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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.

... read more

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.

... read more

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.

Application & Gen AI Integration (Business Leaders) Track
AI Implementation
Systems Selection
AI Investment

Author:

Brandon Walker

Enterprise AI Strategy Lead
Rocket Companies

Brandon Walker is the Enterprise AI Strategy Lead at Rocket Companies. In this role, he is responsible for driving the strategy and architecture for the technology, data and analytics that power the Rocket Company’s fintech platforms—ensuring a consistent, seamless experience for clients across the Rocket Companies ecosystem and driving its growth from mortgage and real estate to personal finance.

Brandon holds a bachelor’s degree from Georgia College and State University and a master’s degree from Harvard University. He and his family reside in Charleston, South Carolina. 

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Brandon Walker

Enterprise AI Strategy Lead
Rocket Companies

Brandon Walker is the Enterprise AI Strategy Lead at Rocket Companies. In this role, he is responsible for driving the strategy and architecture for the technology, data and analytics that power the Rocket Company’s fintech platforms—ensuring a consistent, seamless experience for clients across the Rocket Companies ecosystem and driving its growth from mortgage and real estate to personal finance.

Brandon holds a bachelor’s degree from Georgia College and State University and a master’s degree from Harvard University. He and his family reside in Charleston, South Carolina. 

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.

... read more

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.

Author:

Austin Vance

CEO
Focused Labs

Austin Vance, the co-founder and CEO of Focused Labs, brings a dynamic blend of technical prowess and leadership to the forefront of the software industry. With a career spanning 24 years in software development, he has a rich history of leading high-performing engineering teams at organizations such as Pivotal and PayPal. This extensive experience has not only honed his expertise in the field but also deepened his commitment to delivering exceptional customer service through innovative software solutions. Under his guidance, Focused Labs excels in providing customers with custom software solutions that drive growth, enhance efficiency, and foster innovation, solidifying its position as a trusted partner in the tech ecosystem.

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Austin Vance

CEO
Focused Labs

Austin Vance, the co-founder and CEO of Focused Labs, brings a dynamic blend of technical prowess and leadership to the forefront of the software industry. With a career spanning 24 years in software development, he has a rich history of leading high-performing engineering teams at organizations such as Pivotal and PayPal. This extensive experience has not only honed his expertise in the field but also deepened his commitment to delivering exceptional customer service through innovative software solutions. Under his guidance, Focused Labs excels in providing customers with custom software solutions that drive growth, enhance efficiency, and foster innovation, solidifying its position as a trusted partner in the tech ecosystem.

This talk delves into the forefront of AI reliability, presenting sophisticated strategies that address core challenges in the field. Our focus encompasses the intricacies of hallucination prevention, the refinement of data batching processes, and the criticality of compliance in AI development. Leveraging deep insights from cutting-edge research and practice, we offer a comprehensive perspective on enhancing AI systems' accuracy and ethical integrity. This discourse is designed to equip practitioners and researchers with advanced methodologies, fostering the next wave of AI innovations grounded in robustness and responsibility.

Technologist Deep-Dive (Gen AI & Data Science) Track
Finance
Data Science
AI Technologists
AI Integration

Author:

Sai Teja Akula

Senior Director, Data Science
LTX Trading

With over a decade of dedicated experience in the field of Artificial Intelligence, Sai stands at the forefront of implementing and leveraging Data Science within organizations. A respected figure in the AI community, Sai has played an instrumental role in the transformation of traditional business models by seamlessly integrating advanced data-driven solutions. His expertise extends from developing sophisticated machine learning algorithms to strategizing the holistic implementation of AI within organizational infrastructures

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Sai Teja Akula

Senior Director, Data Science
LTX Trading

With over a decade of dedicated experience in the field of Artificial Intelligence, Sai stands at the forefront of implementing and leveraging Data Science within organizations. A respected figure in the AI community, Sai has played an instrumental role in the transformation of traditional business models by seamlessly integrating advanced data-driven solutions. His expertise extends from developing sophisticated machine learning algorithms to strategizing the holistic implementation of AI within organizational infrastructures

Leading analysts including McKinsey have emphasized the need for business-technology co-ownership of solutions delivery and value creation, to maximize the impact of AI in ways that count for customers.  Intelligence and automation help modernize a whole range of customer service operations by automating routine tasks, allowing human agents to focus on complex problem-solving and empathetic interactions, with enhanced compliance and efficiency for better value and UX delight. In this context, the integration of Generative AI in customer experiences including call transcription, sentiment analysis and similar applications can enable a more empathetic and responsive service model, where efficiency, personalization, and customer satisfaction are paramount.  We present  an AI software engineering best practice focused on integrating new AI capabilities into enterprise wide customer service platforms to deliver incremental value along a multi-year journey of AI adoption. It is based on several technical, product and business criteria essential to optimize the full AI capability stack, to drive greater enterprise value by pacing how and when AI is built and integrated into servicing platforms.

Application & Gen AI Integration (Business Leaders) Track
Business Leader
Finance
Banking
BFSI
C-Suite
Digital Transformation

Author:

Prasad Saripalli

Distinguished Engineer
Capital One

Prasad Saripalli serves as a Distinguished Engineer at Capital One, a technology driven bank on the Fortune 100 list, redefining Fintech and Banking using data, technology, AI and ML in unprecedented ways. Most recently, Prasad served as the Vice President of AIML and Distinguished Engineer at MindBody Inc - a portfolio company of Vista which manages the world's fourth-largest enterprise software company after Microsoft, Oracle, and SAP. Earlier, he served as VP Data Science at Edifecs, an industry premier healthcare information technology partnership platform and software provider, building Smart Decisions ML & AI Platform with Ml Apps Front. Prior to this, Prasad served as CTO and VP Engineering at Secrata.com, provider of Military grade Security and Privacy solutions developed and deployed over the past 15 years at Topia Technology for the Federal Government and the Enterprise, and as CTO & EVP at ClipCard, a SaaS based Hierarchical Analytics and Visualization platform.

At IBM, Prasad served as the Chief Architect for IBM's SmartCloud Enterprise (http://www.ibm.com/cloud-computing/us/en/). At Runaware, he served as the Vice President of Product Development. As a Principal Group Manager at Microsoft, Prasad co-led the development of virtualization stack on Windows 7 responsible for shipping Virtual PC7 and Windows XP Mode on Windows 7.


Prasad teaches Machine Learning, AI, NLP, Distributed Systems, Cloud Engineering and Robotics at Northeastern University and the University of Washington Continuum College.

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Prasad Saripalli

Distinguished Engineer
Capital One

Prasad Saripalli serves as a Distinguished Engineer at Capital One, a technology driven bank on the Fortune 100 list, redefining Fintech and Banking using data, technology, AI and ML in unprecedented ways. Most recently, Prasad served as the Vice President of AIML and Distinguished Engineer at MindBody Inc - a portfolio company of Vista which manages the world's fourth-largest enterprise software company after Microsoft, Oracle, and SAP. Earlier, he served as VP Data Science at Edifecs, an industry premier healthcare information technology partnership platform and software provider, building Smart Decisions ML & AI Platform with Ml Apps Front. Prior to this, Prasad served as CTO and VP Engineering at Secrata.com, provider of Military grade Security and Privacy solutions developed and deployed over the past 15 years at Topia Technology for the Federal Government and the Enterprise, and as CTO & EVP at ClipCard, a SaaS based Hierarchical Analytics and Visualization platform.

At IBM, Prasad served as the Chief Architect for IBM's SmartCloud Enterprise (http://www.ibm.com/cloud-computing/us/en/). At Runaware, he served as the Vice President of Product Development. As a Principal Group Manager at Microsoft, Prasad co-led the development of virtualization stack on Windows 7 responsible for shipping Virtual PC7 and Windows XP Mode on Windows 7.


Prasad teaches Machine Learning, AI, NLP, Distributed Systems, Cloud Engineering and Robotics at Northeastern University and the University of Washington Continuum College.

Author:

Franz Zemen

VP of Software Engineering
Capital One
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Franz Zemen

VP of Software Engineering
Capital One

This talk focuses on how Generative AI is changing digital marketing by speeding up content creation and making it more personal. As the internet gets crowded, creating content quickly and tailored to each person is crucial. We'll show how AI helps marketers make content fast and customize it for each viewer. This session is great for marketers, content creators, and anyone interested in how AI is making digital marketing more efficient and relevant.

Technologist Deep-Dive (Gen AI & Data Science) Track
Marketing
Digital Media
Business Leader
AI Implementation

Author:

Mayank Anand

Machine Learning Engineering Manager
Adobe

Mayank Anand is a Machine Learning Engineering Manager at Adobe, dedicated to enhancing digital marketing with AI and machine learning. His focus lies in harnessing AI for better content creation from texts and images. Presently, he's innovating in the field of generative AI to produce smart, brand-safe content. Mayank holds a Master's in Computer Science from USC, Los Angeles.

... read more

Mayank Anand

Machine Learning Engineering Manager
Adobe

Mayank Anand is a Machine Learning Engineering Manager at Adobe, dedicated to enhancing digital marketing with AI and machine learning. His focus lies in harnessing AI for better content creation from texts and images. Presently, he's innovating in the field of generative AI to produce smart, brand-safe content. Mayank holds a Master's in Computer Science from USC, Los Angeles.

Application & Gen AI Integration (Business Leaders) Track
Business Leader
C-Suite
AI Implementation
AI Technologists

Author:

Hussain Chinoy

Technical Solutions Manager, Applied AI Engineering
Google

Hussain has a background in linguistics and software applications and has been working on building speech and conversational systems for the last 20 years. As a Technical Solutions Manager for Generative AI on the Applied AI Engineering team he focuses on combining Google Cloud services into solutions that accelerate customers usage of AI, including Generative AI for Marketing, Customer Experience, and Website Modernization. His technical areas of interest are in Conversation, Ethics and Governance, and application architecture. Hussain joined Google in 2020 as an application modernization and AI specialist.

... read more

Hussain Chinoy

Technical Solutions Manager, Applied AI Engineering
Google

Hussain has a background in linguistics and software applications and has been working on building speech and conversational systems for the last 20 years. As a Technical Solutions Manager for Generative AI on the Applied AI Engineering team he focuses on combining Google Cloud services into solutions that accelerate customers usage of AI, including Generative AI for Marketing, Customer Experience, and Website Modernization. His technical areas of interest are in Conversation, Ethics and Governance, and application architecture. Hussain joined Google in 2020 as an application modernization and AI specialist.