Trustworthy Artificial Intelligence: 10 challenges for a responsible AI 

Mon Oct 2nd. 9:00 am – 9:45am, Monarchy Ballroom

Abstract:

In recent years, artificial intelligence (AI) is experiencing an eclosion of applications and development of intelligent systems with which we coexist, giving rise to a silent AI that accompanies us in our daily activities, a technological success unprecedented in its short history. Today, AI is considered the general-purpose technology driving the fourth industrial revolution and its impact on society is compared to that of electricity at the beginning of the last century. AI has matured as a technology, which raises the need to establish frameworks for responsible, fair, inclusive, trusted, secure and transparent AI.

In this lecture we delve into trustworthy and responsible AI, presenting essential aspects for its design and use, focusing attention on 10 challenges in the face of the demands of the regulatory debate that has emerged in recent years in Europe, and fueled worldwide by the emergence of creative models of language and image (the two great achievements of homo sapiens 50,000 and 20,000 years ago, respectively).These are focused on: human supervision, cooperation between humans and intelligent systems, robustness in terms of technical soundness and security, transparency and explainability of AI models, fairness and discriminatory behavior of AI models, privacy and data management in federated environments, social and environmental welfare, accountability and responsibility principle, regulation and  sandboxes for testing in risk scenarios, and the integration between general purpose AI systems and trustworthiness.

Bio:

Francisco Herrera (SM’15) received his M.Sc. in Mathematics in 1988 and Ph.D. in Mathematics in 1991, both from the University of Granada, Spain. He is a Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada and Director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI). He is an EurAI Fellow 2009 and IFSA Fellow 2013. He’s an academician at the Spanish Royal Academy of Engineering.

He has been the supervisor of 57 Ph.D. students. He has published more than 600 journal papers, receiving more than 128000 citations (Scholar Google, H-index 170). He has been nominated as a Highly Cited Researcher (in the fields of Computer Science and Engineering, respectively, 2014 to present, Clarivate Analytics). He acts as editorial member of a dozen of journals. His current research interests include among others, computational intelligence, information fusion and decision making, trustworthy artificial intelligence and data science (including data preprocessing, prediction and big data).  Committed to the importance of transmitting to society the results of the research, the formation of new generations of researchers, and the development of an ecosystem of digital innovation and artificial intelligence in Granada.

Website: decsai.ugr.es/~herrera

Machine Learning Approaches for Improving the Quality of Healthcare

Time: Tue, Oct 3rd, 3pm, Monarchy Balroom

Abstract:

Application of Machine Learning (ML) based approaches in healthcare research is increasing at a rapid pace. All areas of healthcare research, from understanding the individual molecules within a cell and their interactions to analysis of images and electronic health records to predicting disease outcomes and health emergencies, have benefitted from the use of ML methods.

In this talk, we will first introduce the central dogma of molecular biology which is fundamental to the understanding of a large class of machine learning applications in biology. Various kinds of data sets emerging in different areas of healthcare research will be mentioned. A few applications of machine learning methods, including deep learning and graph neural network methods, will be described. These will include applications of classification techniques for molecular target prediction, a graph theoretic method for biomarker identification, metaheuristic for drug design and a graph neural network technique for drug to drug interaction prediction. The talk will conclude with a mention of some issues and challenges in this area.

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 is currently 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 many articles in journals, book chapters, and conference proceedings. Prof. Bandyopadhyay has worked in many Institutes and Universities worldwide. She is the recipient of several honors and awards including the Padma Shri from the Government of India – the fourth highest civilian award of India, Shanti Swarup Bhatnagar Prize in Engineering Science, TWAS Prize, Infosys Prize, JC Bose Fellowship, Swarnajayanti fellowship, INAE Silver Jubilee award, INAE Woman Engineer of the Year award (academia), IIT Kharagpur Distinguished Alumni Award, Humboldt Fellowship from Germany, Senior Associateship of ICTP, Italy, young engineer/scientist awards from INSA, INAE and ISCA,  and Dr. Shanker Dayal Sharma Gold Medal and Institute Silver from IIT, Kharagpur, India. She is a Fellow of the three primary science academies and the engineering academy of India, the IEEE, The World Academy of Sciences (TWAS), International Association for Pattern Recognition (IAPR) and West Bengal Academy of Science and Technology. She is a member of the Science, Technology and Innovation Advisory Council of the Prime Minister of India (PM-STIAC).

Telexistence and Society

– Toward the Extension of Human Existence and Capabilities

Oct. 2, 3:00pm-3:45pm, Monarchy Ballroom (Live transmission)

Abstract:

Although telework is feasible for so-called white-collar jobs, many jobs that support society at its core, such as medical care, welfare, water supply, electricity, convenience stores, supermarkets, construction, and civil engineering, require employees to be physically present, making remote working difficult. This is a problem that can be solved if we can use telexistence technology to make physical robots work as our alter egos. Telexistence is a concept that denotes an extension of human existence, wherein a person exists wholly in a location other than their actual current location and can perform tasks freely there. The location that differs from the location where the person physically exists can be a real space or a computer-generated virtual space. The latter case, i.e., telexistence in a virtual space, is usually called virtual reality. Furthermore, by matching the real space with the virtual space, it is possible to telexist in the real space through the virtual space or to act by adding information from the virtual space to the real space.

By distributing avatar robots around the world, users can freely use their avatar robot bodies as substitutes to release their time and space restrictions and enable their unknown experiences. As a result, a “telexistence society” can be realized, in which people’s capabilities can be used freely. It will be possible to respond instantly from a safe place during disasters and emergencies, and this technology can also be used routinely to dispatch medical services, caregivers, physicians, and experts to remote areas. Thanks to the creation of the Virtual Human Teleportation Industry, convenience and motivation in the lives of citizens will greatly improve, and it is anticipated that a healthy and pleasant lifestyle will be realized in a clean and energy-conserving society.

This keynote offers a historical perspective on the present situation and provides an overview of the forthcoming telexistence society from a bird’s-eye view.

Bio:

Professor Susumu Tachi received his Ph.D.  in mathematical engineering and information physics from the University of Tokyo in 1973, after which he joined the Faculty of Engineering of the University of Tokyo. In 1975 he moved to the Mechanical Engineering Laboratory, Ministry of International Trade and Industry, where he served as the Director of the Remote-Control Division and the Biorobotics Division.  In 1989, he rejoined the University of Tokyo, and served as a Professor at the Department of Information Physics and Computing till 2009 when he was conferred Professor Emeritus. Between 2009 and 2015 he was  Professor of Graduate School of Media Design and the Director of the International Virtual Reality Center at Keio University.  In 2017, he founded and became Chairman of Telexistence, Inc.

Currently, he continues his research and activities for the dissemination and social implementation of virtual reality and telexistence at the University of Tokyo’s Research Center for Advanced Science and Technology, and the Institute of Gerontology of the University of Tokyo.

One of his early scientific achievements is the invention (1975) and development of an intelligent mobile robot system for the blind called Guide Dog Robot (1976-1983), which is the first of its kind. This system is known as MELDOG. In 1980, Dr. Tachi invented the concept of telexistence, which enables a highly realistic sense of existence in a remote place without any actual travel, and has been working on the realization of telexistence since then.

Prof. Tachi is a founding director and a fellow of the Robotics Society of Japan (RSJ), the 46th President and a fellow of the Society of Instrument and Control Engineers (SICE), a fellow of the Japan Society of Mechanical Engineers (JSME), and is the founding President of the Virtual Reality Society of Japan (VRSJ). He initiated and founded ICAT (International Conference on Artificial Reality and Telexistence) in 1991 and IVRC (International-collegiate Virtual Reality Contest) in 1993. He was a member of IEEE VR Steering Committee from 1998 to 2017.

Dr. Tachi has received a number of awards, a few of which are listed here:  TIME Magazine Coolest Invention (2003), Laval Mayenne Awards (2003, 2005), Good Design Award (2006), ASIAGRAPH Takumi Award (2007), VRSJ Distinguished Contribution Award (2006), RSJ Founding Contribution Award (2012), Japan Minister of International Trade and Industry Prize (1988), Japan Minister of Education, Culture, Sports, Science and Technology Prize (2011), Governor of Tokyo Prize (2011), IMEKO Distinguished Service Award (1997), and IEEE Virtual Realty Career Award (2007). Dr. Tachi was inducted into the inaugural class of the IEEE Virtual Reality Academy in 2022, and he is the the first recipient of the Research Achievement Award established in 2022 by the Virtual Reality Society of Japan. For more achievements and recognition please see: https://tachilab.org/en/members/susumu_tachi.html

Spacecraft Formation Flying and its Impact on Future Scientific Exploration of Space

Time: Tue Oct 3rd, 9am, Monarchy Ballroom

Abstract:

The use of distributed systems in space for scientific, commercial, and military applications is increasing worldwide. There are two dominant reasons for this interest (a) in most situations, distributed systems provide the capability to enable science, and (b) they provide a more cost-effective, reliable, long-life approach for global communication and remote sensing as compared to their single large monolithic spacecraft. For science, the application of multiple spacecraft systems is considered a way to respond to the fundamental questions of “Are we alone” or “Is there a life” out there? To answer these questions, extraordinary capabilities
are needed that go well beyond those of a single large spacecraft. Technology is now making it possible and there have been many advances in engineering at the nanoscale to develop sensitive new particle detectors capable of determining the direction of the light source, extend sensitive CCD imaging arrays to operate in the ultraviolet and X-ray and generate new approaches to blocking the light from a distant star and observe the faint exoplanets circling nearby. These distributed systems of telescopes, interferometers, and chronographs are capable of selfgoverning and autonomously target/retarget/reconfiguring without depending on ground mission operations. This presentation will provide an overview of the emerging multi-spacecraft space missions, the challenges, and the breakthrough technologies that will enable these missions over the next few decades.

Bio:

Dr. Hadaegh is a Research Professor of Aerospace at Caltech. He earned his Ph.D. in Electrical Engineering from the University of Southern California in 1984, after which he embarked on a career at NASA-Jet Propulsion Laboratory (JPL), the leading center for robotic space exploration. Over the course of nearly four decades, Dr. Hadaegh held various technical and leadership positions at JPL, including serving as the head of the Guidance and Control (G&C) Analysis Group. In this capacity, he spearheaded cutting-edge research in guidance, estimation, and control theory, while also developing advanced algorithms and software for planetary science and astrophysics missions. He led the development of Guidance and Control technologies for spacecraft formation flying, autonomous rendezvous, and docking, playing a crucial role in various NASA missions and Department of Defense programs. Additionally, Dr. Hadaegh served as Principal Investigator for numerous R&D programs, research tasks, and flight experiments. He was a member of the senior leadership, of the Executive Council, and held the position of Chief Technologies until June of 2022. His primary research interests revolve around optimal estimation and robust control of dynamical systems, with a particular focus on distributed spacecraft.

Dr. Hadaegh has been recognized as a JPL Fellow and Senior Research Scientist, as well as a Fellow of both the Institute of Electronics and Electrical Engineers (IEEE) and the American Institute of Aeronautics and Astronautics (AIAA). His accolades include awards such as NASA’s Exceptional Achievement and Exceptional Service Medals, as well as JPL’s Award of Excellence for “Flight Validation of Autonomous Rendezvous in Earth Orbit.” Furthermore, he received AIAA’s 2021 Mechanics and Control of Flight Award and the World Automation of Congress Award. His research contributions in various areas include mathematical modeling of uncertain systems, parameter identifiability of dynamical systems, identification, and control of large space structures, and autonomous control of single and distributed spacecraft systems.

A Robot Just for You

 – Personalized Human-Robot Interaction and the Future of Work and Care –

Time: Tue, Oct 3rd, 9:45 am, Monarchy Ballroom

Abstract:

As robots become part of our world, we demand that they understand us, predict our needs and wants, and adapt to us as we change our moods and minds, learn, grow, and age. The nexus created by major improvements in machine learning for machine perception, navigation, and natural language processing has enabled human-robot interaction in real-world contexts, just as the need for human services continues to grow, from elder care to nursing to education and training, positively impacting user health and quality of life. This talk will discuss work that brings robotics together with machine learning for user modeling, signal processing, and affective computing in order to enable robots to understand, interact, and adapt to users’ ever-changing needs.  The talk will cover methods and challenges of using multi-modal interaction data and expressive robot behavior to monitor, coach, motivate, and support a wide variety of user populations and use cases.  We will cover insights from work with users across the age span (children, adults, elderly), ability span (typically developing, autism, stroke, Alzheimer’s), contexts (schools, therapy centers, homes), and deployment durations (up to 6 months), as well as commercial implications.

Bio:

Prof. Maja Matarić is Chan Soon-Shiong Distinguished Professor of Computer Science, Neuroscience, and Pediatrics at the University of Southern California (USC), founding director of the USC Robotics and Autonomous Systems Center, and Interim Vice President of Research. She received a PhD and MS in Computer Science and Artificial Intelligence MIT, and BS in Computer Science from the University of Kansas. She is a Fellow of the AAAS, IEEE, AAAI, and ACM, recipient of the Presidential Award for Excellence in Science, Mathematics & Engineering Mentoring, the Anita Borg Institute Women of Vision Award for Innovation, the Okawa Foundation Award, NSF Career Award, MIT TR35 Innovation Award, the IEEE Robotics and Automation Society Early Career Award, and the USC Remarkable Woman Award, among others. Prof. Mataric´ is the author of the popular textbook “The Robotics Primer” and has published extensively (h-index 100; 39,594 citations as of 1/30/21). She has served on a number of advisory boards, including the National Science Foundation CISE Division, the Computing Community Consortium (CCC) Council, Willow Garage, and Evolution Robotics.

Prof. Matarić has made a series of pioneering contributions to the field of robotics.  Her early work was in robot navigation and mapping; she then made contributions to multi-robot coordination and swarm control.  Next, she did some of the earliest work in robot learning from demonstration.   She is now best known for founding the field of socially assistive robotics, where her research is developing human-robot interaction algorithms and methods for supporting behavior change in convalescence, rehabilitation, training, and education. Her research has developed robot-assisted therapies for children with autism spectrum disorders, stroke and traumatic brain injury survivors, and individuals with Alzheimer’s disease and other forms of dementia, and has deployed robots in complex environments including homes, nursing homes, hospitals, and classrooms for weeks and months at a time, collecting unprecedented datasets that enabled novel models and personalization strategies . She is also co-founder of Embodied, Inc. where she served as chief science officer in 2016-2018; in May 2020 Embodied launched Moxie, an in-home socially assistive robot for children.

Prof. Matarić is recognized nationally for the scale and impact of her mentoring and K-12 STEM outreach activities. She received the Presidential Awards for Excellence in Science, Mathematics & Engineering Mentoring in 2011 from President Obama, and the USC Mellon Mentoring Award and Provost’s Mentoring Award.  She is the lead of the USC Viterbi School of Engineering K-12 STEM Center, and has raised federal, corporate, and foundation support for and developed free open-source curricular materials for elementary, middle school, and high school robotics courses, K-12 teacher development programs, K-12 STEM equity and inclusion programs, and after-school robotics programs, and competitions training, all aiming to engage student interests in science, technology, engineering, and math (STEM) topics.

Prof. Matarić’s university leadership experience includes serving as the USC Interim Vice President of Research (2020-2021), USC Viterbi School of Engineering Vice Dean of Research (2006-2019), and the elected President of the USC faculty and the Academic Senate (2006-2007).

From Aggregate to Collaborative Teaming in Rescue Oriented Human Robot Interaction

Time: Wed Oct 4th, 9:oo, Monarchy Ballroom

Abstract:

Bio:

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

Time: Wed Oct 4th, 9:45, Monarchy Ballroom

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.

Explainable AI: Reverse engineering solutions with Genetic Programming

Time: Tue Oct 3rd, 11am, Monarchy 4

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: Mon Oct 2nd, 11:30am, Monarchy 4

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.

Decoding the language of “things”: from learning physics grammar to AI-augmented superSensors

Time: Mon, Oct 2nd, 11am

Abstract:

What information is (actually) contained (hidden) in a transducer signal? How can we push the boundary of what is possible to be sensed beyond physical sensor design expectations? Is there more than meets the classical “measurement and instrumentation” eye? The talk will cover an overview AI-augmented sensing capabilities empowered by cross-modal/cross-spectral representation learning and signal-to-signal translation. In particular, we’ll discuss our work in the area of audio AI and novel acoustic sensing capabilities at the intersection of advanced signal processing and deep learning. Highlight research projects will include SoundSee – our mission to the International Space Station (ISS) in partnership with NASA to perform autonomous acoustic monitoring of the station. We will discuss how we are translating these deep audio analytics capabilities into tangible economic and societal benefits here on the ground e.g., improving physical safety, security and wellbeing of our communities. We’ll conclude by sharing our broader vision around foundation AI models for audio and adjacent signal modalities that can potentially transform ubiquitous low-cost sensing at scale and enable a diverse range of application domains including richer UX/interaction, surprisingly responsive objects & environments, augmented situational awareness, digital twins, infrastructure monitoring and ultimately, see and sense the world in a whole new way.

Bio:

Dr. Samarjit Das is the Principal Researcher and AI research group leader at Bosch Research Pittsburgh. His group focuses at the intersection of Artificial Intelligence (AI) and Internet of Things (IoT) including Signal Processing & Deep Learning, Neuro-Symbolic AI and Human-AI Interaction. Dr. Das has successfully led the SoundSee mission to the International Space Station (ISS) in partnership with NASA carrying out novel AI-driven acoustic sensing research in zero-gravity. His work has found extensive media coverages around the world including IEEE Spectrum, MIT Tech Review, NPR, TechCrunch, DigitalTrends, ZDNet, Autonews, Robotics & Automation News to name a few. Dr. Das is the 2021 recipient of the Carnegie Science Award in innovation and Young Alumni Award’22 from ISU College of Engineering. He was featured in keynote presentations at the Consumer Electronics Show (CES) 2020 in Las Vegas, NV and Bosch Connected World 2020 in Berlin, Germany and was a selected panelist at South by Southwest (SXSW) 2020. He received B.Tech degree in Electronics and Communications Engineering in 2006 from IIT Guwahati, India. He then did his PhD in Electrical Engineering at Iowa State University, IA, USA, in 2010 followed by a postdoctoral fellowship at the Robotics Institute, Carnegie Mellon University (CMU) in Pittsburgh. He has been with Bosch Research since 2013 where he currently holds a senior manager role in AI research working with a global team of researchers across the US, Germany and Asia Pacific regions.

Title: From Brain Power to Economic Power

Time: Monday, Oct 2,

Abstract:

Without adequate quality of life, such as good health, relevant education, and shelter, the capacity of humans to produce value that is contributory to society’s economy is significantly diminished. Imagine we compare our world’s economic competitiveness with that of a conceivable alien world that we may encounter in the future. How well are we deploying the brains of our human inhabitants to contribute to the economic competitiveness of our planet? What fraction of the available brains on our planet is healthy and adequately educated to function effectively in the economic enterprise of the earth? Are the governments of our various nations implementing the right policies to maximize the economic power of our planet? My arguments aim to make a case for a commitment to the development of knowledge-based societies everywhere on our planet, with the hope that, in so doing, we will be optimizing the economic potential of our world.

Bio:

Dr. James A. Fabunmi obtained his Ph.D. in Aeronautical Engineering from the Massachusetts Institute of Technology (MIT) in 1978, and has been a Research Engineer at Kaman Aerospace Corporation, Faculty member of Aerospace Engineering at University of Maryland College Park, Founder/Owner of the Advanced Engineering Design and Research (AEDAR) Corporation and Consultant to Major Industry, Government Agencies, Innovation Companies and Universities. He has provided scientific and technical advisory services to various program elements of the United States Department of Defense, and developed initiatives and participated in the conduct of Scientific and Engineering Research and Development in collaboration with Industry, Academia and Government. He is currently the Chief Operating Officer of the American Heritage Defense Corporation (AHDC), a Washington DC 501(c)3 think tank engaged in consulting and advocacy on innovation driven entrepreneurship and diversity. He is the founder of the Adulawo Institute and a co-founder of AWENI the Association of Western Nigeria Innovators – “The Game Changers” for Innovation and Economic Development of Western Nigeria. He is the author of the book “From Brain Power to Economic Power” available on Amazon at:
https://www.amazon.com/dp/1796347728