AISRF2024 - Speakers

Speakers

Ioana Baldini

IBM

Ioana Baldini is a Senior Research Scientist in the IBM Research AI organization. She is currently the tech lead for red-teaming in language models at IBM Research, helping with model evaluation and safety training data generation. As a result of collaborations with non-profit organizations under the IBM Science for Social Good Initiative, Ioana has focused her research efforts on natural language processing with potential for social impact. Previously at IBM Research, Ioana was part of the core research team who developed OpenWhisk, an open source serverless platform, which is offered as IBM Cloud Functions. In her role, Ioana contributed several components to the OpenWhisk infrastructure and helped productize it as the official IBM serverless platform. Ioana holds a Masters and PhD degrees from the University of Toronto. She received the NSERC (NSF-equivalent in Canada) Canada Graduate Scholarship, the IBM PhD Fellowship, the Canada Google Anita Borg Scholarship, and the IBM Research Division Award (contributions to OpenWhisk).

Stefano Pasquali

BlackRock

Stefano Pasquali, Managing Director, is the Head of Liquidity Research Group at BlackRock Solutions. As Head of Liquidity Research, Mr. Pasquali is responsible for market liquidity modelling both at the security and portfolio level, as well as estimating portfolio liquidity risk profiles. His responsibilities include defining cross asset class models, leveraging available trade data, and developing innovative machine learning based approaches to better estimate market liquidity. Mr. Pasquali is heavily involved in developing methodologies to estimate funding liquidity and better estimate funds flows. These models include: the cost of position or portfolio liquidation, time to liquidation, redemption estimation, and investor behavior modelling utilizing a big data approach. Previous to Blackrock, Mr. Pasquali oversaw research and product development for Bloomberg's liquidity solution, introducing a big data approach to their financial analytics. His team designed and implemented models to estimate liquidity and risk across different asset classes with a particular focus on OTC markets. Before this he led business development and research for fixed income evaluated pricing. Mr. Pasquali has more than 15 years of experience examining and implementing innovative approaches to calculating risk and market impact. He regularly speaks at industry events about the complexity and challenges of liquidity evaluation ? particularly in the OTC marketplace. His approach to risk and liquidity evaluation is strongly influenced by over 20 years of experience working with big data, data mining, machine learning and data base management. Prior to moving to New York in 2010, Mr. Pasquali held senior positions at several European banks and asset management firms where he oversaw risk management, portfolio risk analysis, model development and risk management committees. These accomplishments include the construction of a risk management process for a global asset management firm with over 100 billion AUM. This involved driving projects from data acquisition and normalization to model development and portfolio management support. Mr. Pasquali, a strong believer in academic contribution to the industry, has engaged in various conversations and collaborations with universities from the US, UK, and Italy. He also participates as a supervisor in the Experiential Learning Program and Master of Quantitative Finance Program based at Rutgers University, along with tutoring students in research activities. Before his career in finance, Mr. Pasquali was a researcher in Theoretical and Computational Physics (Monte Carlo Simulation, Solid State physics, Environment Science, Acoustic Optimization). Originally from Carrara (Tuscany, Italy), he grew up in Parma. Mr. Pasquali is a graduate of Parma University and holds a master’s degree in Theoretical Physics, as well as research fellowships in Computational Physics at Parma University and Reading University (UK).

Agus Sudjianto

H2O.ai

Agus Sudjianto has a span of 10+ and 20+ years of experience in engineering and banking, respectively. His last position prior to his retirement in banking was an executive vice president, head of Model Risk Management at Wells Fargo. Previously, he held various senior leadership positions in analytics, modeling, credit, and risk management at Lloyds Banking Group in the United Kingdom and Bank of America. Prior to his career in banking, he was an engineer and product design manager in the Powertrain Division at Ford Motor Company for more than a decade.

Agus is the creator of PiML (Python Interpretable Machine Learning), a widely used integrated tool for developing and validating high-risk machine learning models. He extends PiML into a more powerful product called MoDeVa that will be released in early 2025. His recent work with H2O.ai includes the development of software for automated testing and validation for Generative AI to ensure their reliability and will be released in Q4 2024.

He holds 25+ U.S. patents in both finance and engineering. A highly cited author, he has published extensively technical papers in machine learning and statistics and is a co-author of "Design and Modeling for Computer Experiments."

His technical expertise and interests include machine learning/AI and risk management. He serves as a McKinsey Senior Advisor and provides consulting to various industries worldwide, including Financial Services and Digital Media companies. He holds master's and doctorate degrees in engineering and management from Wayne State University and the Massachusetts Institute of Technology.

Francois Buet-Golfouse

Barclays

François is the Global Head of AI and ML for Global Markets at Barclays, where he is building a new team focused on delivering responsible AI solutions tailored to global markets. His approach emphasizes a deep commitment to customer and stakeholder impact. Previously, he founded and led the Decision Science team at JPMorganChase’s International Consumer Bank, driving innovation and impact across various facets of AI and machine learning.

Yi Zeng

Virginia Tech

Yi Zeng is a final-year Ph.D. candidate in Computer Engineering at Virginia Tech, working under the supervision of Prof. Ruoxi Jia. He is affiliated with the Sanghani Center for Artificial Intelligence and Data Analytics. His research focuses on AI safety and responsible AI development. Zeng has established himself as a leading researcher in exploring state-of-the-art safety mechanisms, mitigation strategies, and emerging risks in AI systems. His work has been published extensively in top-tier security and AI conferences, including USENIX Security, ACM CCS, NeurIPS, ICML, ICLR, ICCV, ACL, AsiaCCS, IJCAI, and TMLR. His research has garnered significant media attention, with coverage in prominent outlets such as The New York Times, PCMag, The Register, and VentureBeat. Among his notable achievements are the Best Social Impact Award at ACL 2024, the Amazon Research Fellowship, and the Best Paper Award at ICA3PP 2020.

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