2022 World AI IoT Congress

                                        KEYNOTE SERIES


                                                         Liqun Chen

                                                     (Professor, University of Surrey, UK)

Bio: Liqun Chen is a Professor in Secure Systems at the University of Surrey. Prior to taking this position in 2016, she was a principal research scientist at Hewlett-Packard Laboratories, Bristol, UK. During her 19 years working for the company, she has obtained 78 granted US patents with 28 further applications pending. She developed several cryptographic schemes that were adopted by International Standards bodies, ISO/IEC, IEEE and TCG (Trusted Computing Group). In particular, she designed several cryptographic algorithms (including direct anonymous attestation and the multiple signature interfaces) used in the Trusted Platform Module (TPM). She co-authored the paper “Direct anonymous attestation”, which was originally published at ACM CCS 2004 and received a Test of Time award at ACM CCS 2014. She was the technical leader and principal investigator in the EU H2020 FutureTPM project, which identified and developed algorithms for a TPM that will be secure against quantum computer attacks. She is also a principal investigator in several other EU Horizon projects, which make use of trusted computing and distributed ledger technologies to achieve security and privacy in real world applications. Her current research interests are applied cryptography, trusted computing, and network security.

TITLE FOR TALK: Using Trusted Computing to Secure the Internet of Things

ABSTRACT:  The Internet of Things (IoT) is rapidly growing, involving more and more devices, and affecting ordinary people’s everyday life. Making the IoT safe and trustworthy is challenging but necessary. Some IoT applications also require long-term security, i.e., their security should not be limited by the lifetime of any underlying cryptographic algorithms. This is particularly challenging for low-power and low-cost devices. In this talk, we will discuss how to use trusted computing technologies to help achieve these aims.

                                                            Raj Jain

                                                          (Professor, Washington University in St. Louis, USA)

Bio: Raj Jain is currently the Barbara J. and Jerome R. Cox, Jr., Professor of Computer Science and Engineering at Washington University in St. Louis. Dr. Jain is a Life Fellow of IEEE, a Fellow of ACM, a Fellow of AAAS, and a recipient of the 2018 James B. Eads Award from St. Louis Academy of Science, 2017 ACM SIGCOMM Life-Time Achievement Award. Previously, he was one of the Co-founders of Nayna Networks, Inc., a Senior Consulting Engineer at Digital Equipment Corporation in Littleton, Mass, and then a professor of Computer and Information Sciences at Ohio State University in Columbus, Ohio. With 37,000+ citations, according to Google Scholar, he is one of the highly cited authors in computer science. Further information is at http://www.cse.wustl.edu/~jain/.

TITLE FOR TALK: Challenges and Issues in AI for IoT Security

ABSTRACT: AI is everywhere. It is being applied to IoT Security as well. In our research on the security of medical and industrial IoT, we have uncovered several common mistakes, challenges, and issues in applying AI and securing IoT. In this talk, we will discuss five such challenges and their resolution.

                                                      Nitin H. Vaidya

                                                                        (Professor, Georgetown University, USA)

Bio: Nitin Vaidya is the McDevitt Chair of Computer Science at Georgetown University. He received his Ph.D. from the University of Massachusetts at Amherst. He previously served as a Professor and Associate Head in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. He has co-authored papers that received awards at several conferences, including 2015 SSS, 2007 ACM MobiHoc and 1998 ACM MobiCom. He is a fellow of the IEEE. He has served as the Chair of the Steering Committee for the ACM PODC conference, as the Editor-in-Chief for the IEEE Transactions on Mobile Computing, and as the Editor-in-Chief for ACM SIGMOBILE publication MC2R.

TITLE FOR TALK:  Security and Privacy for Distributed Optimization and Learning

ABSTRACT: Consider a network of agents wherein each agent has a private cost function. In the context of distributed machine learning, the private cost function of an agent may represent the “loss function” corresponding to the agent’s local data. The objective here is to identify parameters that minimize the total cost over all the agents. In machine learning for classification, the cost function is designed such that minimizing the cost function should result in model parameters that achieve higher accuracy of classification. Similar optimization problems arise in the context of other applications as well.

Our work addresses privacy and security of distributed optimization, with applications to machine learning. In privacy-preserving machine learning, the goal is to optimize the model parameters correctly while preserving the privacy of each agent’s local data. In security, the goal is to identify the model parameters correctly while tolerating adversarial agents that may be supplying incorrect information. When a large number of agents participate in distributed optimization, security compromise or failure of some of the agents becomes increasingly likely. The talk will provide intuition behind the design and correctness of the algorithms.

                                                       Gaurav Sharma

                                                                    (Professor, University of Rochester, USA)

Bio:  Gaurav Sharma is a professor in the Departments of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology, and a Distinguished Researcher in Center of Excellence in Data Science (CoE) at the Goergen Institute for Data Science at the University of Rochester. He received the PhD degree in Electrical and Computer engineering from North Carolina State University, Raleigh in 1996. From 1993 through 2003, he was with the Xerox Innovation group in Webster, NY, most recently in the position of Principal Scientist and Project Leader. His research interests include data analytics, cyber physical systems, signal and image processing, computer vision, and media security; areas in which he has 54 patents and has authored over 220 journal and conference publications. He served as the Editor-in-Chief for the IEEE Transactions on Image Processing from 2018 through 2020, and for the Journal of Electronic Imaging from 2011 through 2015, and he currently serves on the Editorial Board of the Proceedings of the IEEE. He is a member of the IEEE Publications, Products, and Services Board (PSPB) and chaired the IEEE Conference Publications Committee in 2017-18. He is the editor of the Digital Color Imaging Handbook published by CRC press in 2003. Dr. Sharma is a fellow of the IEEE, a fellow of SPIE, a fellow of the Society for Imaging Science and Technology (IS&T) and has been elected to Sigma Xi, Phi Kappa Phi, and Pi Mu Epsilon. In recognition of his research contributions, he received an IEEE Region I technical innovation award in 2008 and the IS&T Bowman award in 2021. Dr. Sharma served as a 2020-2021 Distinguished Lecturer for the IEEE Signal Processing Society.

TITLE FOR TALK:  AI and IoT in HealthCare: A Clinical Perspective

Abstract: Clinical healthcare is poised for a data revolution driven by IoT devices and AI algorithms. Networked body-worn sensors can collect health data that is orders-of-magnitude richer than what is available today from observations and measurements in clinical environments. Aggregated over time and across populations such data can fuel modern AI/machine learning algorithms and enable personalization and modernization of care with radical improvements in outcomes and reductions in cost. These drastic changes are driven by a confluence of technology and market trends in sensor miniaturization, communications, and machine learning/AI that enable a host of new physiological and physical measurement based biomarkers for assessing disease condition, treatment effectiveness, and longitudinal progression. In contrast with the subjective, sporadic, in- clinic assessments that are in common use today, the sensor and AI based biomarkers are not only objective and repeatable but used over extended monitoring intervals can provide a comprehensive picture of health conditions and treatment efficacy. We highlight these themes using examples from our recent and ongoing research that features light-weight, low-power sensors that can be affixed to the body like adhesive temporary tattoos, in a diverse set of health monitoring applications including quantification of movement disorders for Parkinson’s and Huntington’s diseases, stroke rehabilitation, and cardiac monitoring. Finally, we highlight emerging directions, open issues, and challenges for research and development in this exciting and increasingly important area.

                                                    Patrick S.P. Wang

                                                                 (Professor, Northeastern University, USA)

Bio:  Prof. Patrick S.P. Wang, PhD. Fellow, IAPR, ISIBM, IETI and IEEE & ISIBM Outstanding Achievement Awardee, Tenured Full Professor, Northeastern University, USA, iCORE (Informatics Circle of Research Excellence) Visiting Professor, MIT, Harvard, University of Calgary, Canada, Otto-Von- Guericke Distinguished Guest Professor, Magdeburg University, Germany, Zijiang Visiting Chair, ECNU, Shanghai, China, as well as honorary advisory professor of many key universities in China, including Sichuan University, Xiamen University, East China Normal University, Shanghai, and Guangxi Normal University, Guilin. Prof. Wang received his BSEE from National Chiao Tung University (Jiaotong University), MSEE from National Taiwan University, MSICS from Georgia Institute of Tech, and PhD, CS, Oregon State U.

Dr. Wang has published over 26 books, 300 technical papers, 3 USA/European Patents, in PR/AI/TV/Cybernetics/Imaging, and is currently founding Editor-in-Chief of IJPRAI (International Journal of Pattern Recognition and Artificial Intelligence) , and Book Series of MPAI, WSP. In addition to his technical interests, Dr. Wang also published a prose book, “Harvard Meditation Melody”《哈佛冥想曲》, 《劍橋狂想曲》and many articles and poems regarding Du Fu and Li Bai’s poems, Beethoven, Brahms, Mozart and Tchaikovsky’s symphonies, and Bizet, Verdi, Puccini and Rossini’s operas.

TITLE FOR TALK:  Intelligent Pattern Recognition (IPR) and Applications to Imaging

Abstract: This talk is concerned with fundamental aspects of Intelligent Pattern Recognition (IPR) and applications. It basically includes the following: Basic Concept of Automata, Grammars, Trees, Graphs and Languages. Ambiguity and its Importance, Brief Overview of Artificial Intelligence (AI), Brief Overview of Pattern Recognition (PR), What is Intelligent Pattern Recognition (IPR) Interactive Pattern Recognition Concept, Importance of Measurement and Ambiguity, How it works, Modeling and Simulation, Basic Principles and Applications to Computer Vision, Security, e-Forensics, Road Sign Design, biomedical diagnosis, Safer biomedical diagnosis, Traffic and Robot Driving with Vision, Ambiguous (design of Road Signs vs Unambiguous (Good) Road Signs, How to Disambiguate an Ambiguous Road Sign? What is Big Data? and more Examples and Applications of Learning and Greener World using Computer Vision. Finally, some future research directions are discussed.

                                           Robert Grossman

                                                                   (Professor, The University of Chicago, USA)

Bio: Robert L. Grossman is the Frederick H. Rawson Distinguished Service Professor of Medicine and Computer Science and the Jim and Karen Frank Director of the Center for Translational Data Science at the University of Chicago. CTDS is a research center that focuses on data science and its applications to problems in biology, medicine, health care and the environment. He is a Director of the Open Commons Consortium, a not-for-profit that develops and operates data commons to support research in science, medicine, health care, and the environment. He is also a Partner at Analytic Strategy Partners LLC, which he founded in 2016. Prior to that, from 2002 to 2015, he was the Founder and Managing Partner at Open Data Group, which built and deployed AI and machine learning models over big data in financial services, insurance, healthcare and IoT.

Title for Talk: Deploying Machine Learning and AI Models in IoT

Abstract: We review some of the options, challenges, trade-offs, and emerging architectures for deploying machine learning and AI models in IoT systems and applications.  


                                      Cynthia Rudin

                                                                     (Professor, Duke University, USA)

Bio: Cynthia Rudin is a professor of computer science and engineering at Duke University. She directs the Interpretable Machine Learning Lab, and her goal is to design predictive models that people can understand. Her lab applies machine learning in many areas, such as healthcare, criminal justice, and energy reliability. She holds degrees from the University at Buffalo and Princeton. She is the recipient of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (the “Nobel Prize of AI”). She received a 2022 Guggenheim fellowship, and is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the Association for the Advancement of Artificial Intelligence. Her work has been featured in many news outlets including the NY Times, Washington Post, Wall Street Journal, and Boston Globe.

Title of Talk: The Extreme of Interpretability in Machine Learning

Abstract: With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice, flawed models in healthcare, and black box loan decisions in finance. Transparency and interpretability of machine learning models is critical in high stakes decisions. In this talk, I will focus on two of the most fundamental problems in the field of interpretable machine learning: optimal sparse decision trees and optimal sparse generalized additive models. I will also discuss a hypothesis for why we can find interpretable models with the same accuracy as black box models and discuss recent and future work on dimension reduction for data visualization and model class visualization in variable importance space.


                                   Prof. Henry TYE

                                         (Professor,The Hong Kong University of Science and Technology,Hong Kong)

Bio: Prof. Henry Tye graduated from California Institute of Technology in 1970 and received his PhD from the Massachusetts Institute of Technology in 1974. He did research at Stanford Linear Accelerator Center, Stanford University and Fermi National Accelerator Laboratory before moving to Cornell in 1978, where he stayed until last year. He is a Fellow of the American Physical Society. Before joining HKUST as the Director of IAS between 2011 and 2016, Prof. Henry Tye was the Horace White Professor of Physics at Cornell University. He was an IAS Professor and Chair Professor of Physics until December 2019. Prof. Tye’s research interest is in theoretical particle physics and cosmology. 
Title: How Our Universe Was Created
Abstract: It is generally accepted that our universe came from nothing. Predictions of this inflationary universe scenario is strongly supported by detailed observational data collected with modern high tech.


                                                    Vasilis Syrgkanis

                                                  (Principal Researcher at Microsoft Research, New England)

Bio:  Dr. Syrgkanis is the Principal Researcher at Microsoft Research, New England, where he is also co-leading the project on Automated Learning and Intelligence for Causation and Economics (ALICE). His research lies at the intersection of theoretical computer science, machine learning and economics/econometrics. He received his Ph.D. in Computer Science from Cornell University, where he had the privilege to be advised by Eva Tardos and then spent two years as a postdoc researcher at Microsoft Research, NYC, as part of the Algorithmic Economics and the Machine Learning groups. He obtained my diploma in EECS at the National Technical University of Athens, Greece.

Title: Towards Automating the Causal Inference Pipeline

Abstract:  With the advent of modern automated machine learning platforms, predictive modelling has been made accessible to almost everyone with access to a dataset and an outcome of interest. However, many data analytic tasks that data shareholders are facing are decision making tasks that require causal modeling; understanding how the outcome of interest will change when we intervene on one of the variables. Making causal inference as accessible as predictive modeling is one of the main challenges of modern data analytics. The causal inference pipeline requires many more cognitive steps than predictive modeling, such as data cleaning, assumption elicitation, causal validation, sensitivity analysis, inference (uncertainty quantification), experimentation. Many of these extra components necessitate a human-in-the-loop architecture. We discuss some recent technical advancements on the research side, such as dealing with corrupted data within a causal task, automating causal estimation with machine learning and automating the construction of confidence intervals. We will also give an overview of the growing software ecosystem that makes causal machine learning more accessible to data scientists and decision makers, such as the EconML and the DoWhy library from Microsoft Research.

                                                Sercan Arik

                                                       (Research Scientist and Manager at Google Cloud AI)

Bio: Sercan Arik is currently working as a Research Scientist and Manager at Google Cloud AI. His current work is motivated by the mission of democratizing AI and bringing it to the most impactful use cases, from Healthcare, Finance, Retail, Media, Education, Communications and many other industries. Towards this goal, he focuses on how to make AI more high-performance for the most-demanded data types, interpretable, trustable, data-efficient, robust and reliable. He led research projects that were launched as major Google Cloud products and yielded significant business impact, such as TabNet and Covid-19 forecasting. Before joining Google, he was a Research Scientist at Baidu Silicon Valley AI Lab. At Baidu, he focused on deep learning research, particularly for applications in human-technology interfaces. He co-developed deep learning-based state-of-the-art speech synthesis, keyword spotting, voice cloning, and neural architecture search systems. He completed his PhD degree in Electrical Engineering at Stanford University. He co-authored more than 50 journal and conference publications.

Title For Talk: Explainable Deep Learning for Structured Data

Abstract: In this talk, we go over our 3 projects developed to push the limits of deep learning for structured data: TabNet, TFT, and DVRL.

TabNet is a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet. TabNet uses sequential attention to choose which features to reason from at each decision step, enabling interpretability and more efficient learning as the learning capacity is used for the most salient features. We demonstrate that TabNet outperforms other neural network and decision tree variants on a wide range of non-performance-saturated tabular datasets and yields interpretable feature attributions plus insights into the global model behavior. Finally, for the first time to our knowledge, we demonstrate self-supervised learning for tabular data, significantly improving performance with unsupervised representation learning when unlabeled data is abundant.
Multi-horizon forecasting problems often contain a complex mix of inputs — including static (i.e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically — without any prior information on how they interact with the target. While several deep learning models have been proposed for multi-step prediction, they typically comprise black-box models which do not account for the full range of inputs present in common scenarios. We introduce the Temporal Fusion Transformer (TFT) — a novel attention-based architecture which combines high-performance multi-horizon forecasting with interpretable insights into temporal dynamics. To learn temporal relationships at different scales, the TFT utilizes recurrent layers for local processing and interpretable self-attention layers for learning long-term dependencies. The TFT also uses specialized components for the judicious selection of relevant features and a series of gating layers to suppress unnecessary components, enabling high performance in a wide range of regimes. On a variety of real-world datasets, we demonstrate significant performance improvements over existing benchmarks, and showcase three practical interpretability use-cases of TFT.
Quantifying the value of data is a fundamental problem in machine learning. Data valuation has multiple important use cases: (1) building insights about the learning task, (2) domain adaptation, (3) corrupted sample discovery, and (4) robust learning. To adaptively learn data values jointly with the target task predictor model, we propose a meta learning framework which we name Data Valuation using Reinforcement Learning (DVRL). We employ a data value estimator (modeled by a deep neural network) to learn how likely each datum is used in training of the predictor model. We train the data value estimator using a reinforcement signal of the reward obtained on a small validation set that reflects performance on the target task. We demonstrate that DVRL yields superior data value estimates compared to alternative methods across different types of datasets and in a diverse set of application scenarios. The corrupted sample discovery performance of DVRL is close to optimal in many regimes (i.e. as if the noisy samples were known apriori), and for domain adaptation and robust learning DVRL significantly outperforms state-of-the-art by 14.6% and 10.8%, respectively.

                                                       Yu (Hugo) Chen

                                                                     (Research Scientist, Facebook Meta AI)

Bio: Yu (Hugo) Chen is a Research Scientist at Meta AI. He got his PhD degree in Computer Science from Rensselaer Polytechnic Institute. His research interests lie at the intersection of Machine Learning (Deep Learning) and Natural Language Processing, with a particular emphasis on the fast-growing field of Graph Neural Networks and their applications in various domains. His work has been published at top-ranked conferences including but not limited to NeurIPS, ICML, ICLR, AAAI, IJCAI, NAACL, KDD, WSDM, TheWebConf, ISWC, and AMIA. He was the recipient of the Best Student Paper Award of AAAI DLGMA’20. He was one of the book chapter contributors of the book “Graph Neural Networks: Foundations, Frontiers, and Applications”. He delivered a series of DLG4NLP tutorials at NAACL’21, SIGIR’21, KDD’21, IJCAI’21, AAAI’22 and TheWebConf’22. His work has been covered in popular technology and marketing publications including World Economic Forum, TechXplore, TechCrunch, Ad Age and Adweek. He is a co-inventor of 4 US patents.

Title For Talk: Graph Structure Learning for Graph Neural Networks

Abstract:  Due to the excellent expressive power of Graph Neural Networks (GNNs) on modeling graph-structure data, GNNs have achieved great success in various applications such as Natural Language Processing, Computer Vision, recommender systems and drug discovery. However, the great success of GNNs relies on the quality and availability of graph-structured data which can either be noisy or unavailable. The problem of graph structure learning aims to discover useful graph structures from data, which can help solve the above issue.

In this talk, I will provide a comprehensive introduction of graph structure learning through the lens of both traditional machine learning and GNNs. Specifically, I will show how this problem has been tackled from different perspectives, for different purposes, via different techniques, as well as its great potential when combined with GNNs. I will cover recent progress in graph structure learning for GNNs and highlight some promising future directions in this research area.

                                           Chris Boshuizen

                                                           (Co-founder of Planet Labs)

Bio: Dr. Chris Boshuizen is an Australian astronaut, scientist, entrepreneur, investor, and musician. Currently a Partner at DCVC, a deep tech investment company in San Francisco where he focuses on funding cutting edge space companies, Boshuizen completed his PhD in physics at The University of Sydney before accepting a position at the NASA Ames Research Center in California. There Dr Boshuizen established Singularity University and most notably co-created the NASA Phonesat. After leaving NASA he co-founded Planet Labs, the first company to employ nanosatellites in a commercial capacity, radically reducing the cost of lifting payloads into space and paving the way for today’s large constellations of spacecraft. Today, Planet operates the largest fleet of Earth-observing satellites and maps the entire surface of the Earth daily, enabling key insights into our changing world that were previously unobtainable. Boshuizen was the 2014 Advance Global Australian of the Year award winner, and has subsequently become a member of the Advance Board of Directors where he is an active spokesperson for successful Australians abroad. Boshuizen is also a musician and releases music under the name “Dr Chrispy”. Dr Boshuizen flew to space as a commercial astronaut on Blue Origin’s New Shepard NS-18 mission on October 13 2021.

Title of Talk: Lessons from the transformation of Space Exploration

Abstract: Space has historically been the domain of large government programs, but over the past two decades new technologies have driven the creation of a thriving commercial space sector. New generations of satellite operators can do more in space than Super Power nations of the past. In this talk Dr Boshuizen will examine lessons from his first hand experience developing these spacecraft, and share exciting news about recent developments in human spaceflight, including his recent trip to space with Star Trek actor William Shatner. 

Important Deadlines

Full Paper Submission:7th May 2022
Acceptance Notification: 19th May 2022
Final Paper Submission:28th May 2022
Early Bird Registration: 27th May 2022
Presentation Submission: 29th May 2022
Conference: 6 – 9 June 2022

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