Gauntlet’s mission is to help make blockchain protocols and smart contracts safer and more trustworthy for users.
Decentralized systems create new challenges for protocol developers, smart contract developers, and asset holders that are not seen in traditional development and investing. Gauntlet is building a blockchain simulation and testing platform that leverages battle tested techniques from other industries to emulate interactions in crypto networks. Simulation provides transparency and greatly reduces the cost of experimentation so that teams can rapidly design, launch, and scale new decentralized systems.
Crypto systems are truly multi-disciplinary as they are at the intersection of distributed systems, cryptography, economics, and game theory. In particular, there is a coupling of economic incentives and product success, which results in a large attack surface that must be analyzed thoroughly.
Tarun is an alumni of D. E. Shaw Research (DESRES), Vatic Labs, an HFT firm, and Cornell. He fell into the blockchain world in 2011, when he was working on ASICs at DESRES and observed Bitcoin ASIC mining rigs substantially delay DESRES’s ASIC production. He has spent the last seven years working on simulation-based R&D at the intersection of high-performance computing and AI and is applying this knowledge to the blockchain ecosystem.
Rei worked at Uber Advanced Technologies Group building marketplace simulations for self-driving vehicles and real-time driver incentives. Prior to that, he spent five years in the HFT industry as a portfolio manager and quant trader at GETCO and 3Red, specializing in market-making strategies for fixed income and commodity futures. Rei received his B.S. in theoretical math and computer science from MIT.
John has spent over a decade working on products in both traditional software and financial systems. He was an early employee at Gigster where he lead fulfillment of client projects and managed the freelance marketplace. Prior to that, he worked at Microsoft as a PM on Windows APIs and HoloLens as well as at Goldman Sachs. He also studied at Cornell where he received a B.S. in Applied Physics and Computer Science.
Most recently, Wei developed low-latency trading strategies at Jump Trading. Before that, he worked at Merrill Lynch, J. P. Morgan, and Goldman Sachs. He has a PhD in Computer Science from UIUC and a Masters in Physics from Peking University.
Guille is researcher at Stanford, currently completing his PhD in electrical engineering under Stephen Boyd. Previously, he has worked at Facebook on fraud detection and at D. E. Shaw Research on topological and manifold methods in data analysis.
Jeremy studied Computer Science at Tufts before working on product development at Cambridge Blockchain and business operations at CoinList.
Tony is an experienced developer and quant trader who has been building secure software systems and accurate financial models for the better part of a decade. He was previously at Jibe Inc and Spot Trading, and has a B.A in Economics from Princeton.
Lawrence is an Insight Consensus fellow and has worked on building scalable networks at OpenDNS and Cisco. He studied CS at the University of Southern California.
Tim Roughgarden is a Professor in the Computer Science Department at Columbia University. He works on the boundary of computer science and economics, and on the design, analysis, applications, and limitations of algorithms. For his research, he has been awarded the ACM Grace Murray Hopper Award, the Presidential Early Career Award for Scientists and Engineers (PECASE), the Kalai Prize in Computer Science and Game Theory, and the EATCS-SIGACT Gödel Prize.
Wei worked with Gauntlet to create our initial simulation platform. Before working with Gauntlet, Wei worked on simulation at Basis (the stablecoin project) and at Tower Research, a quantitative trading firm. He has a PhD in electrical engineering from Princeton University, and was a Professor of computer engineering at Boston University.
Yi is an Assistant Professor in the Department of Mathematics at Columbia University. His research interests are in representation theory and integrable systems and their applications to probability and random matrices. Before Columbia, he received his Ph.D. in mathematics from MIT, advised by Pavel Etingof.