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2022 Past Events

Quantum Paradigm for Machine Learning -- merging quantum computation and machine learning ...
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Quantifying Configuration Health of Software Systems Video: https://ieeetv.ieee.org/video/quantifying-configuration-health-of-software-systems …DevOps, Software configuration health index, diagnosing configuration problems, ... Speaker: IEEE Fellow, Distinguished Visitor, and Past Program Director at the National Science Foundation (NSF), Prof. Krishna Kant Date: November 2, 2022, 9 AM (PT) Virtual Event via Zoom and YouTube live Free Registration: https://www.eventbrite.com/e/quantifying-configuration-health-of-software-systems-tickets-428096777987 Synopsis: Misconfigurations have been Achilles heel in complex software systems and are often responsible for  the downtimes and enablers of the cyber-attacks. With the recent rise of the devOps  paradigm, configuration changes have become even more frequent, sometimes, multiple times a day, thereby underscoring the importance of correct configuration. In this talk, the speaker will discuss the notion of configuration health index to provide an assessment of how well the system is configured with respect to various objectives and how it can be quantified based on a combination of domain knowledge and available data. Prof. Kant will also discuss the challenges in extracting and quantifying the domain knowledge and the selection of tests for diagnosing configuration problems. Speaker Biography: Krishna Kant is a professor in the Computer and Information Science Department at Temple University in Philadelphia, PA. Earlier he was a research professor in the Center for Secure Information Systems (CSIS) at George Mason University. From 2008-2013 he served as a program director at the National Science Foundation (NSF) where he managed the computer systems research (CSR) program and was instrumental in the development and running of an NSF-wide sustainability initiative called SEES (science, engineering, and education for sustainability). Earlier, he served at Intel Corporation for 11 years working on a variety of data center architecture and technology issues. From 1991 to 1997, he held the consultant position at Ericsson (formerly Bellcore) and worked on many broadband and narrowband telecommunications technologies. Before 1991, he was an Associate Professor of Computer Science at the Pennsylvania State University with research contributions in performance modeling and distributed systems. He received his Ph.D. degree in Mathematical Sciences from the University of Texas at Dallas in 1981. He carries extensive experience in academia, industry, and government and has published in a wide variety of areas in computer science. He has authored a graduate textbook on performance modeling of computer systems and coedited several other books.  He is a Fellow of the IEEE and an IEEE distinguished visitor.
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#IEEEday Event 2: Exploring the math in Support Vector Machines Video: https://ieeetv.ieee.org/video/exploring-the-math-in-support-vector-machines …Lagrange Multipliers,  Convex Hulls, Orthogonal Functions, Hilbert Space, Duality, Cover Theorem, ... Speaker: Chapter Chair, Book Author, and SJSU Faculty Member, Vishnu S. Pendyala, PhD Date: October 4, 2022, 6:15 PM (PT) Virtual Event via Zoom and YouTube live Free Registration: https://www.eventbrite.com/e/exploring-the-math-in-support-vector-machines-tickets-425130124647 Synopsis: “SVMs are a rare example of a methodology where geometric intuition, elegant mathematics, theoretical guarantees, and practical algorithms meet” – Bennet and Campbell Support Vector Machines (SVMs) are used for supervised machine learning and have been successful in many applications including those like image classification that favor deep learning. SVM owes its power to the intriguing math involved in its fabrication. This talk will introduce SVM and cover some of that math. Topics covered will include constrained and unconstrained optimization, convexity, the general notion of a function space, minmax equilibrium, duality, Cover theorem, Kernels, and Mercer theorem. Speaker Biography: Dr. Vishnu S. Pendyala is a faculty member of the Department of Applied Data Science at San Jose State University and is the Chair of the IEEE Computer Society, Silicon Valley Chapter. He has over two decades of experience with software industry leaders like Cisco and Synopsys in the Silicon Valley, USA. During his recent 3-year term as an ACM Distinguished speaker and before that as a researcher and industry expert, he gave numerous (50+) invited talks. He holds MBA in Finance and PhD, MS, and BE degrees in Computer Engineering from US and Indian universities. Dr. Pendyala taught a one-week course sponsored by the Ministry of Human Resource Development (MHRD), Government of India, under the GIAN program in 2017 to Computer Science faculty from all over the country and delivered the keynote in a similar program sponsored by AICTE, Government of India in 2022. Dr. Pendyala’s book, “Veracity of Big Data: Machine Learning and Other Approaches to Verifying Truthfulness” is catalogued by several libraries, including those of MIT, Stanford, CMU, and internationally. His edited books, “Tools and Techniques for Software Development in Large Organizations: Emerging Research and Opportunities” and "Machine Learning for Societal Improvement, Modernization, and Progress" are also well-received and are indexed by prominent libraries all over the world. Dr. Pendyala served on the Board of Directors, Silicon Valley Engineering Council during 2018-2019. He received the Ramanujan memorial gold medal at State Math Olympiad and has been a successful leader during his undergrad years. He also played an active role in Computer Society of India and was the Program Secretary for its annual national convention. Marquis Who's Who has selected Pendyala’s biography for inclusion in multiple of its publications for multiple years. Dr. Pendyala spends his fast-vanishing spare time volunteering and has been a reviewer / judge for competitions like the Grace Hopper Celebration of Women in Computing, state level DECA, science fairs and other STEAM events for the last several years. He has traveled widely, covering ~30 states in the US and 23 countries. He resorts to yoga, spirituality, and listening to music, to unwind and be himself.
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#IEEEday Event 1: Android Mobile Malware Detection Models – A Schematic View …Permission pairs in Android phones, dynamic network traffic-based and machine learning based approaches ... Speaker: Indian Institute of Technology Associate Professor Sateesh Kumar Peddoju, PhD Date: October 4, 2022, 9:00 AM (PT) Virtual Event via Zoom and YouTube live Free Registration: https://www.eventbrite.com/e/android-mobile-malware-detection-models-a-schematic-view-tickets-428133417577  Synopsis: In today’s era, smartphones have become ubiquitous because of their fascinating capabilities, for instance, sending and receiving emails, online shopping, mobile Internet browsing, and location-based services, apart from regular calling and messaging features. Additionally, a user-friendly app interface is present in most smartphones allowing users to download various apps according to their needs. However, with an increase in their popularity, there has been an analogous increase in malware attacks targeting smartphones. If a smartphone gets compromised by any malware, it may cause many serious threats, such as financial loss, system damage, data loss, and privacy leakage. Detecting such malware is the key requirement in mobile communications. This talk presents different models developed at our lab to detect Android smartphone malware. The talk first presents an in-depth analysis of how smartphone malware has evolved over the past few years, their ways of infection, threats posed by them, and a comprehensive review of the related works in the field of malware detection. The talk also introduces a static approach that analyzes permission pairs in Android phones. It next discusses a dynamic network traffic-based approach for Android malware detection to analyze the run-time behavior of malicious Android apps. Finally, the talk will present a hybrid model that combines K-Medoids and KNN algorithms on hybrid feature vectors to detect Android malware. Speaker:  Sateesh Kumar Peddoju is currently working with the Indian Institute of Technology Roorkee, India, as an Associate Professor in the Department of Computer Science and Engineering. He did his Ph.D. and M. Tech in Computer Science and Engineering from Osmania University, Hyderabad, India, in 2010 and 2003, respectively. Sateesh is the Senior Member of IEEE and ACM. He acted as the Vice-Chair for IEEE Computer Society, India Council. Currently, he is the Treasurer for the IEEE Roorkee section and Founding Faculty Advisor for ACM Student Chapter-IIT Roorkee. Sateesh received several awards like Faculty Cloud Ambassador for Amazon Web Services Cloud Ambassador Program, IBM SUR Award, Microsoft Educate Award, best paper/presentation awards, best teacher award in previous employment, and travel grants. He received the university merit scholarship for his high rank in the University. Sateesh published several research papers in reputed journals and conferences like IEEE TIFS, IEEE Access, IEEE Potentials, ACM MobiCom, IEEE TrustCom, IEEE MASS, ACM/IEEE ICDCN, and ISC. He co-authored a book titled Security and Storage Issues in the Cloud Environment and edited a book titled Cloud Computing Systems and Applications in Healthcare. Sateesh is the founding Steering Committee Chair for SLICE 2018 and acted as the General Chair, General Co-Chair, Publication Co-Chair, Session Chair, and Program Committee member in several conferences. He is currently acting as the Chair for the Communications sub-group of the IoT Security Guidelines committee constituted by MIETY, Government of India. He is also an expert member of the CERT-Ukt committee constituted by the Government of Uttarakhand, India. His research interests include Cloud Computing, Ubiquitous Computing, and Security.
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Safety for Autonomous Vehicles: Addressing Interoperability and Dependability Challenges with IEEE P2851 Video: https://www.youtube.com/watch?v=84ucEVqC890 Speaker: Jyotika Athavale, NVIDIA Date: Sunday, September 11, 2022, 6:00 PM (PT) In-person at Santa Clara University and online via Zoom and YouTube live Abstract: The IEEE P2851 standard defines a dependability lifecycle of products with focus on interoperable activities related to functional safety and its interactions with reliability, cybersecurity, SOTIF (Safety of the intended functionality) and real time. The standard also describes methods, description languages, data models, and databases that have been identified as necessary or critical, to enable the exchange/interoperability of data across all steps of the lifecycle encompassing activities executed at IP, SoC, system and item levels, in a technology independent way across application domains such as automotive, industrial, medical and avionics safety critical systems. The speaker, who is the chair of the working group on the standard will provide more insights into the topic. Keywords: IEEE P2851, SOTIF, SoC, Safety critical systems, Self-driving cars Biography: Jyotika Athavale is a senior technical leader in automotive functional safety at NVIDIA, driving capability development, safety architectures and methodologies, system safety engineering activities and pathfinding for safety critical markets such as autonomous driving and avionics. Prior to NVIDIA, Jyotika was Principal Engineer (Director) at Intel Corporation where she led functional safety platform architecture for Automotive and Avionics use cases and drove Corporate wide methodologies for radiation effects modeling and product qualification across market segments. She is a Senior IEEE Member and an influencer in emerging technologies and standardization initiatives. She was awarded the IEEE Computer Society Golden Core Award in 2022. Currently, an elected member of the Computer Society Board of Governors and Executive Committee, Jyotika also serves as the 2022 Secretary and Distinguished Visitor of the Computer Society. She chairs the IEEE P2851 Standard on Functional Safety interoperability. Jyotika is a core member of the IEEE Computer Society Functional Safety Standards Committee, Design Automation Standards Committee and Diversity and Inclusion Committee. In addition, she represents the Computer Society at the IEEE USA AI Policy Committee, IEEE SA Industry Connection Group on Mobility as a Service (MaaS) related standardization activities, the IEEE Women in Engineering (WIE) Committee and the IEEE Systems Council. Jyotika also serves on various technical conference committees and has authored patents and technical publications in various conferences and journals. For more information, please refer to jyotikaaathavale.com
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Santa Clara University 500 El Camino Real
34th Annual Hot Chips Symposium -- register for their Dlist to receive the program for the August Symposium ...
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Limitations of AI Systems with Respect to Explainability, Interpretability, Fairness, Ethics, and Causality -- domains, training, trust, predictability, explainable AI, research efforts ...
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(on the Internet)
A New Approach for Storage-Centric Computers -- disjoint node array, large databases, cost, efficiency, speed, standard protocols, power and cooling ...
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(on the Internet)
ICADS 2022: International Conference on Applied Data Science … Fuzzy Loss functions for GANs, Learning Analytics, Next Generation AI and Sustainability, Deep Learning for Melodic Frameworks... Full Papers and abstracts Speakers: Prof. Priti S. Sajja, Sardar Patel University, India Prof. Elvira Popescu, University of Craiova, Romania Dr. Celestine Iwendi, University of Bolton, UK Dr. Vishnu S. Pendyala, San Jose State University, USA Date: Tuesday, July 12, 2022, 8:00 AM - 12 noon (PST) Virtual Event via Zoom and YouTube live Free registration 8:00 am “Fuzzy Loss Functions for Generative Adversarial Neural Network” Prof. Priti Sajja, India (8:30 pm India time) 9:00 am “Applied Data Science for Education” Prof. Elvira Popescu, Romania (7 pm Romania time) 10:00 am “Next Generation Artificial Intelligence and Energy Sustainability” Dr. Celestine Iwendi, Bolton, UK (6 pm UK time) 11:00 am “Deep Learning for Melodic Frameworks” Dr. Vishnu S. Pendyala Synopsis: Fuzzy Loss Functions for Generative Adversarial Neural Network  Generative Adversarial neural Networks (GAN) are very popular for medical image analysis. The paper presents an innovative fuzzy loss function for the GAN in the domain of image analysis. The GAN architecture presented here uses two convolutional neural networks, one of which is a generator and the other is a discriminator. Besides the loss function, the training algorithm is also presented in the paper. The proposed approach is generic and can be used for many domains pertaining to medical image analysis and diagnosing. The proposed work with the modified loss function is experimented on the Covid-19 image set. The results achieved are discussed in brief. In the end, the paper presents limitations and future enhancements possible based on the work. Applied Data Science in Education Learning analytics (LA) is a growing research area, which aims at selecting, analyzing and reporting student data (in their interaction with the online learning environment), finding patterns in student behavior, displaying relevant information in suggestive formats; the end goal is the prediction of student performance, the optimization of the educational platform and the implementation of personalized interventions. The topic is highly interdisciplinary, including machine learning techniques, educational data mining, statistical analysis, social network analysis, natural language processing, but also knowledge from learning sciences, pedagogy and sociology. Despite its increasing popularity, LA has been applied less in the context of social media-based environments; hence in this talk we focus especially on research in social learning analytics area. In particular, we explore academic performance predictors and the relationships between students’ learning styles and their social media use; we also investigate students’ collaboration patterns and the community of inquiry supported by social media tools. Four research directions are tackled: analytics dashboards, predictive analytics, social network analytics and discourse analytics. As far as analysis techniques are concerned, we apply various approaches, such as: classification, regression, clustering and PCA algorithms, textual complexity analysis, social network analysis techniques, content analysis based on Community of Inquiry. We thus address the “trinity” of methodological approaches: i) network analysis (representing actor-actor / social relations); ii) process-oriented analysis (based on action logs and pattern detection); iii) content-oriented analysis (based on learner created artefacts); hence a more comprehensive learning analytics perspective is provided. Next Generation Artificial Intelligence and Energy Sustainability The industry is on a mission to capture the business value that comes with the Next Generation Artificial Intelligence in the context of Industry 5.0 and Energy 5.0 as regarding the provision of renewable energy production facilities. This has greatly increased the need to address the cyber risk landscape with a secure, vigilant, and resilient response and system in place. This marriage of algorithms, processes, and ingenuity will enable humans and machines work hand-in-hand utilising human-centric design solutions in collaboration with human resources to enable sustainable, personalized and autonomous manufacturing through enterprise social networks. This talk will analyse how AI could engender better and more informed decision-making during crisis management scenarios. Furthermore, we shall elaborate how Industry 5.0 actively encourages and enhances the relationship between humans and robots in a cyber-physical domain and in the field of energy consumption, while also relying on advanced five-sense and hologram-based communications. Deep Learning for Melodic Frameworks Audio signals that appeal to the ear and create melody usually confirm to a structural framework. Audio is one of the four datatypes that deep learning works best on, the other three being image, video, and text. This talk will give insights into our deep learning experiments on audio signals particularly with respect to latent melodic frameworks within them. Speaker Biography: Prof. Priti Srinivas Sajja has been working at the Post Graduate Department of Computer Science, Sardar Patel University, India since 1994 and presently holds the post of Professor. She specializes in Artificial Intelligence and Systems Analysis & Design especially in knowledge-based systems, soft computing and multi-agent systems. She is author of Illustrated Computational Intelligence (Springer, 2020) and Essence of Systems Analysis and Design (Springer, 2017) published at Singapore and co-author of Intelligent Techniques for Data Science (Springer, 2016); Intelligent Technologies for Web Applications (CRC, 2012) and Knowledge-Based Systems (J&B, 2009) published at Switzerland and USA, and four books published in India. She is supervising work of a few doctoral research scholars while eight candidates have completed their Ph.D research under her guidance. She has served as Principal Investigator of a major research project funded by the University Grants Commission, India and as a member of Indo-Russian Joint Research Project. She has produced 219 publications in books, book chapters, journals, and in the proceedings of national and international conferences out of which five publications have won best research paper awards. Prof. Elvira Popescu is a Full Professor at the Computers and Information Technology Department, University of Craiova, Romania. Her research interests include technology-enhanced learning, adaptive educational systems, learner modeling, computer-supported collaborative learning, learning analytics, and intelligent and distributed computing. She authored and co-authored over 100 publications, including two books, journal articles, book chapters, and conference papers. In addition, she co-edited six journal special issues, as well as 20 international conference proceedings. She participated in over 15 national and international research projects, three of which as a principal investigator. Prof. Popescu also serves as the Vice Chair for the IEEE Women in Engineering Romania Section Affinity Group and is a board member for the IEEE Technical Committee on Learning Technology and the International Association of Smart Learning Environments. She is also a Distinguished Speaker in IEEE Computer Society Distinguished Visitors Program (2020-2023). She received several scholarships and awards, including five best paper distinctions. She is actively involved in the research community by serving as associate editor for three journals (IEEE Transactions on Learning Technologies, Smart Learning Environments, Frontiers in Computer Science), member of five other journal editorial boards, organizing a series of international workshops (SPeL 2008–2020), serving as a conference chair, program committee chair and track chair for over 20 conferences. Dr. CELESTINE IWENDI is an IEEE Brand Ambassador as well as a visiting Professor with many universities including Coal City University, Nigeria; School of Computing, Faculty of Engineering, BIHER, India; and the School of Computing & Engrg, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, India. He also serves as an Honorary Adjunct Professor and Investigator in the Center of Excellence in Robotics and Mechatronics, ITM Baroda University, India; Adjunct Professor, Delta State Polytechnic, Nigeria; Honorary Professor in the School of Computing Science and Engineering, Galgotias University, India; and an Associate Professor (Senior Lecturer) in the School of Creative Technologies, University of Bolton, United Kingdom. He is a Fellow of the Higher Education Academy, United Kingdom and a Fellow of the Institute of Management Consultants. He authored over 60 peer-reviewed articles. He has delivered over 65 invited and keynote talks. He is a Distinguished Speaker, ACM; Senior Member of IEEE; Member, ACM; Member, IEEE Computational Intelligence Society; Senior Member, Swedish Engineers; Member, Nigeria Society of Engineers; and Member, Smart Cities Community, IEEE. Dr. Celestine is listed among the 2% top-scientist list. The list has been prepared by Stanford University and published by Elsevier. Dr. Vishnu S. Pendyala is a faculty member of the Department of Applied Data Science at San Jose State University and is the Chair of the IEEE Computer Society, Silicon Valley Chapter. He has over two decades of experience in the software industry in the Silicon Valley, USA. During his recent 3 year term as an ACM Distinguished speaker and before that as a researcher and industry expert, he gave numerous (50+) invited talks. He holds MBA in Finance and PhD, MS, and BE degrees in Computer Engineering from US and Indian universities. Dr. Pendyala taught a one-week course sponsored by the Ministry of Human Resource Development (MHRD), Government of India, under the GIAN program in 2017 to Computer Science faculty from all over the country and delivered the keynote in a similar program sponsored by AICTE, Government of India in 2022.
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Open House: Machine Learning, the mortar of modernization and State of the Chapter An introduction / refresher on Machine Learning through exciting analogies, applications, and examples Speaker: Chapter Chair, Book Author, and SJSU Faculty Member, Vishnu S. Pendyala, PhD Date: June 14, 2022, 12:00 PM (PST) Location: SEMI, 673 S Milpitas Blvd, Milpitas, CA 95035 (In-person at SEMI Milpitas and online) Register (free) to attend [Seating is limited. Please be kind to register for in-person attendance only if you are very sure to attend. Masks will be required indoors.] Machine Learning is advancing the civilization and is one of the key drivers of economy today. Machine Learning literacy may one day become essential the way computer literacy is today. This talk intends to explain Machine Learning concepts to wider audiences by using easy to relate real world analogies and serves as a refresher to the already initiated. A fundamental concept in machine learning is similarity. The dot product that is ubiquitously present in machine learning is a measure for similarity. Similarity is key to human learning as well. We learn in delta increments by comparing and contrasting with what we already know. Analogies or similarities help Machine learning is an exciting field for many, but the rigor, math, and its rapid evolution are often found to be formidable, keeping them away from studying and pursuing a career in this area. This talk will use similarity to explain the underpinnings of machine learning to those who want to get insights into machine learning and data science. A number of concepts, techniques, and algorithms in machine learning will be explained relating them to real-world analogies in a way that audiences with little or no background in the area will also be able to appreciate. Speaker: Dr. Vishnu S. Pendyala is a faculty member of the Department of Applied Data Science at San Jose State University and is the Chair of the IEEE CS, Silicon Valley Chapter. He has over two decades of experience in the software industry and recently completed his 3 year term as an ACM Distinguished speaker. He holds MBA in Finance and PhD, MS, and BE degrees in Computer Engineering from US and Indian universities. Dr. Pendyala taught a one-week course sponsored by the Ministry of Human Resource Development (MHRD), Government of India, under the GIAN program in 2017 to Computer Science faculty from all over the country and delivered the keynote in a similar program sponsored by AICTE, Government of India in 2022.
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SEMI, 673 S Milpitas Blvd, Milpitas, CA 95035
Meta-algorithms in Machine Learning ...Ensemble methods; models that help win competitions; Bagging, Boosting, Stacking... Speaker: Chapter Chair, Book Author, and SJSU Faculty Member, Vishnu S. Pendyala, PhD Date: May 31, 2022, 6:00 PM (PST) Virtual Event via Zoom and YouTube live Free registration Synopsis: The word ‘meta’ indicates something beyond, a level up, or a higher layer. Meta-algorithms in Machine learning work on top of the known classification and regression algorithms such as Decision Trees, Logistic Regression, and Support Vector Machines to improve the performance substantially. It is often observed that these algorithms fetch top positions in the competition leaderboards and are now commonly used in the industry as well. This talk will cover some of the popular techniques of Meta-learning and explain why they generally work well. The techniques covered will include bagging, boosting, stacking, and algorithms within those broad categories such as random forest, adaboost, and gradient boosting. Time permitting, related concepts such as one-shot / few-shot learning, siamese neural network, and transfer learning will also be introduced. Speaker Biography: Dr. Vishnu S. Pendyala is a faculty member of the Department of Applied Data Science at San Jose State University and is the Chair of the IEEE Computer Society, Silicon Valley Chapter. He has over two decades of experience in the software industry and recently completed his 3 year term as an ACM Distinguished speaker. He holds MBA in Finance and PhD, MS, and BE degrees in Computer Engineering from US and Indian universities. Dr. Pendyala taught a one-week course sponsored by the Ministry of Human Resource Development (MHRD), Government of India, under the GIAN program in 2017 to Computer Science faculty from all over the country and delivered the keynote in a similar program sponsored by AICTE, Government of India in 2022.
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Design for Sustainability (DFS) - Green Hardware and Software -- greenhouse gases, GHG, DfS, carbon emissions, sustainable design ...
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Ask Me Anything (AMA) session with Björn Schuller -- AI of Covid audio, reducing bias, affective computing, emotions, labeling, lie-detecting, ethics for AI, AEI, ML, deep learning, unsupervised & self-supervised learning, double-blind reviews, careers ...
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Nature-inspired Optimization Algorithms Applied to Control Systems -- transient, steady-state, tuning, meta-heuristic algorithms, self-organizing, fuzzy logic controller ...
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Impact of Blockchain Technologies for Digital Transformation (1:02:10 + Q&A) Blockchain technology is a secure decentralized distributed ledger. Blockchain can create and maintain a continuously growing data transaction blocks of cryptographically secured data records against fraudulence and tamper. Blockchain can also reduce the cost of data and unpredictability of working edge devices or connecting machines. Blockchain technology has potential to drive the digital transformation in different fields with securely, availability and distribution of information with the help of peer-to-peer communication. It can disrupt multiple industries by making transactions with the transparency, security, trusted and securely. It simplifies the development of cost-effective data transactions, where anything can be tracked and exchanged, without requiring a central body. This talk will provide an overview of the impact of blockchain technology in digital transformation with essential concepts of blockchain technology, initially exploring the features of blockchain such as consensus algorithm, distributed ledger, smart contract and cryptography in multiple fields. Audience will be equipped with strong foundation knowledge required to understand the how blockchain can make a difference in the digitalization era. Dr. Madhusudan Singh is the Director of ReBlockchain Group in the department of AI & Big Data, ECIS, at Woosong University, South Korea. He previously worked in the R&D division at Samsung Display, S. Korea, and as a research professor at Seamless Transportation Lab, YICT, Yonsei University, Korea. Dr. Singh serves as a series editor of the Blockchain Technologies in Springer Nature, is a IEEE Computer Society, and ACM Distinguished speaker, a senior member of IEEE and also a Subject Matter Expert for the Automotive Cyber Security Course in IEEE e-learning sponsored by IEEE Computer Society. He is listed in top 2% of scientists published in a study conducted by researchers of ICSR Lab, Elsevier BV & Stanford University, USA. He is actively involved in entrepreneurial endeavors in Blockchain Technologies involving Artificial Intelligence, Information Security, and Autonomous vehicles.
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