Personal statement:
I am a full time researcher at Messari focusing on DeFi and macro valuations. Previously, I worked at BlackRock as a lead Risk Manager covering investment risk for Multi-Asset portfolios. Additionally, I spent my last year at the firm working for the internal systematic fixed income hedge fund building high-performance Python tooling for investment research.
I am thankful for the opportunity to be part of the Risk DAO and very excited to help establish a robust risk management process for Aave.
Conflicts of interest disclosures:
I am a full time employee at Messari. In addition, I hold personal investments in AAVE and competing protocols (Compound and MakerDAO)
Risk DAO Member Information - Callam Ingram representing Blockchain@Berkeley.
Aave forum account:@CallamIngram Discord / telegram / twitter / other contact info: Twitter : @ZukotheDunkman, TG: @ @CallamIngram
Primary focus area: Governance and Project Management
Personal Statement:
Hi, I am a Computer Science at Blockchain@Berkeley and I have been involved in the DeFi space since 2020. I will be spearheading our clubs’ contribution to the Risk DAO but our contribution will be coming from all the members of our governance team. We have a wide range of skills and have already done simulations for assessing risks on the Maker Protocol, specifically the ‘Black Thursday’ crash. (see our StableSims project)
Conflicts of interest disclosures:
Aside from Aave, we have been also delegated voting power across other DAOs such as Uniswap, Compound, and Fei. We are also DeFi loving Degens so it’s likely each member has personal holdings in a variety of different crypto assets but we are also all broke college students so it is safe to assume that each holding is quite small relative to the reputational and future earnings loss we would face by acting corruptly.
I am a finance student in the last year of my Master’s degree.
Currently I am pursuing a Double Degree in Germany in “International Finance” and in “Banking and Financial Markets” in Italy. Additionally I am participating in the training to be a licensed derivatives trader with the German Stock Exchange.
During my studies I’ve specialised on Risk Management which I practice on a daily basis by assisting two professors in their writing on “Quantitative Risk Management in the banking and insurance sector” and “Risk Management in large corporations”.
But my real passion lies in crypto.
I joined in 2017 and have never stopped since. In 2018 I wrote my Bachelor’s thesis as an introduction to Bitcoin, to bring this space to more people. Currently I am working on a crypto fundamentals dashboard, which aims to value or compare projects based on a more traditional approach.
I can commit 10h per week.
Please let me know if you need any additional information.
I’m working on the tool for protocol analysis, which could become handy for the purposes of RiskDAO, in the vein of what Gauntlet is doing, but based on a set of DeFi-native premises:
Gauntlet is running their simulations with agent-based models representing users interacting with a protocol. A model of a user is based on a set of theoretical assumptions about user behaviour patterns. This approach was developed for TradFi, where the bulk of real life data is either not digitised at all (much of the b2c interaction happens offline) or isn’t available (much of market data is private). Hence, agent-based modelling with theoretical assumptions about incomplete data is justifiable. However, for DeFi agent-based modelling is a suboptimal legacy framework. Since all data about transactions and user interactions with the protocol is open and available for modelling, we can learn from real life data a model of a living protocol, or parts of it, and models of user interactions with it. Moreover, this model will be continuously fine-tuned with new data emerging.
Transactions model is only half of the story. The other half is community sentiment manifested on twitter, discord and discourse. In offline economy inflation expectations and consumer sentiment influence consumer behaviour and central banks of the world when modelling national economies gauge it with polls. In DeFi we have the luxury to model community sentiment not with approximating polls, but again with real life data, while constantly fine-tuning the model.
We can learn from real life data models of onchain activity & of community sentiment and then merge them to get a true-to-life DeFi-native model of a protocol, which can be constantly fine-tune. Then it can be used to build tools for explorable + explainable DeFi: running stress tests and alternative scenarios, classifying protocols and tokens, detecting user behaviour patterns, making forecasts about certain protocol KPIs like TVL and that of partnering protocols.
The first example of the community model application is the gauge of Twitter sentiment evolution towards AAVE proto (256k tweets over the period Nov '19 - Oct '21)
As you can see from the intensification of fluctuations real interest skyrockets with the start of DeFi summer (239k out of 256k tweets over the period Jun '20 - Oct '21). If you zoom in, you see details better.
An autoencoding BERT derivative language model trained on the corpus of tweets, then fine-tuned on a different corpus of tweets with sentiment labeled by human annotators, then Twitter feed scrapped with inclusion/exclusion @ # $ and some others for a particular protocol. Then run scrapped Twitter feed through language model, then for each data point in the time series calculate rolling mean with a heuristic window (a sweet spot: less – too much noise, more – too little details).
I became somewhat overwhelmed last year with my existing position at MakerDAO, contributions at Tally, and family health issues, and was not able to continue developing the Risk DAO.
I should have done better to hand this initiative off to some of the eminently capable people in this thread and wider Aave community. I sincerely regret dropping the ball on this and the prospect that I may have obstructed others from taking the lead through my lack of communication.
Aave already has strong risk management capabilities with @Alex_BertoG and others at Aave company, as well as Gauntlet. But I feel there is room for additional community led team to make a positive impact in protocol risk management. I respectfully remove myself from consideration to lead the riskDAO and would encourage interested parties to get in touch with Alex or other Aave contributors to see how they can get involved.
I will try to contribute to betterment of Aave protocol risk management in my personal capacity as a long time AAVE token holder and community member.
I’m a senior Software Project/Test manager with 25+ years of experience in software testing across different branches like Automotive, Web2, Insurance, IPTV, Finance and more. I have a lot of experience with both front/backend high capacity/availability systems. I have been working internationally all my career mainly in North America (US Canada), Europe (Sweden & Germany) and Asia.
Over that, as a hobby, I have been trading for more than 20 years in many different markets like Forex, Commodities, Options, Warrants, Indexes, Stocks and now crypto. In 2012 I had my own Automated Forex trading fund with close to a million euro in capital (Managed account with money from friends and family).
I strongly believe that my participation in the DAO will be beneficial for the Aave Risk DAO.
At the moment I can contribute with 5 hours of my time a week.
Thanks in advance for considering my application and have a beautiful day!
They have a mid-level understanding of both the ins and outs of the Aave protocol.
They’re interested in using GHO as a fully cross-chain stablecoin to be used across all of DeFi.
They can dedicate full time working hours towards this project.
Hi all, great initiative to kick of a risk DAO. What is the current status of this project? @Alex_BertoG, what is your view on this additional risk management initiative?
