QuantUniversity

As many of you know, I have been hosting the QU guest lecture series for many years. Last year, we hosted the first QU AI school with lectures from eminent researchers and leaders! QuantUniversity AI School is a series designed for professionals and enthusiasts at the intersection of AI and finance. The program brings in expert guest speakers from industry and academia to share insights on the latest advancements, challenges, and applications of AI in financial services. Topics range from AI risk management to AI development and topics in model interpretability and evaluation, providing attendees with valuable knowledge to stay ahead in this evolving field. A heart-felt thank you to all the speakers who participated and provided valuable insights to attendees! Today, we are making all the videos and slides on our new platform #Quskillbridge! That's 12+ hours of videos and slides! Special note: For a limited time, if you attend all the sessions and complete the assessment, you can earn a certificate of attendance from QuantUniversity! You can be anywhere in the world to participate in this program and the cost of the program is FREE! Register here: https://lnkd.in/ej_fEaCq Sessions from the 2024-2025 edition of the QU AI School Module 1: Testing & Validating Generative AI Applications Speaker:?Agus Sudjianto?from Ex-Wells Fargo Module 2: The Use and Governance of Generative AI in Large Financial Institutions Speaker:?Jacob Kosoff?from Bank of America Module 3: AI and Investing Speakers: Sri Krishnamurthy, CFA, CAP, QuantUniversity John Harthorne, Founder and Managing Director, Two Lanterns Venture Capital Anish Shah, PhD, CFA, Quant Research, Fidelity Investments Dushyant (D) Shahrawat, CFA, Director, FinTech Investment Banking, Rosenblatt Securities Arun Verma, PhD, Head of Quantitative Research Solutions at Bloomberg, LP John Werner, Managing Director, Link Ventures Module 4: Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigation Speaker:?Apostol Vassilev?from the National Institute of Standards and Technology (NIST) Module 5: Navigating AI and Model Risk Management in Banking: Evolution, Challenges and Best Practices Speakers: Christophe Rougeaux?from TD Hua Julia Li, ex-State Street Module 6: LLMs in Production Speaker:?Uday Kamath, Ph.D.?from Smarsh Module 7: Navigating the Future with Generative AI and Enhanced MRM Frameworks Speaker:?Rafic Fahs?from Fifth Third Bank Module 8: Alien Intelligence, Myth and Reality Speaker:?Frederic Siboulet (MSc, CAIA, CQF, ARPM, AIFI) Module 9: How to Choose a Threshold for an Evaluation Metric for Large Language Models Speaker:?Dhagash Mehta, Ph.D. from BlackRock Inc. Module 10: Poor Measurement in AI: Challenges and a Path Forward Speaker:?Patrick Hall?from the Department of Decision Sciences at The George Washington University University School of Business

Join us for the?QuantUniversity AI Spring School?session: ??? Date & Time:?January 28, 2025, at 12:00 PM ?? Topic:?How to Choose a Threshold for an Evaluation Metric for Large Language Models ?? Speaker:?Dhagash Mehta, Ph.D. Head of Applied Machine Learning Research at BlackRock Inc. ?? What You'll Learn: Identifying Risks:?Understand the importance of recognizing application-specific risks and stakeholder risk tolerance in deploying Large Language Models (LLMs). Setting Robust Thresholds:?Discover a step-by-step methodology to determine statistically rigorous thresholds for LLM evaluation metrics. Practical Application:?See the methodology in action with a case study on the Faithfulness metric using the HaluBench dataset. ?? Why Attend? Ensuring the reliability and safety of LLM deployments is crucial across industries. This webinar offers a systematic approach to selecting evaluation thresholds, enhancing the robustness of AI applications. ?? Learn More: For an in-depth understanding, check out the related research paper:? How to Choose a Threshold for an Evaluation Metric for Large Language Models Bhaskarjit Sarmah,?Mingshu Li,?Jingrao Lyu,?Sebastian Frank,?Nathalia Castellanos,?Stefano Pasquali,?Dhagash Mehta https://lnkd.in/eDcm2CJe

Navigating the Future with Generative AI and Enhanced Model Risk Management QuantUniversity Guest Lecture with Rafic Fahs, Fifth Third Bank and Sri Krishnamurthy, CFA, CAP This talk addresses the critical risks and challenges of generative AI in retail banking, such as confabulation, harmful recommendations, and mishandling sensitive data. It highlights the need for robust initial controls, effective validation, and comprehensive lifecycle management of high-risk AI models. Key solutions include: - AI Governance Working Group: A diverse team ensuring ethical usage and human oversight. - Exemption Criteria: Guidelines for when generative AI systems can be exempt from strict risk management. - Lifecycle Management Framework: Managing high-risk models from development to deployment. - Customized Risk Tiering: Tailoring risk assessments to specific AI use cases. - Enhanced Validation Templates: Standardizing validation processes for consistency and accuracy. This framework balances innovation with robust risk management, ensuring secure and efficient use of generative AI in various applications.

I had the privilege of presenting our work on #QuSkillBridge, developed by QuantUniversity, at the IEEE Global Tech Forum on AI, held on November 7, 2024, at the Microsoft New England Research & Development Center. My talk focused on leveraging agentic architectures for #CareerLifecycleManagement within enterprises. The Imagination in Action segment, curated by John Werner, brought thought leaders and entrepreneurs Michael Stonebraker, Tejas Kulkarni, Tamer Karatekin, Alexander Htet Kyaw & Richa Gupta for a fabulous afternoon filled with intrigue and insights fitting right in the theme of the possibilities with AI!. A heartfelt thank you to IEEE Future Directions, Imagination in Action, and John Werner for hosting this impactful event. For those interested in personalized AI upskilling and cross-skilling solutions, explore more at?https://lnkd.in/eF_HUSc3 About QuSkillBridge.ai: QuSkillBridge.ai offers personalized AI upskilling and cross-skilling solutions tailored to professionals, students, and individuals. Powered by QuantUniversity, our platform identifies unique learning needs and customizes educational journeys, preparing users for AI-driven careers aligned with their backgrounds and interests. Enterprises can craft customized career paths, empowering employees with the skills necessary for future AI-powered roles.

Navigating AI and Model Risk Management in Banking QuantUniversity AI Fall school Lecture 5 Speakers: - Sri Krishnamurthy, CFA, CAP - Christophe Rougeaux, TD - Hua Julia Li, Ex-State Street Summary: This panel will explore the intersection of AI advancements and Model Risk Management (MRM) in the banking industry, past/present/future, focusing on how emerging technologies are transforming risk management practices. Thought leaders will discuss the evolution of AI techniques, real-world applications, and strategies for managing the unique risks posed by AI systems. This discussion will provide insights into challenges, benefits, and practical advice for professionals seeking to upskill in AI risk management.

QuantUniversity's AI Fall School Lecture 4 announcement! Tomorrow Apostol Vassilev from National Institute of Standards and Technology (NIST) will join Sri Krishnamurthy, CFA, CAP for a discussion on adversarial machine learning! Join us over zoom at 12.00pm EDT on October 30th! https://lnkd.in/e5fYdbWc Summary: This talk covers a taxonomy of adversarial machine learning (AML) from the National Institute of Standards and Technology (NIST) Trustworthy and Responsible AI report. It explores types of attacks, attacker goals and capabilities, and mitigation methods, providing a structured understanding of AML concepts and challenges in securing AI systems. Key Takeaways: -Overview of AML attacks (evasion, data poisoning, trojans) and their impact on AI systems. -Insights into attacker objectives and knowledge levels, crucial for developing defense strategies. -Practical methods for mitigating adversarial attacks and managing AI security risks. -Identification of open challenges in AML, guiding future practices for robust AI systems. Reference: https://lnkd.in/eYhQ_ijt