世界の主要大学の研究室を探索 — 研究分野と主要論文を一目で確認できます。
Professor Mugahed A. Al–antari's research lab specializes in advancing artificial intelligence and deep learning for medical image analysis, with a strong focus on computer-aided diagnosis (CAD) systems. The lab develops hybrid AI frameworks that integrate convolutional neural networks, vision transformers, and self-attention mechanisms to improve early detection of diseases such as breast cancer, pneumonia, and COVID-19. Their work emphasizes explainable AI, feature fusion, and transfer learning to enhance diagnostic accuracy and clinical usability. The lab also extends its expertise to agricultural AI, applying similar deep learning techniques to detect crop diseases like potato leaf infections.
Ken Shirasu教授の研究室は、植物の免疫応答とその調節機構に焦点を当てており、特にR遺伝子を介した病原体抵抗性の分子機構を解明しています。Sgt1やRAR1を核とするタンパク質複合体の機能、SCFユビキチンリガーゼ複合体やCOP9シグナルホスホリラーゼとの相互作用を通じて、免疫シグナルの統合的制御を解明しています。また、サリチル酸やMAPKカスケード、リトロトランスポゾンのゲノム進化との関連についても、植物の自己防衛とゲノム安定性の両面から研究を展開しています。
Yoko Masuda教授の研究室では、土壌中の微生物が果たす窒素固定や鉄還元などの生物地球化学的プロセスに注目し、特にデルタプロテオバクテリアに属するアナエロマイクソバクターとゲオバクターの窒素固定能とそのメタゲノム的・メタトランスクリプトーム的基盤を解明しています。paddy soil（水田土壌）という極限的条件下での微生物の生態的役割と、鉄酸化物の添加が窒素固定活性を促進するメカニズムについても実験的に解明しています。また、未培養菌の分類的再編と新種の同定を進め、土壌微生物の多様性と機能の理解を深めています。
Miho Yamauchi教授の研究室では、ナノ材料を用いたエネルギー変換・貯蔵技術の開発を主軸としています。特に、パルス・プラチナ系ナノ粒子や合金ナノ粒子を用いた水素貯蔵・触媒反応のメカニズム解明が進んでいます。金属酸化物や金属有機フレームワーク（MOF）を担体に用いることで、ナノ粒子の電子状態を制御し、効率的な触媒反応を実現する研究が特徴です。また、バイオマス由来原料を用いた持続可能なアミノ酸の電気合成技術の開発も進めています。
Professor Jinkwon Kim's research lab specializes in clinical and translational cardiovascular and cerebrovascular research, focusing on identifying novel biomarkers and hemodynamic parameters that predict outcomes in acute stroke and cardiovascular disease. The lab investigates the prognostic value of routine laboratory markers—such as red blood cell distribution width (RDW), brachial-ankle pulse wave velocity (baPWV), and serum alkaline phosphatase—and hemodynamic parameters like interarm blood pressure differences in predicting mortality, functional outcomes, and long-term prognosis after acute ischemic stroke. The lab also explores the impact of medical therapies, such as high-intensity statin use, on post-stroke outcomes, emphasizing the integration of clinical data with predictive modeling and robust algorithm development for risk stratification. Their work bridges basic biomarker science with real-world clinical application to improve patient outcomes in cerebrovascular and cardiovascular diseases.
Professor Hayeon Song's research lab focuses on the intersection of communication, health behavior, and emerging technologies, with a strong emphasis on how individual differences and contextual factors influence information processing and user experiences. The lab investigates health communication across diverse populations, particularly low-income and pregnant women, while exploring the role of technology—such as virtual reality, exergames, and online education—in shaping perceptions, social presence, and behavioral outcomes. A central theme is understanding how psychological factors like self-awareness, personality traits, and social connection affect engagement with digital health and media content.
Professor C. Michael Hall's research lab specializes in tourism planning, sustainability, and the socio-economic impacts of global events such as pandemics and hallmark events. His work explores the intersection of tourism policy, sustainable development goals (SDGs), and managerial ecologies in shaping tourism systems. The lab focuses on destination resilience, wine tourism, and the strategic planning of tourism at local, national, and international levels, emphasizing sustainability and adaptive governance.
Yuto Ashida教授の研究室は、非エルミート量子系と多体相関の融合を軸に、非平衡量子多体系の新規な臨界現象や量子制御を探究しています。特に、PT対称性や量子測定の効果がもたらす非エルミート的臨界性、光物質結合系における量子相転移の制御、ならびに量子ガス顕微鏡を用いた個別粒子解像度の動的観測を通じた非平衡多体現象の解明が主な研究テーマです。強相関系における非エルミート的効果と量子臨界性の結びつきから、従来にない普遍性クラスの発見を目指しています。
宮之瀬義和教授の研究室は、地球磁圏における高エネルギー粒子の加速・輸送・消失メカニズムを、衛星観測と地上観測を統合した多角的アプローチで解明しています。特に、EMIC波やキロサイクロトロン波といった高周波プラズマ波と粒子の相互作用が、相対論的電子や数十keV級イオンの宇宙空間からの消失に与える影響を、実測データとシミュレーションを用いて解明しています。また、地球磁気圏の嵐（ジオスターム）におけるエネルギー供給源や粒子分布の時間変化の解明にも貢献しています。
Professor Hyanghee Park's research lab focuses on the human-centered design and ethical implications of emerging technologies, particularly in sensitive domains such as mental health, workplace equity, and education. The lab investigates how artificial intelligence, conversational agents, and immersive environments like the metaverse impact human behavior, perception, and well-being across diverse stakeholders. Key research directions include understanding psychological burdens in human-AI interactions, designing inclusive and trustworthy AI systems for high-stakes contexts, and exploring alternative work and support models in post-pandemic and digital societies.
Professor Jae-Hyeung Park's research lab specializes in advanced 3D imaging and display technologies, with a focus on holography, integral imaging, and near-eye displays for augmented and virtual reality. The lab develops innovative optical systems that enable autostereoscopic 3D visualization, dynamic depth-of-field control, and optical see-through functionality, emphasizing compact, lightweight, and immersive display solutions. Key research directions include computer-generated holography, electrically tunable 3D/2D convertible displays, and holographic optical elements for next-generation wearable devices.
Professor Hyunwoo Kim's research lab specializes in the development of innovative transition metal-catalyzed C–H bond functionalization methodologies, with a strong focus on direct and selective C–H amination using ammonia and other nitrogen sources. The lab pioneers electrophotocatalytic strategies and novel catalytic systems—particularly based on iridium and copper—enabling challenging transformations such as aryl radical generation, biaryl coupling, and enantioselective synthesis under mild conditions. Key advances include the design of unique catalytic cycles involving iridacycle intermediates and diimine pathways, as well as mechanistic insights into low-valent copper species for C–N bond formation. The lab’s work bridges fundamental mechanism with practical applications in medicinal chemistry and materials science.
Professor Sung-Bae Cho's research lab specializes in intelligent data analysis and machine learning with a strong focus on bioinformatics, biomedical informatics, and cybersecurity. The lab develops advanced computational methods for gene expression analysis, cancer classification, and intrusion detection systems, integrating techniques such as neural networks, fuzzy logic, hidden Markov models, and feature selection algorithms. A central theme across the research is the fusion of soft computing and machine learning to handle uncertainty, noise, and complexity in real-world biological and security data. The lab also contributes to document image analysis, particularly in the structural understanding of technical publications.
Professor Jae-Sung Woo's research lab specializes in structural biology, molecular pharmacology, and translational biotechnology, with a focus on understanding the molecular mechanisms of ion channel function, RNA editing, antiviral drug discovery, and post-transcriptional gene regulation. The lab integrates cryo-electron microscopy, high-throughput screening, and systems-level omics approaches to study membrane protein dynamics, including connexin hemichannels and ribosome-nascent chain targeting, as well as develop novel biosensors and protein purification platforms. A central theme is the development of precision tools and therapeutic strategies targeting RNA metabolism, viral enzymes, and steroid receptor signaling pathways.
Professor Kuen Yong Lee's research lab specializes in the design and development of smart hydrogels for biomedical applications, with a focus on tissue engineering and regenerative medicine. The lab investigates the synthesis and functionalization of biopolymers such as alginate and chitosan to create injectable, degradable, and mechanically tunable hydrogels that support cell adhesion, proliferation, and differentiation. A key research direction involves decoupling nanoscale ligand presentation from bulk density to precisely control cellular responses, particularly in bone regeneration. The lab also explores the use of chemical cross-linking strategies to independently tune mechanical properties and degradation kinetics in hydrogel systems.
Sasaki教授の研究室は、特に公衆衛生とメンタルヘルス分野に焦点を当てており、COVID-19パンデミック下における職場の対策が従業員の精神的健康に与える影響を解明しています。医療従事者と非医療従事者の精神的健康の変化を縦断的に比較する研究を通じて、ストレス要因や支援の必要性を明らかにしてきました。また、デジタルメンタルヘルスの実装やメディア露出が精神的健康に与える影響についても、実証的・行動的知見を重視した研究を展開しています。
フロリアノ・アミモ教授の研究室は、アフリカをはじめとする発展途上地域における感染症の予防・制御に焦点を当てた公衆衛生研究を推進しています。特に、COVID-19がHIV、結核、マラリアといったエピデミック疾患の制御に与える影響を、疫学的・制度的視点から分析しています。また、医療システムの弱体化や薬剤耐性の拡大といった構造的リスクに対処するための持続可能な政策戦略の構築にも注力しています。
Hayashi教授の研究室は、情報理論と量子情報の交差分野に焦点を当てており、特に情報スペクトル法を用いた非漸近的通信理論の確立を主眼としています。チャネル容量、誤り確率の指数的評価、量子状態の識別、および量子計算のセキュアな実装に関する基礎的で実用的意義の高い研究が進められています。特に、従来の漸近的解析ではカバーできない限界的状況における通信性能の評価が特徴です。
Professor Jeong Min Lee's research lab specializes in biomedical imaging and advanced medical device development, with a focus on improving diagnostic accuracy and therapeutic outcomes in oncology and chronic disease management. The lab investigates innovative imaging techniques such as dynamic 3D-GRE MRI and MR elastography (MRE) for evaluating pancreatic cancer and liver fibrosis, emphasizing non-invasive, high-reproducibility methods. Additionally, the lab explores hybrid energy systems, particularly fuel cell-based power conditioning for clean energy applications, reflecting a multidisciplinary approach bridging medical imaging and sustainable energy technology.
Professor Sungmin Kang's research lab specializes in advancing automated software engineering techniques, with a strong focus on test generation, fault localization, and automated debugging. The lab leverages large language models (LLMs) and AI-driven approaches to bridge the gap between natural language bug reports and executable test cases, enabling more effective and explainable software testing. Key research directions include semantic test generation, reproducible debugging through LLM reasoning, and efficient search over plausible input spaces for deep neural network testing.
