ACSOS 2021
Mon 27 September - Fri 1 October 2021 Washington, DC, United States

We are proud to include three high-profile keynotes into our program:

Vinod Muthusamy (IBM) — Business automation for the masses

Abstract: Business process underpin critical enterprise operations across virtually all industries including health care, finance, and retail, and optimizing and automating these processes can reduce cost or improve customer experience. However, the most common automation approaches, such as structured workflow management systems, operational rules, and robotic process automation (RPA), require skilled developers to implement and only tend to only address the common, anticipated cases. In reality business processes exhibit a long tail of rare or unique situations that often fall on human knowledge workers to handle.This talk will present an approach to business automation that allows business users to use a conversational interface to interact with, compose, and customize automations. This solution relies on recent advances in AI, such as natural language understanding and explainable AI, to enable non-programmers to more effectively and efficiently manage the long tail of situations that are difficult to automate. This talk will also highlight a number of research challenges to fully realize this solution.

Biography: Vinod Muthusamy is a Principal Research Scientist at the IBM T.J. Watson Research Center, and completed his PhD in Computer Engineering at the University of Toronto. His research interests lie at the intersection of business processes and distributed infrastructure, covering topics such as process mining and optimization, AI trust and fairness, serverless middleware, and complex event processing.

Christian Prehofer (DENSO) — Challenges of Big Data and Vehicle Data

Abstract: The number of connected vehicles is rapidly increasing, providing data from a large set of in-vehicle sensors and actuators. For this data, Big Data analysis plays an important role in enabling new services. We discuss the challenges of collecting and processing this data from vehicles, where processing can take place in the vehicle or in the cloud in flexible way. For this environment, we discuss the specific question of application needs and resource management, especially for big data tools in this distributed environment.

Biography: Christian Prehofer (male) works as product director for mobility services at DENSO Germany. Before this, he was research group leader for Internet of Things & Services at Fortiss in Munich and also lecturing at the TU München. Before this, he worked as Chief Researcher at Fraunhofer ESK and professor in Computer Science at TU München. Between 1998 and 2009, he held different management and research positions in the mobile communication industry. Last, he was director at Nokia Research in Helsinki, Finland. He obtained his Ph.D. and his habilitation in computer science from the TU Munich in 1995 and 2000. He is author of more than 150 publications and 34 granted patents.

Ramya Raghavendra (Facebook) — Environmental Impact of AI: Implications, Optimization Opportunities, and Challenges for Sustainable AI Systems

Abstract: The past decade has witnessed a 300,000x increase in the amount of compute for AI. The rapid growth of AI computing comes with significant costs. First, the model size and computation requirement scaling outpace the AI system performance improvement. Second, the energy footprint of AI has massive impact in our environment and profound implications in our industry’s Green and Sustainability strategies. AI accelerators (training and inference) are excellent first steps to bend the growing use cases and system resource demand of AI. However, majority of efficiency opportunities in the AI domain lie above the hardware layers, in model and data efficiency. Advances in AI are currently driven by AI research that seeks to improve accuracy (or related measures) through the use of massive computational power while disregarding the cost. We need to develop AI research while considering their computational (and thus carbon) cost, encouraging a reduction in resources spent. This talk will discuss the research and deployment opportunities to advance sustainability adoption and describe ML system development strategies to scale AI computing sustainably for the next decades to come with the goal of reducing the rising environmental footprint of AI.

Biography: Ramya Raghavendra received her MS & PhD in Computer Science at the University of California, Santa Barbara. She spent the next decade working as a research scientist at IBM Research, during which time she worked on a range of problems that spanned networking, network analytics and large-scale systems for Machine Learning. She has co-authored over 25 papers in top venues including Sigcomm, Infocom, ASPLOS, ICDE, BigData and JMLR, granted over 40 patents and earned the title of ‘Master Inventor’ at IBM. Ramya currently works at Facebook AI where she works on problems at the intersection of systems and AI, leading the Green AI effort aimed at developing and deploying AI that is efficient-by-design.

Mahdi Manesh (Porsche Digital) — Engineering Smart Systems in Practice: Lessons Learned and Challenges Ahead

Abstract: The promising academic results in the field of machine learning have already had an impact in practice. Organizations of different size and domain are aiming at facilitating the seemingly limitless possibilities of this “new” technology. The goals are always the same: increased productivity, quality and the engineering of new product and service offerings to differentiate themselves in the market. However, this is still a hard task to achieve.

In this talk, experiences made in different industrial and consulting settings are presented in a distilled form, to motivate the speaker’s hypothesis: In order to successfully develop the intended products, engineering certainly plays a key role. Yet, people and process are complementing aspects of success, especially when it comes to technology transfer in an environment with lots of uncertainty.The lessons learned can hopefully be discussed and integrated into best practices as well as into the (academic) training of software engineers, thus benefiting the community.

Biography: Mahdi Manesh heads the Software Engineering & Deep Tech Chapters at Porsche Digital. His current research and consulting activities span various aspects of advanced software technologies and CIO advisory with a focus on building digital products and services in the automotive industry. Mahdi received Ph.D. and M.Sc. degrees in Computer Science from the University of Koblenz-Landau as well as a B.Sc. in Computer Science from the Johannes Gutenberg University Mainz

Jonas Ekmark (Zenseact) — Key Research Challenges for development of ADAS and AD

Abstract: We at Zenseact are passionate about life and safety. Our purpose is to protect life on the road by providing a software platform for innovative and safe self-driving features that will change our societies forever. However, the latest advancements in the field of autonomous driving software development resulted in the engineering challenge of the century. Solving that requires razor-sharp focus. Zenseact was born out of the need to move undistracted towards this new trajectory with the purpose to make safe and intelligent mobility real for everyone, everywhere. In order to fully serve our purpose, we are creating an unsupervised autonomous vehicle solution for consumer vehicles together with our lead customer Volvo Cars. In this presentation, I will give an overview of the key research challenges that we currently encounter at Zenseact on our road to unsupervised autonomous vehicle solutions. These challenges represent opportunities for the research community to address exciting problems of current practical importance and offer possibilities for collaboration. Drücken Sie zum Aktivieren des Screenreaders Strg+Alt+Z. Informationen zu Tastaturkürzeln erhalten Sie, indem Sie Strg+Schrägstrich drücken.

Biography:

Azimeh Sefidcon (Ericsson) — 6G Network Compute Fabric

Abstract: We define 6G ( even though still at research phase ) as a trusted intelligent network platform that is delivering ever present intelligent communication. This communication includes connectivity, data and compute in its foundation. Taking into account the driving forces we identify main usecases of 6G to be Internet of senses, Connected intelligent machines, Digitalized and programmable physical world as well as connected sustainable world. In this talk, we share the driving forces behind 6G, its main capabilities with a closer look at the Network Compute Fabric. We design the Network Compute Fabric, as one of the fundamental components of 6G which would act as a first and fast responder to events happening in the real world, hosting computing, which is intertwined with communication. NCF will cater for unified ecosystem, execution environment, data access and application management. Drücken Sie zum Aktivieren des Screenreaders Strg+Alt+Z. Informationen zu Tastaturkürzeln erhalten Sie, indem Sie Strg+Schrägstrich drücken.

Biography: Azimeh Sefidcon is the Research Director in Cloud Systems and Platforms at Ericsson. She is responsible of an international team of researchers in five countries and sets the research agenda for future vision of Network-Compute-Fabric, technologies for edge cloud, network friendly cloud evolution and future computing platforms. She is keen on finding the relevant use cases, algorithms and software stack for which the networking industries needs to be ready to take advantage of future computing paradigms. Having industry 4.0 evolution and cyber physical system in mind, she has focused on various branches including XR, automotive, collaborative robotics and drone traffic managements to capture the requirements on network compute fabric. To facilitate the national effort on technology industrialization, she is active as the board member in few board of directors among which Wallenberg center for quantum technologies (WACQT) as well as process Industry and IT Automation (PiiA). She holds a MSc degree in hardware engineering, and a second MSc degree in intelligent networks, focusing on network services. With the network compute convergence in focus, she continued her research and obtained her PhD degree in mobility and IP. As professional experience, since 2003, she has held various technology development and leadership roles in mobile and core networks as well as software and system design.