R

Roger Luo

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Founder & Managing Partner
California, United States

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Jobs verified_user 0% verified
  • Embedding VC
    Founder & Managing Partner
    Embedding VC
    Nov 2023 - Current (2 years 8 months)
    Embedding VC is an early stage venture firm in Silicon Valley to back US Generative AI startups. Our investors are all current or former builders and operators. As active company builders from the ideation stage, we are often among the first institutional investors to support ambitious and visionary founders.
  • Alumni Ventures
    Super Angel Partner
    Alumni Ventures
    Aug 2023 - Current (2 years 11 months)
    Alumni Ventures collaborates with elite angel investors — a select group of co-investors chosen for their track record, sector expertise, and community connections.
  • ELC
    D2 (DevTools) Partner
    ELC
    Jul 2023 - Current (3 years)
    Building a DevTools ecosystem on top of ELC. Empower and enable engineering leaders with state-of-art DevTools. ELC https://sfelc.com/ is a curated community for engineering leaders in Silicon Valley, with close to 10000 leaders (from CTO/VP to frontline managers) from over 3000+ companies. Our mission is to empower engineering leaders and help evolve the way leadership is implemented in the tech industry. We host monthly events and annual conferences, inviting high-caliber engineering leaders to share their learnings and insights on various topics engineering leaders care about. Our speakers include some of the best engineering leaders, book authors, and entrepreneurs, such as Reid Hoffman, Max Levchin, Eric Yuan and tech executives fr
  • Sequoia Capital
    Scout
    Sequoia Capital
    Oct 2022 - Nov 2023 (1 year 2 months)
    Source and invest in early stage startups.
  • Engineering Leaders Community Angels
    Co-Founder and Managing Partner
    Engineering Leaders Community Angels
    Mar 2022 - Current (4 years 4 months)
    Engineering Leaders Community Angels is an inclusive community of engineering leaders investing in early-stage technology startups founded by engineering and product leaders. The group is managed by a group of Engineering Leaders Community (ELC) members who are also experienced angel investors and operators. We focus on investing in engineering-driven startups with disruptive market opportunities, solid traction, technology advantage, and making a positive impact on society, whether they are in enterprise, consumer, or deep tech. ELC (https://sfelc.com/) is a curated community for engineering leaders in Silicon Valley, with close to 10,000 leaders (from CTO/VP to frontline managers) from over 3,000+ companies. Our mission is to empower en
  • ODF
    Angels Fellow (ODA5)
    ODF
    Oct 2021 - Sep 2022 (1 year)
    Stay on top of the technology and business trends through angel investing.
  • Niantic Inc
    Head of Machine Learning (Director)
    Niantic Inc
    Aug 2021 - Aug 2023 (2 years 1 month)
    Oversee Niantic’s machine learning division, leading a team of applied machine learning scientists & engineers, infrastructure engineers and product manager. Drive the ideation, development, and deployment of machine learning solutions tailored for Niantic’s games and platform customers.
  • Eve
    Co-Founder
    Eve
    Mar 2020 - Aug 2021 (1 year 6 months)
    Eve enables plaintiff law firms to become AI-Native, blending human expertise with cutting-edge AI to transform how justice is delivered.
  • Mosaixai
    Co-Founder, Chief Product Officer, Head of Engineering
    Mosaixai
    Jun 2019 - Mar 2020 (10 months)
    – Develop language AI platform for languages in emerging markets. Work with two of the top ten mobile phone manufactures in the world to build their own white label voice assistant for emerging markets. – Recruit and manage product and engineering teams across three different offices (Palo Alto, Shenzhen and Singapore). Setup engineering offices in Shenzhen and Singapore. – Define product strategy and engineering roadmap for the company. – Shipped 2 million units of Roku like TV operation systems with voice search capability to users covering 6 languages across 40 countries, grew 20% MoM. * The team landed at AWS during COVID in 2020.
  • Mosaixai
    Strategy Advisor
    Mosaixai
    Oct 2018 - May 2019 (8 months)
    A Y-Combinator startup. As an advisor of the company, I helped the company pivot to focus on emerging markets, raised new venture funds. I ended up joining Mosaix.ai full time as a co-founder in Jun 2019.
  • Snap Inc
    Senior Engineering Manager
    Snap Inc
    Sep 2017 - May 2019 (1 year 9 months)
    – User profile service. – Data Privacy Compliance including GDPR. – Machine learning for discover content recommendation. – Content understanding, user understanding and feed ranking.
  • Angel Investor
    Angel Investor
    Angel Investor
    Nov 2016 - Current (9 years 8 months)
    Early-stage startups that I invest in and/or advise: Bacca (AI DevOps), testRigor (Generative AI-based Test Automation Tool, YC), Superlinked (VectorOps, Index Ventures and Theory Ventures), Particle Labs (Leverage Automated Market Maker, Polychain Capital), Onwish (AI Assistant for Professional Financial Analysts), InpharmD (AI Assistant for Clinic Pharmacist, 645 Ventures, YC), MagicSchool.ai (Co-pilot for educators, GSV Ventures), Pinnacle (AI Copilot for learning how to manage engineering team), Monterey.ai (Intelligent customer voice infrastructure, YC), Respell.ai (No-code AI application platform, Craft Ventures), Vinovest (Wine investing and trading), Digma.ai (1st continuous feedback platform for developers), DealDriver.ai (AI-pow
  • China Growth Capital
    Venture Partner
    China Growth Capital
    Oct 2016 - Jun 2019 (2 years 9 months)
    Help CGC, a leading early-stage venture capital firm investing globally with a focus on enterprise software and deep tech, sourcing and performing due diligence on AI and deep tech startups. We invested in ~8 early stage stage startups in AI and enterprise software spaces. Among these investments, Plus.ai (seed), WeRide.ai (seed) become unicorns.
  • Snap Inc
    Principal Research Scientist & Research Manager
    Snap Inc
    May 2015 - Aug 2017 (2 years 4 months)
    – Founding members of Snapchat Research. – Lead the R&D efforts of multiple recommendation, discovery, search and data products. – Lead user growth projects: (new) user behavior analysis, user churn prediction, “friend you may know” (Quick add). – Machine learning platforms and Cloud AI (Vision/Audio/Speech/Text) APIs. – Lead data mining research area.
  • Yahoo Labs
    Senior Research Manager
    Yahoo Labs
    Aug 2014 - May 2015 (10 months)
    – Manage the search federation science team. – Tech owner of Yahoo's aggregated search platform for mobile & desktop search. – Science owner of Yahoo's query analysis and query understanding system. Lead the efforts to improve the performance of query annotation, entity tagging, entity disambiguation and query intent classification. – Build next-generation rich information card based systems for Yahoo's mobile search experiences. – Collaborate with external partners and interns on search, data mining and machine learning research projects.
  • Yahoo
    Senior Research Manager
    Yahoo
    Aug 2014 - May 2015 (10 months)
    – Manage the search federation science team. – Tech owner of Yahoo's aggregated search platform for mobile & desktop search. – Science owner of Yahoo's query analysis and query understanding system. Lead the efforts to improve the performance of query annotation, entity tagging, entity disambiguation and query intent classification. – Build next-generation rich information card based systems for Yahoo's mobile search experiences. – Collaborate with external partners and interns on search, data mining and machine learning research projects.
  • Yahoo Labs
    Research Scientist, Tech Lead
    Yahoo Labs
    Jan 2012 - Aug 2014 (2 years 8 months)
    – Tech lead of Yahoo's aggregated search platform for mobile & desktop search (blend results from vertical search engines like news and local into web search results). Build a system to automatically learn from user feedback data what and where the type of contents to show on the search result page. Product is live and serving 100% of Yahoo Search traffic in more than 10 countries and regions (including USA), improving user metrics and revenue considerably. – Research on large-scale distributed machine learning and web search.
  • Yahoo Labs
    Research Scientist, Tech Lead
    Yahoo Labs
    Jan 2012 - Aug 2014 (2 years 8 months)
    – Tech lead of Yahoo's aggregated search platform for mobile & desktop search (blend results from vertical search engines like news and local into web search results). Build a system to automatically learn from user feedback data what and where the type of contents to show on the search result page. Product is live and serving 100% of Yahoo Search traffic in more than 10 countries and regions (including USA), improving user metrics and revenue considerably. – Research on large-scale distributed machine learning and web search.
  • Yahoo Labs
    Research Intern
    Yahoo Labs
    Jun 2010 - Nov 2010 (6 months)
    I worked with the Page Optimization & Presentation Team, in the Search Science Division. Developed federated search and vertical search algorithms.
  • Idiap Research Institute
    Research Assistant
    Idiap Research Institute
    Dec 2006 - Dec 2011 (5 years 1 month)
    Research on machine learning and computer vision: developing new machine learning algorithms for image categorization and other general classification problems. Research projects (in inverse chronological order): + Learning from weakly supervised data; + Image Retrieval and Categorization; + Multiple Sources Information Fusion; + Visual Place Recognition System.
  • KTH Royal Institute of Technology
    Student Assistant
    KTH Royal Institute of Technology
    Sep 2005 - Nov 2006 (1 year 3 months)
    - Student researcher at Computer Vision and Active Perception Lab (CVAP). - Developed a library for controlling cameras mounted on mobile robot including several image processing tools. - Developed a camera setup for acquiring perspective and omnidirectional images at three different indoor laboratory environments located in three different European cities. (http://cogvis.nada.kth.se/COLD/)
Education verified_user 0% verified
  • ETH Zurich
    Visiting Student Researcher, Computer Vision Laboratory
    ETH Zurich
    Jan 2008 - Dec 2011 (4 years)
  • EPFL École polytechnique fédérale de Lausanne
    Doctor of Philosophy - PhD, Artificial Intelligence
    EPFL École polytechnique fédérale de Lausanne
    Jan 2007 - Dec 2011 (5 years)
  • EPFL
    Doctor of Philosophy - PhD, Artificial Intelligence
    EPFL
    Jan 2007 - Jan 2011 (4 years 1 month)
  • Massachusetts Institute of Technology
    Visiting Student Researcher, Center for Biological & Computational Learning
    Massachusetts Institute of Technology
    Jan 2007 - Dec 2007 (1 year)
  • KTH Royal Institute of Technology
    KTH Royal Institute of Technology
    KTH Royal Institute of Technology
    Jan 2006 - Current (20 years 6 months)
  • KTH Royal Institute of Technology
    Master's degree, Computer Science
    KTH Royal Institute of Technology
    Best Master’s thesis award of Swedish AI Society 2007 https://www.sais.se/mthprize.php
Publications verified_user 0% verified
  • T
    I Know You’ll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application
    The ACM International Conference on Knowledge Discovery and Data Mining KDD
    Aug 2018
  • T
    Bi-directional Joint Inference for User Links and Attributes on Large Social Graphs
    The International World Wide Web Conference WWW
    Apr 2017
  • P
    Learning Entity Types from Query Logs Via Graph-Based Modeling
    Proceedings of ACM International Conference on Information and Knowledge Management CIKM
    Jul 2015
  • P
    NCR: A Scalable Network-Based Approach to Co-Ranking in Question-and-Answer Sites
    Proceedings of ACM International Conference on Information and Knowledge Management CIKM
    Jan 2014
  • C
    OM-2: An Online Multi-class Multi-kernel Learning Algorithm
    CVPR Online Learning for Computer Vision Workshop
    Jan 2010
  • N
    Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree.
    NIPS
    Dec 2008
    Unexpected stimuli are a challenge to any machine learning algorithm. Here we identify distinct types of unexpected events, focusing on 'incongruent events' - when 'general level' and 'specific level' classifiers give conflicting predictions. We define a formal framework for the representation and processing of incongruent events: starting from the notion of label hierarchy, we show how partial order on labels can be deduced from such hierarchies. For each event, we compute its probability in different ways, based on adjacent levels (according to the partial order) in the label hierarchy . An incongruent event is an event where the probability computed based on some more specific level (in accordance with the partial order) is much smaller
  • I
    Object Category Detection Using Audio-visual Cues
    International Conference on Computer Vision System ICVS
    May 2008
    Categorization is one of the fundamental building blocks of cognitive systems. Object categorization has traditionally been addressed in the vision do- main, even though cognitive agents are intrinsically multimodal. Indeed, biological systems combine several modalities in order to achieve robust categorization. In this paper we propose a multimodal approach to object category detection, using audio and visual information. The auditory channel is modeled on biologically motivated spectral features via a discriminative classifier. The visual channel is modeled by a state of the art part based model. Multimodality is achieved using two fusion schemes, one high level and the other low level. Experiments on six different object categories, unde
  • I
    Biologically Motivated Audio-Visual Cue Integration for Object Categorization
    International Conference on Cognitive Systems CogSys
    Apr 2008
    Auditory and visual cues are important sensor inputs for biological and artificial systems. They provide crucial information for navigating environments, recognizing categories, animals and people. How to combine effectively these two sensory channels is still an open issue. As a step towards this goal, this paper presents a comparison between three different multi-modal integration strategies, for audiovisual object category detection. We consider a high-level and a low-level cue integration approach, both biologically motivated, and we compare them with a mid-level cue integration scheme. All the three integration methods are based on the least square support vector machine algorithm, and state of the art audio and visual feature represent
  • K
    A unified search federation system based on online user feedback
    KDD Proceedings of the th ACM SIGKDD
    We model the search federation as a contextual bandit problem. The system uses reward as a proxy for user satisfaction. Given a query, our system predicts the expected reward for each vertical, then organizes the search result page (SERP) in a way which maximizes the total reward. Instead of relying on human judges, our system leverages implicit user feedback to learn the model. The method is efficient to implement and can be applied to verticals of different nature. We have successfully deployed the system to three different markets, and it handles multiple verticals in each market. The system is now serving hundreds of millions of queries live each day, and has improved user metrics considerably.
  • Y
    Oxygen, a novel Oozie framework
    Yahoo Tech Pulse
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