Yingqiang Ge

Yingqiang Ge

Ph.D. in Machine Learning

Yingqiang Ge (葛英强)

About Me

I’m Yingqiang Ge, an Applied Scientist at Amazon. I got my Ph.D. from Rutgers University (2023, advised by Prof. Yongfeng Zhang), where I worked on Trustworthy AI, fairness, and explainability in recommender systems.

During my PhD, I published extensively on fairness-aware recommendation, causal reasoning, and explainable AI at top venues including SIGIR, WSDM, WWW, ACL, and NeurIPS. I also co-created OpenAGI, an open-source research platform for LLM-based agents, and contributed to P5, one of the early works on unifying recommendation as language processing.

My personal research interest is Agentic Safety — making sure autonomous AI agents act reliably and safely in the real world. As agents move from answering questions to taking actions — executing code, calling APIs, managing infrastructure — the safety challenges are fundamentally different from traditional AI. I build open-source systems like AIOS (an LLM agent operating system, published at COLM 2025) and GuardClaw (a real-time safety interception framework), exploring how to give AI agents more autonomy without losing human control.

关于我

我是葛英强，目前在Amazon担任应用科学家。2023年在Rutgers University获得博士学位（导师：张永锋教授），研究方向为可信AI、推荐系统的公平性与可解释性。

读博期间，我在SIGIR、WSDM、WWW、ACL、NeurIPS等顶会上发表了多篇关于公平推荐、因果推理和可解释AI的论文。我参与创建了OpenAGI（基于LLM的智能体开源研究平台），并参与了P5（将推荐统一为语言处理任务的早期工作之一）。

我个人的研究兴趣是智能体安全（Agentic Safety）——确保自主AI智能体在真实世界中可靠、安全地运行。当智能体从回答问题转向执行操作（运行代码、调用API、管理基础设施）时，安全挑战与传统AI截然不同。我构建了AIOS（LLM智能体操作系统，COLM 2025发表）和GuardClaw（实时安全拦截框架）等开源系统，探索如何在赋予AI智能体更多自主权的同时保持人类控制。

自己紹介

葛英強（Yingqiang Ge）と申します。現在Amazonで応用科学者として働いています。2023年にRutgers Universityで博士号を取得しました（指導教員：張永鋒教授）。研究分野は信頼性の高いAI、推薦システムにおける公平性と説明可能性です。

博士課程では、SIGIR、WSDM、WWW、ACL、NeurIPSなどのトップ会議で、公平性を考慮した推薦、因果推論、説明可能なAIに関する論文を多数発表しました。OpenAGI（LLMベースのエージェント研究プラットフォーム）の共同開発や、P5（推薦を言語処理として統合する初期の研究）にも貢献しました。

私の研究関心はエージェント安全性（Agentic Safety）です。自律型AIエージェントが現実世界で信頼性高く安全に動作することを目指しています。エージェントが質問への回答からコード実行、API呼び出し、インフラ管理へと移行する中で、安全性の課題は従来のAIとは根本的に異なります。AIOS（LLMエージェントOS、COLM 2025採択）やGuardClaw（リアルタイム安全インターセプトフレームワーク）などのオープンソースシステムを構築し、人間の制御を維持しながらAIエージェントにより多くの自律性を与える方法を探求しています。

소개

저는 거잉창(Yingqiang Ge)이며, 현재 Amazon에서 응용 과학자로 일하고 있습니다. 2023년 Rutgers University에서 박사 학위를 취득했습니다 (지도교수: 장융펑 교수). 연구 분야는 신뢰할 수 있는 AI, 추천 시스템의 공정성 및 설명 가능성입니다.

박사 과정 동안 SIGIR, WSDM, WWW, ACL, NeurIPS 등 주요 학회에서 공정성 기반 추천, 인과 추론, 설명 가능한 AI에 관한 다수의 논문을 발표했습니다. OpenAGI(LLM 기반 에이전트 연구 플랫폼)를 공동 개발하고, P5(추천을 언어 처리로 통합하는 초기 연구)에 기여했습니다.

저의 연구 관심사는 에이전트 안전성(Agentic Safety)입니다. 자율 AI 에이전트가 실제 환경에서 안정적이고 안전하게 작동하도록 하는 것을 목표로 합니다. 에이전트가 질문 응답에서 코드 실행, API 호출, 인프라 관리로 전환됨에 따라 안전 과제는 기존 AI와 근본적으로 다릅니다. AIOS(LLM 에이전트 운영체제, COLM 2025 채택)와 GuardClaw(실시간 안전 차단 프레임워크) 등의 오픈소스 시스템을 구축하여 인간의 통제를 유지하면서 AI 에이전트에 더 많은 자율성을 부여하는 방법을 연구하고 있습니다.

Sobre mi

Soy Yingqiang Ge, Cientifico Aplicado en Amazon. Obtuve mi doctorado en Rutgers University (2023, dirigido por el Prof. Yongfeng Zhang), donde investigue sobre IA confiable, equidad y explicabilidad en sistemas de recomendacion.

Durante mi doctorado, publique extensamente sobre recomendacion con equidad, razonamiento causal e IA explicable en conferencias de primer nivel como SIGIR, WSDM, WWW, ACL y NeurIPS. Tambien co-cree OpenAGI, una plataforma de investigacion de codigo abierto para agentes basados en LLM, y contribui a P5, uno de los primeros trabajos en unificar la recomendacion como procesamiento de lenguaje.

Mi interes de investigacion personal es la Seguridad Agentica (Agentic Safety) — asegurar que los agentes autonomos de IA actuen de manera confiable y segura en el mundo real. A medida que los agentes pasan de responder preguntas a ejecutar acciones — ejecutar codigo, llamar APIs, gestionar infraestructura — los desafios de seguridad son fundamentalmente diferentes de la IA tradicional. Construyo sistemas de codigo abierto como AIOS (un sistema operativo para agentes LLM, publicado en COLM 2025) y GuardClaw (un framework de intercepcion de seguridad en tiempo real), explorando como dar mas autonomia a los agentes de IA sin perder el control humano.

News

  • 2025-07-01 Our paper “AIOS: LLM Agent Operating System” has been accepted by COLM 2025.
  • 2025-03-01 Our survey “Causal Inference for Recommendation” has been accepted for publication in ACM TIST.
  • 2025-01-15 Our survey on Trustworthy Recommender Systems has been published in ACM TORS.
  • 2024-07-14 Our paper “IDGenRec: LLM-RecSys Alignment with Textual ID Learning” accepted by SIGIR 2024.
  • 2024-03-20 Our paper “GenRec: Large Language Model for Generative Recommendation” accepted by ECIR 2024.
  • 2024-02-19 Our survey on Trustworthy Recommender Systems has been accepted for publication in ACM TORS.
  • 2023-12-11 Started a new position as Applied Scientist at Amazon.
  • 2023-10-24 Received NeurIPS 2023 Scholar Award.
  • 2023-10-19 Gave a guest lecture for CS 483 (Big Data Mining), Department of Computer Science, UIC.
  • 2023-10-17 Successfully defended my PhD degree on the topic of “Towards Trustworthy Recommender Systems” and proudly hold the title of Dr.!
  • 2023-09-22 Our research framework for LLM-based Agents, OpenAGI accepted by NeurIPS’23, Datasets and Benchmarks.
  • 2023-08-04 Our research on “Logistics Audience Expansion” accepted by CIKM’23.
  • 2023-07-17 Our survey on “Fairness in Recommendation” accepted by ACM TIST’23.
  • 2023-07-15 Our research on “User-Controllable Recommendation” accepted by ECAI’23.
  • 2023-05-16 Gave a presentation “When LLMs Meet Domain Experts” at NEC Laboratories America.
  • 2022-12-02 Gave a presentation “Towards Fairer Recommender Systems through Deep Reinforcement Learning” at the Institute of Computing Technology, Chinese Academy of Sciences.
  • 2022-10-18 Our research on “Counterfactual Data Augmentation” accepted by WSDM’23.
  • 2022-06-28 Our research on “Federated Fairness” and “P5” accepted by RecSys’22.
  • 2022-06-09 Gave a talk on “Fairness in Recommender System” at Amazon.
  • 2022-05-24 Successfully passed my PhD Qualifying Exam!
  • 2022-04-26 Received SIGIR 2022 Student Travel Grant.
  • 2022-03-25 Our research on “Explainable Fairness” and “AutoLoss” accepted by SIGIR 2022.
  • 2022-03-09 Our research on “Fairness Evaluation” accepted for publication in JASIST.
  • 2022-02-24 Our research on “Explainable Recommendation through Visualization” accepted by ACL 2022.
  • 2022-01-13 Our research on “Explainable Recommendation over Knowledge Graphs”, “Explainable GNN” accepted by WWW 2022.
  • 2021-12-27 Received ACM WSDM’22 Student Travel Awards.
  • 2021-10-11 Our research on “Pareto Efficient Fairness-Utility Trade-off” accepted by WSDM 2022.
  • 2021-08-09 Our tutorial “Fairness of Machine Learning in Recommender Systems” accepted by CIKM 2021.
  • 2021-08-08 Our research on “Counterfactual Explainable Recommendation” accepted by CIKM 2021.
  • 2021-04-27 Our tutorial “Fairness of Machine Learning in Recommender Systems” accepted by SIGIR 2021.
  • 2021-04-14 Our research on “Personalized Fairness” accepted by SIGIR 2021.
  • 2021-01-17 Received SIGIR Student Travel Grant.
  • 2021-01-16 Papers on “Fairness”, “Generative Recommendation”, “Knowledge Graph Embedding” accepted by WWW 2021.
  • 2020-10-16 Our research on “Long-term Fairness” accepted by WSDM 2021.
  • 2020-08-10 Our research on “Neural Symbolic Reasoning for Explainable Recommendation” accepted by CIKM 2020.
  • 2020-06-20 Our research on “Risk-aware Recommendation”, “Fairness-aware Recommendation”, and “Echo Chamber Analysis” accepted by SIGIR 2020.
  • 2019-01-20 Our research on bridging machine learning and economic principles accepted by WWW 2019.

新闻动态

  • 2025-07-01 论文 “AIOS: LLM Agent Operating System” 被 COLM 2025 录用。
  • 2025-03-01 综述 “Causal Inference for Recommendation” 被 ACM TIST 录用。
  • 2025-01-15 可信推荐系统综述在 ACM TORS 发表。
  • 2024-07-14 论文 “IDGenRec: LLM-RecSys Alignment with Textual ID Learning” 被 SIGIR 2024 录用。
  • 2024-03-20 论文 “GenRec: Large Language Model for Generative Recommendation” 被 ECIR 2024 录用。
  • 2024-02-19 可信推荐系统综述被 ACM TORS 录用。
  • 2023-12-11 入职 Amazon，担任应用科学家。
  • 2023-10-24 获得 NeurIPS 2023 Scholar Award。
  • 2023-10-19 在 UIC 计算机系 CS 483（大数据挖掘）课程做客座讲座。
  • 2023-10-17 成功通过博士学位答辩，论文题目：”Towards Trustworthy Recommender Systems”。
  • 2023-09-22 基于LLM的智能体研究框架 OpenAGI 被 NeurIPS’23 Datasets and Benchmarks 录用。
  • 2023-08-04 论文 “Logistics Audience Expansion” 被 CIKM’23 录用。
  • 2023-07-17 综述 “Fairness in Recommendation” 被 ACM TIST’23 录用。
  • 2023-07-15 论文 “User-Controllable Recommendation” 被 ECAI’23 录用。
  • 2023-05-16 在 NEC Laboratories America 做报告 “When LLMs Meet Domain Experts”。
  • 2022-12-02 在中国科学院计算技术研究所做报告 “Towards Fairer Recommender Systems through Deep Reinforcement Learning”。
  • 2022-10-18 论文 “Counterfactual Data Augmentation” 被 WSDM’23 录用。
  • 2022-06-28 论文 “Federated Fairness” 和 “P5” 被 RecSys’22 录用。
  • 2022-06-09 在 Amazon 做报告 “Fairness in Recommender System”。
  • 2022-05-24 通过博士资格考试！
  • 2022-04-26 获得 SIGIR 2022 学生旅行资助。
  • 2022-03-25 论文 “Explainable Fairness” 和 “AutoLoss” 被 SIGIR 2022 录用。
  • 2022-03-09 论文 “Fairness Evaluation” 被 JASIST 录用。
  • 2022-02-24 论文 “Explainable Recommendation through Visualization” 被 ACL 2022 主会录用。
  • 2022-01-13 论文 “Explainable Recommendation over Knowledge Graphs”、”Explainable GNN” 被 WWW 2022 录用。
  • 2021-12-27 获得 ACM WSDM’22 学生旅行资助。
  • 2021-10-11 论文 “Pareto Efficient Fairness-Utility Trade-off” 被 WSDM 2022 录用。
  • 2021-08-09 教程 “Fairness of Machine Learning in Recommender Systems” 被 CIKM 2021 录用。
  • 2021-08-08 论文 “Counterfactual Explainable Recommendation” 被 CIKM 2021 录用。
  • 2021-04-27 教程 “Fairness of Machine Learning in Recommender Systems” 被 SIGIR 2021 录用。
  • 2021-04-14 论文 “Personalized Fairness” 被 SIGIR 2021 录用。
  • 2021-01-17 获得 SIGIR 学生旅行资助。
  • 2021-01-16 论文 “Fairness”、”Generative Recommendation”、”Knowledge Graph Embedding” 被 WWW 2021 录用。
  • 2020-10-16 论文 “Long-term Fairness” 被 WSDM 2021 录用。
  • 2020-08-10 论文 “Neural Symbolic Reasoning for Explainable Recommendation” 被 CIKM 2020 录用。
  • 2020-06-20 论文 “Risk-aware Recommendation”、”Fairness-aware Recommendation”、”Echo Chamber Analysis” 被 SIGIR 2020 录用。
  • 2019-01-20 关于机器学习与经济学原理结合的研究被 WWW 2019 录用。

ニュース

  • 2025-07-01 論文 “AIOS: LLM Agent Operating System” が COLM 2025 に採択。
  • 2025-03-01 サーベイ “Causal Inference for Recommendation” が ACM TIST に採択。
  • 2025-01-15 信頼できる推薦システムのサーベイが ACM TORS に掲載。
  • 2024-07-14 論文 “IDGenRec: LLM-RecSys Alignment with Textual ID Learning” が SIGIR 2024 に採択。
  • 2024-03-20 論文 “GenRec: Large Language Model for Generative Recommendation” が ECIR 2024 に採択。
  • 2024-02-19 信頼できる推薦システムのサーベイが ACM TORS に採択。
  • 2023-12-11 Amazon で応用科学者として新しいポジションに就任。
  • 2023-10-24 NeurIPS 2023 Scholar Award を受賞。
  • 2023-10-19 UIC コンピュータサイエンス学部 CS 483（ビッグデータマイニング）でゲスト講義。
  • 2023-10-17 博士学位を取得。論文テーマ：”Towards Trustworthy Recommender Systems”。
  • 2023-09-22 LLMベースエージェント研究フレームワーク OpenAGI が NeurIPS’23 Datasets and Benchmarks に採択。
  • 2023-08-04 論文 “Logistics Audience Expansion” が CIKM’23 に採択。
  • 2023-07-17 サーベイ “Fairness in Recommendation” が ACM TIST’23 に採択。
  • 2023-07-15 論文 “User-Controllable Recommendation” が ECAI’23 に採択。
  • 2023-05-16 NEC Laboratories America で “When LLMs Meet Domain Experts” を発表。
  • 2022-12-02 中国科学院計算技術研究所で “Towards Fairer Recommender Systems through Deep Reinforcement Learning” を発表。
  • 2022-10-18 論文 “Counterfactual Data Augmentation” が WSDM’23 に採択。
  • 2022-06-28 論文 “Federated Fairness” と “P5” が RecSys’22 に採択。
  • 2022-06-09 Amazon で “Fairness in Recommender System” を発表。
  • 2022-05-24 博士課程資格試験に合格！
  • 2022-04-26 SIGIR 2022 学生旅行助成金を受賞。
  • 2022-03-25 論文 “Explainable Fairness” と “AutoLoss” が SIGIR 2022 に採択。
  • 2022-03-09 論文 “Fairness Evaluation” が JASIST に採択。
  • 2022-02-24 論文 “Explainable Recommendation through Visualization” が ACL 2022 に採択。
  • 2022-01-13 論文 “Explainable Recommendation over Knowledge Graphs”、”Explainable GNN” が WWW 2022 に採択。
  • 2021-12-27 ACM WSDM’22 学生旅行助成金を受賞。
  • 2021-10-11 論文 “Pareto Efficient Fairness-Utility Trade-off” が WSDM 2022 に採択。
  • 2021-08-09 チュートリアル “Fairness of Machine Learning in Recommender Systems” が CIKM 2021 に採択。
  • 2021-08-08 論文 “Counterfactual Explainable Recommendation” が CIKM 2021 に採択。
  • 2021-04-27 チュートリアル “Fairness of Machine Learning in Recommender Systems” が SIGIR 2021 に採択。
  • 2021-04-14 論文 “Personalized Fairness” が SIGIR 2021 に採択。
  • 2021-01-17 SIGIR 学生旅行助成金を受賞。
  • 2021-01-16 論文 “Fairness”、”Generative Recommendation”、”Knowledge Graph Embedding” が WWW 2021 に採択。
  • 2020-10-16 論文 “Long-term Fairness” が WSDM 2021 に採択。
  • 2020-08-10 論文 “Neural Symbolic Reasoning for Explainable Recommendation” が CIKM 2020 に採択。
  • 2020-06-20 論文 “Risk-aware Recommendation”、”Fairness-aware Recommendation”、”Echo Chamber Analysis” が SIGIR 2020 に採択。
  • 2019-01-20 機械学習と経済学原理の融合に関する研究が WWW 2019 に採択。

소식

  • 2025-07-01 논문 “AIOS: LLM Agent Operating System”이 COLM 2025에 채택됨.
  • 2025-03-01 서베이 “Causal Inference for Recommendation”이 ACM TIST에 채택됨.
  • 2025-01-15 신뢰할 수 있는 추천 시스템 서베이가 ACM TORS에 게재됨.
  • 2024-07-14 논문 “IDGenRec: LLM-RecSys Alignment with Textual ID Learning”이 SIGIR 2024에 채택됨.
  • 2024-03-20 논문 “GenRec: Large Language Model for Generative Recommendation”이 ECIR 2024에 채택됨.
  • 2024-02-19 신뢰할 수 있는 추천 시스템 서베이가 ACM TORS에 채택됨.
  • 2023-12-11 Amazon에서 응용 과학자로 새 직책 시작.
  • 2023-10-24 NeurIPS 2023 Scholar Award 수상.
  • 2023-10-19 UIC 컴퓨터과학과 CS 483 (빅데이터 마이닝) 초청 강의.
  • 2023-10-17 박사 학위 논문 “Towards Trustworthy Recommender Systems” 심사 통과.
  • 2023-09-22 LLM 기반 에이전트 연구 프레임워크 OpenAGI가 NeurIPS’23 Datasets and Benchmarks에 채택됨.
  • 2023-08-04 논문 “Logistics Audience Expansion”이 CIKM’23에 채택됨.
  • 2023-07-17 서베이 “Fairness in Recommendation”이 ACM TIST’23에 채택됨.
  • 2023-07-15 논문 “User-Controllable Recommendation”이 ECAI’23에 채택됨.
  • 2023-05-16 NEC Laboratories America에서 “When LLMs Meet Domain Experts” 발표.
  • 2022-12-02 중국과학원 계산기술연구소에서 “Towards Fairer Recommender Systems through Deep Reinforcement Learning” 발표.
  • 2022-10-18 논문 “Counterfactual Data Augmentation”이 WSDM’23에 채택됨.
  • 2022-06-28 논문 “Federated Fairness”와 “P5”가 RecSys’22에 채택됨.
  • 2022-06-09 Amazon에서 “Fairness in Recommender System” 발표.
  • 2022-05-24 박사 자격시험 통과!
  • 2022-04-26 SIGIR 2022 학생 여행 보조금 수상.
  • 2022-03-25 논문 “Explainable Fairness”와 “AutoLoss”가 SIGIR 2022에 채택됨.
  • 2022-03-09 논문 “Fairness Evaluation”이 JASIST에 채택됨.
  • 2022-02-24 논문 “Explainable Recommendation through Visualization”이 ACL 2022에 채택됨.
  • 2022-01-13 논문 “Explainable Recommendation over Knowledge Graphs”, “Explainable GNN”이 WWW 2022에 채택됨.
  • 2021-12-27 ACM WSDM’22 학생 여행 보조금 수상.
  • 2021-10-11 논문 “Pareto Efficient Fairness-Utility Trade-off”가 WSDM 2022에 채택됨.
  • 2021-08-09 튜토리얼 “Fairness of Machine Learning in Recommender Systems”가 CIKM 2021에 채택됨.
  • 2021-08-08 논문 “Counterfactual Explainable Recommendation”이 CIKM 2021에 채택됨.
  • 2021-04-27 튜토리얼 “Fairness of Machine Learning in Recommender Systems”가 SIGIR 2021에 채택됨.
  • 2021-04-14 논문 “Personalized Fairness”가 SIGIR 2021에 채택됨.
  • 2021-01-17 SIGIR 학생 여행 보조금 수상.
  • 2021-01-16 논문 “Fairness”, “Generative Recommendation”, “Knowledge Graph Embedding”이 WWW 2021에 채택됨.
  • 2020-10-16 논문 “Long-term Fairness”가 WSDM 2021에 채택됨.
  • 2020-08-10 논문 “Neural Symbolic Reasoning for Explainable Recommendation”이 CIKM 2020에 채택됨.
  • 2020-06-20 논문 “Risk-aware Recommendation”, “Fairness-aware Recommendation”, “Echo Chamber Analysis”가 SIGIR 2020에 채택됨.
  • 2019-01-20 머신러닝과 경제학 원리 결합 연구가 WWW 2019에 채택됨.

Noticias

  • 2025-07-01 Nuestro articulo “AIOS: LLM Agent Operating System” ha sido aceptado en COLM 2025.
  • 2025-03-01 Nuestro survey “Causal Inference for Recommendation” ha sido aceptado en ACM TIST.
  • 2025-01-15 Nuestro survey sobre Trustworthy Recommender Systems ha sido publicado en ACM TORS.
  • 2024-07-14 Nuestro articulo “IDGenRec: LLM-RecSys Alignment with Textual ID Learning” aceptado en SIGIR 2024.
  • 2024-03-20 Nuestro articulo “GenRec: Large Language Model for Generative Recommendation” aceptado en ECIR 2024.
  • 2024-02-19 Nuestro survey sobre Trustworthy Recommender Systems aceptado en ACM TORS.
  • 2023-12-11 Comence una nueva posicion como Cientifico Aplicado en Amazon.
  • 2023-10-24 Recibi el NeurIPS 2023 Scholar Award.
  • 2023-10-19 Di una conferencia invitada en CS 483 (Big Data Mining), Departamento de Ciencias de la Computacion, UIC.
  • 2023-10-17 Defendi exitosamente mi tesis doctoral sobre “Towards Trustworthy Recommender Systems”.
  • 2023-09-22 Nuestro framework de investigacion para agentes basados en LLM, OpenAGI, aceptado en NeurIPS’23, Datasets and Benchmarks.
  • 2023-08-04 Nuestro articulo “Logistics Audience Expansion” aceptado en CIKM’23.
  • 2023-07-17 Nuestro survey “Fairness in Recommendation” aceptado en ACM TIST’23.
  • 2023-07-15 Nuestro articulo “User-Controllable Recommendation” aceptado en ECAI’23.
  • 2023-05-16 Presente “When LLMs Meet Domain Experts” en NEC Laboratories America.
  • 2022-12-02 Presente “Towards Fairer Recommender Systems through Deep Reinforcement Learning” en el Instituto de Tecnologia de Computacion, Academia China de Ciencias.
  • 2022-10-18 Nuestro articulo “Counterfactual Data Augmentation” aceptado en WSDM’23.
  • 2022-06-28 Nuestros articulos “Federated Fairness” y “P5” aceptados en RecSys’22.
  • 2022-06-09 Di una charla sobre “Fairness in Recommender System” en Amazon.
  • 2022-05-24 Aprobe exitosamente mi examen de calificacion doctoral.
  • 2022-04-26 Recibi la beca de viaje estudiantil SIGIR 2022.
  • 2022-03-25 Nuestros articulos “Explainable Fairness” y “AutoLoss” aceptados en SIGIR 2022.
  • 2022-03-09 Nuestro articulo “Fairness Evaluation” aceptado en JASIST.
  • 2022-02-24 Nuestro articulo “Explainable Recommendation through Visualization” aceptado en ACL 2022.
  • 2022-01-13 Nuestros articulos “Explainable Recommendation over Knowledge Graphs”, “Explainable GNN” aceptados en WWW 2022.
  • 2021-12-27 Recibi la beca de viaje estudiantil ACM WSDM’22.
  • 2021-10-11 Nuestro articulo “Pareto Efficient Fairness-Utility Trade-off” aceptado en WSDM 2022.
  • 2021-08-09 Nuestro tutorial “Fairness of Machine Learning in Recommender Systems” aceptado en CIKM 2021.
  • 2021-08-08 Nuestro articulo “Counterfactual Explainable Recommendation” aceptado en CIKM 2021.
  • 2021-04-27 Nuestro tutorial “Fairness of Machine Learning in Recommender Systems” aceptado en SIGIR 2021.
  • 2021-04-14 Nuestro articulo “Personalized Fairness” aceptado en SIGIR 2021.
  • 2021-01-17 Recibi la beca de viaje estudiantil SIGIR.
  • 2021-01-16 Articulos “Fairness”, “Generative Recommendation”, “Knowledge Graph Embedding” aceptados en WWW 2021.
  • 2020-10-16 Nuestro articulo “Long-term Fairness” aceptado en WSDM 2021.
  • 2020-08-10 Nuestro articulo “Neural Symbolic Reasoning for Explainable Recommendation” aceptado en CIKM 2020.
  • 2020-06-20 Nuestros articulos “Risk-aware Recommendation”, “Fairness-aware Recommendation” y “Echo Chamber Analysis” aceptados en SIGIR 2020.
  • 2019-01-20 Nuestra investigacion sobre la combinacion de machine learning y principios economicos aceptada en WWW 2019.