Agostino Capponi
@AgostinoCapponi
Followers
269
Following
1
Media
10
Statuses
31
Full Professor at Columbia University, and Director of the Columbia Center for Digital Finance and Technologies. Fellow of CBER and of Luohan academy
New York
Joined May 2017
Where will AI make the biggest difference in Web3? Check out our new paper https://t.co/VnvNENOO3d Across 3K+ proposals, we demonstrate that DAO AI’s simulated votes align with real outcomes in 92% of cases, surpassing the average human benchmark (76.6%).
arxiv.org
This paper presents a first empirical study of agentic AI as autonomous decision-makers in decentralized governance. Using more than 3K proposals from major protocols, we build an agentic AI voter...
0
0
0
Check out the Prediction-Enhanced Monte-Carlo method proposed in our recent paper https://t.co/gzVdnDT5KJ to see how modern machine learning techniques can be used to construct cheap and parallelizable simulations, and attain higher accuracy in variance swaps and swaption pricing
0
0
0
How much additional alpha can you generate if you build new factors out of supply chain data? Check out our new paper to learn more https://t.co/OjnpRjKGmX Reach out at ac3827@columbia.edu too if interested in discussing more
0
0
0
Tremendously honored by this recognition
Professor @AgostinoCapponi is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), the highest U.S. government honor for early-career scientists and engineers. Capponi has developed groundbreaking frameworks and tools to mitigate risks and
0
0
3
Professor @AgostinoCapponi is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), the highest U.S. government honor for early-career scientists and engineers. Capponi has developed groundbreaking frameworks and tools to mitigate risks and
1
1
1
Join me on June 26th at the Digital Assets Connection 2024 https://t.co/85vy0Q3ojI The event is featuring a fantastic lineup of panels that are relevant to all members of the Digital Assets ecosystem – including practitioners, academics, and policy makers
0
1
4
Call for Papers for Gillmore Centre for Financial Technology ( https://t.co/eLujmLuYAp) on DeFi & Digital Currencies 28 September, 2024 at WBS The Shard, London. Delighted to give a keynote speech - for full details, see https://t.co/D9sNXLaott Looking forward to seeing you there
0
1
3
6/N In an environment where vertical integration is not feasible, the authors show how society can end up under investing in capacity. Great paper!🔖
0
1
0
🎙️Our 3rd episode of #CraftingtheCryptoEconomy 📚with @CBER_Forum dives into "Just-In-Time (JIT) Liquidity at Decentralized Exchanges" @AgostinoCapponi breaks it down in our podcast: ➡️ https://t.co/MeTa8GccIV ⬅️ @financeUTM
1
4
3
Can crypto be socially valuable? 👉 https://t.co/EDDMld9d1H 👀 @AgostinoCapponi emphasizes the need for cryptocurrencies to generate value through useful services provided by their underlying blockchains, rather than from speculative activities. @Columbia @DataSciColumbia
2
5
93
I am looking forward to presenting my just-in-time liquidity paper (see https://t.co/RFj3EoqSih) at the 6th Sydney Market microstructure conference in a week. Check out the high-quality program below, with lots of interesting talk on market microstructure and digital finance.
0
0
1
Maximal extractable value is a key concern for smart-contract blockchains which support DeFi and other financial innovations. In this paper, https://t.co/2xXO12inrE joint with Ruizhe Jia and Ye Wang from Macau, we show that private submission pools do not always mitigate MEV.
0
0
4
I am honored to have been the inaugural speaker at the Blockchain Scholars Podcast. Check out the video https://t.co/nQPkLFPpWY In this first episode, I explain the functioning mechanism of automated market makers for practitioners and academics
lnkd.in
This link will take you to a page that’s not on LinkedIn
0
0
6
"Machine Learning And Data Sciences For Financial Markets" https://t.co/ZoLYVZLfcG shows how modern techniques should be used in conjunction with existing knowledge of financial markets. Chapter 7 shows how overcome natural limitations of machine learning and data sciences.
0
0
2
"Machine Learning And Data Sciences For Financial Markets" https://t.co/ZoLYVZLfcG shows how modern techniques should be used with existing knowledge of markets. Chapter 6 focuses on the way ML and Data Sciences (DS) connect financial decisions to real-world observations.
0
0
4
"Machine Learning And Data Sciences For Financial Markets" https://t.co/ZoLYVZLfcG shows how modern techniques should be used in conjunction with existing knowledge of financial markets. This chapter shows how to use ML and GANs to solve control problems, PDEs, and trading games
0
0
5
Machine Learning And Data Sciences For Financial Markets https://t.co/ZoLYVZLN2e shows how modern techniques should be used in financial markets. Chapter 4 gives all that is needed to build portfolios using modern techniques, including statistical arbitrage using neural networks
0
2
5
"Machine Learning And Data Sciences For Financial Markets" https://t.co/ZoLYVZLfcG shows how modern techniques should be used (1) in conjunction with existing knowledge of financial markets, (2) keeping a "model-driven mindset". Chapter 3 focuses on high frequency finance
0
0
4
"Machine Learning And Data Sciences For Financial Markets" https://t.co/ZoLYVZLfcG shows how techniques should be used (1) in conjunction with knowledge of financial markets, (2) keeping a "model-driven mindset". Chapter 2 explains why markets are themselves a learning machine
0
0
0