Invited Speakers

ShanghaiTech Symposium on Information and Science and Technology

T. Russell Hsing
Chair Professor, National Chiao Tung University, Taiwan
Guest Professor, Peking University
Board of Director, OpenFog Consortium

IEEE Life Fellow, Fellow for BCS and SPIE

Yang Yang
Co-Director, Shanghai Institute of Fog Computing Technology (SHIFT)

Professor, School of Information Science and Technology, ShanghaiTech University

IEEE Fellow

Jun Liu
Global Distinguished Engineer, Cisco China

Xiliang Luo
Co-Director, Shanghai Institute of Fog Computing Technology (SHIFT)

Associate Professor, School of Information Science and Technology, ShanghaiTech University

Jianhong Xiao
CEO, Xinyi Information Technology

Rui Fan
Associate Professor, School of Information Science and Technology, ShanghaiTech University

Qing Ling
Professor, School of Data and Computer Science, Sun Yat-sen University

Yuanming Shi
Assistant Professor, School of Information Science and Technology, ShanghaiTech University

Makoto Yasuda
Corporate Officer, Senior Technology Evangelist, Systems & Services Business Division, Hitachi, Ltd.

Ziqiang Hou
Professor, Institute of Acoustics, Chinese Academy of Sciences (CAS)

Committeeman, Telecommunication Science and Technology Committee of the Ministry of Industry and Information Technology(MIIT)

John K. Zao
Associate Professor, National Chiao Tung University, Taiwan

Chair, IEEE Communication Society Standard Working Group on Fog Computing and Networking Architecture Framework

Frank Zhao
Business Development Manager, NI

Jiayou Zong
System Architect, Xilinx

Xuming He
Associate Professor, School of Information Science and Technology, ShanghaiTech University

Qifeng Liao
Assistant Professor, School of Information Science and Technology, ShanghaiTech University

Laurent Kneip
Assistant Professor, School of Information Science and Technology, ShanghaiTech University

Speaker and Speech Information

T. Russell Hsing

Chair Professor
National Chiao Tung University, Taiwan

Board of Director
OpenFog Consortium

Title: Fog Computing and Networking: A Key Pillar for Current 5G Engineering to future 6G Research

Abstract: Pushing processing and storage into the “cloud” has been a key trend in networking and distributed systems in the past decade. In the next wave of network architecture and technology advance, the cloud is now descending to be diffused among the client devices, often with mobility too: the cloud is becoming “fog.” For example, more than just faster speed, many IoT emerging services need to be cognitive of end-user application needs. Questions on fairness, robustness, privacy, security, and efficiency need to be revisited. Furthermore, empowered by chips such as Atom and emergent communication protocols, each client device today is powerful in computation, in storage, and in communication. Yet client devices are still limited in battery power, global view of the network, and mobility support. Recognizing the gap between “Cloud” and “Things,” both IEEE and OpenFog Consortium have stepped up their efforts on filling the “Cloud-to-Thing” continuum through growing its activities in fog computing/networking, communications, storage and control, i.e., “Fog.” Most interestingly, the collection of many Fog-based Networks in a crowd presents a highly distributed, under-organized and dense network.
The goal of starting the Fog Computing & Networking research is to investigate the optimization of resources that are virtualized, pooled, and shared unpredictably. Fog Networking revisits the role of clients in network architectures, more than just an end-user device, but also as an integral part of the control plane that monitors, measures, and manages the network. This is rewriting the traditional practice of using heavy-duty and dedicated network elements for network measurement and management Fog Computing & Networking combine the study of mobile communications, fog-based radio access network (F-RAN) in future 6G wireless, distributed systems, and big data analytics into an exciting new area. Based on our preliminary research, it shows that many new emerging services (such as V2V in Vehicular Telematics Services, Industry 4.0 and e- Healthcare Services) could be realized and implemented easily and economically. It could be also served as core engine to enable many Services in Internet of Things (IoT) applications. Both of Future Research Directions and the ICT Convergence for Entrepreneurs in the area of Fog Computing and Networking will be discussed in this talk. From Technology aspect, this talk will discuss current views on how to identify potential road blocks and then solve these technical challenges for 6G wireless in the future. A few emerging services (such as V2V in vehicular telematics, Industry 4.0, and AR/VR applications) in the 6G-based network environment will be presented in this talk too.

Bio: Prof. T. Russell Hsing, Life Fellow of the IEEE and Fellow for the British Computer Society (BCS) and SPIE. He is now Chair Professor with the National Chiao Tung University in Taiwan. Currently he serves as Member of Harvard Business Review (HBR) Advisory Council, Board of Director for the OpenFog Consortium in US, Board Member/Board Advisor/Investor/Mentor for a few high-tech start-ups via academic spin-offs in US, Hong Kong and Taiwan. He is also Guest Professor of Peking University in China and International Advisor for the Joint Basic Research Lab. (between Peking University and Princeton University) on Communications Theory & Technology, Adjunct Professor with the Arizona State University in US, Visiting Professor with the IIT Bombay in India, Visiting Professor with both Singapore University of Technology Design (SUTD) and Nanyang Technological University (NTU) in Singapore, and the Chinese University of Hong Kong. He has designed and been teaching 3- credit undergraduate and graduate course in Technology Entrepreneurship and Technology Commercialization for students, faculty members and local industry people (since 2014) and another new course of “Internet Economics” starting from 2017. He has been Academic Advisor for the Next Generation Mobile Networks (NGMN) Alliance in Germany since March 2014. His current research efforts are concentrating on current Wireless 5G and future 6G, Next Generation Internet of Things Services and Opportunities, Internet Economics, Fog Networking & Computing, and Technology Entrepreneurship. From 1976 to 2012, He was with the Applied Research Center in Bellcore/Telcordia/Ericsson as Director (1986-1995) and Executive Director (1995-2012). He has also established and supervised the Directors for Telcordia Applied Research Centers in Taiwan (TARC-TW) and Poland (TARC-PL) (2004-2012). He accumulated rich R&D experience of 40 years through affiliations with Burroughs, Xerox, GTE Labs, TASC, Telco Systems Fiber Optics Corporation, and Bellcore/Telcordia/Ericssson. From 1987 to 1995, He has led a research team to propose and then to develop the world’s first working QAM-based ADSL system first, and then designed the world’s first working DCT chip for video communication applications at Bellcore. In 2002, he was given a responsibility to establish a new Emerging Technologies and Services R & D Department for Vehicular Telematics, Healthcare, and physical security systems applications. He pioneered the technology commercialization in emerging technologies and services through joint business ventures with commercial partners. He holds a B.Sc. (1970) from Taiwan, M.Sc. (1974) and Ph.D (1977) of Electrical Engineering, the University of Rhode Island. His research and publications cover communication signal processing, multimedia communications, wireless technologies & sensors network, vehicular networks & telematics, video communications and VLSI implementations. He co-edits the ICT book series for John Wiley & Sons, Inc. since 2007. Within the IEEE Communications Society, he was member (2006-2008) and chair (2010-2011) of the Fellow Evaluation Committee, and a member of the Award Committee (2010-2012). He was founding chair (2010-2012) of Sub-TC on Vehicular Networks and Telematics Applications for the IEEE Communications Society. Within the IEEE, he was a member (2008-2010)/ Chair (2010-2011)/Past Chair (2012) for the IEEE Kiyo Tomiyasu Award Committee, and then the IEEE Eric Sumner Award Committee (2010-2012). He has been a member for the IEE Fellow Committee since 2012, the Strategic Planning Committee in 2013, and Chair for the IEEE Technical Field of Award (TFA) Council & Member for the IEEE Award Board since 2015. He has led an Ad Hoc Committee (June, 2015 – December, 2017) to Review the Fellow Evaluation Criteria and Process for the IEEE Communications Society and made final recommendations to the Board of Governors for the IEEE Communications Society in December, 2017.

Yang Yang

Co-Director
Shanghai Institute of Fog Computing Technology (SHIFT)

Professor
ShanghaiTech University

Title: Fog Computing for Intelligent IoT Applications

Abstract: Fog computing has recently attracted a lot of attentions from communication, computing and control communities. Different from traditional telecom manufacturers, Google, Intel and Facebook actively announced their disruptive approaches for 5G/IoT services based on generic hardware and customized software. In this talk, we will give an introduction of a fog-enabled 5G/IoT R&D platform, which applies SDN and NFV techniques to realize the key functions of a telecom operator according to the 3GPP standard on general CPU/GPU computing platform. It is very adaptive and flexible for supporting a variety of internet of things (IoT) applications in vertical industries. New technical challenges and potential applications of this open platform in delay-sensitive control areas will be fully discussed.

Bio: Dr. Yang Yang is currently a professor with Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences, serving as the Director of CAS Key Laboratory of Wireless Sensor Network and Communication, and the Director of Shanghai Research Center for Wireless Communications (WiCO). He is also a Distinguished Adjunct Professor with the School of Information Science and Technology, ShanghaiTech University. Prior to that, he has held faculty positions at University College London (UCL), Brunel University, and The Chinese University of Hong Kong. Yang is a member of the Chief Technical Committee of the National Science and Technology Major Project “New Generation Mobile Wireless Broadband Communication Networks” (2008-2020), which is funded by the Ministry of Industry and Information Technology (MIIT) of China. In addition, he is on the Chief Technical Committee for the National 863 Hi-Tech R&D Program “5G System R&D Major Projects”, which is funded by the Ministry of Science and Technology (MOST) of China. Since January 2017, he has been serving the OpenFog Consortium as the Director for Greater China Region. Yang’s current research interests include wireless sensor networks, Internet of Things, Fog computing, Open 5G, and advanced wireless testbeds. He has published more than 150 papers and filed over 80 technical patents in wireless communications.

Jun Liu

Global Distinguished Engineer
Cisco China

Title: Fog Application in Industry IoT

Abstract: Introduce the concept of Fog app in industry IoT. A brief of Kinetic, the Cisco solution of IoT. The joint IoT solution of Cisco/Microsoft for IoT. Real case study of Cisco IoT solution.

Bio: Jun Liu has been working for Cisco China since 1998, and now is a Cisco Global Distinguished Engineer. He has more than 20 years’ experience in field of IP network and the Internet of things. He is an expert of fog computing, data centers, and network chip. He is the Cisco convener of China Communications Standards Association (CCSA), and participated development of a number of Chinese industry standards - Communications Standardization Association standard, and hosted edition of books "policy-driven data center", "software-defined network" and some others. He has two US patents, five CCIE certificates and JNICE certificates.

Rui Fan

Associate Professor
School of Information Science and Technology, ShanghaiTech University

Title: Distributed Consensus: A Classic Problem for Modern Times

Abstract: One of the most fundamental tasks in a distributed system is for users to agree on a common course of action. This is encapsulated in the classic consensus problem. Examples of consensus include a network of sensors agreeing on a common reading, a fleet of UAVs deciding on a flight path, or ordering transactions in a blockchain. Consensus has been studied extensively since the 1980s, and research in the area has been reinvigorated by the recent interest in blockchains. In this lecture we lay out the basic setting for distributed consensus and discuss, time permitting, key aspects of the problem including classic algorithms such as Paxos and Practical Byzantine Fault Tolerance, impossibility results such as FLP and CAP, and applications for consensus such as replicated state machines and blockchains.

Bio: Rui Fan is an associate professor in the School of Information Science and Technology at ShanghaiTech University, China. From 2011 to 2016 he was an assistant professor at Nanyang Technological University, Singapore. Dr. Fan received his BSc from Caltech and his MSc and PhD from MIT. His research focuses many aspects of parallel and distributed computing, including algorithm design and optimization, theoretical analysis and lower bounds, as well as applications in domains such as energy efficiency and large scale machine learning.

Qing Ling

Professor
Sun Yat-sen University

Title: Alternating Direction Method of Multipliers for Parallel and Decentralized Optimization

Abstract: This talk shall briefly introduce the alternating direction method of multipliers (ADMM), a powerful tool to solve linearly constrained optimization problems. Particular focus is on its use in parallel and decentralized optimization, with applications in machine learning and fog computing.

Bio: Qing Ling received the B.E. degree in automation and the Ph.D. degree in control theory and control engineering from University of Science and Technology of China, in 2001 and 2006, respectively. He was a Post-Doctoral Research Fellow with the Department of Electrical and Computer Engineering, Michigan Technological University from 2006 to 2009, and an Associate Professor with the Department of Automation, University of Science and Technology of China from 2009 to 2017. He is currently a Professor with the School of Data and Computer Science, Sun Yat-Sen University. His research interest includes decentralized network optimization and its applications. His works received the 2017 IEEE Signal Processing Society Young Author Best Paper Award and the 2017 International Consortium of Chinese Mathematicians Distinguished Paper Award. Dr. Ling is an Associate Editor of the IEEE Transactions on Network and Service Management and the IEEE Signal Processing Letters.

Yuanming Shi

Assistant Professor
School of Information Science and Technology, ShanghaiTech University

Title: Large-Scale Optimization for Machine Learning

Abstract: The focus of this talk is the theory and algorithms for large-scale convex and nonconvex optimization, with particular emphasis on problems that arise in data science and machine learning. Our goal is to gain a fundamental understanding of convex analysis, modeling and approximation, in addition with convergence rates, complexity, scaling behaviors of various optimization algorithms, including both first-order and second-order methods, as well as both deterministic and randomized algorithms.

Bio: Dr. Yuanming Shi received the B.S. degree in electronic engineering from Tsinghua University, Beijing, China, in 2011. He received the Ph.D. degree in electronic and computer engineering from The Hong Kong University of Science and Technology (HKUST), in 2015, under the supervision of Prof. Khaled B. Letaief. Since September 2015, he has been with the School of Information Science and Technology in ShanghaiTech University, as a tenure-track Assistant Professor. He visited University of California, Berkeley, CA, USA, from October 2016 to February 2017, hosted by Prof. Martin J. Wainwright. Dr. Shi is a recipient of the 2016 IEEE Marconi Prize Paper Award in Wireless Communications, and the 2016 Young Author Best Paper Award by the IEEE Signal Processing Society. His research areas include deep and machine learning, large-scale optimization theory and algorithms, high-dimensional probability and statistics, and their applications to artificial intelligence and big data analysis, as well as internet-of-things (IoT) and communication networks.

Makoto Yasuda

Corporate Officer
Senior Technology Evangelist, Systems & Services Business Division, Hitachi, Ltd.

Title: Fog Computing for IT & OT Convergence

Abstract: IoT solutions and technologies will accelerate Convergence of IT: Information Technologies and OT: Operation technologies. IT & OT have lot of similarity but also have big difference in background, legacy, and culture. For Connected Industries, however, IT & OT need to work together using Big Data and advanced analytics. In this session, using OpenFog Consortium documents & experience, challenges of IT & OT integration and how Fog Computing helps will be discussed.

Bio: Mr. Yasuda is a corporate officer of Hitachi Ltd. and also has the title of senior technology evangelist of the Information & Communication Technology Business Division. For the past few years, he has focused on Big Data and its analytics, IoT Technologies & Solutions, and IT & OT Integration. He started his career in the IT industry at Andersen Consulting / Accenture after graduating from university. Before joining Hitachi in 2012, he worked in the software business as the president of Lotus Japan & AP, a marketing director of IBM Japan, and a partner business & marketing SVP of SAP Japan. Additionally, he worked in the network business for Cisco as a partner sales & marketing VP. He was elected as a Director of Japan Region Committee of OpenFog Consortium in 2016, and has been involved OpenFog Consortium activities in Japan.

Ziqiang Hou

Professor
Institute of Acoustics, China Academy of Science (CAS)

Title: Evolution of Fog/Edge Computing

Abstract: This talk presents the latest technological development and trends of fog and edge computing. It compares edge, fog, and cloud, and explores applications of fog and edge computing in fields of blockchain, Information-Centric Networking (ICN), and virtual small cells, and it finally introduces fognomics, the key mechanism enabling applications of fog and edge computing.

Bio: Mr. Ziqiang Hou, is a professor of the Institute of Acoustics, China Academy of Science (CAS). Mr. Hou was the Secretary General of the Chinese Academy of Sciences from 1988 to 1993, and Director of the Institute of Acoustics CAS from 1993 to 1997. Mr. Hou is also an Committeeman of the Telecommunication Science and Technology Committee of the Ministry of Industry and Information technology(MIIT). He leads an R&D group in digital information, consumer electronics products and broadband wired and wireless IP network area in the Institute of Acoustics, CAS. Prof. Hou is the pioneer in China to promote broadband IP network as information infrastructure. He has series paper about WAN, MAN and wire and wireless access network base on broadband IP technology. He has won the 1st Class Chinese National Award of Science and Technology Progress.

John K. Zao

Associate Professor
National Chiao Tung University

Title: Trusted Fog Nodes with Trusted Execution Environment and End-to-End Security Support

Abstract: Fog Computing is an emerging paradigm that promises to extend Cloud Computing to the edge of the Internet. However, before dispersing computing, networking and storage functions among the physically exposed Fog Nodes, as system architects, we must ensure the Fog Nodes as well as the application services running on them can be trusted. We must also protect the information exchanged among the services from various passive and active attacks. In this lecture, we introduce the concepts of Trust Computing and End-to-End Security; we will also demonstrate how they can be implemented on a simple Raspberry Pi platform.

Bio: Dr. John K. Zao is the founding chairman of the new IEEE Standard Working Group on Fog Computing and Networking Architecture Framework and the founding vice-chairman of the new IEEE Standard Committee on Edge/Fog/Cloud Communications with IoT and Big Data. He is also the co-chairman of the Security Working Group of OpenFog Consortium and its Greater China Regional Committee. In his regular job, Dr. Zao is the Chief Technology Officer of NGoggle Inc. in San Diego, USA and CerebraTek Co. Ltd. in the Taiwan Hsinchu Science Park. He is also an Associate Professor of Computer Science in the Taiwan Hsinchu Chiao-Tung University. Dr. Zao is an expert in Internet Security and Pervasive Computing. He received his PhD in Computer Science at Harvard University in 1995 and was elected a Senior Member of IEEE in 2001.

Frank Zhao

Business Development Manager
NI

Title: AI&NI are making SDR smarter

Abstract: 
SDR as an old concept has been enlarged greatly today. Now it is serving for communication, RADAR, EW, navigation, and all kinds of spectrum analysis,such as Software Defined RADAR. As a rising technology, AI enables SDR smarter. It can be implemented in Data Center(Cloud computing) or in End-User equipments(Edge/Fog computing). In this talk, Mr. Zhao will first give several scenarios of different SDR applications which require machine learning. Then, he will share a successful case of Channel Emulator on NI platform to show how AI will enable SDR smarter.

Bio: Mr. Frank Zhao, Business Development Manager of SDR, works at National Instruments. He worked in SJTU for six years in School of Micro-Electronics, Focus on architecture of hardware accelerator for face recognition, and got several patents in this area. As a General Manager, he ran BEEcube China to serve Huawei, ZTE, and many top level universities to do the 5G researching on Cloud RAN, TSN, and IoT. 3 years ago, He joined NI China when BEEcube acquired by NI.

Jiayou Zong

System Architect
Xilinx

Title: FPGA enabled acceleration framework in 5G, edge computing and AI application

Abstract: 
With the slowdown of general-purpose processor scaling due to dark silicon limitations, customized hardware accelerators like FPGAs, CGRAs, and ASICs have gained increased attention in modern application due to their lower power, high performance low latency, and energy efficiency. This includes the Data Center and also Edge/Fog computing, especially in the 5G scenario. In this talk, Mr. Zong will give the latest technology update about the FPGA acceleration for the 5G, Edge computing and also the fancy machine learning applications.

Bio: Mr. Zong Jiayou, Wireless System Architect and Strategic Marketing, works at Xilinx Inc. He is responsible for wireless networking,5G, edge computing, customer engagement from system architecture level, helping customers use Xilinx silicon, solutions and IP to develop their products. Mr. Zong has more than 20 years working experiences in FPGA applications and telecomm equipment developments including wireless networking (CDMA/WCDMA/TD-SCDMA), wired networking, and he has worked at key telecomm equipment companies and FPGA silicon vendor. Most recently he expands his interesting areas to FPGA accelerations in network function virtualization, 5G networking, mobile edge computing. Mr, Zong filed over 80 technical patents in wireless communications. And co-author of the CMCC 5G-CRAN whitepaper.

Xuming He

Associate Professor
School of Information Science and Technology, ShanghaiTech University

Title: Deep Networks for Representation Learning

Abstract: Recently, deep neural networks (DNNs) emerged as a family of models in machine learning that have demonstrated empirical successes in representing and learning complex information from massive data. Such data include but not limited to speeches, natural languages, and images, and the successes have made DNNs a powerful and popular tool in a wide range of applications. This course aims to provide a basic introduction to deep neural networks, including basic model families (CNNs and RNNs etc.), their training methods, implementation issues, and exemplary applications in computer vision and language domains. Through the course, the audience will learn how to use and implement a typical neural network using PyTorch, as well as how to train the network. Moreover, the course will also discuss some of the core issues in deep learning for better conceptual understanding of DNNs, both their capabilities and limitations.

Bio: Xuming He is currently an Associate Professor in the School of Information Science and Technology at ShanghaiTech University. He received Ph.D. degree in computer science from the University of Toronto in 2008. He held a postdoctoral position at the University of California at Los Angeles from 2008 to 2010. After that, he joined in National ICT Australia (NICTA) and was a Senior Researcher from 2013 to 2016. He was also an adjunct Research Fellow at the Australian National University from 2010 to 2016. His research interests include semantic segmentation, 3D scene understanding, and probabilistic graphical models. He is the author of more than 50 papers in top-tier journals and conferences such as IEEE TPAMI, IEEE TIP, CVPR, ICCV, ECCV, NIPS, AAAI, IJCAI. He has served as PC members for CVPR/ICCV/ECCV/AAAI/IJCAI/NIPS/ICML and reviewers for IEEE TPAMI/IJCV/IEEE TIP, etc. He received Shanghai Thousand Talents program award in 2018 and outstanding reviewer award in CVPR 2014 and ECCV 2016.

Qifeng Liao

Assistant Professor
School of Information Science and Technology, ShanghaiTech University

Title: Bayesian inference in scientific computing

Abstract: Inverse problems arise from many fields of science and engineering -- whenever parameters of interest must be estimated from indirect observations. The Bayesian inference method has become increasingly popular as a tool to solve inverse problems. The popularity of the method is largely due to its ability to quantify the uncertainty in the solution obtained. Simply speaking, in the Bayesian framework, the parameters of interest are cast as random variables to which a prior distribution is assigned, and then the posterior distribution of the parameters conditional on the observed data is computed via the Bayes’ rule. The posterior distribution thus provides a probabilistic characterization of the parameters of interest in such problems. Though the idea behind the Bayesian inference method is quite straightforward, the computation of the posterior distribution often poses challenges. In most practical problems, the posterior distributions do not admit a closed-form expression and must be computed numerically. To this end, the Markov chain Monte Carlo (MCMC) method is often used to compute the posterior distributions. In this talk, we discuss the main MCMC algorithm and its MATLAB implementation.

Bio: Dr. Qifeng Liao obtained the PhD degree in applied numerical computing from the School of Mathematics of the University of Manchester in December 2010. During January 2011 to June 2012, He was a postdoc at the Department of Computer Science of the University of Maryland, College Park. During July 2012 to February 2015, He was a postdoc at the Department of Aeronautics and Astronautics of Massachusetts Institute of Technology. Dr. Qifeng Liao joined the faculty of the School of Information Science and Technology at ShanghaiTech University as an assistant professor, PI in March 2015.

Laurent Kneip

Assistant Professor
School of Information Science and Technology, ShanghaiTech University

Title: SLAM: Sensing the world in 3D

Abstract: The course provides an introduction to the topic of Simultaneous Localization and Mapping, in short “SLAM”. As its name reveals, SLAM describes the problem of continuously localizing an agent with sensors in an unknown environment while simultaneously establishing a 2D or 3D map of this environment. This problem lies at the heart of many important emerging applications, such as AR/VR (smart phone/headset localization and perception of environment geometry for virtual content insertion), autonomous driving (real-time vehicle localization and 3D road-scene perception), and, most importantly, robotics. The course focusses on visual SLAM, which has undergone particularly vibrant development over the past decade.

Bio: Dr Laurent Kneip is an Assistant Professor tenure-track within the School of Information Science and Technology at ShanghaiTech. Before, he was a senior researcher, lecturer, and DECRA fellow within the Research School of Engineering at the Australian National University. He owns a PhD degree from ETH Zurich, where he worked at the Autonomous Systems Lab. He is a globally recognized expert in computer vision, and works on enabling autonomous systems and mobile applications to use cameras for real-time 3D perception of the environment. His research interests include visual odometry/SLAM (Simultaneous Localization and Mapping) and structure from motion with single and multi-camera systems, as well as the efficient solution of the more fundamental, underlying algebraic geometry problems. Dr Kneip is the main author of the open-source frameworks OpenGV and polyjam.