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Application & Gen AI Integration (Business Leaders) Track
Data Privacy
Ethical AI
C-Suite
Healthcare
Finance
Manufacturing
Moderator

Author:

Hira Dangol

Vice President, AI/ML & Automation
Bank Of America

Industry experience in AI/ML, engineering, architecture and executive roles in leading technology companies, service providers and Silicon Valley leading organizations. Currently focusing on innovation, disruption, and cutting-edge technologies through startups and technology-driven corporation in solving the pressing problems of industry and world.

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Hira Dangol

Vice President, AI/ML & Automation
Bank Of America

Industry experience in AI/ML, engineering, architecture and executive roles in leading technology companies, service providers and Silicon Valley leading organizations. Currently focusing on innovation, disruption, and cutting-edge technologies through startups and technology-driven corporation in solving the pressing problems of industry and world.

Author:

Andy Lofgreen

AVP, Data Science Practice
DataRobot
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Andy Lofgreen

AVP, Data Science Practice
DataRobot

Author:

Waheed Qureshi

Founder & CEO
WMQ Investments
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Waheed Qureshi

Founder & CEO
WMQ Investments

GAI has driven a huge revolution in how AI platforms are designed, architected, and scaled for training, fine tuning, evaluation, inferencing and GAI application engineering needs using RAG, embeddings and distributed multi-agents frameworks. In this session we will deep dive into the (re)evolution of AI platforms and various technologies to scale this for next generation GAI needs.

AI Agents
C-Suite
Business Leader
AI Implementation

Author:

Animesh Singh

Executive Director, AI & Machine Learning
LinkedIn

Executive Director, AI and ML Platform at LinkedIn | Ex IBM Senior Director and Distinguished Engineer, Watson AI and Data | Founder at Kubeflow | Ex LFAI Trusted AI NA Chair

Animesh is the Executive Director leading the next generation AI and ML Platform at LinkedIn, enabling creation of AI Foundation Models Platform, serving the needs of 930+ Million members of LinkedIn. Building Distributed Training Platform, Machine Learning Pipelines, Feature Pipelines, Metadata engine etc. Leading the creation of LinkedIn GAI platform for fine tuning, experimentation and inference needs. Animesh has more than 20 patents, and 50+ publications. 

Past IBM Watson AI and Data Open Tech CTO, Senior Director and Distinguished Engineer, with 20+ years experience in Software industry, and 15+ years in AI, Data and Cloud Platform. Led globally dispersed teams, managed globally distributed projects, and served as a trusted adviser to Fortune 500 firms. Played a leadership role in creating, designing and implementing Data and AI engines for AI and ML platforms, led Trusted AI efforts, drove the strategy and execution for Kubeflow, OpenDataHub and execution in products like Watson OpenScale and Watson Machines Learning.

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Animesh Singh

Executive Director, AI & Machine Learning
LinkedIn

Executive Director, AI and ML Platform at LinkedIn | Ex IBM Senior Director and Distinguished Engineer, Watson AI and Data | Founder at Kubeflow | Ex LFAI Trusted AI NA Chair

Animesh is the Executive Director leading the next generation AI and ML Platform at LinkedIn, enabling creation of AI Foundation Models Platform, serving the needs of 930+ Million members of LinkedIn. Building Distributed Training Platform, Machine Learning Pipelines, Feature Pipelines, Metadata engine etc. Leading the creation of LinkedIn GAI platform for fine tuning, experimentation and inference needs. Animesh has more than 20 patents, and 50+ publications. 

Past IBM Watson AI and Data Open Tech CTO, Senior Director and Distinguished Engineer, with 20+ years experience in Software industry, and 15+ years in AI, Data and Cloud Platform. Led globally dispersed teams, managed globally distributed projects, and served as a trusted adviser to Fortune 500 firms. Played a leadership role in creating, designing and implementing Data and AI engines for AI and ML platforms, led Trusted AI efforts, drove the strategy and execution for Kubeflow, OpenDataHub and execution in products like Watson OpenScale and Watson Machines Learning.

This presentation explores the integration of generative AI in healthcare and pharmacology, highlighting advancements in prompt engineering and its impact on decision-making. The session will examine the complexities and variability of AI responses and the difficulties in establishing a reliable ground truth, emphasizing the need for structured and reproducible outputs to support clinical and business processes efficiently.

Application & Gen AI Integration (Business Leaders) Track
Healthcare
Pharma
Data Science
AI Technologists

Author:

Zoran Krunic

Principal Product Manager
Amgen

Since joining Amgen R&D in 2018, Zoran Krunic has been at the forefront of applying Machine Learning to enhance patient outcomes and streamline clinical trial enrollment processes, utilizing comprehensive Electronic Health Records and clinical datasets. His pioneering work in the Quantum Machine Learning space, in collaboration with IBM's Quantum team, has been instrumental in integrating machine learning with quantum computing through IBM’s Qiskit platform.

Prior to his tenure at Amgen, Zoran developed Machine Learning algorithms at Optum to predict hardware and software failures within complex enterprise architectures. He has a strong background in data engineering and systems development, having contributed significantly to large-scale projects at renowned organizations such as Capital Group and ARCO Petroleum.

In his current full and part-time endeavors, Zoran is leading the efforts in embracing generative AI technologies, with a particular focus on OpenAI's GPT and Anthropic's Claude-2 models. His work is focused on prompt engineering and its application to code generation, advanced document analysis, and process management, with a commitment to ethical AI practices and data privacy.

A recognized voice in quantum computing circles, Zoran is a regular presenter at industry conferences and has served on numerous panels discussing the integration of quantum computing and generative AI within the Health Sciences sector.

With a Master of Science in Electrical Engineering & Computer Science, Zoran continues to explore and contribute to the evolving relationship between quantum computing and artificial intelligence, fostering groundbreaking advancements in healthcare technology.

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Zoran Krunic

Principal Product Manager
Amgen

Since joining Amgen R&D in 2018, Zoran Krunic has been at the forefront of applying Machine Learning to enhance patient outcomes and streamline clinical trial enrollment processes, utilizing comprehensive Electronic Health Records and clinical datasets. His pioneering work in the Quantum Machine Learning space, in collaboration with IBM's Quantum team, has been instrumental in integrating machine learning with quantum computing through IBM’s Qiskit platform.

Prior to his tenure at Amgen, Zoran developed Machine Learning algorithms at Optum to predict hardware and software failures within complex enterprise architectures. He has a strong background in data engineering and systems development, having contributed significantly to large-scale projects at renowned organizations such as Capital Group and ARCO Petroleum.

In his current full and part-time endeavors, Zoran is leading the efforts in embracing generative AI technologies, with a particular focus on OpenAI's GPT and Anthropic's Claude-2 models. His work is focused on prompt engineering and its application to code generation, advanced document analysis, and process management, with a commitment to ethical AI practices and data privacy.

A recognized voice in quantum computing circles, Zoran is a regular presenter at industry conferences and has served on numerous panels discussing the integration of quantum computing and generative AI within the Health Sciences sector.

With a Master of Science in Electrical Engineering & Computer Science, Zoran continues to explore and contribute to the evolving relationship between quantum computing and artificial intelligence, fostering groundbreaking advancements in healthcare technology.

This engaging panel discussion delves into the critical differences between proprietary and public data, emphasising the distinct advantages and disadvantages associated with each. Explore how the accessibility and vast quantities of public data facilitate robust generalisation within AI models, contrasting with the nuanced strengths of proprietary data.

Public data's accessibility and abundance offer significant advantages, enabling broad generalisation within AI models. Conversely, proprietary data boasts higher quality, enhanced control, and minimal risk of contamination, catering specifically to niche topics with detailed coverage.

Delve into the advantages of public data, its scalability, and the challenges it poses, juxtaposed against the precise and controlled nature of proprietary data. Gain valuable insights into navigating the trade-offs between the two, understanding their impacts on model performance, ethical and regulatory considerations, and innovation within the realm of AI.

Technologist Deep-Dive (Gen AI & Data Science) Track
AI Technologists
Data Science
Digital Infrastructure
MLOps
Moderator

Author:

Tom Kersten

R&D Engineer
Royal NLR - Netherlands Aerospace Centre

Tom is a distinguished R&D Engineer specialising in AI within the aerospace sector. Armed with a background in computer science and AI, Tom possesses a comprehensive understanding of AI systems. Within his company, he stands out as a leading visionary delving into the integration of generative AI in space, in particular to support the efforts of the Dutch government and its military in this domain. His pioneering work involves exploring and harnessing the potential of GenAI models to revolutionise satellite operations, mission planning, earth observation and space exploration. Tom's dedication to pushing the boundaries of AI in aerospace extends to leveraging generative AI's capabilities, envisaging transformative applications that could redefine the landscape of space technology.

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Tom Kersten

R&D Engineer
Royal NLR - Netherlands Aerospace Centre

Tom is a distinguished R&D Engineer specialising in AI within the aerospace sector. Armed with a background in computer science and AI, Tom possesses a comprehensive understanding of AI systems. Within his company, he stands out as a leading visionary delving into the integration of generative AI in space, in particular to support the efforts of the Dutch government and its military in this domain. His pioneering work involves exploring and harnessing the potential of GenAI models to revolutionise satellite operations, mission planning, earth observation and space exploration. Tom's dedication to pushing the boundaries of AI in aerospace extends to leveraging generative AI's capabilities, envisaging transformative applications that could redefine the landscape of space technology.