Title

Time: TBD

Abstract:

TBD

Bio:

Prof. Sanghamitra Bandyopadhyay did her B Tech, M Tech and Ph. D. in Computer Science from Calcutta University, IIT Kharagpur and Indian Statistical Institute respectively. She then joined the Indian Statistical Institute as a faculty member, and became the Director in 2015. Since 2020 she is continuing in her second tenure as the Director of the Institute. Her research interests include computational biology, soft and evolutionary computation, artificial intelligence and machine learning.  She has authored/co-authored several books and numerous articles in journals, book chapters, and conference proceedings and has a citation h-index of 54. Prof. Bandyopadhyay has worked in many Institutes and Universities worldwide. She is the recipient of several awards including the Shanti Swarup Bhatnagar Prize in Engineering Science, TWAS Prize, Infosys Prize, Swarnajayanti fellowship, INAE Silver Jubilee award, INAE Woman Engineer of the Year award (academia), IIT Kharagpur Distinguished Alumni, Humboldt Fellowship from Germany, Senior Associateship of ICTP, Italy, several young engineer/scientist awards,  and Dr. Shanker Dayal Sharma Gold Medal and Institute Silver from IIT, Kharagpur, India, She is a Fellow of the Indian National Science Academy (INSA), National Academy of Sciences, India (NASI), National Academy of Engineers (INAE), Institute of Electrical and Electronic Engineers (IEEE), The World Academy of Sciences (TWAS), International Association for Pattern Recognition (IAPR) and West Bengal Academy of Science and Technology.

Taming Cancer: Personalized Cancer therapy by Model-based Control Engineering Methods

Time: TBD

Abstract:

Imagine if tumor growth would be reduced and then kept in a minimal and safe volume in an automated manner and in a personalized way, i.e. cancer drug would be injected using a continuous therapy improving the patient’s quality of life.

In conventional cancer therapies the doses are chosen to be as high as possible to maximize the effect of the drug and minimize the chance of the development of drug resistance. However, this approach increases side effects and costs.

Optimization based on mathematical models is a promising direction of personalized medicine. Personalizing, thus optimizing treatments may have multiple advantages, from fewer side effects to lower costs. However, personalization is a complicated process in practice. We discuss a mathematical model of tumor growth and therapy optimization algorithms that can be used to personalize therapies. The therapy generation is based on the concept of keeping the drug level over a specified value. The developed control algorithms can handle inter- and intrapatient variability, positivity, and impulsive nature of the control input. In silico tests proved that our algorithms are suitable for optimizing therapies demonstrated on animal experiments as well. The experimental results show that the introduced algorithms significantly increased the overall survival of the mice, demonstrating that by control engineering methods taming the cancer is realistic possibility.

Bio:

Prof. Dr. habil. Levente Kovács  is full professor of the John von Neumann Faculty of Informatics at Óbuda University, Budapest, Hungary. He also currently serves as rector of the University.

Prof. Kovács has MSc degrees in electrical engineering and in biomedical engineering, and a PhD (2008, from Budapest University of Technology and Economics) with a thesis on control methods for insulin dosage in diabetes. His  fields of interest are modern control theory and physiological controls and he published more than 500 articles in these areas.

He is a board member of the Applied Informatics and Applied Mathematics Interdisciplinary Doctoral School and the Habilitation and Doctoral School of Obuda University. He founded the Physiological Controls Research Center at Obuda University in 2013 and is heading it since.  He is member of the IFAC TC 8.2 “Biological and Medical Systems” form 2010, member of the IFAC TC 9.4 “Control Education” from 2017, and member of the Hungarian Diabetes Association from 2010,  in which he founded and chairs the Hungarian Artificial Pancreas Working Group. He is chair of the Hungary Chapters of IEEE SMC and IEEE Control Society; he chaired the IEEE Hungary Section from 2017 to 2020, and has been re-elected in this role in 2022.

In 2015 Prof Kovacs received the highly prestigious ERC StG grant of the European Union in tumor control that aimed to research the context of personalized tumor control by control engineering methods as a double optimization min-max problem: reducing the therapy cost and maximizing the quality of life. In 2016 he founded the Cyber-Medical Technical Committee of the IEEE SMC Society, promoting the theory and practice of personalized healthcare.

Transdisciplinary Systems Engineering: A 21st Century Systems Engineering Imperative

Time: TBD

Abstract:

With ever-increasing complexity of sociotechnical systems, systems engineering is undergoing a historic transformation to increase methodological rigor, flexibility of modeling methods, and exploit the growing convergence of systems engineering with other disciplines. This trend is a key enabler of transdisciplinary systems engineering, which I define as a meta-discipline that exploits the convergence of systems engineering with other disciplines to frame and solve problems that appear intractable when viewed solely through an engineering lens. To illustrate the application of transdisciplinary systems engineering, my talk will focus on exploiting the synergy of Model Based Systems Engineering and Entertainment Arts. Specifically, I will show that by transforming system models into stories that can be executed in virtual worlds, it becomes possible to increase the understanding and participation of all stakeholders, especially in upfront engineering. I will illustrate the use of this approach within the context of a campus security system. I will conclude by reviewing key concepts from other disciplines that can also be exploited in systems engineering to increase system life cycle coverage as well as enhance semantics and flexibility of system modeling approaches.

Bio:

Azad Madni is a University Professor and holder of the Northrop Grumman Foundation Fred O’Green Chair in Engineering in the University of Southern California’s Viterbi School of Engineering, executive director of USC’s Systems Architecting and Engineering Program, and founding director of the Distributed Autonomy and Intelligent Systems Laboratory. He is founder and CEO of Intelligent Systems Technology, Inc., a high-tech R&D company specializing in transdisciplinary approaches to scientific and societal problems of national and global significance. He is also the chief systems engineering advisor to the Aerospace Corp. and was previously a Distinguished Visiting Fellow at NASA’s Jet Propulsion Laboratory.

Dr. Madni defined the field of transdisciplinary systems engineering and is the creator of TRASEE™, a transdisciplinary engineering education paradigm that fosters out-of-the-box thinking while enhancing retention and recall of concepts and facts through innovative storytelling and role-playing approaches. His key areas of research include transdisciplinary and model-based systems engineering methods for realizing intelligent cyber-physical-human systems (such as autonomous vehicles, smart manufacturing, and outcome-driven health care), interactive storytelling in virtual worlds, and augmented intelligence in adaptive human-machine teaming.

He has served as principal investigator on 97 R&D projects totaling over $100M in funding. Federal sponsors of his research include DARPA, NSF, NASA, DHS, NIST, DOE, and AFOSR, among others; industry sponsors include Boeing, General Motors, Northrop Grumman, Raytheon, Lockheed Martin, and SAIC. He is the author of Transdisciplinary Systems Engineering: Exploiting Convergence in a Hyper-Connected World (Springer, 2018) and coauthor of Tradeoff Decisions in System Design (Springer, 2016). He has authored 19 book chapters and more than 400 peer-reviewed publications, and given more than 75 keynote presentations and invited talks at international conferences, major universities, and government agencies.

He was elected to the National Academy of Engineering in 2021, and has been selected to receive the 2023 IEEE Simon Ramo Medal for pioneering contributions to systems engineering and systems science using interdisciplinary approaches. He received the National Academy of Engineering Bernard M. Gordon Prize for Innovation in Engineering and Technology Education. He is a fellow of ten professional science and engineering societies, including AAAS, IEEE, AIAA, INCOSE, and IISE.

He earned his BS, MS, and PhD degrees in engineering from the University of California, Los Angeles. He is a graduate of AEA/Stanford Executive Institute.

Explainable AI: Reverse engineering solutions with Genetic Programming

Time: TBD

Abstract:

Historically, AI had to do with rule bases devised by knowledge engineers.  Of late, however, the term has come to symbolize an implementation of reinforcement learning and Artificial Neural Networks. Yet this representation in terms of weights and connections is problematic for humans to understand.  The need to understand arises not only when we need to more strongly certify the solution; an often ignored need is to know if the problem as posited and solved is the one intended.  Current approaches to the explainability layer go by the name of “explainable AI” and rely on simple regressions in limit input spaces, or struggle with creating models which can replicate the black-box behaviour whilst being distillable to an understandable explanation. An alternative, thus far not well explored, is to reverse engineer the black box by means of Genetic Programming.  Koza’s original intention was the evolution of computer programs and functions that could provide an understanding because the tools of computer programming are, or should be, familiar to the human.  This has great potential to address the shortfalls of existing methods.  I will describe an application of Genetic Programming to reverse engineer the neural network.  Moreover, one example combines grammars to address a problem of COVID lockdown easement.  The talk will discuss challenges to be addressed to deliver capability for the real world, and what can become enabled by such an explainable AI capability, leveraged by these efforts, to be of great value to AI practice. 

Bio:

Daniel Howard holds PhD BS and MSc degrees in chemical and civil engineering.  He was the Rolls-Royce Research Fellow in Computational Fluid Dynamics at University of Oxford and, after a spell in the oil industry, joined the then elite UK Defence Evaluation and Research Agency in Malvern, UK (now QinetiQ) where he pioneered and led the AI team that developed Genetic Programming R&D theory and practice, achieving practical and tested deployments of GP solutions to military Computer Vision, Genomics, Medical Image Analysis and Traffic Modelling for the UK Highways Agency (MIDAS system) and achieved the grade of Fellow.  He was also rewarded by the 2003 accolade from the then Chief Scientist of Ministry of Defence.  Reinstated to the SCR of Pembroke College Oxford in 2002, he was made Honorary Chair Professor at National Taipei University of Technology.  Since 2008 he has run his company specializing in bespoke AI software for clients.  He developed multirun subtree encapsulation co-publishing the method with John Koza. This is the second well known method of explicit modularization representing the function subprogram in GP – the first is Koza’s ADF (automatically defined functions: parameterized subroutines in GP). Since 2001 he has developed a method that uses GP trees to create vectors of numbers that are then interpreted by a user specified grammar and has deployed these for reverse engineering neural networks and other applications.

Autonomous AI (AAI) and  Symbiotic Human-Machine Intelligence Systems

Time: TBD

Abstract: 

It is recognized that the general form of Artificial Intelligence (AI), equivalent to human Natural Intelligence (NI), is Autonomous AI (AAI) underpinned by Intelligence Science, Brain-Inspired Systems (BIS), Cognitive Computers (kC) [3], Intelligent Mathematics (IM), as well as System, Man, and Cybernetics.

This talk presents basic research on fundamental theories, discoveries, and the latest development in AAI and Symbiotic Human-Machine Intelligence Systems (SHMIS) It is revealed that the ultimate aim of AAI is not pre-trained nor pre-programmed intelligence, because both classical approaches may not generate cognitive and autonomous intelligence as that of the brain beyond those of adaptive, imperative, and reflexive intelligence according to the Hierarchical Intelligence Model (HIM) of intelligence science. It explains why the reinforcingly trained Chat-GPT may not carry out inductive reasoning and deductive intelligence generation centric in NI. It explains the difference between machine knowledge (memorization-based) and machine intelligence (reasoning-and-creativity-based). For instance, a trained AI system may memorize an entire encyclopedia, but it may not sufficiently be intelligent for enabling machinable thinking and inference as that of human brains.

This talk queries the intelligent and sociological impacts of AAI towards the Symbiotic Human-Robot Society (SHRS). In SHRS, humans and intelligent robots work symbiotically in a hybrid society that will lead to: a) Sharable wisdoms for coherent intelligence generation for real-time problem solving and decision making; b) Downloadable knowledge acquisition free from learning; c) Mutual augmentation between human and machine intelligence based on their complemental advances. Therefore, SHRS will form a Symbiotic Intelligent Operating Framework (SIOF) for synergizing the advanced inductive reasoning power of humans and the fast knowledge/information processing capability of machines in a coherent platform in order to enable unprecedent human intelligence and unlimited human creativity.

Bio:

Dr. Yingxu Wang is professor of intelligence science, brain science, autonomous systems, software science, and intelligent mathematics. He is the founding President of Int’l Institute of Cognitive Informatics and Cognitive Computing (I2CICC).  He is FIEEE, FBCS, FI2CICC, FAAIA, FWIF, and P. Eng. He has held visiting professor positions at Univ. of Oxford (1995, 2018-2023), Stanford Univ. (2008, 2016), UC Berkeley (2008), MIT (2012), and a distinguished visiting professor at Tsinghua Univ. (2019-2022). He received a PhD in Computer Science from Nottingham Trent University, UK, in 1998 and has been a full professor since 1994. He is the founder and steering committee chair of IEEE Int’l Conference Series on Cognitive Informatics and Cognitive Computing (ICCI*CC) since 2002. He is founding Editor-in-Chiefs and Associate Editors of 10+ Int’l Journals and IEEE Transactions, particularly EiC of IEEE SMC Letters. He is Chair of IEEE SMCS TC-BCS on Brain-inspired Cognitive Systems, Co-Chair of IEEE CS TC-CLS on Computational Life Science, and the steering committee representative of IEEE Computer Science to the Computational Intelligence Society’s Tarns. on Cognitive and Development Systems.

His basic research has spanned across contemporary scientific disciplines of intelligence, mathematics, knowledge, robotics, computer, information, brain, cognition, software, data, systems, cybernetics, neurology, and linguistics. He has published 600+ peer reviewed papers and 38+ books/proceedings. He has presented 70+ invited keynote speeches to international conferences. He has served as honorary, general, and program chairs for 40+ international conferences. He has led 10+ international, European, and Canadian research projects as PI. He is recognized by Google Scholar as world top 1 in Software Science, top 1 in Cognitive Robots, top 8 in Autonomous Systems, top 2 in Cognitive Computing, and top 1 in Knowledge Science with an h-index 67. He is recognized by ResearchGate as among the world’s top 1.0% scholars in general and in several contemporary fields encompassing artificial intelligence, autonomous systems, theoretical computer science, engineering mathematics, software engineering, cognitive science, information science, and computational linguistics. He has created and/or formally proved 100+ theorems in the aforementioned transdisciplinary fields, encompassing two of the world’s top ten hardest mathematical problems known as the Goldbach conjecture and Twin-Prime conjecture in 2022.