Xihui Liu is an Assistant Professor at the Department of Electrical and Electronic Engineering (EEE) and Institute of Data Science (IDS), The University of Hong Kong, with a courtesy appointment in the Department of Computer Science. She holds a PhD from the Chinese University of Hong Kong and a bachelor’s from Tsinghua University. Her research focuses on generative models, multimodal AI, computer vision, and their applications in embodied AI and AI for Science. Education: PhD in Multimedia Lab (MMLab), Chinese University of Hong Kong (2017–2021) Bachelor’s in Electronic Engineering, Tsinghua University (2013–2017) Research Interests: Generative models for 3D content and multimodal systems Embodied AI and vision-language integration Applications in scientific domains Awards: Adobe Research Fellowship (2020) Rising Stars in EECS (2021) WAIC Rising Stars Award (2022) Her work emphasizes interactive generative systems and benchmarks like T2I-CompBench. She co-organized workshops on multimodal foundation models and embodied AI, and currently serves as Area Chair for CVPR, NeurIPS, and ICLR.
Qi Long is a Professor at the University of Pennsylvania, holding joint appointments in the Department of Biostatistics, Epidemiology and Informatics (Perelman School of Medicine), Department of Computer and Information Science (School of Engineering and Applied Science), and Department of Statistics and Data Science (The Wharton School). He serves as Founding Director of the Center for Cancer Data Science, Associate Director of the Penn Institute for Biomedical Informatics, and Associate Director for Quantitative Data Science at the Abramson Cancer Center. His research bridges statistical and machine learning (ML/AI) method development with biomedical applications, focusing on precision medicine and population health. Education : Ph.D. (2005) and M.S. (2003) in Biostatistics from University of Michigan; B.S. (1998) in Computer Science from University of Science and Technology of China. Research interests include: Robust statistical and ML/AI methods for big health data (-omics, EHRs, imaging, mHealth) Multimodal data integration and subgroup heterogeneity analysis Missing data, causal inference, Bayesian methods, and clinical trials Data privacy, algorithmic fairness, and responsible AI in healthcare Foundation models and agentic AI for biomedicine His publications focus on privacy-preserving AI, fairness-aware ML, and integrative models for multi-omics and EHRs. Recent work explores LLMs and watermark detection in hybrid human-AI settings. Scientific Awards : Elected fellow: AAAS, ASA, IMS, ISI, AMIA He leads large NIH- and ARPA-H-funded initiatives, directing statistical coordinating centers for national clinical trials. His lab trains numerous PhD/Master’s students and postdocs, many of whom hold prestigious academic or industry positions.
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.
Dr. Farshid Rahmani is a Lecturer at the School of Property, Construction and Project Management (PCPM), RMIT University, Australia. His research and teaching focus on Construction Procurement , Relational Contracting , and Agile Project Management . He supervises Masters and PhD students in areas including Early Contractor Involvement (ECI) Alliances Public-Private Partnerships (PPP) Collaborative Delivery Systems . His recent publications explore agile frameworks for building adaptation, sustainable materials like recycled concrete, and team dynamics in large infrastructure projects. Key themes include modular construction , project lifecycle management , and innovative material usage . He actively applies Grounded Theory and Abductive Reasoning in construction management research.
Vita Goei is an Associate Professor at the Medical College of Georgia within Augusta University , specializing in Pediatrics with a focus on Pediatric Gastroenterology . Holding an MD from Albert Einstein College of Medicine (1986) and a BA in Biology from Yeshiva University (1979) , she has practiced pediatric gastroenterology since 1996. Education: MD, Albert Einstein College of Medicine (1986) BA, Yeshiva University (1979) Her research career began in molecular biology with seminal work on the GABA(B) receptor gene and MHC class I transcription . She now seeks clinical studies in pediatric GI, particularly liver disease, Cystic Fibrosis , Inflammatory Bowel Disease , and Eosinophilic Esophagitis . Recent publications span antimicrobial therapy, genetic variants, and diagnostic case studies. Certifications: Basic Life Support (AHA, 2023) Georgia Composite Medical Board (2000) Professional service includes continuous affiliation with the Department of Pediatrics (Gastroenterology) since 2007, previously serving in the same department from 2000-2007.
Huaizu Jiang is an Assistant Professor at Khoury College of Computer Sciences, Northeastern University. His research bridges computer vision, graphics, and natural language processing to develop AI systems that understand and reconstruct 3D visual environments. Prior to joining Northeastern, he was a Postdoc Researcher at Caltech and Visiting Researcher at NVIDIA. He holds a Ph.D. from UMass Amherst (advised by Prof. Erik Learned-Miller), and M.E./B.E. degrees from Xi'an Jiaotong University. His research focuses on fundamental challenges in 3D scene understanding, including geometry reconstruction, semantic interpretation, novel view synthesis, motion generation, and optical flow estimation. Core interests span video processing, human-object interactions, multimodal reasoning, and efficient edge-device implementations. Recent publications emphasize diffusion models for motion/scene generation, transformer-based 3D perception, and video interpolation. Key trends include multi-view consistency techniques, text-to-3D synthesis, and efficient real-time algorithms for robotics applications. Awards & Honors: Winner of the VQA Challenge 2020 He advises 15+ graduate students on projects spanning 3D reconstruction, motion synthesis, and vision-language models. His group collaborates with institutions like NVIDIA and Caltech, focusing on generative AI for dynamic scene understanding.
Mirella Lapata is a Professor of Computer Science at the University of Edinburgh , affiliated with the School of Informatics and the EdinburghNLP group. Her research focuses on developing AI systems that reason, generalize, and handle long contexts, with specific interests in compositional generalization, cross-lingual transfer, and verifiable generation. She leads projects funded by UKRI and ERC , including the UKRI AI Centre for Doctoral Training in Responsible NLP and Turing AI Fellowship for human-like reasoning in models. Research Emphasis : Coarse-to-fine decoding in semantic parsing, parameter-efficient LLMs, collaborative writing frameworks, and multimodal summarization. Advising : Supervises current PhD students and has mentored 23 PhD graduates since 2007, including notable alumni like Li Dong and Siva Reddy. Labs & Teams : Co-leads the Generative AI Laboratory (GAIL) and contributes to the Edinburgh Laboratory for Integrated Artificial Intelligence (ELIAI). Her recent work addresses hallucinations in generative models, cross-lingual semantic parsing, and structured reasoning in text-to-SQL tasks. She has co-authored 15+ publications in 2024 alone, spanning journals like TACL , NeurIPS , and ACL .
Dr. Rani Moran is a Lecturer in Psychology at Queen Mary University of London's School of Biological and Behavioural Sciences. She is affiliated with the Centre for Brain and Behaviour. Her research focuses on decision-making, memory, and learning mechanisms, employing methods like computational modeling, neuroimaging, and pharmacological interventions. Key interests include reinforcement learning, cognitive maps, and exploration-exploitation dilemmas. Her work integrates experimental designs with advanced statistical analysis to understand flexible behavioral control. Recent studies explore model-based vs. model-free learning, credit assignment mechanisms, and disinformation's impact on learning biases. She has published in top journals such as Psychological Review and Nature Communications . Dr. Moran collaborates on projects involving neurocomputational models of confidence, meta-cognition, and social learning. Her research aims to develop interventions for optimizing cognitive processes, with applications in mental health and decision-making contexts.
Sonit Bafna is an Associate Professor and Director of the Ph.D. Program in Architecture at the Georgia Institute of Technology's College of Design. He holds a Ph.D. from Georgia Tech (2002), an SMArchS from MIT (1993), and a professional degree in Architecture fromCEPT University (1990). His research bridges empirical studies on space-human interaction and critical architectural theory, focusing on morphology, behavior, perception, and aesthetics. Key research areas include home layout's impact on mental health (e.g., reduced depression risk in living-area-centered apartments), workplace design's role in employee wellbeing, and the relationship between spatial configuration and cognitive abilities. His work is funded by organizations like the General Services Administration and Kaiser Permanente. Bafna supervises doctoral research in morphology, design theory, and cognitive studies. Current projects include a book on imaginative reasoning in architecture and studies on hospital corridor design and film spatial analysis. His courses cover architectural theory, design analysis, and research methods. Notable contributions include interdisciplinary studies on space syntax, the therapeutic potential of domestic environments, and the visual functioning of buildings. His research emphasizes the socio-cultural dimensions of architecture and its imaginative potential beyond functionalism.
Dr. Ben Swift is a Senior Lecturer at the School of Cybernetics, ANU, specializing in AI, computational art, and cybernetics. He leads the Cybernetic Studio, an interdisciplinary collective exploring cybernetic systems through hardware/software/people collaborations. As a livecoding artist, he performs globally and co-founded the ANU Laptop Ensemble. His research spans generative AI, open-source tools like Extempore, and UX design. Education: PhD in Computer Science (ANU) Projects: Australia's Digital Economy (2022), The Augmented Web (2019) Research focuses on AI creativity, biofeedback interfaces, and computational music. His work bridges technical innovation with artistic expression, evident in projects like TSPNet and adversarial camera systems. Key contributions include Extempore’s development and studies in live coding disruption. Awards unspecified but recognized internationally for interdisciplinary impact.
Michele Cagol is a Professor at the Free University of Bozen-Bolzano , Faculty of Educational Sciences. Their work bridges pedagogy, emotional education, and ecological sustainability, with a focus on relational learning frameworks and multisensory educational practices. Research Themes : Emotional education, educational ecology, sustainability pedagogy, media and communication education, teacher training. Projects : Lead participant in the EU Interreg Alpine Space project FRACTAL , promoting green space awareness in the Alpine region. Recent publications emphasize radical relational pedagogy, ecological ethics, and the role of wonder in learning. Notable methodologies include intra-actionist music composition and microteaching for multilingual competencies. Teaching responsibilities span General Pedagogy , Social Education , and teacher training for primary/middle school contexts.
Nakul Gopalan serves as an Assistant Professor at Arizona State University's School of Computing and Augmented Intelligence (SCAI) in Tempe, where he founded and leads the Logos Robotics Lab since joining in August 2022. His academic foundation was established through a PhD in Computer Science from Brown University completed in 2019. Education: PhD in Computer Science, Brown University (2019) Research Focus: Dr. Gopalan pioneers work at the critical intersection of language grounding and robot learning, developing algorithms that enable robots to interpret natural language instructions and learn from human demonstrations. His research directly addresses real-world usability challenges by focusing on hierarchical reinforcement learning, task planning, and human-robot collaboration frameworks that empower non-expert users to train robots for home and office environments. Key innovations include plannable representations for natural language instruction following and transfer learning techniques for robotic task execution. Publication Evolution: Recent publications (2023-2025) demonstrate accelerating specialization in language-conditioned robot learning, with 80% of his latest work exploring compositional instruction following, novice-user teaching interfaces, and explainable AI for robotics. His research trajectory shows a deliberate shift from foundational language grounding (2017-2020) toward practical human-robot collaboration systems, evidenced by increased focus on hardware-software co-design, cross-embodiment transfer, and clinical applications of explainable AI in neurology support systems. Scientific Recognition: Best Paper Award at RoboNLP workshop (Association for Computational Linguistics) 2017 RSS 2023 Best Student Paper Finalist Mentorship & Service: As lab director, Dr. Gopalan actively mentors graduate researchers while teaching core courses including Data Structures and Algorithms (CSE 310) and specialized seminars on robot learning. His significant service contributions include organizing the RSS 2021 "Robotics for People" workshop, serving as Action Editor for ICRA 2023/2024, and extensive reviewing for top-tier robotics conferences (RSS, ICRA, CORL) and AI venues (NeurIPS, AAAI). Research Infrastructure: The Logos Robotics Lab operates as his primary research vehicle, focusing on natural language interfaces for robot training, hierarchical task decomposition, and real-world deployment of language-grounded learning systems. Current projects integrate large language models with robotic control frameworks to enable zero-shot task generalization across different robot embodiments.
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.
Chuchu Fan is the Leonardo Career Development Professor and Director of the REALM Lab (REliable Autonomous system Lab) at MIT's School of Engineering , with a primary appointment in the Department of Aeronautics and Astronautics and Laboratory for Information and Decision Systems . Her work bridges formal methods , control theory , and machine learning to ensure safety in autonomous systems. Ph.D., University of Illinois at Urbana-Champaign (2019) B.E., Tsinghua University (2013) Her research focuses on rigorous safety verification of autonomous systems through neural Lyapunov-barrier functions , control contraction metrics , and formal logic specifications . Recent work emphasizes LLM integration for symbolic planning, multi-robot collaboration , and robustness against model uncertainties . The 15 most recent articles highlight trends in safety-critical control using graph neural networks , reinforcement learning , and temporal logic . Key themes include collision avoidance , multi-agent coordination , and runtime safety filters applied to drones, self-driving cars, and microgrids. Scientific Awards & Honors : 2025 ONR YIP Award 2023 NSF CAREER & AFOSR YIP Awards 2020 ACM Doctoral Dissertation Award 2016 Rising Stars in EECS As head of the REALM Lab, she leads projects on autonomous air taxis , safety verification , and neural certificates for robotic systems. Her teaching includes courses on feedback control and formal methods for autonomous systems.
Andreas Lööw is a Lecturer at Royal Holloway, University of London , focusing on hardware and software verification. Previously, he was a postdoctoral researcher at Imperial College London under Philippa Gardner , contributing to the Gillian Platform . He completed his PhD at Chalmers University of Technology under Magnus Myreen , specializing in interactive theorem proving and hardware verification. His research explores symbolic execution, separation logic, and formal verification of hardware/software systems. Key projects include Betterlog (Verilog semantics reformulation) and foundational work on the Gillian Platform . 2025 : Compositional Symbolic Execution for Memory Models 2025 : Simulation Semantics of Synthesisable Verilog 2024 : Compositional Symbolic Execution for Correctness/Incorrectness 2023 : Exact Separation Logic (Distinguished Paper at ECOOP'24) 2023 : Hardware Verification of Pipelined Processors 2022 : Verilog Concurrency Analysis 2021 : Verified Verilog Compiler (Lutsig) Scientific Awards : Distinguished Paper at ECOOP 2024 He maintains the vv Verilog visualization tool and collaborates on the Gillian Platform . Contact: andreas.loow@rhul.ac.uk
