FRE Students Are VIPs

NYU Tandon’s Vertically Integrated Projects (VIP) program facilitates long-term, large-scale projects that allow for handson application of classroom learning, teamwork-building, and enrichment. This year, two FRE teams, each advised by Prof. Amine Mohamed Aboussalah, participated in real-world initiatives with practical impact.

Team: Anudeep Tubati (Leader), Xinyi Li (Leader), Sihan Wang, Hemaksh Chaturvedi, Divya Agarwal, Avadhoot Kulkarni, Ashwin Guptha, Jiumu Zhu, Penny Yang, Arya Goyal, Utkarshbhanu Andurkar, Bingbing Ke, Chenkai Hu, Yuying Song, Zedong Chen, Pan Hsuan En, Irvin Chadraa, Brendon Jiang, Chenxi Liu, Han Yan, Zoe Zhao, Elaine Chan, Sidhved Warik

Project: Active Portfolio Management with Machine Learning and Time Series Forecasting

While reinforcement learning is being widely adopted for finance, most reinforcement learning (RL) algorithms make strong assumptions such as the Markov property. Moreover, while small players can formulate portfolio management as a contextual bandit problem, large financial companies cannot do so because of the significant effect of their actions on the markets.

We are surveying the literature for recent advances like multiagent or continuous-time RL and developing new RL algorithms. We investigate recurrent techniques like Structured State Space for Sequence Modeling (S4) and Transformers to build the state space with a more relaxed Markov assumption. We aim to increase prediction accuracy through methods like Principal Component Analysis (PCA) and Discrete Wavelet Transforms

(DWT) for effective noise reduction and feature extraction. Our work aims to one–compile the state-of-the-art RL methods for finance with a taxonomy and benchmarks on various datasets. Two, develop a recurrent RL (RRL) algorithm for trading and portfolio management. We aim to integrate backtests and benchmarks into a unified API provided by Backtrader.

Team: Nuo Lei (Leader), Niko Liu (Leader), Jugal Pumbhadia, Sujay Anantha, Allen Abraham, Josie Yang, Kehan Chen, Yu Zheng, Reet Nandy, Victor Pou, Haochen Zhang

Project: Merger & Acquisition Outcome Prediction

In the field of machine learning, particularly within Graph Neural Networks (GNNs), addressing data imbalance remains a significant yet often overlooked challenge. This oversight is partly due to the common practice of evaluating model performance using balanced class samples, which does not reflect the complexity of real-world scenarios. Our research specifically targets this gap by exploring innovative strategies to mitigate imbalance issues in GNN applications, with a focus on node classification and link prediction tasks. We draw attention to the context of Merger and Acquisition (M&A) outcomes, where the natural sparsity of company linkages exemplifies the acute imbalance prevalent in such datasets. Our approach encompasses the development of novel loss functions and evaluation metrics, as well as the adoption of GraphSMOTE, a technique for generating synthetic imbalanced datasets. By applying these methodologies to prominent GNN models like GraphSage and GraphGCN, our work aims to pioneer effective solutions for handling data imbalance. This initiative not only sheds light on the critical but underrepresented issue of imbalanced data in GNNs but also aims to enhance predictive performance in areas of significant economic impact, such as M&A outcomes.