Student Network

The Center for Research toward Advancing Financial Technologies presents the CRAFT Student Network.  The following students have worked alongside principal investigators on NSF-funded I/UCRC CRAFT Fintech Research Projects.

Nan Cui

CRAFT Research Project: Federated Learning for Fairness-aware and Privacy-Preserving Financial Risk Assessment 

Educational Background

  • Ph.D. in Computer Science, Stevens Institute of Technology (SIT), Hoboken, NJ (08/2020 - Present) 

  • M.S. in Computer Science, Stevens Institute of Technology (SIT), Hoboken, NJ (01/2019 - 05/2020) 

  • M.S. in Electrical Engineering, University of Massachusetts Lowell (UMass Lowell), Lowell, MA (08/2015 - 12/2017)

  • B.E. in Electrical Engineering and Automation, Northeast Electric Power University (NEEPU), Jilin, China (08/2011 - 07/2015) 

Bio

Headshot of Nan CuiNan Cui is a Ph.D. student in Computer Science at Stevens Institute of Technology, working under Professor Yue Ning. She has a strong background in Computer Science and Electrical Engineering. She has worked on several projects, including the development of a federated learning framework tailored to support fairness-aware Graph Convolutional Networks (GCNs) and a fair and efficient active learning framework for designing label-efficient algorithms. 

Research Interests

  • Graph Deep Learning

  • Federated Learning

  • Fairness in Machine Learning

Publications

  • Metric-Fair Active Learning. Jie Shen, Nan Cui, and Jing Wang. In Proceedings of the 39th International Conference on Machine Learning, 2022. 

  • Applying Gradient Descent in Convolutional Neural Networks. Nan Cui. In Proceedings of the 2nd International Conference on Machine Vision and Information Technology, 2018. 

History of Research Projects

  • Fair Graph Neural Networks in Federated Learning

  • Individual Fairness in Active Learning

Contact


Zhiyang Deng

CRAFT Research Project: Fast Quantum Methods for Financial Risk Management

Educational Background

  • Ph.D. Student in Financial Engineering, Stevens Institute of Technology

  • M.S. in Financial Mathematics, University of Southern California (USC)

  • B.S. in Mathematics and Applied Mathematics, Nanchang University

Bio

Zhiyang DengZhiyang Deng is a PhD candidate specializing in Financial Engineering at Stevens Institute of Technology. With a strong foundation in mathematics, Deng demonstrates proficiency in complex financial models and quantitative analysis. His research interests focus on Stochastic Control, Mean Field Games, Optimization in Finance.  

Deng has received Provost's Doctoral Fellowships from Stevens due to the recognition from the academic community. Outside his academic pursuits, he's an active participant in industry, and he currently serves as a fixed income Desk Quant intern at RBC Capital Markets.

For further updates or collaboration opportunities, you can connect with him on LinkedIn.

Research Interests

  • Stochastic Control

  • (Numerical) Mean Field Games

  • Risk Measure

  • Reinforcement Learning and Optimal Control

  • Optimization in Finance

History of Research Projects

  • Risk Averse Mean Field Games and Delta Dirac Family Methods

  • Deep learning formulation via optimal control perspective

  • Fast Quantum Methods for Financial Risk Management

Contact


Conor Flynn

CRAFT Research Project: DeFi Data Engine (May 2022 - May 2023)

Educational Background

  • Incoming Ph.D. Student in Computer Systems Engineering at Rensselaer Polytechnic Institute

  • Bachelor of Computer Science with a minor in Math at Rensselaer Polytechnic Institute, Class of 2023

Bio

Conor Flynn Conor Flynn is an incoming Ph.D. student at Rensselaer Polytechnic Institute studying computer systems engineering. Having studied programming since middle school, Conor has since participated in various clubs, coding competitions, and leadership positions revolving around his love for computer science.

Throughout his professional journey, Conor has since worked as an intern as well as an entrepreneur. In 2019, he worked his first professional position as an Information Security Intern at PDX Inc., helping to secure their networking systems. In 2022, Conor was a Computer Science Intern at Convergint Technologies LLC, working on computer vision modeling for their camera systems to help improve their existing infrastructure. His entrepreneurial ventures started in 2020 with the founding of his first company Quantify Enterprises LLC. Throughout his duration working as its sole proprietor, Conor was contracted by various financial professionals and institutions to help improve their existing quantitative trading systems and strategies. Following his success at Quantify, he partook in the founding of ScaleTrade LLC with two colleagues in 2022, aiming to help traders improve their returns through the generation of unique and insightful technical data.

Conor also has done undergraduate research under the supervision of Professors Kristin Bennett and Professor Oshani Seneviratne starting in May of 2022 through May of 2023. During this time, he worked on a programming language and data agnostic, low latency data engine referred to as the DeFi Data Engine. This engine aims to improve the data collection and distribution for the lab for future research.

Research Interests

  • Low Latency Programming

  • Computationally Agnostic System Designs

  • Quantitative Finance

  • Deep Learning

  • Applied Mathematics

Publications

Adams, Kacy, Fernando Spadea, Conor Flynn, and Oshani Seneviratne. "Assessing Scientific Contributions in Data Sharing Spaces." arXiv preprint arXiv:2303.10476
(2023).

Contact


Michael Giannattasio

CRAFT Research Project: Risky Business? Deep Dives into DeFi

Educational Background

Rising Senior at Rensselaer Polytechnic Institute with a major in Mathematics

Bio

Michael GiannattasioMy name is Michael Giannattasio and I am a rising senior at Rensselaer Polytechnic Institute. I have a major in Mathematics specializing in Operations Research with a minor in Economics of Quantitative Modeling. I have been working on the CRAFT project Risky Business? Deep Dives into DeFi from the Summer of 2022 to the present. I have been focused on user-level representation of transactional data and discovering novel user-clustering methods, specifically across multiple datasets, to apply to multiple DeFi ecosystems' users at once. In order to display and facilitate the visualizations of these user clusters along with survival analysis results, I have been developing the DeFi Survival Analysis Toolkit with authored documentation located here

Research Interests

  • Novel Clustering Methods

  • Risk Analysis

  • Data Science

  • Machine Learning

  • Artificial Intelligence

Publications

DeFi Survival Analysis: Insights Into the Emerging Decentralized Financial Ecosystem (Accepted by ACM-DLT); Characterizing Common Quarterly Behaviors in DeFi (Accepted by MARBLE 2023)

Contact


Inwon Kang

CRAFT Research Project: Risk Mitigation in Cross-Platform Decentralized Finance

Educational Background

  • Ph.D. Candidate, Computer Science, Rensselaer Polytechnic Institute (Current) 

  • M.S., Computer Science, Rensselaer Polytechnic Institute (May 2022) 

  • B.S., Computer Science, Rensselaer Polytechnic Institute (May 2020) 

Bio

Inwon KangI am a current Ph.D. student at Rensselaer Polytechnic Institute, working under Professor Oshani Seneviratne. I work mostly with Python for data/analysis oriented projects. I am also familiar with javascript-based frameworks. During my undergraduate and master's program, I focused on explainable machine learning using shallow models. My master's project was on collecting survey data from participants and building explainable ML models to try to explain the results. During this time, I also dabbled in NLP and built an enhanced pipeline of existing state-of-art model for Preference Ellicitation task using GNNs and coreference parsers. My Ph.D. focus is on blockchain systems and integration of machine learning with blockchains.⠀⠀⠀

Research Interests

  • Blockchain Interoperability

  • System Design

  • Deep Learning

  • Natural Language Processing

Publications

  • Dependency and Coreference-boosted Multi-Sentence Preference Model. Farhad Mohsin, Inwon Kang, Yuxuan Chen, Jingbo Shang and Lirong Xia. (DLG-AAAI’23)

  • Learning to explain voting rules. Inwon Kang, Qishen Han, Lirong Xia (Extended Abstract, AAMAS 2023)

  • Analyzing and Predicting Success in Music. Inwon Kang, Michael Manduluk, Boleslaw Szymanski. (Scientific Reports)

  • Blockchain Interoperability Landscape. Inwon Kang, Aparna Gupta, Oshani Seneviratne. (IEEE BigData Distributed Storage Workshop 2022)

  • Landslide Likelihood Prediction using Machine Learning Algorithms. Vasundhara Acharya, Anindita, Inwon Kang, Thilanka Munasinghe, Binita KC (IEEE BigData 2022)

  • Crowdsourcing Perceptions of Gerrymandering. Benjamin Kelly, Inwon Kang, Lirong Xia. (HCOMP 2022)

  • Learning Individual and Collective Priorities over Moral Dilemmas with the Life Jacket Dataset. Farhad Mohsin, Inwon Kang, Pin-Yu Chen, Francesca Rossi and Lirong Xia. (MPREF-22)

  • Making group decisions from natural language-based preferences. Farhad Mohsin, Lei Luo, Wufei Ma, Inwon Kang, Zhibing Zhao, Ao Liu, Rohit Vaish and Lirong Xia. (COMSOC-21)

History of research projects 

Robust Federated Learning

Contact


Md. Saikat Islam Khan

CRAFT Research Project: Efficient, Private, and Explainable Federated Learning for Financial Crime Detection

Educational Background

  • Ph.D. Candidate, Computer Science, Rensselaer Polytechnic Institute (August 2023 – May 2028)

  • B.Sc. in Computer Science and Engineering (February 2015 – January 2020)

Bio

Headshot Md. Saikat Islam KhanI am currently a Ph.D. student at Rensselaer Polytechnic Institute, working under Professor Oshani Seneviratne. My research activities span a broad spectrum of applications in machine learning, computer vision, and data science. During my undergraduate studies, I developed various classification models to classify medical images. My Ph.D. focus is on federated large language models and explainable federated learning. Through my involvement with CRAFT, I introduced Federated Learning for Relational Data (Fed-RD), a novel privacy-preserving federated learning framework specifically developed for financial transaction datasets that are partitioned both vertically and horizontally across parties. Fed-RD strategically employs differential privacy and secure multiparty computation to ensure the privacy of training data. I now aim to improve this project by enhancing the explainability of the black-box model.⠀⠀⠀⠀⠀⠀⠀⠀⠀

Research Interests

  • Distributed Systems

  • Federated Large Language Model

  • Explainable Federated Learning

  • Health Informatics

Publications

  • Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection MSI Khan, A Gupta, O Seneviratne, S Patterson - arXiv preprint arXiv:2408.01609, 2024

  • A differentially private blockchain-based approach for vertical federated learning L Tran, S Chari, MSI Khan, A Zachariah, S Patterson… - arXiv preprint arXiv:2407.07054, 2024

  • Accurate brain tumor detection using deep convolutional neural network MSI Khan, A Rahman, T Debnath, MR Karim, MK Nasir… - Computational and Structural Biotechnology Journal, 2022

  • Water quality prediction and classification based on principal component regression and gradient boosting classifier approach MSI Khan, N Islam, J Uddin, S Islam, MK Nasir - Journal of King Saud University-Computer and …, 2022

  • MultiNet: A deep neural network approach for detecting breast cancer through multi-scale feature fusion SI Khan, A Shahrior, R Karim, M Hasan, A Rahman - Journal of King Saud University-Computer and …, 2022

  • Using fused Contourlet transform and neural features to spot COVID19 infections in CT scan images A Nur, MSI Khan, MK Nasir - Intelligent Systems with Applications, 2023

  • Proposing a hybrid technique of feature fusion and convolutional neural network for melanoma skin cancer detection MM Rahman, MK Nasir, M Nur-A-Alam, MSI Khan - Journal of Pathology Informatics, 2023

  • BlockSD‐5GNet: Enhancing security of 5G network through blockchain‐SDN with ML‐based bandwidth prediction A Rahman, MSI Khan, A Montieri, MJ Islam, MR Karim… - Transactions on Emerging Telecommunications …, 2024

  • Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities A Rahman, T Debnath, D Kundu, MSI Khan, AA Aishi… - AIMS Public Health, 2024

  • Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues A Rahman, MS Hossain, G Muhammad, D Kundu… - Cluster computing, 2023

  • Biorthogonal wavelet based entropy feature extraction for identification of maize leaf diseases B Mazumder, MSI Khan, KMM Uddin - Journal of Agriculture and Food Research, 2023

Contact


Tasha Kholsa

CRAFT Research Project: Comprehensive Financial Disclosure Lexicon  

Educational Background

B.S. in Quantitative Finance, Stevens Institute of Technology, Class of 2025 

Bio

Headshot of Tasha KholsaTasha Khosla is a rising third year quantitative finance student pursuing a concentration in quantitative methods. A passion for mathematics, programming, and finance is what led her to pursue this degree at Stevens Institute of Technology. Outside of academics, she is involved in various clubs and organizations on campus. In her free time, Tasha enjoys reading, writing, and exploring new programming languages and techniques.   

An interest in financial technology (fintech) prompted her to work on a CRAFT research project this summer, studying the application of natural language processing (NLP) in finance and accounting research through financial literature reviews.  ⠀⠀⠀⠀⠀⠀⠀⠀⠀

Research Interests

  • Artificial Intelligence

  • Green Finance,

  • Machine Learning,

  • Natural Language Processing

Contact


Dominick Varano

CRAFT Research Project: Federated Learning for Fairness-Aware and Privacy-Preserving Financial Risk Assessment

Educational Background

B.S. in Computer Science at Stevens Institute of Technology

Bio

Headshot of Dominick VaranoDominick Varano is a fourth-year undergraduate student in Computer Science at Stevens Institute of Technology. He has a strong passion for Machine Learning and Artificial Intelligence. His research has focused on studying and developing individual and group fairness algorithms within federated learning frameworks. 

Research Interests

  • Machine Learning

  • Artificial Intelligence

  • Natural Language Processing

Contact


Bolun “Namir” Xia

CRAFT Research Project: Predictive Learning from Long Financial Documents 

Educational Background

  • Ph.D. Candidate, Computer Science, Rensselaer Polytechnic Institute (August 2022 - ) 

  • M.S., Computer Science, Rensselaer Polytechnic Institute (August 2020 - May 2022) 

  • B.S., Finance and Computer Science, New York University, Stern School of Business (Fall 2016 - May 2020) 

Bio

Bolun “Namir” XiaBolun "Namir" Xia is a Ph.D. candidate in Computer Science at Rensselaer Polytechnic Institute, with an undergraduate background in Finance and Computer Science. His research focuses on Natural Language Processing (NLP) and its real-world applications in Finance. Being both versed in Finance and Computer Science, he aims to bridge the gap between both, contributing to the growing field of Financial Technologies in the NLP sphere. His work mainly focuses on developing better representations of textual data for predictive analytics, with cutting-edge NLP methods such as Transformer Language Models and Graph Deep Learning. Through his involvement with CRAFT, he has been able to produce favorable results in long document regression tasks, which predict numerical target variables using long documents, such as using 10-K reports to predict firm performance indicators and analyzing earnings calls to forecast its immediate impact in temporal stock price dynamics. He now aims to improve the craft by designing novel graph-based deep learning methods specialized for prediction tasks based on financial text. In his free time, he likes to practice traditional archery and learn different languages. 

Research Interests

  • Natural Language Processing

  • Predictive Analytics

  • Long Document Regression

  • Graph Deep Learning

  • Machine Learning

  • Transformer Language Models

Publications

Bolun "Namir" Xia, Vipula D. Rawte, Mohammed J. Zaki, and Aparna Gupta. FETILDA: an effective framework for fin-tuned embeddings for long financial text documents. arXiv Computing Research Repository, Jun 2022.

History of research projects 

Predictive Learning from Long Financial Documents   

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