Background on Symposium
Industry Research and User Symposium (IRUS) has always been at the forefront in bringing together solution providers and users of HPC, Cloud and Data platforms. This has been an open and engaging platform where they can share their challenges and successes and discuss relevant technology issues related to computational and data sciences. Previous IRUS sessions (2018, 2017, 2016 links) had tremendous participation from conference attendees and included senior speakers from industry like Amazon, Flipkart, Intel, Microsoft, Shell and Xilinx, academia like CERN, IISC and TIFR and start-ups such as Khosla Labs and Forus Health.
2019 IRUS Sessions
This year’s IRUS will continue to bring attention to emerging themes in the areas of computational and data sciences from leading academics and industry practitioners. The sessions have been specially designed to have a diverse topics and speakers that not only matter to deepening our understanding of cutting-edge breakthroughs but also enables how their impact can be widened across industry and society.
2019 IRUS Session Chair
Laks Raghupathi is a data analytics leader delivering value from globally diverse, high-performing teams leveraging advanced analytics insights and digital platforms. Laks currently works as Analytics Manager in Shell Downstream’s global lubricants supply chain business. He has developed and deployed business-critical analytics for Shell Upstream to predict extreme storm exposure to offshore platforms and Siemens Healthcare in computer-aided diagnosis – both of which were also widely published. He holds a PhD in Applied Math and Computer Science
For any questions you may reach out to 2019 IRUS Chair Dr. Laks Raghupathi ([email protected]) or Chiranjib Sur (HiPC 2019 Conference General Chair) – [email protected].
IRUS Session #1: Artificial Intelligence (AI) in Drug Development
Moderator: Dr. Sudip Roy
Dates: December 18-19, 2019 (10:00 am – 12:00 noon)
Abstract: The scientific world is moving towards adopting machine learning (ML), deep learning (DL) technologies which are termed as artificial intelligence (AI). The pharmaceutical industry to quickly accessing the progresses in these new technologies and rapidly integrating them in their drug development cycle. The overarching goal of AI in healthcare is to improve human life. So, the innovation is going on with high hopes around better drugs, better diagnostics, medical applications and cure for life threatening diseases.
Here we will address some key developments in the drug discovery area and see how current state of AI is getting implemented in the pharmaceutical industry. Alongside we will bring to attention some areas where research and innovation are progressing rapidly and globally researchers, government, social organizations are started finding ways to implement these technologies.
IRUS Session #2: AI @ Scale – Challenges and Best-Practices
Moderator: Sundar Dev, Google, USA
Dates: December 19, 2019 (10:00 am – 12:00 noon)
Abstract: The field of Artificial Intelligence (AI) has emerged as the most key technological revolutions of this century. AI has evolved from being a niche research area to one with vast and profound impact on the day to day lives of billions of people all over the world. Today, AI has applications in the fields of Agriculture, Aviation, Education, Entertainment, Finance, Healthcare, Industrial Manufacturing and Transportation among other things. Application of AI as a method to solve previously intractable problems presents a huge opportunity as well as many concerns. Particularly, a systems-level concern is scalability. In this Industry Research User Symposium session on AI @ scale, we want to explore some of these issues, get leaders from academia and industry to share their thoughts, experiences, and best practices on how they are thinking about addressing the problem of scalability in the field of AI, and also discuss how HPC can help power the field of AI to address some of these challenges.
- What are the biggest challenges facing the field of AI as it scales to more applications and users?
- How many of these challenges are due to the limitations of today’s AI infrastructure and/or H/W platforms?
- How is the field of AI trying to solve these problems?
- The field of HPC has solved similar problems facing the field of AI today but for other applications (like Weather Forecasting, Bio-molecular analysis, etc.,) that share very similar characteristics to typical AI workloads (i.e., data intensive, concentrated kernel computations on the data such as fully connected layers, convolutions, sequence models, embeddings, etc). How can HPC help the field of AI scale?
- Can HPC help AI break new ground by tapping into new applications and/or use cases? If so, what are some examples of new AI applications and/or use cases that will be enabled by HPC infrastructure?
- Conversely how can AI techniques help the HPC community? What are the similarities? What are the differences?
IRUS Organizing Committee
Laks Raghupathi, Shell, India
Sundar Dev, Google, USA