STAC Summit, 16 Oct 2018, Chicago

• Travis Oliphant, founder of NumFOCUS, Anaconda, and Quansight

Over the last two decades, Travis has authored and/or directed the teams for several popular Python tools for data science, including NumPy, SciPy, Numba, Dask, JupyterLab, and Ananconda. Today he and his colleagues spend their time at Quansight creating new tools while helping organizations leverage the Python ecosystem to improve their data science, machine-learning (ML), and "Augmented Intelligence" (AI) processes. In this talk, Travis will provide his points of view (and take your questions) on key issues facing trading and investment firms using Python, including its relation to other languages in common use like R, Scala, and Julia. Which approaches to ML and AI on Python have the most traction and future? Which Python tools are best suited to which challenges? How far can you take a notebook like Jupyter today and in the near future? What is the future of Julia and R in a Python-based enterprise? What is the state and future of Python on accelerators like GPUs and FPGAs?

• Will Vick, AI Business Development, HPE

One of the blessings of the tech industry’s huge investment in AI is the proliferation of tools to make the production of models easier, faster, and less expensive. But this proliferation is also something of a curse. With dozens of software frameworks and hardware architectures to choose from, figuring out which combinations are best for a given set of AI objectives can be daunting. In this talk, Will will sketch out the AI ecosystem and a way of thinking about its components. He will then suggest some tools firms can use to make evaluating AI solutions easier.

• Michel Debiche, Guest Analyst, STAC

It's a non-trivial challenge to benchmark machine-learning techniques and technologies in a way that captures the key elements that are important to decision makers in a business. Investment managers and trading desks care about many things beyond raw performance, such as model quality, time to market, and cost. In this talk, Michel will use early test results from a common text-processing use case in finance to illustrate a general framework for ML benchmarks that can be applied by financial firms and technology vendors (oh, and by benchmarking firms).

• Peter Lankford, Founder & Director, STAC

Peter will present the latest benchmark results for big data workloads like tick analytics and big compute workloads like derivatives valuation. He will also provide an update on the STAC Cloud Special Interest Group SIG, a group of financial firms and vendors focused on improving assessments and dialog relating to public, private, and hybrid cloud solutions.

• Jerry Nelligan, CTO, XR Trading
• Edouard Alligand, CEO, QuasarDB
• Joel Sehr, VP Americas, SQream Technologies
• Venkatesh Nagapudi, VP Product Management, Vexata

The challenges inherent in data analytics to support trading and investment, as well as the potential of new technologies to help, have been big topic of research and discussion at STAC for about a decade. Even after all that time, it's still a hot topic. Why is that? What are the most recent challenges, and how can firms solve them? Our panel will bring together several innovators with unique angles on the topic. To kick off, the vendors on the panel will each provide a short presentation:

• Mike Strickland, Data Center Solutions Architect, Intel

FPGA technology has traditionally required custom hardware design and development in RTL. But according to panelists at the STAC Summits last spring, hardware and software is now available to ease creation of FPGA-accelerated algorithms and to deploy them quickly in the datacenter. The question is: are these new products up to the challenging demands of financial analytics? In this talk, Mike will discuss what kinds of workload make good targets for FPGA and present recent case studies where FPGA acceleration has been used for financial applications. While the main focus of these cases is the acceleration achieved, Mike will also discuss other key attributes such as scaling and ease of use.

• Dr David Snowdon, Director of Engineering, Arista

In 2013, Dave Snowdon helped launch Metamako to deliver network hardware solutions to the ultra-low latency market. Five years and many plane trips from Sydney later, Dave and the Metamako team became part of Arista. In this talk, Dave will catch his breath and consider the big picture of networks in financial services enterprises. How do recent business and regulatory trends affect customer requirements around networking? What potential synergies exist between front-office and back-office networks? Dave will offer some opinions on these questions and take more from you.

• Paul Famighetti, Global Head of Exchange Technology, Cumberland/DRW (moderator)
• Brian Peterson, Managing Director, Hehmeyer Trading + Investments
• Amit Kaushik, Head Of Research and Portfolio Management, Blockseed Investments
• Derek Groothuis, MD & Head of Engineering, Coinbase Markets
• Chase Boyle, Director, Product Management - Blockchain, Zayo Group

Institutional trading and investment firms are taking cryptocurrencies very seriously. Even after the recent bubble burst, the top 100 cryptocurrencies reportedly have a total value of 187 billion dollars. The excitement is pulling many firms in from the sidelines. According to one report, over the last two years the number of hedge funds in crypto has gone from 20 to 287. But with the excitement comes challenges. There's significant regulatory uncertainty. Crypto markets work differently from other markets. And while the blockchain technology underlying cryptocurrencies is quite advanced, the technology used to trade them can be woefully inappropriate. Our expert panel will discuss the current situation, including: How do market structures differ from other assets? What it’s like to trade on crypto exchanges, and how do the technologies differ? What directions could or should crypto trading technology take? What role are traditional exchanges playing and how could they be more helpful? How could potential market innovations or regulatory decisions impact technology directions?