Alvin Li
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Alvin Li Email & Phone Number

MIT CS + ML | Quant Trading @ SIG at Susquehanna International Group, LLP (SIG)
Location: Palo Alto, California, United States 7 work roles 1 school
1 work email found @amazon.com LinkedIn matched
✓ Verified July 2026 4 data sources Profile completeness 100%

Contact Signals · 1 work email

Work email a****@amazon.com
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Role
MIT CS + ML | Quant Trading @ SIG
Location
Palo Alto, California, United States

Who is Alvin Li? Overview

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Quick answer

Alvin Li is listed as MIT CS + ML | Quant Trading @ SIG at Susquehanna International Group, LLP (SIG), based in Palo Alto, California, United States. AeroLeads shows a work email signal at amazon.com and a matched LinkedIn profile for Alvin Li.

Alvin Li previously worked as Quant Trader at Susquehanna International Group, Llp (Sig) and Machine Learning Researcher (SuperUROP) at Mit Computer Science And Artificial Intelligence Laboratory (Csail). Alvin Li holds Bachelor Of Science - Bs, Computer Science And Engineering from Massachusetts Institute Of Technology.

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Email format at Susquehanna International Group, LLP (SIG)

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*@amazon.com
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Profile bio

About Alvin Li

Hello! I’m an MIT CS grad with a background in quant trading, machine learning research, software engineering, entrepreneurship, and venture capital.

Listed skills include Neural Networks, Data Science, Algorithms, Cloud Firestore, and 22 others.

Current workplace

Alvin Li's current company

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Susquehanna International Group, LLP (SIG)
Susquehanna International Group, Llp (Sig)
MIT CS + ML | Quant Trading @ SIG
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7 roles

Alvin Li work experience

A career timeline built from the work history available for this profile.

Machine Learning Researcher (Superurop)

Cambridge, Ma, Us

Published a paper to the AdvML Frontiers @ ICML2023Liquid time-constant networks (LTCs) have recently emerged as a promising method for processing complex continuous time-series data. To enhance the robustness of LTCs against adversarial attacks, we propose several novel adversarial training pipelines. These training pipelines introduce disruptive elements, such as data perturbations and temporal manipulation. We evaluate the performance of a standard LSTM network, a standard LTC network, and their adversarially trained counterparts on their performance across uniformly sampled, irregularly sampled, adversarially perturbed, and raw time series data. When evaluated on adversarially perturbed irregularly sampled time series data, our adversarial LTC model outperforms the prediction accuracy of state of the art standard LSTMs by over 35%, standard LTCs by over 20%, and adversarial LSTMs by 4%.Mentored by Alexander Amini, Mathias Lechner, Professor Daniela Rus (Director of MIT CSAIL)

Sep 2022 - Jul 2023

Machine Learning Researcher (Urop)

Cambridge, Ma, Us

Completed the primary proof of concept for an enhanced ML image search querying process. We developed a novel deep feature variational autoencoder network with superior reconstructions compared to state of the art convolutional VAEs. Our model achieves this by incorporating a perceptual loss network (EfficientNet B0) that extracts differences in feature representations between ground truth and output images rather than evaluating them on a traditional pixel-by-pixel basis. We implemented a t-SNE algorithm to generate a visualization of the encoded data's latent space, then constructed a separate interface that displays a smooth linear interpolation of the data across each latent dimension. Our model allows users to construct a set of desired latent features, then reverse engineer the closest image in the dataset that matches their search query.Mentored by Alexander Amini, Igor Gilitschenski, Wilko Schwarting, Professor Daniela Rus (Director of MIT CSAIL)

Jan 2021 - Sep 2022

Quant Trading Intern

Amsterdam, Nl

Jun 2022 - Aug 2022

Software Development Engineering Intern

Seattle, Wa, Us

Launched a backend feature for Amazon Connect that automates agent rerouting, addressing a critical pain point and reducing downtime for over 1,000,000 user interactions

Jun 2021 - Sep 2021

Cofounder

Hatch enables customers to seamlessly reserve a table and pre-order their food through a mobile app so that their dishes are served immediately upon arrival. Our team is working to revolutionize the restaurant industry through pioneering a novel dine in experience and optimizing operational efficiency for restaurants.

Jun 2020 - Oct 2020

Summer Intern

Mountain View, Ca, Us

HDR+, Portrait Mode, Nightsight for Pixel 4.

Jul 2019 - Aug 2019
1 education record

Alvin Li education

  • Massachusetts Institute Of Technology
    Massachusetts Institute Of Technology
    Computer Science And Engineering
FAQ

Frequently asked questions about Alvin Li

Quick answers generated from the profile data available on this page.

What company does Alvin Li work for?

Alvin Li works for Susquehanna International Group, LLP (SIG).

What is Alvin Li's role at Susquehanna International Group, LLP (SIG)?

Alvin Li is listed as MIT CS + ML | Quant Trading @ SIG at Susquehanna International Group, LLP (SIG).

What is Alvin Li's email address?

AeroLeads has found 1 work email signal at @amazon.com for Alvin Li at Susquehanna International Group, LLP (SIG).

Where is Alvin Li based?

Alvin Li is based in Palo Alto, California, United States while working with Susquehanna International Group, LLP (SIG).

What companies has Alvin Li worked for?

Alvin Li has worked for Susquehanna International Group, Llp (Sig), Mit Computer Science And Artificial Intelligence Laboratory (Csail), Optiver, Amazon Web Services (Aws), and Hatch.

How can I contact Alvin Li?

You can use AeroLeads to view verified contact signals for Alvin Li at Susquehanna International Group, LLP (SIG), including work email, phone, and LinkedIn data when available.

What schools did Alvin Li attend?

Alvin Li holds Bachelor Of Science - Bs, Computer Science And Engineering from Massachusetts Institute Of Technology.

What skills is Alvin Li known for?

Alvin Li is listed with skills including Neural Networks, Data Science, Algorithms, Cloud Firestore, Start Ups, Figma, Tensorflow, and Investments.

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