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UCB and Domino Data Lab Collaborate to Modernize Statistical Computing Environment in Life Sciences

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Collaboration Blends Advanced Cloud Technologies, Open-Source Collaboration, and Data Science Integration to support UCB’s development pipeline

BRUSSELS and SAN FRANCISCO, May 19, 2025 /PRNewswire/ — UCB, a global biopharmaceutical company, and Domino Data Lab, provider of a leading data science platform trusted by the world’s largest enterprises, today announced a strategic collaboration aimed at modernizing a Statistical Computing Environment (SCE) for the life sciences industry. This joint effort aims to transform the legacy SCE into unified, scalable, and flexible platforms to meet the evolving demands of clinical research, regulatory compliance, and efficient data analysis.

The two companies will work together to implement a modern cloud-enabled environment that integrates diverse analytical tools, such as SAS, R, and Python, within a unified framework. This platform will leverage Domino’s expertise in cloud technologies and open-source support, combined with UCB’s deep clinical research and regulatory knowledge. Together, this effort aims to enhance research capabilities and maintain compliance with industry standards like GxP, FDA 21 CFR Part 11, and GDPR.

“At UCB, we believe that innovation thrives through collaboration, and this partnership exemplifies our commitment to evolving and pushing the boundaries of what’s possible in life sciences. By harnessing advanced technologies to modernize our statistical computing environment and combining our expertise with Domino’s cutting-edge platform, we aim to accelerate the development of innovative therapies for people living with severe diseases,” said Mike Branson, SVP, Head Biometrics and Data Science, UCB. He added, “This new SCE will embed critical metadata and workflow management to enable searchable, responsible data re-use, accelerating our cutting-edge Data Science and enabling more efficient drug development and evidence-based decision making.”

The collaboration is aligned with UCB’s broader scientific innovation strategy, which includes leveraging pathways, populations, and technology platforms to uncover the root biological causes of diseases, deepen understanding of patient populations, and accelerate R&D pipelines.

“We’re proud to partner with UCB to help patients get new therapies faster,” said Nick Elprin, CEO and co-founder of Domino Data Lab. “Together, we aim to lead the way in modernizing Statistical Computing Environments to accelerate clinical development with greater speed, flexibility, and confidence.”

The modernized SCE will bring immediate benefits to researchers, data scientists, and patients by providing streamlined access to state-of-the-art tools. Enhancing UCB’s capabilities in this way should expedite the development of differentiated therapies, offering faster, more tailored treatment options for those living with severe diseases. Additionally, the integration of advanced data science will enable more efficient clinical studies, leveraging large-scale data analysis to enhance predictions about efficacy and tolerability.

This collaboration also emphasizes UCB and Domino’s shared commitment to driving innovation across the life sciences industry. By contributing to initiatives like PHUSE, and the SCE Coalition, the partnership aims to foster open-source principles, challenge industry norms, and establish global standards for efficient and compliant statistical environments. Together, UCB and Domino are not only advancing their own operations but also paving the way for broader industry transformation.

About UCB

UCB, Brussels, Belgium (www.ucb.com) is a global biopharmaceutical company focused on the discovery and development of innovative medicines and solutions to transform the lives of people living with severe diseases of the immune system or of the central nervous system. With more than 9 000 people in approximately 40 countries, the company generated revenue of € 6.1 billion in 2024. UCB is listed on Euronext Brussels (symbol: UCB). Follow us on Twitter: @UCB_news.

About Domino Data Lab

Domino Data Lab empowers the largest data-driven enterprises to build and operate data science at scale. Domino’s Enterprise Data Science Platform provides an integrated experience encompassing analysis and reporting, collaboration, and governance. With Domino, global enterprises can develop better medicines, grow more productive crops, develop more competitive products, and more. Founded in 2013, Domino is backed by Sequoia Capital, Coatue Management, NVIDIA, Snowflake, and other leading investors. Learn more at domino.ai.

Investor Relations
UCB, Antje Witte
T +32.2.559.94.14
email antje.witte@ucb.com

Head of Global R&D and Business Communications
UCB, Scott Fleming
T +447702777378
email scott.fleming@ucb.com

For Domino Data Lab
David Conner
T +1 415-907-0400
email david.conner@dominodatalab.com 

Forward looking statements

This document contains forward-looking statements, including, without limitation, statements containing the words “potential”, “believes”, “anticipates”, “expects”, “intends”, “plans”, “seeks”, “estimates”, “may”, “will”, “continue” and similar expressions. These forward-looking statements are based on current plans, estimates and beliefs of management. All statements, other than statements of historical facts, are statements that could be deemed forward-looking statements, including estimates of revenues, operating margins, capital expenditures, cash, other financial information, expected legal, arbitration, political, regulatory or clinical results or practices and other such estimates and results. By their nature, such forward-looking statements are not guaranteeing future performance and are subject to known and unknown risks, uncertainties, and assumptions which might cause the actual results, financial condition, performance or achievements of UCB, or industry results, to be materially different from any future results, performance, or achievements expressed or implied by such forward-looking statements contained in this document. Important factors that could result in such differences include but are not limited to: global spread and impacts of wars, pandemics and terrorism, the general geopolitical environment, climate change, changes in general economic, business and competitive conditions, the inability to obtain necessary regulatory approvals or to obtain them on acceptable terms or within expected timing, costs associated with research and development, changes in the prospects for products in the pipeline or under development by UCB, effects of future judicial decisions or governmental investigations, safety, quality, data integrity or manufacturing issues, supply chain disruption and business continuity risks; potential or actual data security and data privacy breaches, or disruptions of our information technology systems, product liability claims, challenges to patent protection for products or product candidates, competition from other products including biosimilars or disruptive technologies/business models, changes in laws or regulations, exchange rate fluctuations, changes or uncertainties in tax laws or the administration of such laws, and hiring,  retention and compliance of its employees. There is no guarantee that new product candidates will be discovered or identified in the pipeline, or that new indications for existing products will be developed and approved. Movement from concept to commercial product is uncertain; preclinical results do not guarantee safety and efficacy of product candidates in humans. So far, the complexity of the human body cannot be reproduced in computer models, cell culture systems or animal models. The length of the timing to complete clinical trials and to get regulatory approval for product marketing has varied in the past and UCB expects similar unpredictability going forward. Products or potential products which are the subject of partnerships, joint ventures or licensing collaborations may be subject to disputes between the partners or may prove to be not as safe, effective or commercially successful as UCB may have believed at the start of such partnership. UCB’s efforts to acquire other products or companies and to integrate the operations of such acquired companies may not be as successful as UCB may have believed at the moment of acquisition. Also, UCB or others could discover safety, side effects or manufacturing problems with its products and/or devices after they are marketed. The discovery of significant problems with a product similar to one of UCB’s products that implicate an entire class of products may have a material adverse effect on sales of the entire class of affected products. Moreover, sales may be impacted by international and domestic trends toward managed care and health care cost containment, including pricing pressure, political and public scrutiny, customer and prescriber patterns or practices, and the reimbursement policies imposed by third-party payers as well as legislation affecting biopharmaceutical pricing and reimbursement activities and outcomes. Finally, a breakdown, cyberattack or information security breach could compromise the confidentiality, integrity and availability of UCB’s data and systems. Given these uncertainties, the public is cautioned not to place any undue reliance on such forward-looking statements. These forward-looking statements are made only as of the date of this document, and do not reflect any potential impacts from the evolving event or risk as mentioned above as well as any other adversity, unless indicated otherwise. The company continues to follow the development diligently to assess the financial significance of these events, as the case may be, to UCB. UCB expressly disclaims any obligation to update any forward-looking statements in this document, either to confirm the actual results or to report or reflect any change in its forward-looking statements with regard thereto or any change in events, conditions or circumstances on which any such statement is based, unless such statement is required pursuant to applicable laws and regulations.

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SOURCE Domino Data Lab; UCB

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BinBase Expands 2026 BIN Dataset with Instant Payout Intelligence for iGaming, Gambling, and Cross-Border Transfers

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BinBase updates its 2026 dataset with specialized Fast Funds, Visa Direct, and Mastercard MoneySend indicators to help iGaming operators and payout platforms execute seamless, instant card disbursements.

MIAMI, July 23, 2026 /PRNewswire-PRWeb/ — BinBase, a global provider of payment routing intelligence and card issuing data, has introduced specialized instant payout indicators as part of its upgraded 2026 BIN Database. Tailored for iGaming operators, online gambling platforms, crypto-to-fiat ramps, and payout aggregators, the updated dataset helps platform engineers streamline real-time card disbursements and Push-to-Card (P2C) transactions.

In high-velocity sectors such as online betting and gaming, instantaneous player payouts are a primary driver of customer retention. However, executing Push-to-Card transactions through protocols like Visa Direct and Mastercard MoneySend requires knowing whether the receiving card issuer supports Fast Funds for specific merchant category codes (MCCs). Attempting instant payouts on non-eligible cards leads to declined transactions, elevated processing fees, and poor user experiences.

The 2026 BinBase release solves this operational bottleneck by delivering dedicated attributes for real-time fund disbursements:

Fast Funds Eligibility: Granular indicators identifying domestic and cross-border Fast Funds support across global Visa and Mastercard ranges.Online Gambling Fast Funds (OG FF): Dedicated flags specifically identifying card ranges authorized to receive real-time gambling and betting payouts.Mastercard MoneySend & Visa Direct Indicators: Precise protocol compatibility markers (MS Ind & MT Ind) ensuring push transactions are routed only to eligible recipient cards.Direct Debit & Pull-Funds Support: Indicators for recurring collections and account-funding transactions.

“Player payouts in iGaming cannot wait for standard 2-to-3-day ACH settlements,” said a spokesperson for Damiko Inc. “By embedding our Fast Funds and Gambling FF flags into their payment engines, operators can instantly validate recipient cards before initiating a transfer, guaranteeing high success rates and instant liquidity for their users.”

Fintech engineers and payout architects can examine the full 29-field database schema and access a free 2026 sample dataset on GitHub.

To explore commercial licensing, bulk database downloads, or custom data feeds, visit BinBase at https://binbase.com.

About Damiko Inc

Damiko Inc is a US-based fintech data provider specializing in card issuer analytics, payment routing data, and global BIN database solutions. Operating through its flagship product, BinBase.com, the company supplies high-precision transaction intelligence to help merchants and payment facilitators worldwide optimize approval rates and mitigate processing fees.

Media Contact
Fedor Lavrikoff, BinBase, 1 7866133334, sales@binbase.com, www.binbase.com 

View original content:https://www.prweb.com/releases/binbase-expands-2026-bin-dataset-with-instant-payout-intelligence-for-igaming-gambling-and-cross-border-transfers-302829344.html

SOURCE BinBase

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Walnut Coding’s Young Coders Serve as ‘Instructors’ at Huawei Cloud Developer Training Camp

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Ages 8 and 15, students showcase AI-era project-building skills from concept to working application

BEIJING, July 23, 2026 /PRNewswire/ — Walnut Coding (the “Company”), a leading online platform for youth coding education, said two of its students – ages 8 and 15 – have joined the “instructor” lineup at Huawei Cloud Developer Training Camp, making them among the youngest “instructors” in the program’s history. The Company cites the pair as a prime example of how young learners can combine coding fundamentals with AI tools to turn ideas into working applications.

The two students, Bolin Du, 8, and Peiqi Gao, 15, built working applications using Huawei Cloud CodeArts, an AI coding assistant, then presented the projects to the training camp themselves – walking the audience through their design choices, technical builds, and debugging process.

The move lands at a moment when AI coding tools are forcing a rethink across the education sector. Tools that can generate functioning code from a plain-language prompt have undercut the traditional argument for teaching children to program – that they need the skill to build things themselves. Walnut Coding’s answer is that the more valuable skill now is judgment – knowing what problem to solve, breaking it into parts, and determining whether an AI’s output actually works.

Gao built a travel-planning application that generates routes, itineraries, and recommendations based on user input, handling the project end to end, from requirements and design through coding and debugging. Du, the younger of the two, built an interactive calendar application, using HTML for page structure, CSS for visual design, and JavaScript for interactive features. Both students then took on an instructor’s role at the camp, presenting their project goals and technical implementation to the audience – a step Walnut Coding says separated the work from a typical classroom assignment.

These were not classroom exercises but working projects, built and presented inside a professional developer-training environment. The experience demanded more from both students than simply producing something functional – they needed to articulate their reasoning, defend technical choices, and refine the final result under scrutiny. Their participation signals a broader shift underway in what youth coding education can deliver.

AI is making code generation easier, but it is also redrawing which skills actually matter. A student who relies only on one-click generation may get a rough prototype quickly, but still struggle to spot logical flaws, judge whether the output is reliable, or turn an abstract idea into a product that actually works. Students with programming foundations, by contrast, are better positioned to define requirements, evaluate what the AI produces, correct its errors, and treat the technology as a tool rather than a shortcut to lean on.

“AI can help children generate code faster, but it cannot decide for them what problem they should solve, nor can it make the final judgment about whether the result is truly effective,” said Pengxuan Zeng, founder and CEO of Walnut Coding. “What these two students demonstrated is not just coding technique, but the ability to define needs, break down tasks, verify outcomes, and turn an idea into a working product. That is why we believe young people still need to learn programming in the AI era.”

Walnut Coding structures its courses around that thesis, pairing student-led project work with teaching-assistant guidance and AI-assisted support. According to the Company, this data is continuously fed back into its systems to refine the personalization of AI-assisted feedback — a closed-loop process linking teaching, practice, feedback, and curriculum development.

The Company frames the payoffs less around producing professional software engineers than around a broader form of literacy. As AI continues to reshape how tasks get done, the ability to understand the technology, structure problems clearly and collaborate effectively with intelligent tools may prove one of the most durable skills a young learner can develop.

Walnut Coding says it plans to keep expanding opportunities for students to build practical projects, partner with industry technology platforms such as Huawei Cloud, and develop the core capabilities needed to build with technology in the AI era.

View original content:https://www.prnewswire.com/news-releases/walnut-codings-young-coders-serve-as-instructors-at-huawei-cloud-developer-training-camp-302833884.html

SOURCE Walnut Coding

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MOREH Showcases High-Performance LLM Inference on AMD GPUs at AMD Advancing AI 2026

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SAN FRANCISCO, July 23, 2026 /PRNewswire/ — Moreh, an AI infrastructure software company, led by CEO Gangwon Jo, participated in AMD Advancing AI 2026, AMD’s flagship annual AI event held in San Francisco on July 22–23 (local time), where it demonstrated its distributed inference solution, the MoAI Inference Framework, running on AMD GPUs.

At the event, Moreh presented a live demonstration of the GLM-5.1 large language model (LLM) powered by the MoAI Inference Framework on a system equipped with 32 AMD Instinct™ MI300X GPUs across four nodes. Visitors experienced the chatbot firsthand, evaluating its response speed and service quality while observing performance across a range of real-world use cases.

Unlike conventional demonstrations that simply run an AI model, Moreh’s showcase displayed key inference service metrics in real time, including GPU utilization, Tokens Per Second (TPS), Time To First Token (TTFT), and Time Per Output Token (TPOT). This enabled attendees to directly verify both inference performance and GPU resource efficiency in a production-like service environment.

Global AI industry leaders and enterprise customers attending the event expressed strong interest in the system’s fast response times and stable performance. In particular, the live deployment of the computationally demanding GLM-5.1 model on AMD GPUs at production-grade service levels received positive feedback from visitors.

Moreh’s MoAI Inference Framework is widely recognized as the world’s first commercially deployed distributed inference solution built for the AMD ecosystem. Its distributed inference and heterogeneous computing technologies are designed to dramatically reduce AI service costs, enabling broader adoption of AI worldwide. The technology addresses one of the industry’s biggest challenges-the rapidly rising infrastructure and service costs caused by increasingly larger AI models-by delivering a more efficient inference infrastructure.

Moreh CEO Gangwon Jo stated, “This event provided an opportunity for global customers to verify firsthand that top-tier inference performance can be achieved on AMD GPU environments,” and added “We will continue advancing our AI infrastructure software so enterprises can operate AI services as efficiently as possible, regardless of the underlying GPU platform.”

Moreh develops its own AI infrastructure engine and has expanded its end-to-end AI capabilities through its foundation LLM subsidiary, Motif Technologies, covering both AI infrastructure and foundation models. The company is also strengthening its presence in the global AI market through strategic partnerships with leading technology companies, including AMD and Tenstorrent.

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SOURCE Moreh

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