Organizers

Leman Akoglu is the Heinz College Dean's Associate Professor of Information Systems at Carnegie Mellon University. She holds courtesy appointments in the Computer Science Department (CSD) and Machine Learning Department (MLD) of School of Computer Science (SCS). Dr. Akoglu's research awards include the Best Research Paper (SIAM SDM 2019), Best Student Machine Learning Paper Runner-up (ECML PKDD 2018), Best Paper Runner-up (SIAM SDM 2016), Best Research Paper (SIAM SDM 2015), Best Paper (ADC 2014), and Best Knowledge Discovery Paper (ECML PKDD 2009). She holds 3 U.S. patents filed by IBM T. J. Watson Research Labs. Her research has been supported by the NSF, US ARO, DARPA, Adobe, Capital One Bank, Facebook, Northrop Grumman, PNC Bank, PwC, and Snap Inc.

Lavanya Basavaraju, Vice President Research Data Scientist, within AI Center of Excellence (AI CoE) at U.S. Bank, specializing in building AI applications for financial services. She previously led AI technical programs at Ameriprise Financial and in Consulting for various CPG organizations. Her expertise spans language models, computer vision, multimodal architectures, generative AI, and ensemble methods for financial document analysis, credit risk assessment, and fraud detection. She holds patents in document processing and fraud detection and has published research on AI-assisted automation systems. She has presented her publications at premier conferences including ICAIF, KDD, and GHC. She has served as program committee for AAAI, as ambassador for WiDS and as invited speaker/panelist at various technical workshops.

Dr. Cristián Bravo is Professor and the Canada Research Chair in Banking and Insurance Analytics at Western University, Canada, where he also serves as the Director of the Banking Analytics Lab. His research lies at the intersection of artificial intelligence, analytics, and banking, researching how techniques such as multimodal deep learning, social network analysis, and causal inference can be used to understand relations between consumers and financial institutions. He has close to 100 academic works in high-impact journals and conferences in operational research, finance, and computer science. He serves as senior area chair of the KDD conference Applied Data Science Track, and as editorial board member in Applied Soft Computing and the Journal of Business Analytics. He is the co-author of the book “Profit-Driven Business Analytics”, which has sold over 6,000 copies to date, and the book “Deep Learning in Banking”, published by Wiley. Dr. Bravo has been quoted by The Wall Street Journal, WIRED, RFE (France), CTV, The Toronto Star, The Globe and Mail, the Financial Post, and Global News, among other international media. He is also a regular panelist at the CBC News’ Weekend Business Panel where he discusses the latest news in Banking, Finance and Artificial Intelligence.

Holly Ferguson is an independent research consultant specializing in semantic knowledge architecture and multimodal AI solutions. Prior to this, she served as a Research Scientist and Tech Lead at Siemens AG within the Smart Machine Vision Research Team, where she spearheaded the development of five major projects integrating Semantic Knowledge Graphs (SKGs) with Vision-Language Models (VLMs) and Augmented Reality (AR). In this role, she led the filing of seven patents focused on groundbreaking advancements in AI-driven automation and multimodal data linking. Prior to Siemens, Dr. Ferguson was a Lead Data Architect and Research Scientist at Booz Allen Hamilton, where she engineered de-novo ontologies and NLP rules for sensitive US-agency-based research. She holds a PhD in Computer Science and Engineering from the University of Notre Dame, with a dissertation focused on semantic infrastructure for eco-friendly building design. An active contributor to the academic community, she has published over ten papers in venues such as IEEE Big Data and the International Journal of Semantic Computing. She also serves as a Program Committee member and reviewer for top-tier conferences, including KDD, SDM, and CODS-COMAD.

Dr. Shalini Ghosh is a Principal (Director-level) in the Knowledge and Information org of Google, working as a Tech Lead on AI Safety. Prior to this, she was a Senior Principal Research Scientist at Amazon Science in the Alexa AGI team, where she co-led a cross-org effort to train the Multimodal Nova LLM, with a focus on designing and training any-to-any LLMs for content understanding and generation tasks across text, audio, speech, image, and video modalities. Dr. Ghosh was a Director in Samsung Research America for 2+ years, where she led the Multimodal AI R&D team for video understanding in Samsung Smart TV. Prior to that, she was a Principal Scientist at SRI International where she worked on applying machine learning to a broad set of domains, including trustworthy computing (e.g., security) and dependable computing (e.g., reliability). Dr. Ghosh was also a Visiting Scientist at Google Research, where she worked on large-scale language understanding and dialog modeling, in collaboration with Google Brain. Dr. Ghosh received her Ph.D. from the University of Texas at Austin. She serves as the Area Chair for multiple ML conferences (e.g., NeurIPS, ICLR, ICML), and her research has won a Best Paper award and a Best Student Paper Runner-up award for applications of ML to dependable computing. She is the lead/co-author of 60+ publications. She was selected as one of the “30 Influential Women Advancing AI” in 2019, was an Invited Speaker at the KDD Conference, and her ReWork talk was selected as one of the “Top 5 AI talks” in 2020.  More information about her work can be found at https://sites.google.com/site/shalinighosh.

Panos Ipeirotis is the Merchant’s Council Professor of Technology and Business at the Department of Technology, Operations, and Statistics (TOPS) at the Leonard N. Stern School of Business of New York University. He is also an associated faculty member at the Center for Data Science and the Computer Science department.

He is the recipient of the 2015 Lagrange Prize in Complex Systems, for his contributions in the field of social media, user-generated content, and crowdsourcing, and received the 2020 Test of Time award at KDD. He has also received more than ten “Best Paper” awards and nominations, and a CAREER award from the National Science Foundation. He got his Ph.D. in Computer Science from Columbia University in 2004 and his undergraduate degree from CEID, University of Patras, Greece.

Dhagash Mehta is the Head of Applied Artificial Intelligence Research at BlackRock, Inc. and an Editorial Board Member at the Journal of Financial Data Science and Journal of ESG and Impact Investing (both PMR journals). Previously he was a Senior Manager, Investment Strategist (Machine Learning – Asset Allocation) at Investment Strategy Group at The Vanguard Group. Before joining Vanguard, he was a Senior Research Scientist at United Technologies (UTX) Research Center. Prior to that, he was a Research Assistant Professor at the Department of Applied and Computational Mathematics and Statistics in conjunction with the Department of Chemical and Biomolecular Engineering at University of Notre Dame. He was a Fields Institute Postdoc Fellow for the Thematic Program on Computer Algebra at Fields Institute, Toronto, in Fall 2015 and a Visiting Fellow at Simons Institute for Theory of Computing at Berkeley in Fall 2014. Previously, he has held various research positions at the University of Cambridge (the UK), Imperial College London (the UK), the University of Adelaide (Australia), Syracuse University (USA) and National University of Ireland Maynooth (Ireland).

He was General Chair of large conferences such as ACM-ICAIF’24, Fields Workshop on Machine Learning for Investor Modeling, Sanya Workshop on Algebraic Geometry and Machine Learning, Conference on Statistical Topology and Random Manifolds at ICTP Trieste, etc., and  has organized numerous emerging area workshops at SIAM, ACM ICAIF, AAAI, Neurips, etc.

Saurabh Nagrecha is a senior technical lead in applied research at Google, overseeing fraud defenses across all Google Ad serving surfaces. His work focuses on leveraging multimodal AI, graph neural networks (GNNs), foundation models, and adversarial red teaming to combat sophisticated invalid traffic (IVT) and emerging AI-generated fraud threats. He has led the development of large-scale detection systems protecting multi-billion-dollar ad revenues and holds multiple patents in generative AI applications. Before Google, Saurabh held leadership roles at eBay, Capital One, and served as an independent fraud and AML consultant for startups. He has deep expertise in graph-based machine learning, scalable fraud detection, and compliance-driven AI solutions. He earned his PhD in Computer Science from the University of Notre Dame, specializing in cost-sensitive imbalanced graph classification. Saurabh is also an active researcher, serving on program committees for top-tier conferences (KDD, NeurIPS, ICML) and collaborating on open-source AI initiatives for election security in Southeast Asia. He has developed and taught courses on network science and ML applications in finance.