Rationale-Guided Machine Learning Professors and Supervisors

Sean Renمشاهده پروفایل

دانشیار

University of Southern CaliforniaUnited States

Sean Ren is an Associate Professor in Computer Science at the University of Southern California, where he holds the Andrew and Erna Viterbi Early Career Chair. He directs the INK Research Lab and serves as Research Team Leader at USC's Information Sciences Institute. Affiliated with the USC NLP Group and Machine Learning Center, his research focuses on developing robust NLP systems through knowledge-aware architectures and data-efficient learning. His research interests include: Evaluation methods exposing NLP limitations in reasoning tasks Augmenting models with commonsense/knowledge via novel algorithms Graph neural networks for relational inference Model robustness verification and enhancement Neural-symbolic integration for interpretable AI Recent publications demonstrate strong emphases on language model reasoning, knowledge distillation, and compositional generalization. His group's ACL/NeurIPS papers frequently address robustness gaps in state-of-the-art models. Honors include: ACL Outstanding Paper (2023) MIT TR Innovator 35 Asia Pacific (2023) NSF CAREER Award (2021) Forbes 30 Under 30 (2019) ACM SIGKDD Dissertation Award (2018) Research is supported by NSF, DARPA, IARPA, and industry partners (Google, Amazon, Meta). He leads the INK Lab with focuses on label-efficient learning and knowledge-guided NLP, while actively recruiting PhD students for projects bridging symbolic and neural paradigms.

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George Vosselmanمشاهده پروفایل

استاد

University of TwenteNetherlands

George Vosselman is a Full Professor at the University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), specializing in Geo-Information Extraction with Sensor Systems. Educated with honours at Delft University of Technology (1986) and PhD in Photogrammetry from Rheinische Friedrich Wilhelms University of Bonn (1991), he has held academic roles at the University of Stuttgart, University of Washington, and Delft University of Technology (1993–2004). Since 2004, he has been a key figure at ITC, serving as department head (2012–2018, 2023–). Education: Delft University of Technology (BSc with honours, 1986), Rheinische Friedrich Wilhelms University of Bonn (PhD with honours, 1991) His research focuses on leveraging sensor technology advancements for large-scale geo-information production. Key expertise includes quality analysis of laser altimetry data, point cloud segmentation/classification, 3D building/road modeling, and model-driven imagery analysis. He has published over 220 papers and co-edited the textbook Airborne and Terrestrial Laser Scanning (2010). Recent work integrates deep learning with geospatial data, addressing semantic segmentation, visual question answering, and drone-based mapping. Recent publications (2025–2023) highlight trends in deep learning for remote sensing , including multimodal question answering benchmarks (HRVQA), vectorized building extraction (RoIPoly), latent diffusion for road modeling (LDPoly), and drone obstacle avoidance systems. His work bridges photogrammetry , computer vision , and robotic mapping , with applications in urban planning, disaster management, and informal settlement monitoring. Scientific Awards : Hansa Luftbild (1993), ISPRS Otto von Gruber (2000), Schwidefsky Medal (2012), Karl Kraus Medal (2012), ASPRS Fairchild Award (2015), ISPRS Fellow (2020) As an educator, Vosselman has taught photogrammetry, remote sensing, and laser scanning at Delft University of Technology and globally. He chaired the ITC Examination Board (2015–2023) and modernized geo-information education in Asia/Africa. His software for point cloud processing is commercialized in Europe, and he currently leads ISPRS working groups on point cloud methodologies. Labs/teams include the Earth Observation Science Chair Group at ITC, collaborating on UAV-based datasets (UAVid, UAVPal) and indoor laser scanning systems. Recent activities (2025) involve invited talks on pulse matching limitations in laser scanning and deep learning for point cloud classification.

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Lorraine Liمشاهده پروفایل

استادیار

University of PittsburghUnited States

Lorraine Li is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh, affiliated with the Interdisciplinary Science Program (ISP) and the School of Computing and Information. She holds a PhD from the University of Massachusetts Amherst (2022) and conducted postdoctoral research at AI2's Mosaic team. Her work focuses on NLP, machine learning, and socially responsible AI systems. Education: PhD in Computer Science (UMass Amherst, 2022) Research explores evaluation frameworks for commonsense knowledge, model interpretability, and ethical AI applications in domains like education and law. Key interests include probabilistic models, long-tail reasoning, and geographic robustness in LLMs. Recent publications address confirmation bias in reasoning chains (ACL 2025), geographically diverse prompting (CVPR 2024), and uncommon scenario reasoning (NAACL 2024). She co-organized the AAAI 2024 Make symposium and serves on committees for ACL, EMNLP, and NAACL. Grants: Pitt Cyber funding (2024) Lab: Pitt NLP Seminar group

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Ryan Ruvaldمشاهده پروفایل

پژوهشگر ارشد

Blekinge Institute of TechnologySweden

Ryan Ruvald is a Postdoctoral Fellow at Blekinge Institute of Technology (BTH) in the Department of Mechanical Engineering, Karlskrona, Sweden. His academic journey began with a Bachelor of Science in Mechanical Engineering from the California Maritime Academy, where he gained extensive hands-on training on ships and power plants. Prior to academia, he worked in the solar industry building utility-scale solar power plants for First Solar in Phoenix, Arizona, and served in commercial shipping on container ships, tankers, and deep-sea drillships. Bachelor of Science in Mechanical Engineering, California Maritime Academy Master of Science in Mechanical Engineering with emphasis on Sustainable Product Service System Innovation (MSPI program), Blekinge Institute of Technology Ruvald's research centers on product development, with particular focus on developing methods to generate data for data-driven design in scenarios where knowledge about both machine and environment is sparse or non-existent. His current research focuses on addressing the human element in autonomous system design through experiential prototyping platforms developed in partnership with Volvo CE. These platforms simulate user scenarios to guide empathic design of systems and human-machine interactions. His work spans across Knowledge Management, Design Decision Rationale, Artificial Intelligence, Augmented Reality, and Internet of Things, with strong emphasis on Product-Service Systems (PSS) innovation. His publication record reveals a consistent trajectory in PSS design methodologies, with increasing integration of AI and autonomous systems in recent years. Early work focused on prototyping techniques and PSS implementation in construction equipment, while his most recent publications explore AI-driven approaches to predictive maintenance, autonomous equipment behavior comprehension, and data mining through experience prototyping. This evolution demonstrates a clear progression from traditional product-service system design toward intelligent, data-driven approaches that incorporate human factors in autonomous systems. Ruvald collaborates extensively with industry partners, most notably Volvo CE, to develop experiential prototyping platforms that bridge theoretical research with practical applications. His work has significant implications for the construction equipment industry and autonomous vehicle development, focusing on how humans interact with increasingly autonomous systems in real-world environments.

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Yangyang Shuمشاهده پروفایل

مدرس

University of New South WalesAustralia

Dr. Yangyang Shu serves as an Associate Lecturer in the School of Systems and Computing at the University of New South Wales (UNSW) Canberra campus. Previously, he held a Research Fellow position at the Australian Institute for Machine Learning (AIML) at the University of Adelaide, where he contributed to the Centre for Augmented Reasoning (CAR) project. His academic foundation includes a Ph.D. in Computer Science from the University of Technology Sydney. His research spans cutting-edge areas in artificial intelligence, with core expertise in Machine Learning and Computer Vision . Key focus areas include: Low-supervised paradigms (Weakly/Semi-/Self-Supervised Learning) Rationale-Guided Machine Learning systems Generative AI applications Machine Learning in Music and affective computing Fine-grained visual recognition in data-scarce environments Dr. Shu's publication record demonstrates consistent contributions to top-tier venues including CVPR, ECCV, IEEE Transactions on Multimedia, and Pattern Recognition. His work bridges theoretical advances in learning with practical applications in photo aesthetic assessment, semantic segmentation, and emotion recognition, showing particular strength in developing methods for low-data regimes and leveraging privileged information. He maintains active research collaborations through the School of Systems and Computing at UNSW Canberra, building on prior affiliations with AIML and the Centre for Augmented Reasoning. His current teaching responsibilities include ZEIT 2103 (Data Structures and Representation) for Semester 1, 2025.

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Tove Helldinمشاهده پروفایل

مدرس ارشد

University of SkövdeSweden

Tove Helldin is a Senior Lecturer at the School of Informatics, University of Skövde, Sweden. Her research focuses on anomaly detection , topic modeling , and human-computer interaction , particularly in telecommunications networks and decision support systems . She has led projects in AI for climate adaptation and future sepsis diagnostics , emphasizing team effectiveness and trust calibration in automated systems. PhD in Computer Science (2014), University of Skövde Licentiate in Computer Science (2012) MSc in Computer Science (2009) Her scientific work spans interactive machine learning , visualization of causal relationships , and transparency in military threat evaluation . She has contributed to automotive UI design and fighter aircraft automation , with publications in venues like ACM Computing Surveys and IEEE conferences. Notably, her work explores topic modeling applications in network monitoring and interpretable AI frameworks.

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Nadine von Frankenberg und Ludwigsdorffمشاهده پروفایل

پژوهشگر

Technical University of MunichGermany

Dr Nadine von Frankenberg und Ludwigsdorff is a Postdoctoral Researcher at the Technical University of Munich (TUM) , affiliated with the Institute for Informatics I1 . She conducts research and teaches in the areas of ubiquitous computing, smart environments, and occupant-centric building control systems. Her office is located in Garching b. München, and she can be reached at nadine.frankenberg@tum.de . Education & Dissertation: In 2022 she defended her doctoral dissertation “Rationale Management for Event-Based Arbitration of Thermal Quality in Shared Spaces” at TUM, laying the groundwork for her subsequent work in human-in-the-loop building controls. Research Interests: Ubiquitous and pervasive computing Smart environments and IoT Building control systems with human-in-the-loop Occupant-centric & personalized indoor environmental quality Digital twins, machine learning, and occupant health Her work unites computer science, HCI, and building technology to create adaptive systems that balance energy efficiency with individual comfort and well-being. Publication Profile: Across 2016-2022 she authored or co-authored more than a dozen peer-reviewed papers in top venues such as BuildSys , CHI , Ubicomp , ICSE , and SIGCSE . A clear trend emerges: early work (2016-2017) focused on visualization and assessment tools for smart buildings, followed by 2018-2020 contributions on sensor-based thermal comfort inference, machine-learning control algorithms, and cloud architectures for multi-device studies. Most recently, her 2021-2022 publications center on conflict resolution and rationale management for shared thermal spaces, culminating in her 2022 dissertation. Scientific Service & Awards: Program Committee member: ICSE 2021-2023, SIGCSE 2017-2023, HICSS-56 2023, CSEE&T 2020-2022, SEUH 2020 Publicity Chair: CSEE&T 2020, SEUH 2020 Publication Co-Chair: BuildSys 2020 Teaching & Student Supervision: She has been a recurring instructor and lab supervisor for Introduction to Software Engineering (EIST) , iPraktikum , and Interactive Learning seminars since 2018. Additionally, she has advised or co-advised more than 25 Bachelor’s, Master’s, and Guided Research theses on topics spanning thermal comfort systems, digital twins, mHealth applications, and IoT network simulation. Labs & Teams: She is a core member of the Research Group for Applied Software Engineering (ASE) at TUM, collaborating closely with Prof. Bernd Brügge’s chair and interdisciplinary partners in architecture, psychology, and health informatics.

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