# NAVER AI LAB

<mark style="color:green;">**Introducing NAVER AI LAB**</mark>**&#x20;&#x20;**&#x20;[**Click!**](https://www.youtube.com/watch?v=pDXxvBZ-5tQ)&#x20;

<mark style="color:green;">**Meet Research Scientists at NAVER AI LAB**</mark> <mark style="color:green;"></mark>   [**Click!**](https://www.youtube.com/watch?v=3-y9mADv8J8) &#x20;

**\[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**&#x20;
    * 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**&#x20;
    * Controllable LM, Hallucination&#x20;
    * Prompt optimization&#x20;
    * 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**&#x20;
    * **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&#x20;
      * Overcoming limitations of current hyperscale LMs (hallucination, prompt optimization, bias)
      * Advanced large-scale self-supervised learning&#x20;
    * **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**](https://kyunghyuncho.me/) **(NYU): NLP, hyperscale LM**
    * [**Andrew Zisserman**](https://scholar.google.com/citations?hl=ko\&user=UZ5wscMAAAAJ) **(U. of Oxford): Speech recognition and audio-visual representation learning**
    * [**Jun-Yan Zhu**](https://www.cs.cmu.edu/~junyanz/) **(CMU): Generative models**
    * [**Jonghyun Choi** ](https://ppolon.github.io/)**(GIST): Continual and online learning**
    * [**Joseph Lim**](https://viterbi-web.usc.edu/~limjj/) **(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).**&#x20;
    * **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:**&#x20;
  * **Research intern:**&#x20;
    * Algorithm coding test > Paper implementation or tech talk > Job interview

[Full publication list](https://naver-career.gitbook.io/en/publications/all)&#x20;