Pan Yushan (IEEE Member, 2022; IEEE Senior Member, 2024) was born in Xi'an, China. He studied and worked in Norway for twelve years. Influenced by the Nordic higher education system, he grew into an outstanding researcher and educator after completing his PhD and his habilitation. In 2022, he returned to China and joined Xi'an Jiaotong-Liverpool University, where he works on interdisciplinary research in affective computing and multimodal perception.

Prior to joining XJTLU, he worked as a Senior Researcher at the Norwegian University of Science and Technology (NTNU) and the Norwegian Maritime Competence Center (2019–2021), as a Postdoctoral Researcher at NTNU (2018–2019), and earned his PhD from the University of Oslo (2013–2017). His PhD research group, which pioneered object-oriented programming and Nordic system development, was the academic home of Turing Award laureates Prof. Ole-Johan Dahl and Prof. Kristen Nygaard. He also worked as a Technical Consultant at Parametric Technology Corporation (PTC) (2011–2013).

He is currently a member of the Chinese Association for Artificial Intelligence (CAAI), an IEEE Senior Member, an ACM member, a member of the China Computer Federation (CCF), and a lifelong member of the Chinese Association of Automation (CAA).

His research focuses on affective computing, multimodal emotion recognition, and psychological state modeling, with an emphasis on emotion perception and emotional dynamics in extreme scenarios. Targeting the domains of sports health, geriatric care, and other applications, he aims to build generalizable and deployable affective intelligence algorithms to support the mental health and psychosomatic wellbeing of special populations.

 潘昱杉（IEEE 会员，2022；IEEE 高级会员，2024），生于西安，留学并工作于挪威十二年。受北欧高等教育体系熏陶，他在完成博士与任教资格论文后成长为优秀科研与教育工作者。2022年回国加入西交利物浦大学，深耕民事与军事医学领域，专注开展情感计算与多模态感知交叉方向的科研工作。此前，他于2019–2021年任挪威科技大学及挪威海事中心高级研究员，2018–2019年从事博士后研究，2013–2017年在奥斯陆大学攻读博士（所在团队为图灵奖得主Nygaard与Dahl的学术归属，开创面向对象程序设计与北欧系统开发先河）。2011–2013年任美国参数技术公司技术顾问。

现任中国人工智能学会会员、IEEE高级会员、ACM会员、中国计算机学会会员及中国自动化学会终身会员。

研究方向为情感计算、多模态情绪识别与心理状态建模，聚焦极端场景下的情绪感知与情绪动力学。面向运动健康、医疗康养及作战等其他领域，研究可落地情感智能算法体系，支撑特殊人群心理健康与身心养护。工作构建了坚实的军民医工交叉研究基础。

Hans forskning fokuserer på affektiv databehandling, multimodal følelsesgjenkjenning og modellering av psykologiske tilstander, med vekt på følelsespersepsjon og følelsesdynamikk i ekstreme situasjoner. Målet er å utvikle generaliserbare og implementerbare affektive intelligensalgoritmer for domener som idrettshese, eldreomsorg og andre anvendelser, for å støtte psykisk helse og psykosomatisk velvære hos spesielle befolkningsgrupper.   

Ettersom flere nordiske studenter kommer til Kina for utveksling og gradsstudier, tar jeg gjerne imot masterstudenter og doktorgradsstudenter til forskningssamarbeid.

Dr. Pan's research is centered on affective computing and multimodal perception, guided by three core missions:

Mission 1: Equip machines with perceptual and cognitive capabilities to read, reason, and respond like empathic partners.

• Enable robots to perceive, express, and regulate emotions, adding an affective layer to every human–robot interaction
• Anchor AI decisions in validated cognitive models so that system logic mirrors human thought
• Co-create control loops that keep humans optimally "in the loop"
• Application examples:

• • Sports health domain: Investigate the impact of emotional fluctuations on athletic performance, exercise fatigue, and physical-mental recovery to enable intelligent regulation of sports psychology
  • Medical scenarios: Introduce empathic-cognitive capabilities into surgery-grade human-robot interaction, achieving collaborative operation with millimeter precision and millisecond feedback
  • Geriatric care domain: Focus on emotion recognition, psychological depression screening, and intelligent physical-mental intervention for the elderly population

• • Other domains: Target high-intensity field training, outdoor duty, emergency response and other real-world scenarios to build accurate psychological state assessment models for active-duty personnel, enabling dynamic mental monitoring and graded psychological load evaluation

Mission 2: Guarantee that every agent-to-agent and human-to-agent loop is trustworthy, information-rich, and fast to retrieve.

• Liveness detection: Verify the authenticity of the interacting subject in real time, blocking spoofing attacks at the gate
• Adaptive information retrieval: Align user intent with content in sub-second latency, acting as the system's information concierge
• Hyperspectral imaging: Capture spectral signatures beyond the visible spectrum to harden biometric security and enable pixel-level lesion mapping
• Application examples:

• • Medical domain: Use hyperspectral imaging for pixel-level spectral analysis of skin lesions to assist early cancer screening; combined with multimodal emotion perception, enable dynamic pre- and post-operative psychological state monitoring
  • Geriatric care domain: For elderly-oriented human-robot companion systems, rapidly respond to elderly users' intentions (e.g., medication reminders, emotional conversation) through adaptive information retrieval, improving interaction fluency and age-friendly experience
  • Other domains: Introduce liveness detection into identity authentication and remote psychological assessment of active-duty personnel to prevent biometric spoofing and ensure trustworthy command-link interactions

Mission 3: Turn algorithms into dependable, real-time, source-aware system capabilities.

• Embedded software engineering: Develop safety-critical firmware for robotics, biometric terminals, and spectral imagers that meet hard real-time deadlines
• Cross-Pillar hardware/software co-design: Deliver the drivers, middleware, and compute substrates that let Missions 1 and 2 run flawlessly under strict power, size, and safety constraints
• Application examples:

• • Sports health domain: For sports fatigue monitoring terminals, hardware-software co-design ensures real-time synchronization and processing of multimodal sensor data (heart rate, galvanic skin response, infrared), supporting real-time psychological regulation decisions in competition settings
  • Medical scenarios: Provide safety-certified middleware for surgical assistance systems, ensuring medical-grade control under strict power and size constraints
  • Geriatric care scenarios: Provide embedded firmware support for elderly companion robots, ensuring real-time response and low-power endurance for affective interaction
  • Other domains: Develop low-power, hard real-time embedded firmware for wearable psychological monitoring devices for active-duty personnel, enabling edge computing-based emotional state assessment and graded early warning in field environments without network access

潘昱杉博士的研究以情感计算与多模态感知为核心，包含三大使命/Dr. Pan Yushans forskning har affektiv databehandling og multimodal persepsjon som kjerne, og omfatter tre hovedoppdrag:

使命一：赋予机器感知与认知能力，使其能够像共情伙伴一样理解、推理与响应。/Oppdrag én: Gi maskiner evnen til persepsjon og kognisjon, slik at de kan forstå, resonnere og reagere som empatiske samarbeidspartnere.

• 使机器人具备情感感知、表达与调节能力，为每一次人机交互构建情感交互层
• 基于经验证的认知模型支撑 AI 决策，使系统逻辑贴合人类思维方式
• 构建协同控制闭环，确保人类处于最优“人在回路”状态
• 应用示例：

• • 运动健康领域：探究情绪波动对竞技状态、运动疲劳及身心恢复的影响机制，实现运动心理智能调控
  • 医疗场景：将共情-认知能力引入手术级人机交互，在毫米级精度与毫秒级反馈中实现协同操作
  • 康养领域：聚焦老年群体情绪识别、心理抑郁筛查及智能化身心康养干预
  • 其他领域：面向高强度驻训、野外执勤、应急处置等实战化场景，构建现役人员心理状态精准评估模型，实现心智动态监测与心理负荷分级研判

使命二：确保智能体间、人机间的每一次交互都具备可信性、信息丰富性与快速检索能力。/Oppdrag to: Sikre at all interaksjon mellom intelligente agenter og mellom menneske og maskin er troverdig, informasjonsrik og gir rask tilgang til data.

• 活体检测：实时验证交互对象的真实性，从源头阻断欺骗攻击
• 自适应信息检索：亚秒级匹配用户意图与目标内容，充当系统的智能信息管家
• 高光谱成像：捕捉可见光之外的光谱特征，增强生物识别安全性，并实现病灶像素级定位
• 应用示例：

• • 医疗领域：利用高光谱成像对皮肤病灶进行像素级光谱分析，辅助早期癌变筛查；同时结合多模态情绪感知，实现术前术后心理状态动态监测
  • 康养领域：面向老年群体的人机陪伴系统，通过自适应信息检索快速响应老人意图（如用药提醒、情感倾诉），提升交互流畅性与适老化体验
  • 其他领域：在现役人员身份认证与远程心理评估中引入活体检测，防止生物特征伪造，保障指挥链路交互可信

使命三：将算法打造为可靠、实时、可溯源的系统能力。/Oppdrag tre: Utvikle algoritmer til pålitelige, sanntidsbaserte og sporbare systemfunksjoner.

• 嵌入式软件工程：面向机器人、生物识别终端与光谱成像设备，研发满足强实时性要求的安全关键固件
• 跨维度软硬件协同设计：开发驱动程序、中间件与计算支撑平台，使前两大使命在严格的功耗、体积与安全约束下稳定可靠运行
• 应用示例：

• • 运动健康领域：面向运动疲劳监测终端，软硬件协同设计保障多模态传感器（心率、皮电、红外）数据同步与实时处理，支撑赛场即时心理调控决策
  • 医疗场景：为手术辅助系统提供符合安全认证的中间件，确保医疗级控制在严格功耗与体积约束下稳定运行
  • 康养场景：为老年陪伴机器人提供嵌入式固件支持，保障情感交互的实时响应与低功耗续航
  • 其他领域：为现役人员可穿戴心理监测设备研发低功耗、强实时的嵌入式固件，在野外无网条件下实现情绪状态边缘计算与分级预警

• XJTLU University Academic Board Member/西交利物浦大学学术委员会委员, 2025-
• XJTLU PHCI Programme Director/西交利物浦大学人机交互（PHCI）项目主任, 2025-
• Xi'an ACM Chapter, Vice Chair/ACM 西安分会 副主席, 2025 - 2027
• ACM-W Suzhou, Member, 2025-2027
• Xi'an ACM Chapter, Treasurer/ACM 西安分会 财务主管, 2024-2025
• XJTLU SAT School Work Placement Officer/西交利物浦大学智能工程学院实习就业主管, 2022-2025
• ACM CARES Steering Committee, Committee Member/ACM 全球学术关怀指导委员会（CARES）委员, 2022-
• Lifetime Member, Chinese Association of Automation/中国自动化学会 终身会员
• Senior Member, IEEE, ACM, China Computer Federation/IEEE 高级会员、ACM 高级会员、中国计算机学会高级会员
• Member, Association for the Advancement of Affective Computing, The Institution of Engineering and Technology, Chinese Society for Stereology, China Ordnance Society, Chinese Association for Artificial Intelligence, Chinese Information Processing Society of China/英国工程技术学会（IET）会员、情感计算促进会会员、中国体视学学会会员、中国兵工学会会员、中国人工智能学会会员、中国中文信息学会会员
• Assistant Professor, Xian Jiaotong-Liverpool University/西交利物浦大学 助理教授, 2022-2025
• Senior Researcher, Norwegian Maritime Competence Center, Norges teknisk-naturvitenskapelige universitet/挪威科技大学 挪威海事能力中心 高级研究员, 2020-2021
• Postdoc, Norges teknisk-naturvitenskapelige universitet/挪威科技大学 博士后, 2017-2019
• Research Fellow, Universitetet i Oslo奥斯陆大学 博士研究员, 2013-2017
• Consultant(PLM), Parametric Technology Corporation/美国参数技术公司（PTC）产品生命周期管理（PLM）顾问, 2012-1013
• Associate Consultant(PLM), Parametric Technology Corporation, 2009-2010

• First Prize in Natural Science, Suzhou Association for Artificial Intelligence, Suzhou Science and Technology Association(Municipal-level), 2026/苏州市人工智能自然科学奖一等奖、苏州市人工智能学会，2026。
• Winning Prize in the 18th China Chengdu International Software Design and Application Competition, jointly issued by Chengdu Municipal People's Government, Sichuan Provincial Department of Education and Sichuan Provincial Department of Economy and Information Technology, 2024/第18届中国国际软件设计与应用大赛优胜奖，成都市人民政府/四川省经济和信息化厅/四川省教育厅，2024年。
• Double Innovation Doctor under Jiangsu Provincial Talent Program, JSSCBS20230474, Talent Office of CPC Jiangsu Provincial Committee, 2024/江苏省人才计划双创博士，JSSCBS20230474, 江苏省委人才办公室, 2024年
• Best SURF supervisor, XJTLU, 2024.
• Excellence Award in the 1st Air Force Creative Challenge, Awarded by the Chinese People's Liberation Army Air Force, 2023/首届空军创意挑战赛，优秀奖，授予单位：中国人民解放军空军，2023年
• The Eighth China International "Internet Plus" University Student Innovation and Entrepreneurship Competition, Bronze Award, Ministry of Education/第八届中国国际“互联网+”大学生创新创业大学，铜奖，授予单位：教育部， 2023年
• Best paper award. The 2023 IEEE International Conference on Cyber, Physical and Social Computing. (CCF B)，2023年
  
• Regonised Associate Chair. ACM CHI conference 2023 (CCF A)，2023年
• Regonised reviewer. Ocean Engineering (SCI Q1)，2022年
• Best paper award. The 18th ACM SIGGraph International Conference on Virutal-Reality and Its Applications in Industry. 2022年
  
• Chinese Government Award for Outstanding Students Abroad, China Scholarship Council (CSC) ID: ZF2016045003/2016 年度国家优秀自费留学生奖学金 教育部国家留学基金委编号 ID: ZF2016045003
• 2013. PhD Fellowship, The Research Council of Norway/挪威研究理事会, ca. 2.56M RMB（约2560000人民币）

Joining us:

We seek to recurit highly qualified individuals from China and from around the world. We offer studentships. I will be happy to help you explore topics and programs that would suit your background and aspirations. 

Ph.D. Students - You should have an excellent academic record, a Master's degree from a recognized program, and deep interest and commitment in pursuing research. Writing skills are important. 

Summer Studentships - I typically have openings for several summer positions in research projects for senior undergrads. You should have high academic standing. This is an excellent opportunity for learning about the research environment and graduate school while being gainfully employed. Masters students interested in contributing to our research projects are also welcome. Please send me your resume by email to register your interest. Having some of the following as background would be helpful but not essential:

• programming experience
• real-world work experience
• mulitimodal learning and representation
• machine learning, data science
• AI, knowledge-based systems, knowledge representation and reasoning, AI programming
• software engineering work experience
• cognitive science, neuroscience, and cognitive psychology

However, ethusiasm, self-motivation, and dedication are essential :-)

Current Postgraduates and Undergraduates (UoL):

• Y3 PhD student, 1(Primary)
• Y2 PhD, 2(Primary)
• Y1 PhD, 2(Primary), 2(Co)
• 5 Ongoing BS students(Computer Science, UoL, UK)

Postgraduates and Undergraduates as co-supervisor/external supervisor:

• 5 PhD students (Osaka, GeorgiaTech, Xidian University and Xi'an Jiaotong University), 4 MS students (XJTU, Xidian, ZZULI), 3 BS students (SJTU)

Completed PhD, MS and BS students:

• 1 Completed PhD(co-advisor, NTNU), 1 completed MS student (NTNU, Norway), 25 completed MS students(UoL, UK), and 13 completed BS students(UoL, UK).

Current teaching:

• CPT412 Human-Robot Interaction; PG
• CPT105 Introduction to Programming in Java; UG, ca. 900 students per academic year
• SAT306 Final Year Project; UG
• SAT405 MRes Final Year Project; PG
• SAT406 MSc Final Year Project; PG

Taught:

• CPT302 Multiagent Systems; UG