Panels

Panels below are common to all three co-located conferences: IEEE CIC, IEEE CogMI and IEEE TPS

Panel 1: Collaboration at Edge: Edge AI and Edge Computing in 2020

Thursday, Dec. 12th, 2019 10:15AM-11:45AM Room Avalon (3rd Floor)

Edge Computing has been a heated topic in the last decade with many buzz words such as edge cloud and edge AI. On one hand, artificial intelligence and machine learning continue to enjoy its initial success in many business, science and engineering applications. On the other hand, 5G and fast networks are fueling many initiatives of the 2020, ranging from Internet of Things, self-driving cars, smart cities to smart planet. This panel consists of top notch research experts in cloud computing, edge computing and mobile and wireless communication. They will share their vision and perspectives on how edge AI and edge computing will transform collaboration at edge of the Internet in the presence of 5G, Cloud, Big Data-powered artificial intelligence and machine learning.

Panel Moderator

LingLiu
Ling Liu
IEEE Fellow
Professor, College of Computing, Georgia Institute of Technology, USA
Director, Distributed Data Intensive Systems Lab (DiSL)

Bio - Prof. Dr. Ling Liu is a full professor in the College of Computing at Georgia Institute of Technology and an elected IEEE Fellow. She directs the research programs in Distributed Data Intensive Systems Lab (DiSL). Her current research interests are centered on data and intelligence powered computing, such as artifical intelligence, machine learning, knowledge discovery and data mining, big data systems and analytics. Concretely, she is interested in developing innovative and efficient learning algorithms and systems for multi-modality of data, as well as algorithms and optimizations for improving performance, availability, security, privacy, trust of data and intelligence powered computing systems and applications, such as cloud and edge computing, distributed computing, Internet of smart things, Mobile computing and location based services, wireless and sensor networked computing, peer to peer and blockchain computing. Prof. Liu has published over 300 international journal and conference articles with high citations at Google scholar. Her research group has produced a number of open source software systems, among which the most popular ones include WebCQ, XWRAPElite, PeerCrawl, GTMobSIM, SHAPE, NEAT, TripleBit (jointly with Prof. Pingpeng Yuan), MemFlex, MemPipe, FastSwap, AdaTrace, GTDLBench.
Prof. Dr. Liu is a recipient of IEEE Computer Society Technical Achievement Award (2012) and an Outstanding Doctoral Thesis Advisor award from Georgia Institute of Technology in 2012. Her research group has been a recipient of the best paper awards from numerous top venues, including ICDCS 2003, WWW 2004, 2005 Pat Goldberg Memorial Best Paper Award, IEEE Cloud 2012, IEEE ICWS 2013, Mobiqutious 2014, APWeb 2015, IEEE/ACM CCGrid 2015, IEEE Symposium on Big Data 2016, IEEE Edge 2017 and IEEE IoT 2017. Prof. Dr. Ling Liu has served as a general chair or a PC chair of numerous IEEE and ACM conferences in data engineering, very large databases, Big data, and distributed computing fields, and most recently, co-PC chair of the 2019 International Conference on World Wide Web. Prof. Liu has been on editorial board of over a dozen international journals, and served as the Editor In Chief of IEEE Transactions on Service Computing (2013-2016). Currently, Prof. Liu is the Editor in Chief of ACM Transactions on Internet Technology (TOIT). Prof. Dr. Liu's current research is primarily sponsored by NSF, IBM and Intel.


Panelists(alphabetical by last name)

Aberer
Karl Aberer
Professor, EPFL-IC, Station 14, CH-1015 Lausanne, Switzerland
Distributed Information Systems Laboratory (LSIR)
Academic co-director and member, CHD

Bio - Karl Aberer is a professor in the School of Computer and Communications Sciences at EPFL. He received his PhD in Mathematics, ETH Zürich, 1991. From 1991 to 1992 he was postdoctoral fellow at the International Computer Science Institute (ICSI) at the University of California, Berkeley. In 1992 he joined the Integrated Publication and Information Systems Institute (IPSI) of GMD in Germany, where he was leading the research division Open Adaptive Information Management Systems. In 2000 he joined EPFL as full professor.
His research interests are on foundations, algorithms and infrastructures for distributed information management, including semantic interoperability, information retrieval, social networks, trust management and applications to scientific and sensor data management. He has produced more than 400 scientific publications, including more than 70 peer-reviewed journal articles and 300 peer-reviewed conference proceeding publications.
He was the director of the Swiss National Centre for Mobile Information and Communication Systems NCCR MICS from 2005 to 2012, Vice-President for Information Systems of EPFL from 2012 to 2016, and has been consulting the Swiss Government as a member of the Swiss Research and Technology Council from 2004 to 2011. He is currently member of the expert group on cyber-defense, consulting the department of defense on questions related to cyber-security. He is co-founder of LinkAlong, a startup established in 2017 providing data analytic capabilities for open source documents based on technologies for knowledge extraction developed in his research.

Brian
M. Brian Blake
Professor, George Mason University, USA

Bio - M. Brian Blake, PhD was Executive Vice President for Academic Affairs and Nina Henderson Provost at Drexel University. As the highest ranking academic officer, he oversees all academic programs across the 15 schools and colleges and over 26,000 students. Blake came to Drexel from the University of Miami, where he set research and teaching priorities and led faculty enhancement efforts as vice provost for academic affairs, and oversaw 155 graduate programs serving more than 5,700 students as dean of the Graduate School. Previously he was associate dean for research and graduate studies in the University of Notre Dame College of Engineering, and chaired the Georgetown University Department of Computer Science as it launched its first graduate program. Blake has directed computer science labs funded by more than $10 million in sponsored research awards; authored 170-plus publications and chaired six conferences; edited major journals including his current service as editor-in-chief of IEEE Internet Computing; and advised dozens of students at every level from postdoctoral fellowships through doctoral, master’s and undergraduate studies. Blake is a Senior Member of the IEEE and ACM Distinguished Scientist. Blake’s industry experience includes six years as a software engineer and architect at Lockheed Martin, General Dynamics and The MITRE Corporation before entering academia full time. Blake also holds appointments in the College of Engineering (as professor in the Department of Electrical and Computer Engineering) and in the College of Medicine (as professor of neuroengineering).

Dimitrios
Dimitrios Georgakopoulos
Professor, Swinburne University, Australia
Director, IoT Lab, Digital Innovation Platform
Industry 4.0 Program Leader, Manufacturing Futures Research Institute

Bio - Prof. Georgakopoulos is currently a professor at Swinburne University of Technology and leads the University’s IoT Lab and Industry 4.0 Program. Dimitrios came to Swinburne from his roles as Research Director at CSIRO’s ICT Centre and Professor at RMIT. Dimitrios has held research and management positions in several industrial labs in the USA, including Telcordia Technologies (where he helped found Telcordia’s research laboratories in Austin, Texas and Poznan, Poland), Microelectronics and Computer Corporation, GTE (now Verizon) Laboratories, and Bell Communications Research. Dimitrios is an internationally known expert in IoT, process management, and data management. He has won more than twenty major research awards, produced 200 publications that have been cited 15,000+ times, and attracted significant external research funding ($42M+) from both industry and government in the USA, EU, and Australia.

Weisong
Weisong Shi
IEEE Fellow
Professor and Associate Dean for Research, College of Engineering, Wayne State University, USA
Director, Connected and Autonomous dRiving Lab (CAR Lab)
Director, Mobile and Interent Systems Laboratory (MIST)
Director, Cyber-Physical Systems Program

Bio - Dr. Weisong Shi is a Charles H. Gershenson Distinguished Faculty Fellow and a Professor of Computer Science at Wayne State University. There he directs the Mobile and Internet Systems Laboratory, Connected and Autonomous Driving Laboratory, Cyber-Physical Systems Program, and Wayne State's Wireless Health Initiative, investigating performance, reliability, power- and energy-efficiency, trust and privacy issues of networked computer systems and applications. He founded the Metro Detroit Workshop on Connected and Autonomous Driving (MetroCAD).





Panel 2: Can Machine Learning be Private, Secure and Trusted?

Friday, Dec. 13 th, 2019 9:30AM-10:45AM Room San Gabriel (1st Floor)

Big data powered artificial Intelligence and machine Learning have paved way for accelerating scientific discoveries and innovations and creating next generation smart and intelligent systems and applications. However, as we enjoy more advances in AI and ML, there is a growing concern on whether and to what extent machine learning can be private, secure and trusted. This panel consists of top notch security research experts. They will share their vision and perspectives on the security, privacy and trust issues and challenges and debate on the following research questions: can machine intelligence be learned privately? what types of security guarantee the AI and ML powered intelligent systems should provide? how much can we trust the machine intelligence learned from statistical models and algorithms?

Panel Moderator

Joshi
James Joshi
Program Director, NSF, USA
Professor, School of Computing and Information, University of Pittsburgh, USA
Director, The Laboratory for Education and Research on Security Assured Information Systems (LERSAIS)

Bio - James Joshi is a professor of School of Computing and Information at the University of Pittsburgh, and the director/founder of the Laboratory of Education and Research on Security Assured Information Systems (LERSAIS), which has been designated as a Center of Academic Excellence in Information Assurance /Cyber Defense Education and Research (CAE and CAE-R). He is currently serving as a Program Director of CNS/SaTC Program. He is an elected Fellow of the Society of Information Reuse and Integration (SIRI), a Senior member of the IEEE and a Distinguished Member of the ACM. His research interests include access control models, security and privacy of distributed systems, trust management and network security. He is a recipient of the NSF-CAREER award in 2006. He has served as program co-chair and/or general co-chair of several international conferences/workshops, including, ACM SACMAT(2009/10), IEEE BigData2016, IEEE/EAI CollaborateCom, IEEE IRI, IEEE CIC, IEEE ISM2014. He currently serves as the steering committee chair of IEEE CIC, TPS and CogMI, and has served as the steering committee member of ACM SACMAT, IEEE IRI, IEEE/EAI CollaborateCom and IEEE ICME. He currently serves as the EiC of the IEEE Transactions on Services Computing, and was a founder and co-Editor-in-chief of EAI Endorsed Transactions on Collaborative Computing. He had also served in or is in the editorial board of several international journals. His work has been recognized with Best Paper award in ACM CODASPY 2011 and BigData Congress in 2017. He has published over 120 articles as book chapters and papers in journals, conferences and workshops, and has served as a special issue editor of several journals including Elsevier Computer & Security, ACM TISSEC (now TOPS), Springer MONET, IJCIS, and Information Systems Frontiers. His research has been supported by NSF, NSA/DoD, and Cisco. Earlier in 1995, he had led the efforts to establish the first Computer Science undergraduate degree program in Nepal. He had also established and managed the NSF CyberCorp Scholarship for Service program at Pitt since 2006.


Panelists(alphabetical by last name)

Elisa
Elisa Bertino
Samuel Conte Professor, Department of Computer Science, Purdue University, USA
Director, Cyber Space Security Lab (Cyber2Slab)

Bio - Elisa Bertino is professor of Computer Science at Purdue University and a professor of Electrical and Computer Engineering (courtesy appointment). She serves as Director of the Purdue Cyberspace Security Lab (Cyber2Slab). In her role as Director of Cyber2SLab she leads multi-disciplinary research in data security and privacy. Prior to joining Purdue, she was a professor and department head at the Department of Computer Science and Communication of the University of Milan. She has been a visiting researcher at the IBM Research Laboratory (now Almaden) in San Jose, at the Microelectronics and Computer Technology Corporation, at Rutgers University, at Telcordia Technologies. Her main research interests include security, privacy, database systems, distributed systems, and sensor networks. Her recent research focuses on cybersecurity and privacy of cellular networks and IoT systems, and on edge analytics for cybersecurity. Elisa Bertino is a Fellow member of IEEE, ACM, and AAAS. She received the 2002 IEEE Computer Society Technical Achievement Award for “For outstanding contributions to database systems and database security and advanced data management systems”, the 2005 IEEE Computer Society Tsutomu Kanai Award for “Pioneering and innovative research contributions to secure distributed systems”, and the 2019-2020 ACM Athena Lecturer Award.

Murat
Murat Kantarcioglu
Professor, Computer Science, University of Texas at Dallas, USA
Director, UTD Data Security and Privacy Lab

Bio - Dr. Murat Kantarcioglu is a Professor in the Computer Science Department and Director of the Data Security and Privacy Lab at The University of Texas at Dallas (UTD). He received a PhD in Computer Science from Purdue University in 2005 where he received the Purdue CERIAS Diamond Award for Academic excellence. He is also a visiting scholar at Harvard Data Privacy Lab. Dr. Kantarcioglu's research focuses on the integration of cyber security, data science and blockchains for creating technologies that can efficiently and securely process and share data.
His research has been supported by grants including from NSF, AFOSR, ARO, ONR, NSA, and NIH. He has published over 170 peer reviewed papers in top tier venues such as ACM KDD, SIGMOD, ICDM, ICDE, PVLDB, NDSS, USENIX Security and several IEEE/ACM Transactions as well as served as program co-chair for conferences such as IEEE ICDE, ACM SACMAT, IEEE Cloud, ACM CODASPY. Some of his research work has been covered by the media outlets such as the Boston Globe, ABC News, PBS/KERA, DFW Television, and has received multiple best paper awards. He is the recipient of various awards including NSF CAREER award, the AMIA (American Medical Informatics Association) 2014 Homer R Warner Award and the IEEE ISI (Intelligence and Security Informatics) 2017 Technical Achievement Award presented jointly by IEEE SMC and IEEE ITS societies for his research in data security and privacy. He is also a Distinguished Scientist of ACM.

Jaideep
Jaideep Vaidya
RBS Dean’s Research Professor, Rutgers University, USA
EiC of TDSC

Bio - TBA Dr. Jaideep Vaidya is a Full Professor in the MSIS Department at Rutgers University. He received the B.E. degree in Computer Engineering from the University of Mumbai, the M.S. and Ph.D. degree in Computer Science from Purdue University. His general area of research is in data mining, data management, security, and privacy. He has published over 130 technical papers in peer-reviewed journals and conference proceedings, and has received several best paper awards from the premier conferences in data mining, databases, digital government, security, and informatics. He has also received the NSF Career Award, the Rutgers Board of Trustees Research Fellowship for Scholarly Excellence, and the Junior Faculty Research Award from Rutgers Business School. He is a senior member of the IEEE and ACM and has been recognized as an ACM Distinguished Scientist.





Panel 3: Human Intelligence and Machine Intelligence: Friend or Foe

Friday, Dec. 13 th, 2019 11:00AM-12:30PM Room San Gabriel (1st Floor)

Artificial intelligence and machine learning have enjoyed unprecedented advances in business, science and engineering applications, including self-driving cars, smart cities, and cyber manufacturing. At the same time, there are increasing debates on the relationships between human intelligence and artificial machine intelligence. Some debates argue the importance of putting human in the loop of machine learning to ensure fairness, accountability, and transparency, while other debates conjecture that machine intelligence may supersede human intelligence and will eventually dominate and control the human society. This panel consists of top notch AI, ML and data mining research experts. They will share their vision and perspectives on the intertwined relationships between machine intelligence and human intelligence from multiple data mining and machine learning subareas, including natural language understanding, behavior modeling and reasoning, graph mining, network representation learning, mobile mining, social media and crowd mining.

Panel Moderator

Christin Seifert
Christin Seifert
Professor, University of Twente, Netherlands

Bio - I am an assistant professor at the Data Science group at University of Twente. I have been excited about all flavours of machine learning for a long time. In particular, I am passionate about bridging the gap between algorithmics and the human. I strongly believe that algorithms should be designed in a way that tightly integrates the user and her mental models of her material world, and the (immaterial) data collected to describe it; Artificial intelligences should be able to explain their reasoning and basis of decision making to end users — in a way that relates to their physical reality and that they can understand. In consequence, my research is characterised by interdisciplinarity and a broad range of application areas. My core research interests are explainable machine learning, and data mining, as well as intersections with human-computer interaction, information visualization, and information retrieval; detours include natural language processing and technology enhanced learning. My publication list gives an impression of how my research interests evolved. Today, it includes 97 peer-reviewed papers, 11 journal articles and 5 book chapters that mark different stages of my scientific (and personal) development. I hold a Ph.D. from the Technical University Graz, Austria (Thesis Topic: Visually Supported Supervised Machine Learning) and a diploma (equiv. M.Sc.) in computer science from the Technical University Chemnitz, Germany.


Panelists(alphabetical by last name)

Tarek
Tarek Abdelzaher
Professor and Willett Faculty Scholar, University of Illinois at Urbana Champaign, USA

Bio - Tarek Abdelzaher received his Ph.D. in Computer Science from the University of Michigan in 1999. He is currently a Professor and Willett Faculty Scholar at the Department of Computer Science, the University of Illinois at Urbana Champaign. He has authored/coauthored more than 300 refereed publications in real-time computing, distributed systems, sensor networks, and control. He is an Editor-in-Chief of the Journal of Real-Time Systems, and has served as Associate Editor of multiple journals, including IEEE TMC, IEEE TPDS, ACM ToIT, ACM TIoT, ACM ToSN, and ACM TCPS. He chaired (as Program or General Chair) several conferences in his area including RTAS, RTSS, IPSN, Sensys, DCoSS, ICDCS, and ICAC. Since 2017, he leads the Internet of Battlefield Things Collaborative Research Alliance. Abdelzaher's research interests lie broadly in understanding and influencing performance and temporal properties of networked embedded, social and software systems in the face of increasing complexity, distribution, and degree of interaction with an external physical environment. Tarek Abdelzaher is a recipient of the IEEE Outstanding Technical Achievement and Leadership Award in Real-time Systems (2012), the Xerox Award for Faculty Research (2011), as well as several best paper awards. He is a member of IEEE and ACM.

Paolo
Paolo Boldi
Professor, University of Milano, Italy

Bio - Paolo Boldi is full Professor at the Università degli Studi di Milano from 2015, where he is currently the co-ordinator of the PhD Program in Computer Science. His main research topics are algorithms and data structures for big data, web crawling and indexing, graph compression, succinct and quasi-succinct data structures, distributed systems, anonymity and alternative models of computation. Recently, his works focused on problems related to complex networks (especially, the World-Wide Web, social networks and biological networks), a field where his research has also produced software tools used by many people working in the same area. He chaired many important conferences in this sector (e.g., WSDM, WWW, ACM WebScience), and published over one hundred papers; he was also recipient of three Yahoo! Faculty Awards and co-recipiend of a Google Focused Award, and member of many EU research projects. He was keynote speaker at many conferences such as ECIR, SPIRE, MFCS, IIR and invited scholar at the Institut des Hautes Études Scientifiques.

Dustdar
Schahram Dustdar
IEEE Fellow, ACM Distinguished Scientist, Member of Academia Europaea
Professor, Vienna University of Technology, Vienna
Head of the Distributed Systems Group

Bio - Schahram Dustdar is Full Professor of Computer Science (Informatics) with a focus on Internet Technologies heading the Distributed Systems Group at the Vienna University of Technology (TU Vienna). From 2004-2010 he was Honorary Professor of Information Systems at the Department of Computing Science at the University of Groningen (RuG), The Netherlands. He is a member of the Academia Europaea: The Academy of Europe, Informatics Section (since 2013) and an IEEE Senior Member (2009). He is recipient of the ACM Distinguished Scientist award (2009), the IBM Faculty Award (2012), the IEEE TCSVC Outstanding Leadership Award (June 2018), the IEEE TCSC Award for Excellence in Scalable Computing (June 2019). He is an Associate Editor of IEEE Transactions on Services Computing, ACM Transactions on the Web, and ACM Transactions on Internet Technology and on the editorial board of IEEE Internet Computing. He is the Editor-in-Chief of Computing (an SCI-ranked journal of Springer).

Huan
Huan Liu
ACM Fellow, IEEE Fellow, AAAI Fellow, AAAS Fellow, Member of ASEE and SIAM
Professor, Arizona State University, USA

Bio - Dr. Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He obtained his Ph.D. in Computer Science at University of Southern California and B.Eng. in Computer Science and Electrical Engineering at Shanghai JiaoTong University. Before he joined ASU, he worked at Telecom Australia Research Labs and was on the faculty at National University of Singapore. At Arizona State University, he was recognized for excellence in teaching and research in Computer Science and Engineering and received the 2014 President's Award for Innovation. His research interests are in data mining, machine learning, social computing, and artificial intelligence, investigating interdisciplinary problems that arise in many real-world, data-intensive applications with high-dimensional data of disparate forms such as social media. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He is a co-author of a text, Social Media Mining: An Introduction, Cambridge University Press. He is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction, and Chief Editor of Data Mining and Management in Frontiers in Big Data. He is a Fellow of ACM, AAAI, AAAS, and IEEE. More can be found at his home page.





Panel 4: From Federated Learning, Multi-Agent Systems, to Distributed AI: Research Challenges and Killer Applications

Saturday, Dec. 14 th, 2019 1:30PM-3:00AM Room San Gabriel (1st Floor)

Federated Learning, Multi-agent systems and Distributed AI are buzz words that have been revised recently with the advances in cloud data centers, machine learning and AI as a service, and AI on the Edge. Federated learning refers to technologies and learning algorithms that can construct cognitive learner models from a collection of independent learners, representing autonomous organizations or agents. Multi-agent systems typically refer to systems with multiple agents interacting in a spatially and/or temporally constrained environment. However, multi-agent systems tend to focus on multiple software agents and their environment, whereas distributed AI consists of multiple types of agents, ranging from robots, software agents, humans or human teams, to any combination of human, robot, software-agent teams.

This panel consists of experts from industry in cloud data centers, cognitive computing, cyber manufacturing, distributed AI, distributed systems, and multi-agent systems. This industrial panel will present industrial perspectives on Federative Learning and Distributed AI, leveraging the decades research on multi-agent systems, including interesting research and development problems, potential killer applications, and open research challenges ahead.

Panel Moderator

Christin Seifert
Christin Seifert
Professor, University of Twente, Netherlands

Bio - I am an assistant professor at the Data Science group at University of Twente. I have been excited about all flavours of machine learning for a long time. In particular, I am passionate about bridging the gap between algorithmics and the human. I strongly believe that algorithms should be designed in a way that tightly integrates the user and her mental models of her material world, and the (immaterial) data collected to describe it; Artificial intelligences should be able to explain their reasoning and basis of decision making to end users — in a way that relates to their physical reality and that they can understand. In consequence, my research is characterised by interdisciplinarity and a broad range of application areas. My core research interests are explainable machine learning, and data mining, as well as intersections with human-computer interaction, information visualization, and information retrieval; detours include natural language processing and technology enhanced learning. My publication list gives an impression of how my research interests evolved. Today, it includes 97 peer-reviewed papers, 11 journal articles and 5 book chapters that mark different stages of my scientific (and personal) development. I hold a Ph.D. from the Technical University Graz, Austria (Thesis Topic: Visually Supported Supervised Machine Learning) and a diploma (equiv. M.Sc.) in computer science from the Technical University Chemnitz, Germany.


Panelists(alphabetical by last name)

Luis
Luis Garcés-Erice
Research Staff Member, IBM Zurich Research Laboratory, Switzerland

Bio - Luis Garcés-Erice is a Senior Research Staff Member in the Cognitive Systems group at IBM Research, Zurich Lab, Switzerland. His research interests are mainly in distributed systems, network protocols and middleware, with his current work focused on large data processing systems such as Data Lakes and how to scale and govern them between the Enterprise and the Cloud. He has published many papers on international conferences and journals, disclosed tens of patents and received outstanding recognition for his work on products and projects for the IBM Corporation. He obtained his PhD with a thesis on peer-to-peer systems in 2004 from Télécom Paris University (France) after getting his MSc in Telecommunication Engineering (EE+CS) from the Public University of Navarra (Spain) in 2001. Luis Garcés-Erice is a professional member of the IEEE Computer Society and the ACM.

Sudhanshu
Sudhanshu Gaur
Senior Director at Hitachi Center of Industrial IoT, USA

Bio - Sudhanshu heads the IoT Edge Lab at Hitachi America R&D, responsible for incubating industrial IoT solutions for multiple industries including Manufacturing and Transportation. His research interests span distributed systems, sensor fusion, Edge AI, and automation. His current work focuses on scalable IoT system design, real-time unstructured data analysis, and operationalization of ML/AI models at Edge. He is currently leading initiatives for IoT solutions deployment in over 150 manufacturing plants worldwide including field trial of emerging technologies to augment/replace skilled workforce. He has authored over 100 patents, peer-reviewed publications, and a book on LTE Advanced. He received his Ph.D. degree in Electrical & Computer Engineering from Georgia Institute of Technology. Sudhanshu is a Senior Member of IEEE.

Ashish Kundu
Ashish Kundu
Head of Cybersecurity at Nuro.ai, USA

Bio - Dr. Ashish Kundu is an ACM Distinguished Member, ACM Distinguished Speaker and a worldwide leader in Security, Privacy, and Compliance. He is currently at Nuro.AI as its Head of Cybersecurity, Dr. Kundu was a Master Inventor and Research Staff Member in Security Research at IBM T J Watson Research Center, Yorktown Heights, New York. He has been working in the area of Cybersecurity, Privacy and Compliance for more than 15 years. Dr. Kundu received Ph.D. in Computer Science from Purdue University. His work has led to more than 130 patents filed with more than 100 patents granted, and more than 40 research papers. He was recognized with the prestigious CERIAS Diamond Award for his outstanding contribution to cybersecurity research during his Ph.D.

Paolo
Mudhakar Srivatsa
Distinguished Research Staff Member and Manager (Distributed AI) Thomas J. Watson Research Center, USA

Bio - Mudhakar Srivatsa is a distinguished research staff member and manager at the Distributed AI department in IBM T. J. Watson Research Center. His work is focussed on distributed learning over IoT/sensor data gathered from heterogeneous data sources. He serves as technical area leader on joint US/UK consortium of 20 academic and industrial labs on distributed analytics and information sciences. He has led the deployment of large scale IoT solutions in the smarter health domain for monitoring activities of daily living, connected cars domain for smarter cities, maritime domain for detecting piracy, and in dense urban environments such as stadiums and music festivals for public safety.