Dr. M. Tanaveer


M. Tanveer is Associate Professor and Ramanujan Fellow at the Discipline of Mathematics of the Indian Institute of Technology Indore. Prior to that, he worked as a Postdoctoral Research Fellow at the Rolls-Royce@NTU Corporate Lab of the Nanyang Technological University, Singapore. He received the Ph.D degree in Computer Science from the Jawaharlal Nehru University, New Delhi, India. Prior to that, he received the M.Phil degree in Mathematics from Aligarh Muslim University, Aligarh, India. His research interests include support vector machines, optimization, machine learning, deep learning, and applications to Alzheimer’s disease and dementia. He has published over 100 referred journal papers of international repute. His publications have over 3000 citations with an h index of 30 (Google Scholar, December  2022). Recently, he has been listed in the world’s top 2% of scientists in the study carried out by Stanford University, USA. He has served on review boards for more than 100 scientific journals and served for scientific committees of various national and international conferences. He is the recipient of the 2022 APNNS Young Researcher Award, 2017 SERB-Early Career Research Award in Engineering Sciences and the only recipient of 2016 DST-Ramanujan Fellowship in Mathematical Sciences which are the prestigious awards of INDIA at early career level. He is currently the Associate Editor – IEEE Transactions on Neural Networks and Learning Systems (2022 – ), Associate Editor – Pattern Recognition, Elsevier (2021 – ), Action Editor – Neural Networks, Elsevier (2022 – ), Board of Editors – Engineering Applications of Artificial Intelligence, Elsevier (2022 – ), Associate Editor – Neurocomputing, Elsevier (2022 – ), Associate Editor – Cognitive Computation, Springer (2022 – ), Editorial Board – Applied Soft Computing, Elsevier (2022 – ), International Journal of Machine Learning and Cybernetics, Springer (2021 – ), Associate Editor – Frontiers in Applied Mathematics and Statistics (Aug 2020 – ), Editorial Review Board – Applied Intelligence, Springer. He is/was Guest Editor in Special Issues of several journals, including ACM Transactions of Multimedia (TOMM), Applied Soft Computing, Elsevier, IEEE Journal of Biomedical Health and Informatics, IEEE Transactions on Emerging Topics in Computational Intelligence, Multimedia Tools and Applications, Springer and Annals of Operations Research, Springer. He has also co-edited one book in Springer on machine intelligence and signal analysis. He has organized many international/national conferences/symposium/workshop as General Chair/Organizing Chair/Coordinator, and delivered talks as Keynote/Plenary/invited speaker in many international conferences and Symposiums. He has organized several special sessions in top-ranked conferences, including WCCI, IJCNN, IEEE SMC, IEEE SSCI, ICONIP. Amongst other distinguished international conference chairing roles, he is the General Chair for 29th International Conference on Neural Information Processing (ICONIP2022) (the world’s largest and top technical event in Computational Intelligence). Tanveer is currently the Principal Investigator (PI) or Co-PI of 11 major research projects funded by Government of India including Department of Science and Technology (DST), Science & Engineering Research Board (SERB), and Council of Scientific & Industrial Research (CSIR), MHRD-SPARC, ICMR. 

Title: Large Scale Machine Learning Algorithms and Applications


With the explosive growth in technology, the amount and the variety of data has grown tremendously leading to new challenges in classification scenarios. Support vector machine (SVM) algorithm is considered one of the most popular classification paradigms in machine learning owing to its strong mathematical background, and has lately faced criticism due to its limitations such as unscalability, high time complexity and sensitivity to feature and label noise. Over the past decade, several advancements have been made such as twin SVM and variants which led to significant improvements in terms of fast learning speed, ease of implementation and ability to capture diversity among classes. These models have attracted considerable research attention due to promising results shown in the various real-world applications including Image Retrieval, Computer Vision, Financial Regression, Biomedical Analysis etc. However, there have emerged new challenges along with the existing ones such as high dimensionality in kernel implementations, need for large training data and sensitivity to outliers. There is, thus, a need to improve upon these methods and devise new ones to tackle the limitations. In this talk, some novel large-scale twin SVM based algorithms will be discussed to overcome these shortcomings.