Speakers

Prof. Ujjwal Maulik

Professor, Dept. of Comp. Sc. and Engg., Jadavpur University

Fellow - FNAE, FNASc, FIAPR, FAAIA, FIEEE, Fulbright Fellow, USA, AvH - Humboldt Fellow, Germany, Senior Associate, ICTP, Italy, IEEE CIS Distinguished Lecturer, ACM Distinguished Speaker

Biography: Dr. Ujjwal Maulik is a Professor in the Dept. of Comp. Sc. and Engg., Jadavpur University since 2004. He was also the former Head of the same Department. He also held the position of the Principal in charge and the Head of the Dept. of Comp. Sc. and Engg., Kalayni Govt. Engg. College. Dr. Maulik has worked in many universities and research laboratories around the world as visiting Professor/ Scientist including Los Alamos National Lab., USA in 1997, Univ. of New South Wales, Australia in 1999, Univ. of Texas at Arlington, USA in 2001, Univ. of Maryland at Baltimore County, USA in 2004, Fraunhofer Institute for Autonome Intelligent Systems, St. Augustin, Germany in 2005, Tsinghua Univ., China in 2007, Sapienza Univ., Rome, Italy in 2008, Univ. of Heidelberg, Germany in 2009, German Cancer Research Center (DKFZ), Germany in 2010, 2011 and 2012, Grenoble INP, France in 2010, 2013 and 2016, University of Warsaw in 2013 and 2019, University of Padova, Italy in 2014 and 2016, Corvinus University, Budapest, Hungary in 2015 and 2016, University of Ljubljana, Slovenia in 2015 and 2017, International Center for Theoretical Physics (ICTP), Trieste, Italy in 2014, 2017 and 2018. He is the recipient of Alexander von Humboldt Fellowship during 2010, 2011 and 2012 and Senior Associate of ICTP, Italy during 2012-2018 and Fulbright Fellowship in 2024-2025. He is the Fellow of Indian National Academy of Engineers (INAE), India, National Academy of Science India (NASI), International Association for Pattern Recognition (IAPR), USA, The Institute of Electrical and Electronics Engineers (IEEE), USA and Asia-Pacific Artificial Intelligence Association (AAIA), Hongkong. He is also the Distinguish Member of the ACM. He is a Distinguish Speaker of IEEE as well as ACM. His research interest include Machine Learning, Pattern Analysis, Data Science, Bioinformatics, Multi-objective Optimization, Social Networking, IoT and Autonomous Car. In these areas he has published ten books, more than four hundred papers, mentoring several start-ups, filed several patents and already guided twenty five doctoral students. His other interest include mentoring young students, traveling extensively around the globe, outdoor Sports and Classical Music.

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DBLP

Title of the Talk: AI for Healthcare

Dr. Imran Razzak

Senior Lecturer in Human-Centered Machine Learning, School of Computer Science & Engineering, University of New South Wales, Sydney

Associate Editor- IEEE TNNLS, Neural Network, IEEE TCSS, IEEE JBHI

Biography: Imran Razzak is a Senior Lecturer in Human-Centered Machine Learning in the School of Computer Science and Engineering at University of New South Wales, Sydney, Australia. Previously, he was as a Senior Lecturer in Computer Science at School of IT, Deakin University, Victoria. His area of research focuses on connecting language and vision for better interpretation of multidimensional data and spans over three broad areas: Machine Learning, Computer Vision, and Natural Language Processing with special emphasis on healthcare and use of natural language to explain the rationale and decision-making process behind the use of machine learning algorithms and models. With a strong background in computer vision and deep learning techniques, he has developed expertise in analysing and interpreting medical images (US, mammogram, CT, PET, MRI, fMRI) to improve patient outcomes. Throughout his career, he has worked on a range of projects that involve developing and implementing machine learning algorithms for medical image analysis. His work has resulted in more than 200 publications citation in leading journals and conferences in the field of medical image analysis. Through his research, he has made significant contributions to the field of medical image analysis, particularly in the area of early diagnosis. Additionally, he has collaborated with medical professionals to develop tools for early diagnosis of various medical conditions, including cancer, neurodegenerative disease, and heart disease.

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DBLP

Title of the Talk: Unlocking Early Prognosis and Tailored Treatment Plans: Intersection of AI and Medical

Abstract: Most neurodegenerative diseases are characterised by progressive and irreversible neuronal damage and functional impairment. Traditional diagnostic and monitoring protocol rely heavily on expert clinical evaluation, including a focus on symptoms such as bradykinesia, tremor, rigidity, and postural instability, alongside examination of patients’ response to dopaminergic therapy. However, by the time these symptoms manifest, often 5, 10, or even 20 years after disease onset, significant neuronal loss has already occurred and diminished the efficacy of potential interventions. First part of this talk focuses on the transformative potential of AI in enhancing early prognosis, impact of medication, identification of biomarkers and personalized treatment plans, particularly in retinal imaging for early diagnosis. In the second part, I will highlight our recent work on AI in Health such as cardiovascular imaging and multiomics. By leveraging advanced multiomics techniques and AI algorithms, we can detect subtle changes in at their early stages, allowing for timely intervention and improved patient outcomes.

Dr. Puneet Gupta

Associate Professor, Department of Computer Science and Engineering, Indian Institute of Technology Indore

Biography: Dr. Puneet Gupta is currently working as an Associate Professor in the Department of Computer Science and Engineering, Indian Institute of Technology, Indore. Before that, he was a postdoctoral researcher at Tampere University, Finland, working to make Deep Learning architectures more secure and reliable to increase their applicability in real-world applications. Before that, he was a member of the Machine Vision group in Embedded Methods and Robotics, TCS Research and Innovation. His work in the group mainly focuses on improving the efficacy of remote heart rate estimation and exploring its application area, like human expression understanding. He received his Doctoral degree from the Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, India, in 2016. CUrrently, he is working to make technology beneficial for human beings by analyzing their behavior. To this end, he has worked on fusing multiple biometric traits for authentication, analyzing facial expressions using deep learning, measuring the human-vital parameters using unobtrusive and non-contact human videos, cross-modal learning, hyperspectral imaging, Federated learning, Medical imaging and fortifying Deep learning architectures using adversarial defenses. These are indispensable in security, affective computing, ambient intelligence, and health care. His research areas include affective computing, computer vision, and deep learning. He has published several papers in reputed International Journals and Conferences.

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Title of the talk: Role and Estimation of Remote Photoplethysmography in the realm of AI

Abstract: Human vital parameters, like heart rate, blood pressure, and respiratory rate, are crucial for diagnosing various medical conditions and require frequent monitoring for accurate assessment. Traditional methods for estimating these parameters require skin contact, which can be uncomfortable and sometimes incompetent in several situations. Thus, there is a growing need for remote estimation of these parameters, and remote photoplethysmography (rPPG) techniques are becoming increasingly popular in this context. The rPPG provides information about arterial blood flow by analyzing non-contact face videos captured using camera sensors. This talk will discuss the importance of rPPG and some of its real-world applications, where traditional heart rate estimation devices are incompetent. Subsequently, it will unravel the principles, challenges, and working methodology for the rPPG-based HR estimation. This talk will conclude by providing some future research directions.