NAVER AI LAB

NAVER AI LAB aims to achieve results that will surprise the world through impactful research, thus contributing to the global AI research community, as the mid&long-term AI research team of NAVER.

Introducing NAVER AI LAB Click!

Meet Research Scientists at NAVER AI LAB Click!

[NAVER AI LAB Job Description]

• Research topics

  • Including, but not limited to -

  • Next generation of backbones for image, video, and audio recognition.

    • Multimodal hyperscale representation

    • Novel neural architecture design (e.g., NAS).

    • Object recognition (e.g., classification, detection, segmentation, retrieval, etc.).

    • Lightweight and energy-efficient models (e.g., pruning, quantization, compression).

    • Learning with large-scale insufficient annotations (e.g., weakly- / self- / semi-supervised learning).

    • Novel learning algorithms for NNs (e.g., normalization, optimization, etc.).

  • Generative models for image, video, text, and audio

    • Uncoditional & conditional image generation

    • Image-to-image and vid-to-vid translation

    • Disentanglement and controllability

    • Cross-modal generation

    • Audio and music generation

    • Effective learning algorithm for generative models

    • Style transfer and super-resolution for images and videos

    • Neural render (NeRF) and super-resolution

  • Hyperscale language models and their extensions

    • Controllable LM, Hallucination

    • Prompt optimization

    • Multi-modal and Multi-lingual extension.

    • New evaluation metrics

    • Extension to dialogs, QA, summarization, content generation, etc.

  • Human computer interaction and interactive AI.

    • Accessibility

    • Computational Interaction

    • Computational Social Science and Social Computing

    • Data-driven Interface Design

    • Human Computation

    • Visualization

  • Representation learning for semi-structed or structured data.

    • Graph representation learning

    • Time-series prediction and representation learning

  • Trustworthy AI.

    • Explainable AI and causal inference.

    • Robust machine learning (adversarial robustness, domain generalization).

    • De-biased and fair machine learning.

    • Proper uncertainty estimation (e.g. prediction calibration, probabilistic machine learning).

    • Privacy-preserving AI (e.g. differential privacy, federated learning, etc.).

  • Audio recognition.

    • Big representation learning for automatic speech recognition (ASR).

    • Audio-visual speech recognition.

  • Healthcare AI

    • EMR/EHR based foundation models (large-scale pre-trained language models) for healthcare

    • Clinical predictive modeling with EMR/EHR (e.g., disease prediction, ICD code mapping)

    • Clinical decision support system

    • Medical image analysis for otorhinolaryngology & dentistry

    • Interpretability of AI models (XAI)

    • Causal inference in machine learning & intervention modeling for healthcare services

  • Other topics.

    • AI for social good.

    • Reinforcement learning in the wild.

• AI Research with External Collaboration

  • SNU-NAVER Hyperscale AI Center

    • Professors: Byung-Gon Chun, Gunhee Kim, Seungwon Hwang, Hyunoh Song, Byoung-Tak Zhang, Taesup Moon, Sang-Goo Lee, Kyomin Jung, Kyoung Mu Lee

    • Main topics (not limited)

      • Advanced hyperscale language models (multimodal, multi-lingual)

      • Reliable and efficient distributed training

      • Overcoming limitations of current hyperscale LMs (hallucination, prompt optimization, bias)

      • Advanced large-scale self-supervised learning

    • Some members will contribute as an adjunct professor of SNU.

  • KAIST-NAVER Hypercreative AI Center

    • Professors: Jaegul Choo, Jinwoo Shin, Sung Ju Hwang, Eunho Yang, Jae-Sik Choi, Juho Lee, Kee-Eung Kim, Alice Oh, Juho Kim, Edward Choi, Minjoon Seo

    • Main topics (not limited)

      • Multi- and cross-modal content generation

      • Generation controllability and quality measurement

      • Representation learning for content generation

    • Some members will contribute as an adjunct professor of KAIST.

  • Academic Advisors

    • Kyunghyun Cho (NYU): NLP, hyperscale LM

    • Andrew Zisserman (U. of Oxford): Speech recognition and audio-visual representation learning

    • Jun-Yan Zhu (CMU): Generative models

    • Jonghyun Choi (GIST): Continual and online learning

    • Joseph Lim (USC): Reinforcement learning in the wild

• Requirements

  • Research intern

    • Experience on research collaborations and paper writing in related fields.

    • Proficient programming skills in Python (PyTorch).

    • Preferred

      • Currently in an MS or PhD programme in CS, EE, mathematics or other related technical fields.

      • Proficient track record of publications at top-tier conferences in machine learning, computer vision, natural language processing, audio, hci, and speech.

• Hiring process:

  • Research intern:

    • Algorithm coding test > Paper implementation or tech talk > Job interview

Full publication list