Starting with any relevant education, walk me through the twists and turns of your career to date? How did one opportunity lead to the next + what was the key takeaway/ experience in each role + how did this lead you to where you are now)

Donna Schut
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Vibhor Rastogi
Global Head AIML Data InvestingCiti VenturesNick Nystrom
Chief technology officerPeptilogicsJeff Herbst
Founding Managing PartnerGFT VenturesJeff Herbst is Co-Founding Managing Partner of GFT Ventures, an early stage AI-focused venture capital fund with approximate $140M in assets under management. He brings to the fund over three decades of venture capital, operational, business development and M&A experience. Prior to launching GFT, Jeff spent 20 years as NVIDIA's Vice President of Business Development where he built an ecosystem of accelerated computing applications spanning the domains of AI, Data Science, Autonomous Machines, and Graphics and Visualization. During his tenure at the company, among other things he created the Nvidia GPU Ventures program, overseeing more than 40 global investments and 20 acquisitions valued over $8B. He also led the Nvidia Inception global startup accelerator, now comprised of more than 10,000 AI, Data Science and High Performance computing companies.
Shreesha Jagadeesh
Associate Director of Applied Machine LearningBestBuyShreesha Jagadeesh is an Associate Director of Machine Learning at Best Buy. He leads a multi-national team of ML Scientists and Engineers building models that power the online customer journey through personalized recommendations and ads. He leverages his expertise in Multi-Stage Recommender Systems, LLMs, Embeddings, Multi-Arm Bandits, Offline Policy Evaluation and A/B testing to help digital teams to personalize the experiences deepening customer relationship & driving ecommerce revenue for Best Buy.
Prior to Best Buy, he has worked in a variety of corporate and consulting roles including at Amazon, EY and Cisco building Data Science models in a diverse set of domains spanning HR, Tax, Legal and Supply Chain. Outside of his day job, he advises early-stage startup, reviews pre-publication books/courses and has also published 2 online Data Science courses. He lives in Boston with his wife and enjoys travelling to exotic locations with an Antarctica expedition coming up in December 2024.
Bias in AI systems can lead to harmful outcomes. We investigate methods to increase model transparency and explainability in order to detect, understand, and mitigate risks from bias. Techniques like saliency maps, attention mechanisms, and adversarial testing can shed light on model behavior. Improving model transparency and reducing bias is key to developing safer, more trustworthy AI.
Risk MitigationModel DevelopmentData ScientistJon Bennion
Machine Learning Engineer and LLMOpsFOXRegulationEthicsBusiness LeaderMelissa Harup
SVP and Chief CounselMondelez International