Timezone: »
- Clustering and Learning from Imbalanced Data
- Deep Hedging: Hedging Derivatives Under Generic Market Frictions Using Reinforcement Learning
- Generating User-friendly Explanations for Loan Denials using GANs
- Practical Deep Reinforcement Learning Approach for Stock Trading
- Idiosyncrasies and challenges of data driven learning in electronic trading
- Machine learning-aided modeling of fixed income instruments
- An Interpretable Model with Globally Consistent Explanations for Credit Risk
- Continuous learning augmented investment decisions
- HELOC Applicant Risk Performance Evaluation by Topological Hierarchical Decomposition
- Looking Deeper into the Deep Learning Models: Attribution-based Explanations of TextCNN
- Matrix Regression and Its Applications in Cryptocurrency Trading
- Sensitivity based Neural Networks Explanations
- On the Need for Fairness in Financial Recommendation Engines
- Read the News, not the Books: Predicting Firms’ Financial Health
Author Information
Ramya Malur Srinivasan (Fujitsu Laboratories of America)
Miguel Perez (Ernst and Young LLP)
I'm a data scientist at a Financial Services Organization focused on developing algorithms and optimizations to crowdsource subject matter experts' evaluation of risk for anti-money laundering and employee conduct, and developing graph convolution neural networks and statistical learning models for studying behavior captured in banking customers' transaction records. The teams that I work with and oversee are applying these supervised and unsupervised models on graph databases of Global Forbes 500 banking transaction records. My academic background is in mathematical physics as an expert in Quantum Algorithmic Complexity, Category and Topos Theory, and quantum foundations. My research interests focus on (1) applying information theory to decision systems for algorithmic optimization, (2) applications of rating-ranking algorithms with geometric and non-trivial loss functions, (3) critical phenomenon on time varying graphs.
Yuanyuan Liu (AIG)
Ben Wood (JP Morgan)
Dan Philps (City, University of London. Rothko Investment Strategies)
Dan Philps is a Computer Scientist, head of Rothko Investment Strategies and is an artificial intelligence (AI) researcher. He has 20 years of quantitative investment experience. Prior to Rothko, he was a senior fund manager at Mondrian Investment Partners. Before 1998, Philps worked as an analyst/programmer at a number of investment banks, specializing in trading and risk models. He has a BSc (Hons) from King’s College London, is a CFA charterholder, a member of CFA Society of the UK and holds a post graduate research role at City, London University.
Kyle Brown (Wright State University)
Daniel Martin (Carnegie Mellon University)
Mykola Pechenizkiy (TU Eindhoven)
Luca Costabello (Accenture Labs)
Rongguang Wang (Cornell University)
Suproteem Sarkar (Harvard University)
Sangwoong Yoon (Seoul National University)
Zhuoran Xiong (Columbia University)
Enguerrand Horel (Stanford University)
Zhu (Drew) Zhang (Iowa State University)
Ulf Johansson (Jönköping University)
Jonathan Kochems (JP Morgan)
Gregory Sidier (JP Morgan)
Prashant Reddy (J.P. Morgan)
Lana Cuthbertson (ATB Financial)
Yvonne Wambui (Carnegie Mellon University)
Christelle Marfaing (Lydia Solutions)
Galen Harrison (University of Chicago)
Irene Unceta Mendieta (BBVA Data & Analytics)
Thomas Kehler (CrowdSmart)
Tom Kehler is President, Chief Scientist, and Co-Founder at CrowdSmart; a technology-based investment company dedicated to transforming seed investing and radically improving outcomes for investors and startups. Dr. Kehler has over 30 years of experience as an entrepreneur and CEO. He was CEO of InelliCorp the first AI/Expert Systems Company to go public. He was CEO of Connect one of the first ecommerce companies to go public. He was CEO of Recipio, an early social marketing company that enabled large scale brainstorming between companies and their customers. Recipio’s customers included LEGO, NBC, and Procter & Gamble. Recipio was sold to Informative where he later because CEO. Informative was sold to Satmetrix where he headed up Community Solutions. After Satmetrix, Dr. Kehler was one of the founders of ClearStreet, a fintech company. Dr. Kehler has served on the Information Technology Advisory Board of the National Research Council. He has served on various corporate, academic, and non-profit boards. Dr. Kehler received a Ph.D. in applied physics from Drexel University and has over 20 publications in artificial intelligence, natural language processing, and physics. Currently he is focused on predictive analytics models for startups.
Mark Weber (MIT-IBM Watson AI Lab)

Mark Weber is the Senior Lead of Business Innovation at IBM Research, where he leads a global team of designers, engineers, and strategists supporting lab-to-market impact for clients and partners. As a researcher, Mark has published peer-reviewed work in blockchain technology for supply chain finance, graph deep learning for anti-money laundering, and algorithmic fairness in lending, as well as a thought-leadership pieces such as “The Scientific Method for Business” with IBM Consulting. Prior to his current role, Mark led Strategy & Operations and ran the membership program at the MIT-IBM Watson AI Lab. Mark is a former Fellow at the MIT Legatum Center for Development & Entrepreneurship, and actively serves as an advisor to the Harambe Alliance of African Entrepreneurs. He earned an MBA in finance from the MIT Sloan School of Management, where he also worked as a graduate researcher and corporate membership manager in the MIT Media Lab’s Digital Currency Initiative. Mark earned his bachelors at the University of Notre Dame’s classical great books program and began his career producing documentary films on political economy and international development, most notably the award-winning and internationally distributed documentary Poverty, Inc. In his personal life, Mark is an ultramarathon trail runner, free diver, back country skier, and adventure motorcyclist who enjoys books, chess, and cooking.
Li Ling (JP Morgan Chase)
Ceena Modarres (Capital One)
Abhinav Dhall (Indian Institute of Technology Ropar)
Arash Nourian (UC Berkeley)
David Byrd (Georgia Institute of Technology)
Ajay Chander (Fujitsu Labs of America)
Xiao-Yang Liu (Columbia University)
Hongyang Yang (Columbia University)
Shuang (Sophie) Zhai (Iowa State University)
Freddy Lecue (INRIA)
Sirui Yao (Virginia Polytechnic Institute and State University)
Rory McGrath (Accenture Labs)
Artur Garcez (City, University of London)
Vangelis Bacoyannis (J.P. Morgan)
Alexandre Garcia (Telecom ParisTech)
Lukas Gonon (ETH Zurich)
Mark Ibrahim (Capital One, Center for Machine Learning)
Mark Ibrahim is a senior machine learning engineer with a background in mathematics, deep learning, and knowledge graphs. He has worked on methods to interpret neural network predictions and applications of deep learning to forecasting. He enjoys good coffee, eating well, and editing text in Vim.
Melissa Louie (Capital One)
Omid Ardakanian (University of Alberta)
Cecilia Sönströd (University of Borås)
Kojin Oshiba (Harvard College)
Chaofan Chen (Duke University)
Suchen Jin (J.P. Morgan)
aldo pareja (IBM)
Toyo Suzumura (IBM Thomas J. Research Center)
More from the Same Authors
-
2021 : Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding »
Benedikt Wagner · Artur Garcez -
2021 : GPU-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning »
Xiao-Yang Liu · Zhuoran Yang · Zhaoran Wang · Anwar Walid · Jian Guo · Michael Jordan -
2021 : Graph-Tensor Singular Value Decomposition for Data Recovery »
Lei Deng · Haifeng Zheng · Xiao-Yang Liu -
2021 : High Performance Hierarchical Tucker Tensor Learning Using GPU Tensor Cores »
hao huang · Xiao-Yang Liu · Weiqin Tong · Tao Zhang · Anwar Walid -
2021 : Codee: A Tensor Embedding Scheme for Binary Code Search »
Jia Yang · Cai Fu · Xiao-Yang Liu -
2021 : Deep variational reinforcement learning by optimizing Hamiltonian equation »
Zeliang Zhang · Xiao-Yang Liu -
2021 : Spectral Tensor Layer for Model-Parallel Deep Neural Networks »
Zhiyuan Wang · Xiao-Yang Liu -
2022 : An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement Learning »
Danil Provodin · Pratik Gajane · Mykola Pechenizkiy · Maurits Kaptein -
2022 : An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement Learning »
Danil Provodin · Pratik Gajane · Mykola Pechenizkiy · Maurits Kaptein -
2022 Poster: Dynamic Sparse Network for Time Series Classification: Learning What to “See” »
Qiao Xiao · Boqian Wu · Yu Zhang · Shiwei Liu · Mykola Pechenizkiy · Elena Mocanu · Decebal Constantin Mocanu -
2022 Poster: Where to Pay Attention in Sparse Training for Feature Selection? »
Ghada Sokar · Zahra Atashgahi · Mykola Pechenizkiy · Decebal Constantin Mocanu -
2022 Poster: Formalizing Consistency and Coherence of Representation Learning »
Harald Strömfelt · Luke Dickens · Artur Garcez · Alessandra Russo -
2022 Poster: Homomorphic Matrix Completion »
Xiao-Yang Liu · Zechu (Steven) Li · Xiaodong Wang -
2022 Poster: FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning »
Xiao-Yang Liu · Ziyi Xia · Jingyang Rui · Jiechao Gao · Hongyang Yang · Ming Zhu · Christina Wang · Zhaoran Wang · Jian Guo -
2021 : Discussion Pannel »
Xiao-Yang Liu · Qibin Zhao · Chao Li · Guillaume Rabusseau -
2021 : The Impact of Batch Learning in Stochastic Bandits »
Danil Provodin · Pratik Gajane · Mykola Pechenizkiy · Maurits Kaptein -
2021 : High Performance Computation for Tensor Networks Learning »
Anwar Walid · Xiao-Yang Liu -
2021 Workshop: Second Workshop on Quantum Tensor Networks in Machine Learning »
Xiao-Yang Liu · Qibin Zhao · Ivan Oseledets · Yufei Ding · Guillaume Rabusseau · Jean Kossaifi · Khadijeh Najafi · Anwar Walid · Andrzej Cichocki · Masashi Sugiyama -
2021 : Opening Remarks »
Xiao-Yang Liu -
2021 Poster: Sparse Training via Boosting Pruning Plasticity with Neuroregeneration »
Shiwei Liu · Tianlong Chen · Xiaohan Chen · Zahra Atashgahi · Lu Yin · Huanyu Kou · Li Shen · Mykola Pechenizkiy · Zhangyang Wang · Decebal Constantin Mocanu -
2021 Poster: Grounding inductive biases in natural images: invariance stems from variations in data »
Diane Bouchacourt · Mark Ibrahim · Ari Morcos -
2021 Poster: CrypTen: Secure Multi-Party Computation Meets Machine Learning »
Brian Knott · Shobha Venkataraman · Awni Hannun · Shubho Sengupta · Mark Ibrahim · Laurens van der Maaten -
2020 : Closing Remarks »
Xiao-Yang Liu -
2020 : Spotlight Talk 7: Hidden Technical Debts for Fair Machine Learning in Financial Services »
Chong Huang · Arash Nourian · Kevin Griest -
2020 : Spotlight Talk 2: Black Loans Matter: Distributionally Robust Fairness for Fighting Subgroup Discrimination »
Mark Weber -
2020 : Panel Discussion 2: Software and High Performance Implementation »
Glen Evenbly · Martin Ganahl · Paul Springer · Xiao-Yang Liu -
2020 : Panel Discussion 1: Theoretical, Algorithmic and Physical »
Jacob Biamonte · Ivan Oseledets · Jens Eisert · Nadav Cohen · Guillaume Rabusseau · Xiao-Yang Liu -
2020 Workshop: First Workshop on Quantum Tensor Networks in Machine Learning »
Xiao-Yang Liu · Qibin Zhao · Jacob Biamonte · Cesar F Caiafa · Paul Pu Liang · Nadav Cohen · Stefan Leichenauer -
2020 : Opening Remarks »
Xiao-Yang Liu -
2019 : Coffee + Posters »
Changhao Chen · Nils Gählert · Edouard Leurent · Johannes Lehner · Apratim Bhattacharyya · Harkirat Singh Behl · Teck Yian Lim · Shiho Kim · Jelena Novosel · Błażej Osiński · Arindam Das · Ruobing Shen · Jeffrey Hawke · Joachim Sicking · Babak Shahian Jahromi · Theja Tulabandhula · Claudio Michaelis · Evgenia Rusak · WENHANG BAO · Hazem Rashed · JP Chen · Amin Ansari · Jaekwang Cha · Mohamed Zahran · Daniele Reda · Jinhyuk Kim · Kim Dohyun · Ho Suk · Junekyo Jhung · Alexander Kister · Matthias Fahrland · Adam Jakubowski · Piotr Miłoś · Jean Mercat · Bruno Arsenali · Silviu Homoceanu · Xiao-Yang Liu · Philip Torr · Ahmad El Sallab · Ibrahim Sobh · Anurag Arnab · Krzysztof Galias -
2019 : Poster Session »
Gergely Flamich · Shashanka Ubaru · Charles Zheng · Josip Djolonga · Kristoffer Wickstrøm · Diego Granziol · Konstantinos Pitas · Jun Li · Robert Williamson · Sangwoong Yoon · Kwot Sin Lee · Julian Zilly · Linda Petrini · Ian Fischer · Zhe Dong · Alexander Alemi · Bao-Ngoc Nguyen · Rob Brekelmans · Tailin Wu · Aditya Mahajan · Alexander Li · Kirankumar Shiragur · Yair Carmon · Linara Adilova · SHIYU LIU · Bang An · Sanjeeb Dash · Oktay Gunluk · Arya Mazumdar · Mehul Motani · Julia Rosenzweig · Michael Kamp · Marton Havasi · Leighton P Barnes · Zhengqing Zhou · Yi Hao · Dylan Foster · Yuval Benjamini · Nati Srebro · Michael Tschannen · Paul Rubenstein · Sylvain Gelly · John Duchi · Aaron Sidford · Robin Ru · Stefan Zohren · Murtaza Dalal · Michael A Osborne · Stephen J Roberts · Moses Charikar · Jayakumar Subramanian · Xiaodi Fan · Max Schwarzer · Nicholas Roberts · Simon Lacoste-Julien · Vinay Prabhu · Aram Galstyan · Greg Ver Steeg · Lalitha Sankar · Yung-Kyun Noh · Gautam Dasarathy · Frank Park · Ngai-Man (Man) Cheung · Ngoc-Trung Tran · Linxiao Yang · Ben Poole · Andrea Censi · Tristan Sylvain · R Devon Hjelm · Bangjie Liu · Jose Gallego-Posada · Tyler Sypherd · Kai Yang · Jan Nikolas Morshuis -
2019 : Poster Session »
Nathalie Baracaldo · Seth Neel · Tuyen Le · Dan Philps · Suheng Tao · Sotirios Chatzis · Toyo Suzumura · Wei Wang · WENHANG BAO · Solon Barocas · Manish Raghavan · Samuel Maina · Reginald Bryant · Kush Varshney · Skyler D. Speakman · Navdeep Gill · Nicholas Schmidt · Kevin Compher · Naveen Sundar Govindarajulu · Vivek Sharma · Praneeth Vepakomma · Tristan Swedish · Jayashree Kalpathy-Cramer · Ramesh Raskar · Shihao Zheng · Mykola Pechenizkiy · Marco Schreyer · Li Ling · Chirag Nagpal · Robert Tillman · Manuela Veloso · Hanjie Chen · Xintong Wang · Michael Wellman · Matthew van Adelsberg · Ben Wood · Hans Buehler · Mahmoud Mahfouz · Antonios Alexos · Megan Shearer · Antigoni Polychroniadou · Lucia Larise Stavarache · Dmitry Efimov · Johnston P Hall · Yukun Zhang · Emily Diana · Sumitra Ganesh · Vineeth Ravi · · Swetasudha Panda · Xavier Renard · Matthew Jagielski · Yonadav Shavit · Joshua Williams · Haoran Wei · Shuang (Sophie) Zhai · Xinyi Li · Hongda Shen · Daiki Matsunaga · Jaesik Choi · Alexis Laignelet · Batuhan Guler · Jacobo Roa Vicens · Ajit Desai · Jonathan Aigrain · Robert Samoilescu -
2019 Poster: This Looks Like That: Deep Learning for Interpretable Image Recognition »
Chaofan Chen · Oscar Li · Daniel Tao · Alina Barnett · Cynthia Rudin · Jonathan K Su -
2019 Spotlight: This Looks Like That: Deep Learning for Interpretable Image Recognition »
Chaofan Chen · Oscar Li · Daniel Tao · Alina Barnett · Cynthia Rudin · Jonathan K Su -
2018 Poster: A Structured Prediction Approach for Label Ranking »
Anna Korba · Alexandre Garcia · Florence d'Alché-Buc -
2017 Poster: Beyond Parity: Fairness Objectives for Collaborative Filtering »
Sirui Yao · Bert Huang -
2016 : Summary/Goodbye »
Tarek R. Besold · Artur Garcez · Antoine Bordes · Gregory Wayne -
2016 : Welcome/Opening »
Tarek R. Besold · Antoine Bordes · Gregory Wayne · Artur Garcez -
2016 Workshop: Cognitive Computation: Integrating Neural and Symbolic Approaches »
Tarek R. Besold · Antoine Bordes · Gregory Wayne · Artur Garcez -
2015 : Discussion Panel with Morning Speakers (Day 2) »
Giovanni S Carmantini · Gustav Sourek · Artur Garcez · Daniel Silver · Michael J Witbrock -
2015 : Relational Knowledge Extraction from Neural Networks »
Artur Garcez -
2015 Workshop: Cognitive Computation: Integrating neural and symbolic approaches »
Artur Garcez · Tarek R. Besold · Risto Miikkulainen · Gary Marcus