Keynote Speakers
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LIST OF KEYNOTE SPEAKERS FOR AIM-CON 2026
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Prof. Ponnuthurai Nagaratnam Suganthan Department of Computer Science and Engineering, Qatar University, Qatar. Research Professor, KINDI Center for Computing Research, Qatar University, Qatar. |
Prof. P. N. Suganthan (Fellow, IEEE) received the UG and PG degrees from the University of Cambridge, Cambridge, U.K. in Electrical and Information Engineering. Later he served as a Pre-Doctoral Research Assistant with the Department of Electrical Engineering, University of Sydney, and then a Lecturer with the Department of Computer Science and Electrical Engineering, University of Queensland, from 1996 to 1999. Since August 2022, he has been with the KINDI Center for Computing Research, Qatar University, Doha, Qatar, as a Research Professor. His research interests include randomization-based learning methods, swarm and evolutionary algorithms, pattern recognition, deep learning, and applications of swarm, evolutionary, and machine learning algorithms. Dr. Suganthan was a recipient of the IEEE Transactions on Evolutionary Computation Outstanding Paper Award in 2012 and the Highly Cited Researcher Award by Thomson Reuters in computer science in 2015. He is currently an Associate Editor for IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Information Sciences, and Pattern Recognition, and the Founding Co-Editor-in-Chief for Swarm and Evolutionary Computation journal. |
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Keynote Talk Randomization Based Deep and Shallow Learning Methods for Classification and Forecasting Abstract : This talk will first introduce the main randomization-based feedforward learning paradigms with closed-form solutions. The popular instantiation of the feedforward neural networks is called random vector functional link neural network (RVFL). Other feedforward methods included in the presentation are random weight neural networks (RWNN), extreme learning machines (ELM), Stochastic Configuration Networks (SCN), and Broad Learning Systems (BLS). We will also present deep random vector functional link implementations. Hyperparameter tuning will be addressed in detail. The talk will also present extensive benchmarking studies using classification and forecasting datasets. |
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Dr. Ahmad Y. Javaid EECS Department, The University of Toledo, USA. Founder and Director of the Cyber Security and Teaming Research (CSTAR) Laboratory |
Dr. Ahmad Y Javaid is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at the University of Toledo, Ohio, USA. He also serves as the Associate Chair and Undergraduate Program Director for the department, as well as the Founding Director of the Cybersecurity Solutions & Teaming Research (CSTAR) Lab. Dr. Javaid’s research expertise lies at the intersection of Cybersecurity, Artificial Intelligence, and Machine Learning. His work is particularly focused on developing resilient AI-driven solutions for critical infrastructure, including integrated energy systems and communication networks. He has authored numerous high-impact publications and has been a lead investigator on several research projects funded by federal and state agencies. Dr. Javaid is also the Director of International Partnerships for the College of Engineering at UToledo, where he fosters global academic and research collaborations. |
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Keynote Talk Empowering Sustainable and Resilient Integrated Energy Systems (IES) through Deep Reinforcement Learning Abstract: As the global energy landscape transitions toward decentralized and integrated architectures, ensuring both sustainability and resilience against cyber-physical threats has become a paramount challenge. Integrated Energy Systems (IES) offer a promising pathway to sustainability by optimizing multiple energy carriers; however, their complexity and stochastic nature make traditional control methods insufficient. This keynote explores the application of Deep Reinforcement Learning (DRL) as a transformative tool for managing Network-Resilient Integrated Energy Systems (NR-IES). We will discuss how DRL agents can be trained to navigate high-dimensional, dynamic environments to optimize energy dispatch while simultaneously detecting and mitigating cyber-anomalies in real-time. By leveraging the self-learning capabilities of DRL, we can enhance the "intelligence" of the grid, ensuring it remains robust against sophisticated attacks and operational uncertainties. The session will conclude with a vision of how AI-driven resilience directly contributes to the long-term sustainability and reliability of critical infrastructure. |
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Prof. Qin Xin Department of Computer Science, Faculty of Science and Technology, The University of the Faroe Islands, Denmark. |
Prof. Qin Xin received the Ph.D. degree from the Department of Computer Science, University of Liverpool, U.K., in December 2004. Currently, he is a Full Professor in computer science with the Faculty of Science and Technology, The University of the Faroe Islands (UoFI), Faroe Islands, Denmark. Prior to joining UoFI, he had held variant research positions in world leading universities and research laboratory, including a Senior Research Fellowship with Universite Catholique de Louvain, Belgium, a Research Scientist/Postdoctoral Research Fellowship with the Simula Research Laboratory, Norway, and a Postdoctoral Research Fellowship with the University of Bergen, Norway. His main research interests include the design and analysis of sequential, parallel and distributed algorithms for various communication and optimization problems in wireless communication networks, and cryptography and digital currencies, including quantum money. Moreover, he also investigates the combinatorial optimization problems with applications in bioinformatics, data mining, and space research. |
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Keynote Talk Almost Optimal Deterministic Rendezvous, Treasure Hunt, and Strongly Universal Exploration Sequences |
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Dr. Fahim Mohammad Senior AI & Machine Learning Scientist, Cisco Systems, USA. |
Dr. Fahim Mohammad, based in Portland, Oregon, USA, is currently a Machine Learning Engineering Technical Leader at Cisco, USA. Dr. Fahim Mohammad brings experience from previous roles viz. Senior AI Framework Engineer & Data Scientist at Intel Corporation and Microsoft, USA. He has also worked as Clinical Data Scientist and Research Fellow at Harvard University from Jul 2012 to May 2014, Boston, MA. Dr. Fahim Mohammad holds a 2008 - 2012 Doctor of Philosophy (Ph.D.) in Computer Science and Engineering from University of Louisville. His area of research interests includes Machine Learning, Deep Learning, Applied Artificial Intelligence, and Computer Vision. |
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Keynote Talk Large Language Models Beyond the Hype: Real Applications, Real Limitations, and the Road Ahead |
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Prof. Brejesh Lall Department of Electrical Engineering, IIT Delhi, India. |
Prof. Brejesh Lall received the B.E. degree in electronics and communication engineering and the M.E. degree in signal processing from the Delhi College of Engineering, New Delhi, in 1991 and 1992, respectively, and the Ph.D. degree in signal processing from the IIT Delhi, New Delhi, in 1999. From 1997 to 2005, he was with Hughes Software Systems, where he worked in the signal processing group on source coding and PHY layer solutions for many communication technologies, such as terrestrial wireless, GEO and LEO satellite communication systems, and satellite broadband. He joined the Department of Electrical Engineering, IIT Delhi as a Faculty Member in 2005, where he is currently a Professor. He has served as the Head of the Bharti School of Telecommunication Technology and Management, IIT Delhi, and the Coordinator of the National Cadet Corps, IIT Delhi. His research interests are broadly in signal processing, image processing, and communications, including the areas of object representation, tracking, and classification, odometry, depth map generation, representation, and rendering, vector sensor-based underwater acoustic communications, and performance issues in molecular communications. |
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Prof. M. Tanveer Department of Mathematics, IIT Indore, Indore (M.P.) India. INSA Distinguished Lecture Fellow, IEEE CIS Distinguished Lecturer (2024-2026), INSA Associate Fellow and SERB Ramanujan Fellow |
Prof. M. Tanveer received the Ph.D. degree in computer science from JNU, New Delhi, India, in 2013. He is a Professor at IIT Indore, Indore, India. He has published more than 165 journal articles with more than 8100 citations (H-index 43). Dr. Tanveer is an IEEE CIS Distinguished Lecturer from 2024 to 2026. He was a recipient of numerous awards including the IIT Indore Excellence Research Award, the INNS Aharon Katzir Young Investigator Award, and the INSA Distinguished Lecture Fellow. He is/was the Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Fuzzy Systems (TFS), IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) and Elsevier’s Neural Networks (NeuNet), Pattern Recognition (PR), and Applied Soft Computing (ASOC). |
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Keynote Talk Advancing Alzheimer’s Diagnosis: The Role of Shallow and Deep Learning Algorithms |
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Prof. Mhamed Sayyouri Sidi Mohamed Ben Abdellah University, Fes, Morocco |
Prof. Mhamed Sayyouri is a qualified professor at the Department of Industrial Engineering, National School of Applied Sciences of Fez (ENSAF), Sidi Mohamed Ben Abdellah University, Morocco. With a multidisciplinary background in physics, applied mathematics, automation, and information processing, he earned a DESA in Automation and Information Processing (2004) and a Ph.D. in Signals, Systems, and Computer Science (2014). His research focuses on signal and image processing, computer vision, artificial intelligence, evolutionary optimization, and feature extraction. He develops advanced methods for the analysis, recognition, and security of visual and multimedia data. An author of over 120 international publications, he is widely recognized for his contributions to these fields. |
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Keynote Talk From Data to Intelligence: Optimized AI Models for Signal, Image, and Multimedia Systems |
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Prof. P. Shanti Thilagam National Institute of Technology Karnataka, Suratkal, India |
Prof. P. Shanti Thilagam is a distinguished faculty in the field of Data Analytics, Data Security, and Data Management at National Institute of Technology (NITK), Suratkal. She is honoured with a Distinguished Alumnus Award from NITK, Surathkal, India, in Nov 2019. She is the recipient of M.S Ramanujan Lecture Award, 30th National convention of Computer Engineers, The Institution of Engineers (India), 18-19 Feb, Salem. She also has received BITES Best PhD Thesis Award for the year 2009 in the category of "Computer Science & Engineering". |
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Dr. Koushik Guha National Institute of Technology Silchar, India |
Dr. Koushik Guha received the B.Tech. degree in electronics and communication engineering from Techno India, Salt Lake, Kolkata, under the West Bengal University of Technology, India, in 2005, the M.Tech degree in electronics and communication engineering (RF and Microwaves) from Burdwan University, West Bengal, India, in 2007, and the Ph.D. degree in design and modeling of RF MEMS shunt switch from NIT Silchar, in 2016. He was a Lecturer with the Department of ECE, Haldia Institute of Technology (HIT), West Bengal, from 2007 to 2010. He has been an Assistant Professor with the National Institute of Technology, Silchar, since 2010. He served as a Visiting Faculty of NIT Mizoram, from 2012 to 2014. |
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Keynote Talk Role of AI ML in Semiconductor and VLSI Technology |
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Prof. Sabrina Tiziana Gaito University of Milan, Italy |
Prof. Sabrina Tiziana Gaito received a degree in Physics in 1996, a Master Degree in Material Science in 1998 and a Ph.D. in Applied Mathematics in 2002 from the University of Milan, Italy. I am currently full professor at the Computer Science Department of the University of Milan, where I am the Director of the CONNETS Lab (Computer Network and Network Science Lab, connets.di.unimi.it). She is Editor in Chief of “Applied Network Science”, Springer-Nature and also Member of the Editorial Board of “PLOS One”. She has published 90+ articles with peer reviews and indexed by Scopus in prestigious magazines and conferences, including Elsevier Computer Networks, Elsevier Ad hoc Networks, Elsevier Computer Communications, Elsevier Theoretical Computer Science, Springer Mobile Networks and Applications. |
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