Find Academic Supervisors

Ethan MollickView profile

Associate Professor

• Artificial Intelligence
• Generative AI Applications
• Entrepreneurship
• +5 more

Ethan Mollick is the Ralph J. Roberts Distinguished Faculty Scholar, Rowan Fellow, and Associate Professor of Management at the Wharton School of the University of Pennsylvania. He serves as Co-Director of the Generative AI Labs at Wharton, where he researches AI's impact on work, entrepreneurship, and education. His academic background includes a PhD and MBA from MIT's Sloan School of Management and a bachelor's degree from Harvard University. Mollick's research spans artificial intelligence applications in business and education, entrepreneurship dynamics, and innovation management. He explores how generative AI transforms classroom education through simulations like PitchQuest, and investigates startup funding disparities with a focus on gender representation. His work on the 'jagged technological frontier' demonstrates how humans integrate AI capabilities through Centaur and Cyborg approaches. His publications reveal consistent focus on practical AI implementation, with recent work examining prompt engineering for diverse idea generation, AI-assisted teaching strategies, and gender representation tipping points in venture capital. The research shows increasing emphasis on educational applications since 2022, coinciding with the ChatGPT breakthrough. MBA Professor of the Year Award, Poets & Quants (2024) Named to TIME Magazine's Most Influential People in Artificial Intelligence ASQ Award for Scholarly Contribution (2024) Greif Research Impact Award (2020) McGraw-Hill/Academy of Management Innovation in Entrepreneurship Pedagogy Award (2016) As Co-Director of Wharton's Generative AI Labs, Mollick leads prototype development and research on human-AI collaboration. His teaching includes Technology Strategy, Entrepreneurship, and Innovation courses where he implements AI simulations. He has advised numerous organizations and co-founded a startup prior to academia, bringing practical experience to his research on solo versus team ventures. Mollick directs Wharton Interactive's game development initiatives, creating educational simulations including PitchQuest (AI-powered VC simulator), The Saturn Parable (crisis management), and The Entrepreneurship Game. His lab work focuses on creating scalable AI educational tools that provide personalized learning experiences through simulated practice environments.

• Applied Linguistics
• Language Testing and Assessment
• Language Assessment Literacy
• +6 more

Professor Luke Harding is a faculty member at the Department of Linguistics and English Language , Lancaster University , within the School of Social Sciences . His work bridges applied linguistics, language assessment, and critical discourse studies, with a focus on the ethical and societal implications of testing. Research Interests : Language testing and assessment, World Englishes and English as a Lingua Franca (ELF), second language listening and pronunciation assessment, diagnostic approaches to language evaluation, and language assessment literacy. Recent projects integrate digital technology and corpus linguistics into testing frameworks. Publications : Published extensively in Language Testing , Applied Linguistics , and Language Assessment Quarterly . Co-edited the Routledge Handbook of Language Testing (Second Edition) (2022), a key reference work in the field. Teaching : Leads modules in Language Test Construction and Evaluation , Issues in Language Testing , and Statistical Analysis for Language Testing within the university's distance MA program. Leadership : Convened the Language Testing Research Group with colleagues Tineke Brunfaut and John Pill, advancing interdisciplinary approaches to assessment.

• Artificial Intelligence
• Natural Language Processing
• Multimodal Reasoning
• +4 more

Graham Neubig is an Associate Professor at the Language Technologies Institute (LTI) within Carnegie Mellon University (CMU). His research focuses on advancing artificial intelligence, particularly in natural language processing (NLP), multimodal reasoning, and large language models (LLMs). He explores topics such as AI safety, generative AI, and human-AI interaction, with an emphasis on practical applications like machine translation and web-agent systems. His work often involves developing frameworks for evaluating AI systems, such as OpenAgentSafety and BehaviorBox, which assess real-world agent performance and model behavior. Neubig's research also delves into improving LLM capabilities through reasoning analysis, hallucination detection (e.g., ZINA), and culturally aware systems (e.g., CAIRe). He has contributed to open-source projects like Pangea (a multilingual LLM) and frameworks such as Cmulab for model deployment. His recent work addresses challenges in agentic tasks, self-improving agents (Skillweaver), and benchmarking across domains like visual reasoning (VisualPuzzles) and software engineering. Notable achievements include advancing evaluation methodologies for LLMs, developing tools for ethical AI, and creating benchmark suites that test systems under realistic conditions. His lab collaborates on projects like the BrowserGym ecosystem and OpenHands platform, which aim to standardize web-agent research and AI-driven software development. Neubig's contributions span theoretical advancements and practical implementations, bridging the gap between cutting-edge research and real-world applications. He advises students such as Apurva Gandhi and actively publishes in top venues, addressing topics from instruction-following improvements to the societal impacts of AI. His work frequently emphasizes the importance of transparency, controllability, and cultural awareness in AI systems.

David HsuView profile

Professor

• Robotics
• Artificial Intelligence
• Machine Learning
• +2 more

David Hsu is Provost's Chair Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing, where he founded and directs the NUS Artificial Intelligence Laboratory (NUSAIL) and leads the Smart Systems Institute. His academic leadership includes chairing major conferences such as Robotics: Science & Systems (2015) and IEEE ICRA (2016), alongside editorial roles in IEEE Transactions on Robotics and the Journal of Artificial Intelligence Research. He earned a B.Sc. in Computer Science & Mathematics from the University of British Columbia and a Ph.D. in Computer Science from Stanford University. His research spans robotics, AI, and computational biology, with recent focus on robot planning under uncertainty and human-robot collaboration. Current work integrates machine learning with decision-theoretic planning to enable robust human-robot co-existence in unstructured environments. Analysis of his 2023-2025 publications reveals dominant trends in deformable object manipulation (e.g., clothes handling via semantic keypoints), open-world navigation using scene graphs, and LLM-driven multi-agent reasoning for complex tasks. Key innovations include perspective-aware visual grounding for human-centric interaction and functional object arrangement through compositional generative models, reflecting a strong emphasis on real-world applicability. His scientific contributions have earned prestigious recognition: IJCAI-JAIR Best Paper Prize (2022) for foundational AI research Robotics: Science & Systems Test of Time Award (2021) IEEE Fellowship (2018) for contributions to robotic planning RSS Best Systems Paper Award (2017) RoboCup Best Paper Award at IROS (2015) Humanitarian Robotics Award at ICRA (2015) As director of the Adaptive Computing Laboratory, Hsu drives research on fundamental computational frameworks for human-robot interaction. The lab's work on uncertainty-aware decision-making has secured significant research funding through grants from Singapore's National Research Foundation and industry partnerships with robotics firms. While specific student names aren't publicized, his leadership in the NUSAIL indicates extensive mentorship of doctoral candidates in AI and robotics.

John R AndersonView profile

Professor

• Cognitive Neuroscience
• Cognitive Science
• Computational Psychology
• +3 more

John R Anderson is the Richard King Mellon University Professor of Psychology and Computer Science at Carnegie Mellon University (CMU), affiliated with the Department of Psychology within the Dietrich College of Humanities and Social Sciences. His research focuses on understanding higher-level cognition, particularly mathematical problem-solving, through the development of the ACT-R cognitive architecture—a computational framework simulating human cognitive processes. This architecture integrates behavioral, neural, and educational data to model learning and decision-making. Anderson’s work bridges cognitive science, neuroscience, and educational technology. He investigates how brain imaging (e.g., fMRI, EEG) can reveal the temporal dynamics of cognitive processes and improve instructional methods. His research emphasizes analyzing brain activity time courses to uncover underlying mechanisms of problem-solving and skill acquisition. Key Research Themes: Cognitive architectures, neural correlates of learning, computational models of memory, and intelligent tutoring systems. Notable Contributions: Development of the ACT-R architecture, integration of neuroimaging with cognitive modeling, and studies on skill transfer and learning strategies. Anderson’s publications include seminal books like Cognitive Psychology and Its Implications and How Can the Human Mind Occur in the Physical Universe? His work has advanced understanding of associative memory, strategic decision-making, and the application of cognitive models in educational technology. His lab, the ACT-R Research Group, collaborates across disciplines to model complex cognitive tasks and their neural foundations. Current projects analyze real-time brain activity to refine educational interventions and improve human-machine interaction.

• Learning Engineering
• Technology-Enhanced Learning
• Question-Based Learning
• +12 more

Olof Bälter is a Professor in Computer Science at KTH Royal Institute of Technology, affiliated with the Division of Media Technology and Interaction Design within the School of Electrical Engineering and Computer Science. He is the founder of the Technology-Enhanced Learning research group and holds a focus on learning engineering and human-computer interaction. His research interests center on technology-enhanced learning , question-based learning , learning analytics , AI in education , and inclusive pedagogy . A consistent theme in his work is improving efficiency in education and daily life through digital tools. He developed the Pure Question-Based Learning (Pure QBL) methodology, a digital Socratic approach that enhances student engagement and learning outcomes. His work extends to wellness in education through initiatives like walking seminars, and he investigates digital interventions for mental health, such as online Cognitive Behavioral Therapy (CBT) courses. His recent publications highlight trends in AI-generated educational content , learning efficiency , digital pedagogy , and inclusive course design , with applications in computer science education, language instruction, and global development. His research often employs experimental and data-driven methods, including randomized controlled trials and learning analytics. Teacher of the Year at the Surveying program KTH's Pedagogical Prize Higher Education Hero STINT Excellence in Teaching Scholarship (2008 and 2013) Olof Bälter has supervised numerous courses in programming, computer science, media technology, and learning engineering. He has collaborated with institutions such as Stanford University, Williams College, Region Stockholm, Stockholm University, and organizations like Promobilia and Begripsam. His projects aim to scale effective learning methods globally and make education more accessible and efficient. He leads research on the effectiveness of Pure QBL for students with ADHD and is involved in developing digital tools for health literacy and professional development in Ethiopia and Rwanda. His work bridges theory and practice, aiming to transform educational delivery through innovation and evidence-based design.

Sherry Tongshuang WuView profile

Assistant Professor

• Human-Computer Interaction
• Natural Language Processing
• Artificial Intelligence
• +6 more

Sherry Tongshuang Wu is an Assistant Professor at Carnegie Mellon University's School of Computer Science, with primary appointments in the Human-Computer Interaction Institute (HCII) and secondary affiliation with the Language Technology Institute (LTI) . Trained at the University of Washington under Jeffrey Heer and Dan Weld, she bridges HCI and NLP to study human interactions with AI systems across diverse user groups. Her educational background includes a Ph.D. (2016-22) and M.S. (2016-18) in Computer Science and Engineering from the University of Washington, and a B.Eng. (2012-16) from Hong Kong University of Science and Technology. Industry experience includes research internships at Google Brain, Microsoft Research, and Apple. Wu's research focuses on three interconnected pillars: Real-world AI Evaluation (developing frameworks like SPHERE for systematic assessment), Task-specific AI Test & Distill (optimizing general-purpose models for specific use cases), and Human-AI Task Delegation (designing optimal collaboration between humans and AI). Her work emphasizes practical deployment, user-specific net gains, and error recovery mechanisms. Analysis of her 15 most recent publications reveals a strong trend toward evaluation frameworks (35%), human-AI collaboration systems (30%), and specialized model distillation (25%), with growing emphasis on educational applications (10%). Key methodological themes include checklist-based evaluation, perspective-aware retrieval, and structural analysis of AI outputs. Google Academic Research Award (2024) Amazon Research Awards (2024) AIED 2024 Best Paper Award ACL 2020 Best Paper Award Rising Stars in EECS Workshop (2020) Wu actively mentors 19 students across PhD, Master's, and undergraduate levels, with notable projects including synthetic data generation, LLM literacy tools, and retrieval system optimization. She leads significant grant-funded work through Amazon Research Awards and Google Academic Research Awards, focusing on deployable model generation and human-AI collaboration frameworks. Her lab develops practical tools like Promp2Model and SPHERE that bridge theoretical research with industry applications.

Alison Louise BaileyView profile

Professor

• Developmental Psycholinguistics
• Academic Language Pedagogy
• Educational Assessment
• +7 more

Alison Louise Bailey is a Professor and Division Head in Human Development & Psychology at UCLA's Graduate School of Education and Information Studies. She specializes in developmental psycholinguistics, focusing on multilingual education, academic language pedagogy, and assessment practices for English learners. Education: Ed.D. and Ed.M. from Harvard University; B.A. (Hons) in Linguistics with a minor in American Literature from The University of Hull, England Her research explores language learning progressions, equity in educational assessment, and the intersection of language with STEM disciplines. She leads two U.S. Department of Education-funded ExcEL Peer Network projects and collaborates on NSF and W.T. Grant Foundation grants. Recent publications emphasize translanguaging, automated language assessment, and bias mitigation in educational technologies. Her work integrates Bayesian modeling for dual-language immersion outcomes and examines racial injustices in assessment systems. Awards: Bobbie and Mark Greenfield Distinguished Faculty Award, Haynes Foundation Faculty Fellow, Harold and Lois Haytin Award, Ann C. Rosenfield Distinguished Community Partnership Prize She contributes to national assessment policy as a NAEP Reading Standing Committee member and NCME Classroom Assessment Task Force Co-Chair, advocating for culturally responsive assessment frameworks.

• Machine Learning
• Artificial Intelligence
• Combinatorial Optimization
• +13 more

Yiming Yang is a Professor at the Language Technologies Institute and Machine Learning Department within the School of Computer Science at Carnegie Mellon University , where he has held faculty positions since 2003. His research spans foundational and applied aspects of machine learning , artificial intelligence , and scientific computing . Professor, Carnegie Mellon University (2003–Present) Associate Professor, Carnegie Mellon University (1996–2003) Yang's research focuses on LLM-based problem-solving agents , combinatorial optimization , and scalable oversight frameworks . His work explores diffusion models, Langevin dynamics, and Fourier neural operators for NP-hard problems, while advancing reinforcement learning techniques for self-play supervision and principle-driven fine-tuning of large language models. Recent publications highlight his contributions to code synthesis , PDE solving , and multi-agent reinforcement learning . Key methodologies include demonstration-guided control, retrieval-augmented reasoning, and test-time scaling laws. His team has developed frameworks like FEEDER for efficient in-context learning and μTransfer-FNO for zero-shot hyperparameter transfer in PDE solvers. Notable scientific achievements include: Best Student Paper Runner Up (2013) Best Theoretical Paper Award (1994) Best Theoretical Paper Award (1993) Yang has mentored over 20 PhD students and postdocs, including Shengyu Feng , Zhiqing Sun , and Aman Madaan , across domains like graph learning , extreme multi-label classification , and language model alignment .

Fethiye Irmak DoganView profile

Researcher

• Human-Robot Interaction
• Machine Learning
• Deep Learning
• +4 more

Fethiye Irmak Dogan is a Postdoctoral Research Associate at the University of Cambridge's Department of Computer Science and Technology, working in the Affective Intelligence and Robotics Laboratory. She holds a Ph.D. in Computer Science from KTH Royal Institute of Technology (2023), an M.Sc. and B.Sc. in Computer Engineering from Middle East Technical University (METU). Her research focuses on human-robot interaction, continual learning, and socially appropriate robot behaviors leveraging explainability. She has conducted robotics research at KTH's Division of Robotics, Perception and Learning and collaborated internationally, including a visiting scholar stint at Georgia Institute of Technology. Education highlights include: B.Sc., Computer Engineering, METU (2015) M.Sc., Computer Engineering, METU (2018), with research at Kovan Robotics Lab Ph.D., Computer Science, KTH (2023), with visiting research at Georgia Tech Research interests emphasize deploying autonomous robots in human environments, resolving ambiguous user instructions through explainability, and enabling socially intelligent robot behaviors. Recent work explores continual learning for context adaptation, multimodal frameworks for human-robot collaboration, and vision-language models for wellbeing assessment in children. Key projects include BT-ACTION (modular instruction understanding), GRACE (LLM-driven socially appropriate actions), and STREAK (continual learning for household tasks). Her contributions span robotics, AI ethics, and human-centered design, with a focus on real-world applications in healthcare and education.

Hamid MansoorView profile

Assistant Professor

• Data Visualization
• Digital Health
• Digital Phenotyping
• +6 more

Hamid Mansoor is an Assistant Professor in the Department of Computer Science at the University of Manitoba. He holds a PhD in Computer Science from Worcester Polytechnic Institute under Prof. Emmanuel Agu, and was part of the DARPA-funded WASH project. His research focuses on data visualization, digital health, and smartphone-based behavioral analysis. He previously served as a Postdoctoral Fellow at the VIXI Lab, University of Victoria, Canada, under Prof. Miguel Nacenta. Education: PhD in Computer Science, Worcester Polytechnic Institute Research Interests: Interactive data visualization frameworks for health monitoring Mobile and ubiquitous computing for behavioral analysis Smartphone-sensed human behavior and health informatics Visual representation of text-based and sensor data Publications highlight trends in visual analytics for healthcare, including tools like ARGUS and INPHOVIS for detecting bio-behavioral disruptions and smartphone-based phenotyping. His work integrates machine learning with visualization to address challenges in health data interpretation. Awards: Best short paper honorable mention (EuroVis 2020) His contributions span academic collaborations in health informatics and mobile computing, with a focus on bridging theory and practical applications in healthcare technology.

Christoforos MavrogiannisView profile

Assistant Professor

• Human-robot interaction
• shared autonomy
• multiagent and multirobot systems
• +4 more

Christoforos Mavrogiannis is an Assistant Professor of Robotics at the University of Michigan, leading the Fluent Robotics Lab within the Department of Robotics. He holds a Ph.D. and M.S. from Cornell University and a Diploma in Mechanical Engineering from the National Technical University of Athens. His research focuses on enabling robots to seamlessly integrate into dynamic, unstructured environments through advancements in human-robot interaction, shared autonomy, multiagent systems, and navigation algorithms. Education: Ph.D. and M.S., Cornell University Diploma in Mechanical Engineering, National Technical University of Athens Research Interests: His work spans human-robot interaction , shared autonomy , and multiagent systems . He develops algorithms for navigating dynamic environments , decentralized control , and behavior prediction , with applications in robotic manipulation and collaborative transport . Recent projects include the HOUND off-road robot and pixel-art generation with mobile robots. Awards: Best paper award at the RSS Social Navigation Workshop (2024) Grants & Advising: He advises the Fluent Robotics Lab and contributes to organizing conferences like ICRA and HRI. His lab focuses on human-centered robotics and socially competent navigation . Labs & Teams: Fluent Robotics Lab at the University of Michigan, collaborating on projects like the HOUND platform and electrostatic brake systems for manipulation.

David LindlbauerView profile

Assistant Professor

• Human-Computer Interaction
• Computer Graphics
• Computer Vision
• +7 more

David Lindlbauer is an Assistant Professor at the Human-Computer Interaction Institute (HCII) within Carnegie Mellon University's School of Computer Science. He leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center (XRTC) . His research bridges Human-Computer Interaction, Computer Graphics, and Computer Vision to create adaptive interfaces that enhance human-digital interaction. Education : PhD (summa cum laude) from Technische Universität Berlin , MSc and BSc from University of Applied Sciences Upper Austria Previous Affiliation : Postdoctoral Researcher at ETH Zurich (2018-2020) David's research focuses on understanding human perception of digital information and developing computational approaches to optimize AR/VR interface usability. Key areas include: Context-aware adaptive interfaces Visual saliency and attention modeling Spatial audio-haptic systems Optimal placement algorithms Object manipulation in Remixed Reality Diminished/ambient MR interfaces His 15 most recent publications (2024-2025) span topics in adaptive XR interfaces, multimodal notifications, haptic systems, and spatial cognition. These works appear at venues like ACM CHI, ACM UIST, IEEE VR, and Frontiers in VR. Common themes include: Machine learning for interface adaptation Human factors in XR design Real-time environment analysis Privacy-aware display systems Collaborative MR interfaces Accessibility enhancements Scientific Recognition : Best Paper Honorable Mention Award (ACM CHI 2024) Best Paper Award (ACM ISS 2023) ETH Zurich Postdoctoral Fellowship Multiple best paper recognitions at CHI, UIST, and IEEE VR Teaching & Leadership : Course developer for CMU's "Interactive Extended Reality" Mentor for NASA SUITS Challenge team Co-chair roles at CHI and UIST Overseeing PhD students and research interns

Salem LahlouView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• GFlowNets
• +7 more

Salem Lahlou is an Assistant Professor in the Machine Learning Department at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), having joined in September 2024. He previously served as a Senior Researcher at the Technology Innovation Institute (TII) in 2024. His academic background includes a PhD from Mila and Université de Montréal (UdeM) under Yoshua Bengio (2023), with prior studies in applied mathematics at École Polytechnique and statistical learning at École Normale Supérieure Paris-Saclay. His research focuses on developing more capable and reliable AI systems through three interconnected pillars: Novel Method Development : Core contributions to Generative Flow Networks (GFlowNets), uncertainty estimation techniques (DEUP), and curriculum learning frameworks Large Language Model Advancement : Enhancing reasoning capabilities and alignment through preference optimization and trace-based learning Community Tooling : Creation of torchgfn library for GFlowNets and benchmarks including BabyAI, FinChain, and LLM-BabyBench Core research areas span Machine Learning, GFlowNets, Uncertainty Estimation, LLM Reasoning, Reinforcement Learning, and AI for Science. Recent publications (2023-2025) demonstrate strong emphasis on GFlowNet theory/improvements (8+ papers), LLM reasoning evaluation (FinChain, LLM-BabyBench), uncertainty quantification, and societal AI impacts. Key application domains include mathematical reasoning, financial systems, privacy preservation, and cognitive science. He currently advises graduate students including Junyi (privacy risks in SNNs) and Abhijith (LLM reasoning). His group collaborates with MBZUAI faculty (Nils Lukas, Alham Fikri, Mingming Gong, Martin Takac) and industry partners on projects involving Conversational AI, Personalization, and Affective AI.

Alexander KollerView profile

Professor

• Computational Linguistics
• Neural-Symbolic Models
• Semantic Parsing
• +5 more

Professor Alexander Koller is a leading academic in Computational Linguistics at Saarland University's Department of Language Science and Technology. He holds a courtesy appointment in Computer Science and contributes to the Saarland Informatics Campus - one of Europe's premier computer science research centers. He leads the Computational Linguistics group and serves as speaker for the DFG-funded Research Training Group 'Neuroexplicit Models of Language, Vision, and Action'. PhD in Computer Science (Saarland University) Former positions: University of Potsdam, Columbia University, University of Edinburgh Sabbatical experiences: Meta AI (Paris), Allen Institute for AI (Seattle) His research focuses on computational modeling of meaning and reasoning in NLP, combining neural and symbolic approaches. Key contributions include semantic parsing systems like the AM parser and Alto, neurosymbolic models, and the GIVE Challenge for NLG evaluation. His recent work explores LLMs' limitations in problem-solving and compositional generalization. Recent publications highlight diverse applications across semantic parsing, dialogue systems, and LLM evaluation. Awards include ACL 2020 Best Theme Paper and multiple Outstanding Paper recognitions at ACL conferences. 2025 - AI Action Summit keynote speaker 2023 - ACL Outstanding Paper Awards 2022 - ELLIS Faculty appointment He maintains the DialogOS system for spoken dialogue development and teaches advanced computational linguistics topics. His group includes multiple postdocs and PhD students working across LLMs, dialogue systems, and semantic modeling.