STAC improves technology discovery and assessment in the finance industry through dialog and research guided by the STAC Benchmark Council, a group of self-described leading financial firms and technology vendors.

STAC

STAC - Strategic Technology Analysis Center | 1,156 followers on LinkedIn. Hands-on research. Testing systems. Hype-dispelling discussions. | STAC is a company that coordinates a community called the STAC Benchmark Council. Understanding who we are means understanding not just the company leadership, but more importantly the members of the Council and some of the many individuals from that community who contribute requirements, proposals, and code.

The STAC Benchmark Council consists of over 350 financial institutions and more than 50 vendor organizations whose purpose is: 
1) to conduct substantive discussions on important technical challenges and solutions in financial services, and 
2) to develop technology benchmark standards that are useful to financial organizations.

CEO @ STAC ► Ex-Head of FX Technology / FXall / Trading / REDI / Wealth Management Tech / Elektron / Market Data ► Research ► Ultra Low Latency ►…

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Head of Research at STAC - Strategic Technology Analysis Center

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STAC - Strategic Technology Analysis Center

As AI continues to revolutionize the finance industry, understanding its practical applications from those on the front lines is crucial. How can AI agents transform financial decision-making? At the upcoming AI STAC conference in London on December 4th, Hanane D. , Director and Algorithmic Trader at Bernstein Societe Generale, will present a talk entitled "AI agents in finance: A perspective from the front lines". Drawing from her experience in developing AI-driven approaches, Hanane will ✅ Explain what AI agents are and when they should be used ✅ Discuss agentic design patterns ✅ Examine open-source and proprietary frameworks for building agentic systems. Register today to discover how AI agents could become your secret weapon in the high-stakes world of trading and investment management. 🔗 Registration: https://lnkd.in/eCwd4Q5a

💡Looking for a way to speed up ML development? ML development today is often fragmented: a researcher working in a hosted notebook with sampled data hands off to an ML engineer, who then repackages the work for an orchestrator to handle execution. At the AI STAC in New York on December 10th, Paul Yang from Runhouse will present his talk, “A Lean Orchestration Manifesto”. Paul will argue for a different, "lean orchestration" approach to managing ML workflows, which he believes restores the development flywheel. In this pattern, code that defines an ML program is serverless and runs identically from a researcher’s local IDE and an orchestrator task in production. The orchestrator's role is limited to scheduling and monitoring execution. Paul will explain this pattern in detail and how it provides much of the reproducibility, developer experience, and fault tolerance that so many ML pipelines lack. 🔗 Register now for December 10th: https://lnkd.in/eUSdEW7P

Recent advancements in GenAI are transforming document classification in clinical research. At AI STAC in New York on December 10th, Nataraj Dasgupta , VP Engineering at Syneos Health and former technologist at a major bank, will present a novel methodology for auto-classifying clinical trial documents using GenAI. He will demonstrate how this approach, which combines contextual RAG, fine-tuned models, and prompt optimization with columnar databases and highly parallel processing, is achieving a remarkable 1000x reduction in both time and cost during initial testing. And he’ll discuss its applicability in finance. 🔗 Register: https://lnkd.in/eUSdEW7P

The AI arms race is on, but it’s not just about the latest models; it's about creating the right architectural backbone that can scale intelligently, minimize latency, and optimize computational efficiency. In my role at STAC - Strategic Technology Analysis Center , I’m fortunate to have eye-opening conversations with industry leaders and practitioners that are shifting my perspective on AI infrastructure. What I’ve learned from these conversations: 👀 You’ll hit compute bottlenecks if you rely solely on existing hardware. 👀 Traditional network architectures are struggling to maximise GPU utilisation. 👀 There’s still a lot of room to improve how basic mathematical operations are executed at the hardware level. Building future-proof AI infrastructure is about understanding and leveraging three emerging paradigms: 1️⃣ Heterogeneous computing: The days of one-size-fits-all hardware are over. We're witnessing a shift towards specialized architectures optimized for specific parts of AI workloads. 2️⃣ AI-specific network fabrics: Firms fine-tuning or training models are investing heavily in GPUs. But a crucial enabler is often overlooked: the network. When network architecture respects AI workloads' unique requirements, GPU utilisation rates can soar. 3️⃣ Algorithmic hardware transformation: The boundaries between mathematical algorithms and hardware implementation are blurring, leading to more efficient and specialized computational approaches. These infrastructure patterns are rapidly reshaping how we build, deploy, and scale AI systems, and we’ve got a full workshop track dedicated to them at our developer event on December 5th in London. Product and technology leaders from AMD , Xelera Technologies , Intel Corporation and Arista Networks are leading sessions on the fundamental building blocks of modern AI infrastructure, and practitioners from leading investment banks, hedge funds, exchanges and asset managers will be there. The workshops take place the day after the AI STAC conference main agenda and are a great way to dive deeper into the topics covered on day 1. Workshop tracks: ▶️ AI infrastructure ▶️ AI data management ▶️ AI applications Curious? You can register here: https://lnkd.in/eqDNTPUM Stay tuned - tomorrow, I'll be sharing more details about workshop track 2.

James Corcoran

🤔Can IT trading ops benefit from AI? Fergal Toomey believes that integrating LLMs with recent advancements in deep learning for time-series analysis offers a transformative approach to automating operations management—enabling more effective detection and resolution of performance issues. Register here to hear his insights: 💬 “Combining LLMs and numerical AI: The case of trading-platform telemetry” 📅 4 Dec 2024, AI STAC London 👉 https://lnkd.in/eMunJWjC Pico #AISTAC #ai #machinelearning #hft #trading #financialmarkets #marketdata #Analytics #FinTech #LLM #deeplearning

“Where did the AI get the data for that response?” This question of traceability is increasingly critical as AI solutions gain access to broader training and augmentation datasets and become better at using tools. This need extends from an organization's data scientists to managers, regulators, and even the public. Join us at AI STAC in London (December 4) or New York (December 10) for a panel titled "Who says?": Tracing AI outputs to their source. London Panelists: ▪️ Margaret Hartnett , Co-Founder, Progressio.ai ▪️ Aaron Armstrong , Partner, Intellimation.ai ▪️ More speakers to be announced New York Panelists: ▪️ Deepak Dube, PhD. , CEO, EazyML ▪️ Niamh O'Connell , Enterprise Lead, Prove AI ▪️ Pete Harris , Partner, Lighthouse Partners These experts will focus on: ✅ Framing questions of traceability in a productive way ✅ Sharing lessons learned from deploying traceable AI systems at scale ✅ Offering advice on cutting-edge approaches to ensure AI systems provide the right answers for the right reasons, compensate for biases in underlying datasets, and respect IP. Anyone responsible for AI in the finance industry is welcome to attend. Register now to secure your spot. 🔗 London: https://lnkd.in/eCwd4Q5a 🔗 New York: https://lnkd.in/eUSdEW7P

Today we’re announcing that AI STAC dev in London is expanding with more sessions on December 5th! This is your chance to connect with a vibrant community of AI engineers, developers, and architects from the financial services sector. Join us for hands-on workshops featuring the latest in AI technology and techniques. The event has 3 tracks, covering: 1️⃣ AI infrastructure 2️⃣ AI data management 3️⃣ RAG applications Sessions include speakers from AMD , Xelera Technologies , Intel Corporation , Weights & Biases , KX , Arista Networks , TurinTech AI , DataStax and more. Why You Should Attend: Practical Workshops: Each session is designed for hands-on learning in specific AI engineering domains. Networking Opportunities: Engage with industry experts and peers from leading companies in AI. Free Admission for Financial Technologists: Our sessions are completely free! With limited seats available, we’re encouraging early registrations to secure your spot. And remember, AI STAC kicks off the day before on December 4th, focusing on the AI infrastructure needs of finance. Check out the current sessions posted and register today! 🔗 https://lnkd.in/eTKqfn6X

🚀 Are you maximizing the ROI of your GPUs? Firms planning to fine-tune or train models on prem are making a significant hardware investment, particularly in GPUs. It's crucial that they get the best possible ROI by maximizing GPU utilization. This reduces the time to train/fine tune, which reduces the cost. In @Chris Russell's view, a key enabler of AI cluster performance is often overlooked: the network. In his experience, AI workloads have key differences from other workloads, and when the network architecture respects those differences, performance soars. He’ll make his case at the upcoming AI STAC event in London with his talk, “Proper Pipes: How Networks Can Reduce the Cost of Fine-Tuning and Training.” In it, he explores the unique demands of AI and offers insights into designing future-ready networks for the AI data center. Don’t miss this chance to discuss the requirements of networking approaches with your peers. 📅 4 Dec 2024, London 👉 https://lnkd.in/eMunJWjC #ArtificialIntelligence #GPU #GenAI #Training #DataCenter #NetworkInfrastructure #AIInfrastructure #NetworkEngineering #AIFabric #FineTuning #AISTAC

Track 3 at AI STAC Dev focuses on the key aspects of developing cutting-edge AI applications. From model training to deployment and optimization, this track will provide you with the latest tools and strategies for building efficient, high-performance AI systems. This track features leading experts from Weights & Biases and TurinTech AI : • Weights & Biases: Train, fine-tune, and manage models from experimentation to production. • Turin Tech: Maximizing Code Performance with AI: Evolutionary, Context-Aware, and LLM-Agnostic Approaches to Scalable Optimization. These hands-on workshops will equip you with the practical skills and knowledge to accelerate your AI application development. 🔗 Register now for December 5th in London: https://lnkd.in/eTKqfn6X

Gradient boosting is an ML workhorse today in use cases that prioritize inference speed and explainability. Are GPUs the best platform for it? The question affects applications in many industries—from automated trading to fraud detection to machine-fault prediction—and the stakes are significant. Andrea Suardi believes the architecture of FPGAs (field-programmable gate arrays) make them a better fit for the bottlenecks in tree-based algorithms like gradient boosting. He’ll make his case at the upcoming AI STAC events in London and New York and offer tips on integrating FPGA into existing software-based application stacks. Anyone responsible for such a stack is welcome to attend. 📅 4 Dec 2024, London 👉 https://lnkd.in/eMunJWjC 📅 10 Dec 2024, New York 👉 https://lnkd.in/esZEaVnN Andrea is also part of a technical workshop with AMD the following day at AI STAC dev London (Dec 5), where he’ll walk through the details of integrating an FPGA accelerator into a software application for low-latency gradient boosting. If you’re hands-on, you’ll enjoy this session, too. 📅 5 Dec 2024, London 👉 https://lnkd.in/e3558y6C XGBoost architecture image from “Waste Detection and Water Quality Assessment in Aquatic Environments,” Parasar et al, DOI:10.21203/ rs.3.rs -3829799/v1 Xelera Technologies Michael McGuirk Felix Winterstein

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STAC improves technology discovery and assessment in the finance industry through dialog and research guided by the STAC Benchmark Council, a group of financial firms and technology vendors.

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