signal processing and artificial intelligence have garnered significant recognition. Notable among his accolades is the 2019 ICASSP Best Student Paper Award for groundbreaking work presented by L. Yuan. His research excellence was further acknowledged with the 2018 IEEE Signal Processing Magazine Best Paper Award, authored by A. Cichocki and team. Dr. Zhao’s impact extends to Japan, where he received the 3rd IEEE Signal Processing Society Japan Best Paper Award in 2018. Additionally, he has been honored with the 5th Research Incentive Award by the RIKEN President in 2014, among other prestigious recognitions for his pioneering research in brain signal decoding and affective brain-computer interfaces. 🌟

 

Research Focus:

🔬 Dr. Qibin Zhao’s research primarily focuses on advanced techniques in tensor decomposition and multiway data analysis, leveraging the power of tensor networks in various applications. His work encompasses areas such as semi-supervised multi-view concept decomposition, robust kernel PCA for multidimensional data, Bayesian tensor factorization for scalable analysis, and noisy tensor completion methods. With expertise in tensor ring factorization, he explores innovative approaches for image completion, fusion, and analysis in hyperspectral and multispectral domains. Dr. Zhao’s contributions extend to exclusive and consistent NMF for multi-view representation learning, deep matrix factorization with hypergraph regularization, and novel tensorized transformer networks for medical image segmentation. 🧠

 

Publication Top Notes:

  1. Semi-supervised multi-view concept decomposition – Jiang, Q., Zhou, G., Zhao, Q. (2024) Expert Systems with Applications 📝
    • Citations: 0
  2. Noisy Tensor Completion via Low-Rank Tensor Ring – Qiu, Y., Zhou, G., Zhao, Q., Xie, S. (2024) IEEE Transactions on Neural Networks and Learning Systems 📝
    • Citations: 8, Cited by: Unknown
  3. Exclusivity and consistency induced NMF for multi-view representation learning – Huang, H., Zhou, G., Zheng, Y., Yang, Z., Zhao, Q. (2023) Knowledge-Based Systems 📝
    • Citations: 0, Cited by: Unknown
  4. Diverse Deep Matrix Factorization with Hypergraph Regularization for Multi-View Data Representation – Huang, H., Zhou, G., Liang, N., Zhao, Q., Xie, S. (2023) IEEE/CAA Journal of Automatica Sinica 📝
    • Citations: 3, Cited by: Unknown
  5. TT-Net: Tensorized Transformer Network for 3D medical image segmentation – Wang, J., Qu, A., Wang, Q., Liu, J., Wu, Q. (2023) Computerized Medical Imaging and Graphics 📝
    • Citations: Unknown, Cited by: Unknown