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* 20+ years extensive R&D experience on machine learning, natural language processing…
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Ruofei (Bruce) Zhang reposted thisRuofei (Bruce) Zhang reposted thisWe're setting up a new AIML team at Apple in Bengaluru to build next-gen AI powered Search and Q&A experiences. Looking for strong ML engineers and researchers with background in search, recommendations, personalization, or similar technologies to join this new initiative. You’ll have the opportunity to develop and enhance cutting-edge search technologies and large language models to shape how billions of people get information on their devices. Apply here: https://lnkd.in/ddgkFArG #apple #ai #search #hiring #appleindia
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Ruofei (Bruce) Zhang shared thisOur Bangalore team is hiring MLEs to work on NLP and RAG for Apple Intelligence, check out!Ruofei (Bruce) Zhang shared thisWe're setting up a new AIML team at Apple in Bengaluru to build next-gen AI powered Search and Q&A experiences. Looking for strong ML engineers and researchers with background in search, recommendations, personalization, or similar technologies to join this new initiative. You’ll have the opportunity to develop and enhance cutting-edge search technologies and large language models to shape how billions of people get information on their devices. Apply here: https://lnkd.in/ddgkFArG #apple #ai #search #hiring #appleindia
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Ruofei (Bruce) Zhang shared thisWe are currently hiring Machine Learning Engineers (MLEs) and Software Engineers (SWEs) at multiple levels, including Entry-Level, Senior, Staff, and Principal, as well as Senior Engineering Managers. Positions are available in both the Bay Area and Seattle, with flexible location options. Take a look and apply if you're interested in joining a dynamic and innovative team working on NLP, generative AI based Question Answering, on-device and on-server Agentic LLM, and AI Search. https://lnkd.in/gFySBDbR https://lnkd.in/guK3pFh9 https://lnkd.in/gTkEp4sk https://lnkd.in/gYuV4AN https://lnkd.in/g-_kzB5b
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Ruofei (Bruce) Zhang shared thisWe have Distinguished Engineer and Sr. Engineering Manager roles open in AIML Siri & Information Intelligence team working on generative AI powered knowledge question answering and Apple Intelligence features. The locations are flexible, can be either in the Bay Area or Seattle. Check out. https://lnkd.in/gsWJxXC9 https://lnkd.in/gSNcBmkTAIML - Distinguished Engineer, Siri & Information Intelligence - Careers at AppleAIML - Distinguished Engineer, Siri & Information Intelligence - Careers at Apple
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Ruofei (Bruce) Zhang shared thisWe have several MLE, SWE, and Applied Scientist positions open in both the Bay Area and Seattle. The level ranges from junior, senior, to principal and distinguished engineer, check out. https://lnkd.in/gqctTpzx https://lnkd.in/gRtgaPgC https://lnkd.in/gFthqrMN https://lnkd.in/gJrPx56UAIML - Machine Learning Engineer (Knowledge Extraction), Siri and Information Intelligence - Jobs - Careers at AppleAIML - Machine Learning Engineer (Knowledge Extraction), Siri and Information Intelligence - Jobs - Careers at Apple
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Ruofei (Bruce) Zhang posted thisTeam recently had a work accepted by ICML 2021. We propose a new attention computation method called EL-attention. It avoids heavy operations for building multi-head keys and values in attention, with no need of cache. Inference speed can be up to 5X compared to baseline multi-head attention. More appealingly, it produces identical therefore lossless result, applicable to all Transformer-based NLG models such as BART, GPT. Check out our paper (https://lnkd.in/gDxqMcK) and open source (https://lnkd.in/giCUpGA).
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Ruofei (Bruce) Zhang shared thisKeng-hao Chang presented our paper "Generating Better Search Engine Text Advertisements with Deep Reinforcement Learning" (https://lnkd.in/e5q6q-S) at KDD 2019 in Anchorage this week, received lots of interests from the attendances.
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Ruofei (Bruce) Zhang shared thisWe are hiring. Please contact me if you are interested.Principal Data & Applied Scientist in Bellevue, Washington, United States | Engineering at MicrosoftPrincipal Data & Applied Scientist in Bellevue, Washington, United States | Engineering at Microsoft
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Ruofei (Bruce) Zhang liked thisRuofei (Bruce) Zhang liked thisTen years ago today(April 27, 2016), I sat quietly in the corner of Stanford University’s EE380 class and listened to Prof. Geoffrey Hinton speak about deep learning and back-propagation. I probably understood only 10% of the technical details at the time. But I walked away with something far more important—a clear sense that AI would reshape the world. That moment changed my trajectory. From that day on, I went all in on AI. I started by building relationships with top researchers and founders through an AI-focused recruiting platform. Over time, we worked with 100+ AI startups and 10,000+ researchers and engineers. In 2021, together with many top AI fellows, we took the next step—launching Fellows Fund to support founders not just with talent, but with insights, community, and capital. Today, we’ve invested in 50+ fast-growing AI companies, including 8 unicorns, and recently launched a new $200M fund, backed by 37 AI fellows. Looking back, none of this would have happened to me without that lecture 10 years ago. Prof. Hinton—thank you for your pioneering work and for inspiring so many of us to build in this field. We’re all beneficiaries of your curiosity, rigor, and generosity in sharing knowledge. (Attached is a photo I took from the corner of the classroom that day.) Eric S. Yuan Jianchang (JC) Mao Lucas Sheiner Xuedong D. Huang Amit Zavery Michele Catasta William (Liam) Fedus Lilian Weng Andrew Mauboussin Deepak Agarwal Taroon Mandhana Nick Chong Vijay K Narayanan Rik van der Kooi Anshul Pande Shiva Rajaraman Karen Ng Dragomir Anguelov Li Deng Charles Elkan Gang Hua Dawn Song Eric Xing Kristin Gleitsman Ravi Ithal Xinran Wang Haixun Wang Zachary Lipton Rong Yan Evan Cheng Todd Parsons Yiran Chen Yiwen Rong Hp Jin
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Ruofei (Bruce) Zhang liked thisRuofei (Bruce) Zhang liked thisEarlier this week at the MSA Spring Summit, I had the opportunity to connect with brands, publishers, partners, and business leaders on how we’re building new tools and solutions to help businesses succeed in the AI economy and lay the foundation for a more agentic web. This work spans the stack, from new Copilot experiences to underlying infrastructure - like our announcement of support for UCP-ready feeds in Microsoft Merchant Center, making it easier for merchants to bring structured product data into Copilot. Today, we’re building on that momentum by joining the Universal Commerce Protocol (UCP) Tech Council to help shape the open standard for agentic commerce. Excited for Microsoft to play a role in advancing open, interoperable systems that will define this next era. https://lnkd.in/g3iS_cNnAmazon, Meta, Microsoft, Salesforce, and Stripe Join the Universal Commerce Protocol Tech CouncilAmazon, Meta, Microsoft, Salesforce, and Stripe Join the Universal Commerce Protocol Tech Council
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Ruofei (Bruce) Zhang liked thisRuofei (Bruce) Zhang liked thisYesterday marked the end of an era at Apple. I'm walking away with lifelong friendships and pride in the technologies we built together. Now, it's time for a quick reset before joining Microsoft Superintelligence (MSI) later this month. Let’s go!
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Ruofei (Bruce) Zhang liked thisRuofei (Bruce) Zhang liked thisLast week at the Microsoft Advertising Spring Summit, I spoke about trust in AI-led advertising. Sharing what I told the room. AI is moving from assisting marketing decisions to making them. That changes the bar for how we build. Trust can no longer be just a feature: it has to be built, tested, and earned in code. At Microsoft Advertising, three principles guide how we engineer for that: 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆: assumptions are load-bearing. If an assumption is hidden, it's a bug and usually a trust gap waiting to happen. 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: if the platform automates the decision, the platform owns the outcome. 𝗥𝗲𝗮𝗹𝗶𝘁𝘆-𝗯𝗮𝘀𝗲𝗱 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁: a platform can't be the sole referee of its own impact. Measurement has to hold up outside our walls. Clean-room and incrementality approaches are a big part of that. Thank you to the clients and partners who joined us for a day of clarity, product news, and AI-powered solutions across the human web, the LLM web, and the emerging agentic web. For more from the Summit, here's Tim Frank's recap: https://msft.it/6040v8hc6
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Ruofei (Bruce) Zhang liked thisRuofei (Bruce) Zhang liked thisBing just reached a major milestone: 1 billion monthly active users, as Satya Nadella shared on today’s earnings call. We’ve added more users in the last 5 years than in the previous 10. I’m so grateful for everyone in Bing and our partners across Microsoft and beyond who have contributed over the years to this achievement, driving advances in fundamentals, differentiation, and growth with passion and tenacity. Few products reach the 1 billion user mark, and few technologies in history have been as transformative as web search. I am excited for our team to continue to drive innovation and competition in this critical area for society.
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Ruofei (Bruce) Zhang liked thisRuofei (Bruce) Zhang liked thisIt’s wonderful to be in Vegas for #GoogleCloudNext to celebrate the incredible momentum of our partners and customers. Tomorrow morning, we will talk about how Gemini Enterprise for Customer Experience is bringing shopping and customer service together on a single intelligent interface to turn every customer interaction into an opportunity to inform, delight, and drive loyalty.
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Ruofei (Bruce) Zhang liked thisRuofei (Bruce) Zhang liked thisI've been invited to give a talk on the evolution of AI and value creation at an alumni event. I want to show the audience how fast AI startups reached to $100M ARR from their public launches. So, I asked Claude Cowork to research and rank startups by time-to-$100M ARR. Here is what Claude came up. I am super pleased to see 4 portfolio companies of Fellows Fund ranked in the top-10: Higgsfield AI, Replit, Abridge, and Gamma! Super proud of these companies and their founders & teams! Alex Ren Lucas Sheiner Alex Mashrabov Michele Catasta Zachary Lipton Grant Lee
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Ruofei (Bruce) Zhang liked thisRuofei (Bruce) Zhang liked thisForbes 2026 AI 50 List | Top Artificial Intelligence CompaniesForbes 2026 AI 50 List | Top Artificial Intelligence Companies
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Ruofei (Bruce) Zhang liked thisRuofei (Bruce) Zhang liked thisIn the agentic economy, trust is no longer just a brand promise — it’s an engineered outcome. And as AI increasingly acts on behalf of humans, trust will be determined by how decisions are built into systems. Next week at the Microsoft Advertising Spring Summit, we’ll be sharing new announcements and capabilities for brands and businesses to succeed throughout their AI transformations. Stay tuned!
Experience
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Education
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State University of New York at Binghamton
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Machine learning, data mining, optimization, multimedia information retrieval, image/video processing and analysis
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Licenses & Certifications
Publications
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Deep Learning Models and Applications
Publishing House of Electronics Industry, China
See publicationMonograph book on Deep Learning models and their applications in NLP, CV for Internet products
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Multimedia Data Mining
Chapman and Hall/CRC
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My DBLP
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See publication60+ papers on NLP, machine learning, predictive analysis, and multimedia information retrieval published on leading academic journals and top international conferences.
Patents
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Look ahead strategy for trie-based beam search in generative retrieval
Issued US-12353454-B2
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Multi-task sequence tagging with injection of supplemental information
Issued US-12353998-B2
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Transformer-based neural network including a mask attention network
Issued US-12260338-B2
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Resource-efficient sequence generation with dual-level contrastive learning
Issued US 11966428
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Neural network categorization accuracy with categorical graph neural networks
Issued US 11960573
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Generating electronic summary documents for landing pages
Issued US 11921766
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Learning graph representations using hierarchical transformers for content recommendation
Issued US 11676001
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Query rewriting and interactive inquiry framework
Issued US 11603017
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Neural network categorization accuracy with categorical graph neural networks
Issued US 11551039
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Assessing Semantic Similarity Using A Dual-encoder Neural Network
Issued US 11,461,415
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Generating Electronic Summary Documents For Landing Pages
Issued US 11,449,536
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Processing image-bearing electronic documents using a multimodal fusion framework
Issued US 11,301,732
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Multi-task GAN, and image translator and image classifier trained thereby
Issued US 11,263,487
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Taxonomy enrichment using ensemble classifiers
Issued US 11,250,042
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Pipeline for identifying supplemental content items that are related to objects in images
Issued US 11,163,940
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Query rewriting and interactive inquiry framework
Issued US 10,654,380
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Using a generative adversarial network for query-keyword matching
Issued US 10,642,846
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System and method for multimedia ranking and multi-modal image retrieval using probabilistic semantic models and expectation-maximization (EM) learning
Issued US 10,614,366
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Increasing inclusiveness of search result generation through tuned mapping of text and images into the same high-dimensional space
Issued US 10,614,118
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Dynamic Tensor Attention for Information Retrieval Scoring
Issued US 10,459,928
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Rewriting Keyword Information using Search Engine Results
Issued US 10,445,376
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Identifying Relevant Content Items using a Deep-structured Neural Network
Issued US 10,354,182
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Compressing an original query while preserving its intent
Issued US 10,248,967
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Latency-efficient multi-stage tagging mechanism
Issued US 9,659,259
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Search service advertisement selection
Issued US 9,589,277
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Convolutional Latent Semantic Models and their Applications
Issued US 9,477,654
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27. System and method for multimedia ranking and multi-modal image retrieval using probabilistic semantic models and expectation-maximization (EM) learning
Issued US 9,280,562
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Apparatus and methods for detecting adult videos
Issued US 8,358,837
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System and method for image annotation and multi-modal image retrieval using probabilistic semantic models comprising at least one joint probability distribution
Issued US 8,204,842
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Optimization framework for tuning ranking engine
Issued US 8,108,374
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System and method for modeling user selection feedback in a search result page
Issued US 7,827,184
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Automatically generating a content-based quality metric for digital images
Issued US 7,826,657
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Video channel creation systems and methods
Issued US 7,814,513
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System and method for image annotation and multi-modal image retrieval using probabilistic semantic models
Issued US 7,814,040
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Honors & Awards
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Distinguished Alumni Award
Thomas J Watson College of Engineering & Applied Science, State University of New York at Binghamton
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Distinguished Engineer
Microsoft
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Distinguished Alumni Award
Department of Computer Science, State University of New York at Binghamton
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IEEE Senior Member
IEEE
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Yahoo! Super Star Team Award
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Distinguished Ph.D. Dissertation Award
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Research Excellence Award
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Yahoo! Best Overall Hack Award
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IEEE Computer Society Student Travel Award, CVPR 2004
IEEE
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Dr Haluk D.
A 12-month professional, voluntary collaboration has finally borne fruit. This morning, I was honored to see the University of Washington News (https://lnkd.in/gRRpDqjA) and the UW Milgard School of Business (https://lnkd.in/gKJ_pwbm) highlight our Responsible GenAI Framework. Why do we care about responsible generative AI? Because GenAI systems are no longer experimental curiosities. They shape decisions, influence behavior, generate content at scale, and increasingly affect trust, fairness, privacy, and accountability in organizations and society. Without intentional design and governance, these systems can amplify bias, create opaque risks, and erode confidence rather than enable innovation. This framework is about making GenAI useful, ethical, and sustainable at the same time. It emphasizes responsibility not as a constraint, but as an enabler of long-term value, credibility, and impact. I am grateful to the collaborators, reviewers, and institutions who supported this work, and excited to see responsible GenAI move from aspiration to practice. To learn more: - Read the RGAF Framework (PDF): RGAF Framework https://lnkd.in/gTaxnndV #GenAI #ResponsibleGenAI International Society of Service Innovation Professionals (ISSIP) Milgard CBA
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Scott Falconer
#1 - Toss a ball to a friend that is running at an angle and have them catch it. Not that hard for humans in general. Very hard for AI. #2 - Document and explain the math behind #1, with variants for wind speed, elevation, etc. Very hard for humans, relatively trivial for AI. There's a lot of opportunity at the jagged frontier.
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Marc Brooker
Barbarians at the Gate (https://lnkd.in/gkEBX79s) is a very interesting new paper from some great folks at Berkeley, with some exciting results showing the potential for AI in systems research. But I think the authors aren't quite asking the hardest problem about where this takes systems as a field. I wrote a new blog post about it: https://lnkd.in/gxS3s4v4 The core point of the paper is that AI can be effective at automatically driving the 'number go up' part of systems research. Given an evaluator, AI can automate progress on implementation. I believe this is 100% correct. The paper also talks about how more focus will have to go into developing evaluators. Again, I 100% agree, but think they're understating their case. Building evaluators (basically tests) is, I believe, going to become the focus of systems and software over the next decade. Which leaves researchers with the hard problem of choosing which problems to go after. That's never been easy in academic systems, and is only going to become more of a challenge. But the best systems research is driven by choosing problems well! This vision for AI could lead to lots more of the least interesting systems research. The "number go up" kind. Which could make it harder than ever to identify the truly innovative and visionary work. We're already seeing that elsewhere in science. So, mostly, I think the paper is right, but not yet asking the biggest question: what should systems research be?
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Alex Coqueiro
Every research has a story. The published note rarely tells it. Most of my previous research started with an answer already in my head. This one did NOT. When I started exploring how Shared API Services fit into the age of #agentic AI, I hit a wall. The conventional wisdom says: build #APIs, enable reuse, accelerate delivery. But the more I dug in, the more I realized the challenge is not purely technical. Technology is part of the answer, but so are design best practices, people, and team structure. So, some customer inquiries from organizations facing this challenge every day made a huge difference. Those conversations changed my perspective. Thank you for your trust. The #Gartner research is now out: Unlock Agentic AI by Evolving Shared API Services with MCP (Model Context Protocol). Shared API Services were designed with a single consumer in mind. Agentic AI is fundamentally different. It does not read documentation. It does not ask for help. It must discover, interpret, and orchestrate capabilities on its own. That single shift changes almost every assumption behind how shared services are scoped, owned, and governed today. If your organization is scaling AI beyond isolated pilots, this note is for you. Is your team already rethinking how shared services are structured for agentic AI? I'd love to hear what you are seeing. 👉 Link in the comments. A huge thank you to Steve Schwent, who started this research and built on Anne Thomas key insights around Shared API Services. Thank you, Charles Smulders, Mike Gilpin, Tigran Egiazarov, Keith Guttridge, Andrew Humphreys, and Bhagyanshi Pathak, for all your contributions to make this research better. #AgenticAI #MCP #API #SoftwareEngineering #PlatformEngineering
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Laurence Moroney
To FTP or to sFTP? Been using these technologies for 30+ years, and somehow I find myself still wondering when to use one or the other. The folks at #kiteworks have a nice article: https://gag.gl/oi1wa2 SFTP (Secure File Transfer Protocol) offers a highly secure method for transferring files by using encryption, authentication, and data integrity checks, making it a significant improvement over the older, unencrypted FTP protocol. However, it comes with drawbacks such as increased complexity, slower performance, and limited native support for compliance features like audit logging or encryption at rest. Tools like Kiteworks enhance SFTP by adding security, governance, and compliance capabilities, making it more suitable for regulated environments.
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Vijay Ramachandran
Regarding the AI disruption of SW freakout - I am interested in knowing if there is demand/supply based market analysis of this? If so, please do comment and let me know. The current repricing of SW seems to assume SW is demand constrained?? - i.e. the same amount of SW can be generated much cheaper, and thus software vendors will face pricing pressure and competition. But what if SW is supply constrained? Why not a scenario where 10 times as much software can be sold for 2 times as much revenue using the same number of software engineers? Considering the premium multiple that SW companies have commanded, and the high salaries of SW engineers, I would guess that the market is more likely to be supply constrained.
4 Comments -
Leon Song
I'm excited to share three research papers from our talented team at Together AI that are advancing the frontiers of efficient and effective AI systems: ## 🚀 Accelerating Large Model Inference Our researchers have developed "Ladder-residual," a novel parallelism-aware architecture that significantly accelerates large model inference through communication overlapping. This innovation addresses one of the key bottlenecks in deploying large language models at scale, making AI systems faster and more cost-effective. 📄 Read more: [Ladder-residual: parallelism-aware architecture for accelerating large model inference with communication overlapping](https://lnkd.in/g825uRRz) ## 🤝 Enhancing Model Alignment Through Collective Intelligence By leveraging the collective intelligence of open-source models, we've developed new methods to improve AI alignment without the computational expense of traditional alignment techniques. This research demonstrates how cooperation between diverse models can lead to superior performance and better alignment with human values. 📄 Read more: [Improving Model Alignment Through Collective Intelligence of Open-Source Models](https://lnkd.in/g9FZ83P8) ## 📱 Optimizing On-device and Cloud Collaboration Our collaboration with researchers from Stanford introduces cost-efficient methods for on-device and cloud language model cooperation. This work enables more powerful AI capabilities on edge devices while intelligently leveraging cloud resources, resulting in better user experiences and reduced costs. 📄 Read more: [Cost-efficient Collaboration between On-device and Cloud Language Models](https://lnkd.in/ggM6v3R3) These papers represent our commitment to developing AI that is more efficient, aligned, and accessible. I'd love to hear your thoughts on these research directions or discuss potential collaborations! #TogetherAI #MachineLearning #AI #ResearchAndDevelopment #LLM #EdgeComputing #AIAlignment
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