Isha Chaturvedi is an AI researcher with over six years of industry experience in machine learning, specializing in Natural Language Understanding (NLU) and Generative AI. She is currently an applied scientist on the Amazon AGI team. She has worked at multiple companies, including Articul8 (an Intel spin-off), Capital One, and Ericsson. Her recent work at Articul8 focused on building multilingual evaluation models based on reasoning. Isha holds multiple patents in the NLU domain and is actively involved in open-source projects. She is an author of the Maya paper (an instruction-finetuned multilingual multimodal model) with Cohere for AI. She has also contributed to research at the Urban Observatory and Sounds of New York City labs at New York University, where she earned her master’s degree in urban data science. She completed her undergraduate studies in environmental technology and computer science at the Hong Kong University of Science and Technology (HKUST). As a research assistant at the HKUST-Deutsche Telekom Systems and Media Lab, she worked on augmented reality and computer vision. Beyond research, Isha has been an O’Reilly instructor, teaching courses on Few-Shot Learning and Prompt-Based Learning. She has also served as an Advisory Board Member at the University of California, Riverside.
Eren Kurshan currently leads Research and Methodology efforts at Morgan Stanley towards building capabilities in emerging AI/ML techniques such as graph AI/ML, KG based reasoning etc. Prior to this role, she was the Executive Head of AI and Machine Learning for Client Protection at Bank of America Corporation, where she was responsible for leading the development of custom Machine Learning and Deep Learning solutions for fraud detection, prevention and operational improvement. Dr. Kurshan and her team built the first generation of in-house AI and Machine Learning models for Bank of America’s payment systems portfolio (including Credit Card, Debit Card, ATM, Wires, ACH, P2P Payments, Checks, Deposits, Online/Bill Pay transactions, Alert Processing and Prioritization etc). Dr. Kurshan has served as the technical lead for various AI and Data Science programs at Columbia University, J.P. Morgan Corporate and Investment Bank, and IBM. She was a Visiting Fellow at Princeton University Center for Information Technology Policy during 2015-2016 and served as an Adjunct Professor of Computer Science at Columbia University since 2014. Dr. Kurshan received her Ph.D. in Applied Algorithms and Theoretical Computer Science from the University of California. She has over 60 peer reviewed technical conference and journal publications and over 100 patents. She was the recipient of 2 Best Paper Awards from IEEE and ACM Conferences, Outstanding Research and Corporate Accomplishment Awards from IBM.
Senthil Kumar is the Head of Emerging Research at Capital One where his research focuses on Generative AI, Anomaly Detection, Representation Learning, Model Interpretability and Reinforcement Learning as applied to Capital One business problems. He is a member of the Capital One Machine Learning Oversight Committee, and a faculty member at Capital One Tech College where he is involved in strategies related to ML education, talent development and university engagements. Prior to joining Capital One, he was at Bell Labs where he developed and managed several successful products that have been licensed around the world. Most recently, he co-organized the 2021 ICML Workshop on Representation Learning for e-Commerce and Finance, and the 2024 KDD Workshop on ML in Finance. He also served as the co-chair of the 2022-3 ACM International Conference on AI in Finance.
Mahashweta Das is a Senior Director, AI at Visa Research where she leads a group of AI researchers and engineers focused on conducting foundational research, creating new products/early prototypes that incorporate research breakthroughs, and delivering innovative technologies to Visa's strategic products and businesses. She is also affiliated with Northeastern University Silicon Valley Campus as part-time lecturer. Previously, she has held positions at Hewlett Packard Lab, Yahoo! Research, Technicolor Research, and IBM Research. Mahashweta received her Ph.D. in Computer Science from the University of Texas at Arlington in 2013. Her research interests include machine learning, deep learning, data mining, and algorithms. She has published over thirty refereed articles at premier international research conferences and journals, and regularly serves on the program committee of these conferences. Her PhD dissertation received Honorable Mention at ACM SIGKDD 2014 Doctoral Dissertation Award.
Jose A. Rodríguez-Serrano is a Senior Lecturer at the Esade Business School (Barcelona) since 2022. Formerly he was Data Science Program Manager at BBVA, and Machine Learning Area Manager at Xerox Research. His trajectory is a mix of applied research and technology transfer: he has published papers in top AI conferences and journals such as CVPR, ICCV, NeurIPS, IEEE PAMI, IJCV, holds over 20 patents and has participated, both as a contributor and as manager, in delivering machine learning functionalities both into functioning prototypes as well as products that are commercially available. In the last 8 years he has worked in applications of machine learning for retail banking: at BBVA, he led the team deploying the first ML models in the app and set up an internal innovation program which was awarded by FastCompany. Now at Esade he continues a research line in real estate valuation and other financial applications of AI.
Daksha Yadav is a Senior Applied Scientist at Amazon, where she leverages cutting-edge machine learning techniques, including LLMs and Deep Learning, to extract valuable insights from real-time accounting and financial data. Her work focuses on driving impactful business solutions through analysis of large-scale datasets, including innovative approaches for anomaly detection in time-series data using generative and transformer models. Prior to Amazon, Daksha pioneered machine learning solutions in the Internet of Things domain at Philips Lighting Research. She holds a Ph.D. in Computer Science from West Virginia University and brings a strong research and development background, evidenced by her 30+ peer-reviewed publications, book chapters, and patents in areas such as machine learning, biometrics, and computer vision.