The AIconics Awards Shortlist 2023 - Solution Implementer of the Year

Akshata Upadhye is an innovative data scientist who has dedicated her career to harnessing the transformative power of AI and data across various industries. 
At Randstad, Akshata played a pivotal role in enhancing the recruitment process by building advanced features for our in-house search engine and optimizing the sales algorithm. These innovations helped in reducing candidate-to-hire time in recruitment and ensured the delivery of high-quality leads and recommendations to our sales teams, aligning perfectly with Randstad's vision to become the world's leading specialized talent company.

She has also developed robust data pipelines and dashboards that provide invaluable insights, empowering business leaders to make critical decisions informed by data-driven intelligence. Beyond her work at Randstad, she is deeply committed to staying at the forefront of AI technology, particularly in the field of generative AI, through research and development initiatives. This passion for digital transformation is driven by the belief that innovation and technology can create a more human-centric experience for job seekers and employers alike

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Rohit has over a decade of leadership and innovation in AI, his influence has embedded AI into the very heart of Mastercard's cyber security and identity solutions. This has been a game changer for our customers in terms of increased fraud detection.

A seasoned leader with over 20 years of progressive experience and responsibilities in analytics and AI, Rohit leads a centralized function in a complex global organization with multiple stakeholders. Rohit has a proven record of delivering cutting edge solutions to help deepen customer relationships, maximize financial performance and create operational efficiencies. 

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Syed Haider leads intelligent automation with human-in-the-loop systems for creating operational efficiencies in clinical trials while aspiring for faster drugs to market. Syed is an AI Evangelist at Merck focusing on AI strategy and governance aiming to create safe AI solutions at scale.

The inherent ethical and moral characteristics of LLM-based generative AI provide valuable insights into its effectiveness when deployed. However, existing research literature suggests full comprehension of the impact it has on the user experience is an active open area. AI Ethics policy and governance are key requirements for the successful activation of Generative AI use cases in the enterprise environment while ensuring safety. Syed is a veteran AI leader with over 17 years of experience in AI strategy and governance, commercial innovations, and global enterprise solution development.

Syed is passionate about giving back to the community and supporting new talent by sharing his knowledge and experience. In the pursuit of his passion, he has joined Columbia University as an Adjunct Associate Professor in the Data Science Institute, Department of IEOR where he teaches a graduate course in AI.

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Sreya has a unique blend of excellence in core and applied AI research, industrial innovation and technical leadership. She invented one of the very first approaches to merge distributed machine learning, causal machine learning and blockchain to decentralize AI, and proposed and developed the very first implementation of 'Causal Federated Learning' which has been adopted by top research labs and industries worldwide. She has given talks in numerous international conferences and mentored several young women in the field of AI. Sreya has received multiple accolades including the prestigious Microsoft Research Diversity award twice.

Previously, Sreya has led multiple projects in the field of AI and Robotics, including Privacy Preserving Machine Learning Research at MILA AI Lab and Smart Home Automation Applied Research at Panasonic AI R&D Lab, Tata Elxsi. Sreya has experience partnering with top brand names (including Wipro, MILA, CHUM Medical Research) globally to make innovative technology, especially AI, accessible to all sectors worldwide.

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​​​​​​Dr. Theo Zanos is the head of the Division of AI in Health, Quantitative Intelligence at Northwell Health, as well as an Associate Professor at the Institute of Health System Science and Institute of Bioelectronic Medicine, at the Feinstein Institutes for Medical Research and the Zucker School of Medicine, Hofstra Northwell. 

His current research focuses on developing and applying machine learning methods on multimodal healthcare, neural and physiological data to enable early diagnosis, disease severity assessment, and personalization and adaptability of therapies. To that end, his lab focuses on combining machine learning methods with multiple healthcare data modalities (Electronic Health Records, continuous vitals, imaging, and unstructured notes) to develop and deploy clinical predictive and diagnostic models.
 
His most recent use case includes developing a model that predicts medical staff churn and hiring patterns.

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