This talk will explore the portfolio construction process at Trexquant and the practical application of deep learning methods in quantitative trading.
Denis will introduce Mixture of Experts (MoE) models and demonstrate their behavior through a toy example using synthetic data. He will then discuss how neural networks are constructed and applied within Trexquant's real-world trading strategies, including aspects of model setup and expert construction that are specific to quantitative trading.
Add to calendarAmerica/New_YorkTrexquant Tech Talk: Applications of Deep Learning Methods in Quantitative Trading 09/30/2926
Join us for a CSAIL Alliances Tech Talk!
This talk will explore the portfolio construction process at Trexquant and the practical application of deep learning methods in quantitative trading.
Denis will introduce Mixture of Experts (MoE) models and demonstrate their behavior through a toy example using synthetic data. He will then discuss how neural networks are constructed and applied within Trexquant's real-world trading strategies, including aspects of model setup and expert construction that are specific to quantitative trading.
The lecture will also cover other deep learning approaches used in quantitative research, including Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and Contrastive Learning, and conclude with examples comparing portfolios constructed using these methods with those based on traditional linear models.
The deployment of automated software systems called AI agents has recently exploded. A November 2025 report by MIT Sloan School of Management and Boston Consulting Group found that 35 percent of surveyed businesses had already deployed AI agents, while another 44 percent planned to implement agentic AI soon.
A new chip developed by MIT researchers could help tiny, low-power UAVs avoid obstacles as they zip around tight corners inside an industrial HVAC system to check for gas leaks.
There’s a delicate art to teaching robots, even when you’re preparing them for predictable environments like factories, where they’ll repeat the same tasks a little differently depending on the obstacles they face. Whether a human is suddenly in their way or there’s new clutter, the machine must closely mimic its operator’s actions by staying on a trajectory (or motion path).
For MIT Professor Armando Solar-Lezama, one of the most common misunderstandings about AI is the notion that it can be dropped into existing human roles like a plug-and-play replacement.
In 2026, the hype for artificial intelligence (AI) agents is louder than ever before. These semi-autonomous programs can “think” and execute well-defined tasks in areas like customer service and software development, typically using language models (LMs). But fields like medical diagnosis and scientific discovery require them to inquire about a vast range of solutions in uncertain environments, which LMs struggle with.
Researchers from MIT and elsewhere have developed a more user-friendly and efficient method to help networking engineers identify potential system failures before they cause major problems, like a cloud service outage that leaves millions of users unable to access applications.
AI models are proliferating fast. There’s Claude, ChatGPT, Gemini, Copilot, DeepSeek, Grok, Mistral, Llama, and many more emerging every day. But which ones to work with? And why? We asked MIT CSAIL faculty and students which AI tools they’re reaching for right now. The responses showed a variety of preferences, a clear winner in one area, and a word of caution about what goes into any public model’s memory.
On January 30, student teams will present their projects to the class, guest lecturers, and invited colleagues. This capstone session will be followed by an after-party celebrating the close of Nexus II.
