Abstract: In early 90s we proposed optoacoustics (photoacoustics) for imaging in tissues and medical diagnostics. Since then many research groups started working in this field and at present optoacoustic technique became an emerging diagnostic modality. It has a great potential to become an invaluable tool for early, cost-efficient, and safe diagnosis. It is based on thermoelastic generation of optoacoustic waves in tissues with optical pulses and provides images with high molecular contrast and high spatial resolution. We proposed to use optoacoustics for diagnostics of a number of diseases including cancer, stroke (ischemic and hemorrhagic), circulatory shock; intracranial hematoma detection, monitoring of cerebral blood oxygenation in patients with traumatic brain injury (TBI), neonatal patients, and fetuses during late-stage labor. This technique is particularly suitable for quantitative, real-time, continuous measurements and monitoring of blood oxygenation including cerebral blood oxygenation with high accuracy and resolution. We developed and built medical grade laser-based systems for optoacoustic imaging, monitoring, and sensing. We developed software and algorithms for real-time, continuous imaging and monitoring in tissues with high accuracy and resolution. Unique ultra-sensitive, wide-band optoacoustic detectors were developed by our group for a number of clinical applications. We performed successful pre-clinical and clinical tests of the systems in healthy volunteers, patients with TBI, and critically ill patients. For further improvement accuracy and resolution machine learning can be used. Machine learning can substantially enhance the diagnostic capabilities of this emerging imaging technique. Applications of machine learning may also significantly shorten time for optoacoustic diagnosis.

Keywords: Optoacoustic, photoacoustic, imaging, monitoring, machine learning

Acknowledgements: We thank the National Institutes of Health (NIH), other funding agencies, and industry for support of these studies.

Biography: Rinat O. Esenaliev, PhD, is a Professor at the Department of Neurobiology, Director of Laboratory for Optoacoustic Imaging and Monitoring at the University of Texas Medical Branch (UTMB), USA. He is a Fellow of SPIE and OSA/Optica. Dr. Esenaliev was working at Rice University and MD Anderson Cancer Center in Houston (Texas) from 1993 to 1997. Since 1997 he has been working at UTMB. Dr. Esenaliev is a pioneer in biomedical optoacoustic imaging and monitoring and has over 30 years of experience in biomedical optics and optoacoustics, optoacoustic instrumentation, and optoacoustic diagnostics, therapy and theranostics. His research interests include also noninvasive biosensors, nanotechnology, laser-based therapies of cancer and other diseases, anti-cancer drug delivery, OCT, and high-resolution ultrasound. Dr. Esenaliev has over 180 publications (excluding abstracts) that have been cited over 10,000 times, and his publication h-index is 50. He is an inventor of 30 patents (including 22 issued patents) and author of 282 conference presentations, most of them on optoacoustic imaging and monitoring. In 2010 he was a winner of the University of Texas System Chancellor’s Innovation and Entrepreneurship Award for “Multiple Therapeutic and Diagnostic Methods and Devices”. He received 33 grants for development of novel technologies for noninvasive diagnostics, therapy, and theranostics. Dr. Esenaliev has been serving as an editorial board member of Photoacoustics.

Abstract: The AI frenzy started several years ago, and it doesn't seem to stop any time soon. Moreover, with the advancement of Large Language Models (LLMs) and, more recently, Large Vision Models (LVMs), we are witnessing how everything is changing at an overwhelming pace.

This talk surveys recent trends in AI and how relevant stakeholders – industry, academia, governments, common people, and society at large – react to it. The talk stresses the fading of borders between AI and other disciplines, such as data science, neuroscience, and linguistics. It also highlights how the notion of AI as an entity is becoming increasingly common. Generative AI is seeing enormous investments, but there is simultaneously a constant increase in the costs of developing new LLMs and LVMs. On the other hand, this fact has prompted researchers to start proposing AI models that do not have billions of parameters and are still a promising alternative in a number of tasks.

Yet there are phenomena that indicate huge changes in the game. For example, for quite some time it is the industry that dominates developments in AI, not academia. Likewise, although people have realized the need for responsible AI, industry leaders use different benchmarks to test their models, which makes it difficult to compare the risks that different models bring. Many countries and governments are developing regulations to guardrail AI developments, but new developments in AI largely outpace the development of regulations.

Biography: Vladan Devedzic is a Professor of Computer Science and Software Engineering at the University of Belgrade, Faculty of Organizational Sciences. He is the founder and Head of the research group focused on Artificial Intelligence (GOOD OLD AI research network). He is also the founder of the Artificial Intelligence Laboratory at his home faculty. Since 2021, he has been a corresponding member of the Serbian Academy of Sciences and Arts (SASA) at the Department of Technical Sciences. According to the list of world's top scientists, published by Stanford University, he is among 0.6% of the most cited researchers in the "career" category in the field of Artificial Intelligence (for the period 1996-2023). Vladan Devedzic continuous professional goal is to bring together ideas from the field of Artificial Intelligence / Intelligent Systems and Software Engineering. His current interests include artificial intelligence, programming education, software engineering, and technology-enhanced learning.

Abstract: Self-driving technology is poised to revolutionize transportation infrastructure globally, offering a unique opportunity to enhance the quality of life. As urban areas face challenges such as rapid growth, avoidable collisions, vehicle emissions, and congestion from single-occupant commuters, autonomous vehicles promise to transform transportation systems by delivering significant environmental, social, and economic benefits. However, autonomous ground vehicles (AGVs) must overcome various challenges to navigate safely from origin to destination. In this lecture, we will explore these challenges across different self-driving models. We will delve into self-driving algorithms, the integration of supervised learning and reinforcement learning, fundamental driving functions, and collision avoidance using deep reinforcement learning. The talk will conclude with test results and an assessment of risk levels for self-driving technology.

Biography: Prof. Ankush Ghosh is Senior member of IEEE, Fellow of IETE working as Vice-President at ADSRS Education and Research Foundation, India. He has received his Ph.D. (Engg.) degree from Jadavpur University, India in 2010. He was a research fellow of the Advanced Technology Cell- DRDO, Govt. of India. He was awarded National Scholarship by HRD, Govt. of India. He has outstanding research experiences and published more than 20 edited books from Springer & Elsevier; 3 National & 8 International patents and more than 150 research papers indexed in Scopus/Web of Science. He is serving as an editorial board member of several international journals including Chief Editor. He has more than 20 years of experience in teaching, research as well as industry. His UG and PG teaching assignments include Microprocessor and microcontroller, AI, IOT, Embedded and real time systems etc. He has delivered Keynote/Invited lecture in a number of international seminar/conferences, refreshers courses, and FDPs. He has guided a large number of M.Tech and Ph.D. students. Dr. Ghosh is an active member of IEEE and organized a number Seminars and workshops in association with IEEE. He is an editor & organizing committee member of the Conference series GUCON, GlobConET, GlobConPT, GlobConHT, ICACIS ICCCA, ICEEE, ICACIT. He is a Global Jury member of National Entrepreneurship Network- Mentor Group. He has received award for contributing in Innovate India programme from AICTE- DST, Govt. of India in 2019 and 2020.