Kit Srinivasan will give an introduction to FICORE (Finnish Indian Consortia for Research and Education) network involving 23 IITs and 15 Finnish higher educational institutions.

Siddharth Naithani will address the hands-on approach from Business Finland to AI collaborations in India.

Artificial Intelligence (AI) and Machine Learning (ML), including deep learning (DL), continue to transform the world around us. However, in order to avoid the risk of "plateauing out", we need to make the next leap. In this talk, I will provide an overview of some exciting work we've been doing to design cutting-edge technologies and push the boundaries of what can be achieved with AI/ML/DL in several important domains such as biopharma, IoT, quantum computing, multiagent systems, and e-commerce. I'll also mention relevant research being conducted at FCAI.

For the benefit of our audience - both within academia and industry - I'll present several motivating examples from recent AI/ML/DL success stories, and sketch how critically dissecting the (complex) problems of interest for scientists and industrial practitioners, and combining intuition with mathematical rigor is often the key to making these scientific advances.

Artificial Intelligence has become impactful during the last few years in chemistry and the life sciences, pushing the scientific boundaries forward as exemplified by the recent success of AlphaFold2. In this presentation, I will provide an overview of how AI has impacted drug design in the last few years, where we are now and what progress we can reasonably expect in the coming years. The presentation will have a focus on deep learning based molecular de novo design, however, also aspects of synthesis prediction, molecular property predictions and chemistry automation will be covered.

An overview will be presented how AI/ML methods are taken into use and developed for Medicine Design in Orion Pharma R&D for different tasks: molecular property predictions, automated ML, AI-assisted structure-based virtual screening and generative models for designing novel small molecule drugs.

Combinatorial treatments involving two or more drugs have become a standard of care for various complex diseases, including tuberculosis, malaria, HIV and other viral infections, as well as most of the advanced cancers. High-throughput screening in preclinical model systems (e.g. cancer cell lines or viral infection models) is the state-of-the-art approach to systematically identify candidate drug combinations. However, due to the exponential number of possible drug combinations and the extensive heterogeneity of the target systems, computational methods, and in particular, machine learning, are critically needed to guide the discovery of effective combinations to be prioritized for further pre-clinical validation and clinical development.

In this talk, I present our recent work on a highly accurate machine learning framework for predicting the responses of drug combinations in preclinical studies. Our methods model the cell context-specific drug interactions through higher-order tensors, and efficiently learns latent factors of the tensor using powerful factorization machines and latent tensor reconstruction. The approach enables our methods to leverage information from previous experiments performed on similar drugs and cells when predicting responses of new combinations in so far untested cells; thereby, it achieves highly accurate predictions despite sparsely populated data tensors. In conclusion, the presentation discusses the open challenges and potential extensions of the methods.

Finnish Center for Artificial Intelligence (FCAI) is a community of experts that worldwide brings together top talents in academia, industry and the public sector to solve real-life problems using both existing and novel AI. Our mission is to create AI that is data-efficient, trustworthy and ethically sustainable, and that can operate with people in an understandable manner. Our activities span a wide spectrum of fields. We collaborate with academic, industrial and societal actors, and have high-volume strategic initiatives as well as numerous smaller-scale bottom-up projects. We also have a wide and outstandingly popular education program. In her talk, Kajaste will invite the audience into collaboration with FCAI and give concrete case examples of various kinds of successful collaboration

In a multi-x edge environment, it is a challenge to timely share partially overlapping data streams for multiple AI functions in several edges doing simultaneous inference on the data. The challenge becomes even harder, if the same data is needed for off-line training done later elsewhere. In this presentation, I will discuss some aspects of the problem and present related findings of recent research projects that studied domains of air quality monitoring and energy ecosystems.

• Arun Rajkumar, Assistant Professor, IIT Madras
• Ashwin Rao, Assistant Professor, University of Helsinki
• Vikas Garg, Assistant Professor, Aalto University
• Harish Guruprasad, Assistant Professor, IIT Madras
• Chandrashekar Lakshminarayanan, Assistant Professor, IIT Madras
• Amauri Holanda De Souza Junior, Postdoctoral Researcher, Aalto University
• Naser Motlaugh, Postdoctoral Researcher, University of Helsinki
• Pranvera Kortoçi, Postdoctoral Researcher, University of Helsinki