Find Academic Supervisors

Guofu ZhouView profile

Professor

• Empirical Asset Pricing
• Machine Learning in Finance
• Financial Economics
• +10 more

Guofu Zhou , the Frederick Bierman & James E. Spears Professor of Finance at Washington University's Olin Business School , has been a faculty member since 1990. His academic career includes multiple Reid Teaching Awards (2020, 2019, 2018, 2014, 2010) Best Paper Awards (Institute for Quantitative Investment Research 2019, Chinese Finance Association 2010, Inquire UK/Europe 2019 & 2024) and affiliations with journals like the Journal of Financial Economics and Management Science . Education: PhD, Duke University (1990) MA, Duke University (1987) MS, Academia Sinica (1985) BS, Chengdu University of Technology (1982) His research bridges empirical asset pricing and applied AI/machine learning , with a focus on market efficiency anomaly exploitation Bayesian inference option pricing Chinese financial markets behavioral finance He has contributed to understanding equity risk premium predictability, technical analysis, and portfolio optimization techniques. Key trends in his Journal of Financial Economics , Journal of Finance , and Review of Financial Studies publications include machine learning applications in asset pricing , anomaly-market linkages , and fear sentiment in Treasury markets . Recent work with ChatGPT explores textual analysis of earnings calls. Scientific Awards: Best Paper Award, Institute for Quantitative Investment Research (2019) Finalist for Crowell Memorial Prize (2024) Led multiple Best Paper Awards at conferences like FMA and SIF Contact: zhou@wustl.edu | Office: Simon Hall Room 207

• Creative Cognition
• Psychology of Creativity
• Metacognition
• +3 more

Mathias Benedek is an Associate Professor at the Institute of Psychology, Faculty of Natural Sciences, University of Graz, Austria. He directs the Creative Cognition Lab and is actively involved in several research networks, including the "Complexity of Life" profile area, the "Brain and Behavior" research network, and the "FUTURE EDUCATION" research network at the University of Graz. His research focuses on the cognitive and neural mechanisms underlying creative thinking, with particular emphasis on the role of memory processes, metacognition, and eye movement patterns during creative ideation. Dr. Benedek's work bridges psychological theory with empirical research methods including eye tracking, neuroimaging, and computational modeling of creative processes. His research has important implications for understanding how creative potential develops and how it can be assessed and nurtured in educational and professional contexts. Dr. Benedek's publication record demonstrates a consistent focus on creative cognition across multiple dimensions. His recent work has expanded into emerging areas such as human-AI collaboration for creative tasks, automated assessment of creativity using large language models, and the relationship between physical activity and creative performance. His research shows a strong trajectory toward more ecologically valid methods for studying creativity in real-world contexts, moving beyond traditional laboratory paradigms. Seraphine Puchleitner Anerkennungspreis (PhD Supervision Award), University of Graz, 2021 William-Stern-Preis, German Psychological Society, 2019 Research Prize, University of Graz, 2017 Research Prize (Publication Category), Initiative Gehirnforschung, 2016 Berlyne Award, Division 10, American Psychological Association, 2015 Dr. Benedek has demonstrated strong commitment to mentoring the next generation of researchers, as evidenced by his 2021 PhD Supervision Award. His research has been supported by multiple grants from national and international funding bodies, though specific grant details are not provided in the available information. His professional service includes leadership roles in the Initiative Gehirnforschung Steiermark since 2010 and active membership in several psychological societies across Europe and North America. Dr. Benedek leads the Creative Cognition Lab at the University of Graz, which employs a multidisciplinary approach to studying creative processes. The lab integrates methods from cognitive psychology, neuroscience, and computational modeling to investigate the mechanisms underlying creative thought. Current research projects examine the relationship between eye movements and internal cognitive processes, the development of automated assessment tools for creativity, and the application of creativity research to educational contexts.

Xin PengView profile

Professor

• Software Analytics
• Intelligent Software Development
• Microservice
• +4 more

Xin Peng is a Professor and Deputy Dean at the School of Computer Science, Fudan University, China. He leads the CodeWisdom research team focusing on intelligent software engineering techniques for development, maintenance, and operation of software systems. His educational background includes a PhD in Computer Science (2001-2006) and Bachelor's degree in Computer Science (1997-2001), both from Fudan University. He progressed through the academic ranks from Assistant Professor (2006-2010) to Associate Professor (2010-2015) and finally to Professor (2015-present). Professor Peng's research interests span Software Analytics, Intelligent Software Development, Microservice systems, and AIOps. His work leverages AI technologies including deep learning and knowledge graphs to develop intelligent software engineering techniques. A significant portion of his recent work focuses on applying Large Language Models to various software engineering tasks, including vulnerability detection, API usage analysis, and test automation. His publication record shows a clear trend toward increasingly sophisticated applications of AI in software engineering, with recent work heavily featuring LLMs for tasks ranging from vulnerability patch porting to resource leak detection. The research spans multiple domains including microservice systems, automotive software, and Web of Things security. Best Paper Award of ICSM 2011 ACM SIGSOFT Distinguished Paper Award of ASE 2018 and 2021 IEEE TCSE Distinguished Paper Award of ICSME 2018, 2019, and 2020 IEEE Transactions on Software Engineering Best Paper award for 2018 Professor Peng serves in numerous leadership roles including Deputy Director of CCF Technical Committee on Software Engineering, Co-Editor-in-Chief of Journal of Software: Evolution and Process, and Associate Editor for ACM Transactions on Software Engineering and Methodology. He has been actively involved in program committees for major software engineering conferences including ICSE, ASE, ESEC/FSE, and ICSME. He leads the CodeWisdom research team at Fudan University, which has developed several benchmark systems including TrainTicket for microservice research. The team's work bridges academic research with industrial applications, particularly in microservice systems analysis and intelligent software development tools.

Juho LeeView profile

Associate Professor

• Bayesian deep learning
• Bayesian inference
• meta learning
• +5 more

Juho Lee is an Associate Professor at the Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST). Previously, he worked as a research scientist at AITRICS and completed his PhD in Computer Science & Engineering at Pohang University of Science and Technology (POSTECH) under Professor Seungjin Choi, followed by postdoctoral research at University of Oxford with Professor François Caron. His research focuses on: Bayesian deep learning Bayesian inference Meta learning Generative models Uncertainty quantification Graph representation learning Professor Lee's work bridges theoretical Bayesian methods with practical deep learning applications. His recent publications demonstrate significant contributions to neural processes, Bayesian optimization, and scalable inference methods. He has developed novel architectures like Set Transformer for permutation-invariant modeling and advanced techniques for uncertainty quantification in deep networks. His research shows a clear trajectory toward making Bayesian principles applicable to large-scale, real-world machine learning problems. Notable contributions include: Set Transformer: A framework for attention-based permutation-invariant neural networks (ICML 2019) Bootstrapping neural processes (NeurIPS 2020) Deep amortized clustering (NeurIPS 2019 workshop) Learning to pool in graph neural networks for extrapolation Professor Lee actively mentors graduate students and has advised numerous PhD candidates who co-author papers with him across NeurIPS, ICML, and ICLR. His research group SIML@KAIST develops scalable and interpretable machine learning methods with strong theoretical foundations.

Peng LiView profile

Professor

• Integrated Circuits and Systems
• Brain-Inspired Computing
• Electronic Design Automation
• +6 more

Peng Li is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. His research focuses on integrated circuits, brain-inspired computing, electronic design automation, and hardware machine learning systems. He holds Fellow status in the Institute of Electrical and Electronics Engineers (IEEE). His work emphasizes neuromorphic engineering, spiking neural networks, and the intersection of machine learning with analog circuit design. Education includes a PhD in Electrical and Computer Engineering from Carnegie Mellon University, an MS in Systems Engineering from Xi'an Jiaotang University, and a BS in Information Science and Engineering from the same institution. His research has been recognized with prestigious awards including the ICCAD Ten-Year Retrospective Most Influential Paper Award and multiple Design Automation Conference Best Paper Awards. Key research trends in his articles include advancements in spiking neural networks (SNNs), hardware accelerators for neuromorphic computing, Bayesian optimization for analog circuit design, and robustness in machine learning systems. He explores topics like adversarial robustness, energy-efficient architectures, and data-efficient prediction techniques. His work bridges theoretical machine learning models with practical hardware implementations, particularly in 3D integration and systolic array acceleration. Notable contributions include pioneering hybrid approaches combining formal verification with machine learning for analog circuits (HFMV framework), and innovations in neuromorphic processors such as the 3D Liquid State Machine architecture. His research also addresses challenges in semiconductor manufacturing, including wafer map pattern recognition and failure detection through semi-supervised learning and contrastive methods. Awards highlight his impactful contributions to both design automation and neural computing. His grants and collaborations likely span industry partnerships in semiconductor technology and neuromorphic computing. He leads a lab focused on next-generation hardware-software co-design for intelligent systems, emphasizing energy efficiency and scalability.

• Computer Vision
• Deep Learning
• Robotics
• +7 more

Dr. João Henriques is a Research Fellow of the Royal Academy of Engineering (RAEng) at the Visual Geometry Group (VGG), University of Oxford. His research focuses on advancing computer vision, deep learning, and robotics, particularly in areas like 3D scene understanding, reinforcement learning, and multi-agent systems. He is renowned for developing the KCF and SiameseFC visual trackers, which won the VOT Challenge and are deployed in consumer hardware. His work spans 3D geometry, self-supervised learning, causal inference, and neuro-symbolic systems. Key contributions include methods for egocentric video analysis, unsupervised reconstruction, and robot navigation. He leads the VGG's research on neural feature fields, hierarchical scene understanding, and real-time 3D perception. Recent publications emphasize 3D-aware segmentation, universal place recognition, and neuro-symbolic world modeling for robotics. His research often bridges theoretical guarantees with practical applications, such as medical imaging and autonomous systems. Dr. Henriques collaborates with industry and academia on AI ethics, friendly AI, and interpretable learning. His lab hosts DPhil students advancing creative AI applications, such as generative models for gameplay design and LLM evaluations in real-world editorial workflows.

Tim OatesView profile

Professor

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

Dr. Tim Oates is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County . His research spans machine learning, artificial intelligence, and brain-machine interfaces, with a focus on weakly supervised methods, human-in-the-loop reinforcement learning, and grounded policy development for robotics. Ph.D., Computer Science, University of Massachusetts, Amherst, 2000 M.S., Computer Science, University of Massachusetts, Amherst, 1997 B.S., Computer Science and Electrical Engineering, 1989 Current research threads include: Developing non-invasive brain injury severity assessment via medical time series Modeling human brain development through computational frameworks Designing algorithms for autonomous robotic learning Recent publications highlight AI security mechanisms (backdoor detection via tensor decomposition, matrix factorization) Medical applications (3D artery reconstruction, skin lesion diagnosis, EEG denoising) Neuro-symbolic integration (holographic representations, language-guided reinforcement learning) Mathematical reasoning (schema-based problem solving, subitizing algorithms) Contact: oates@cs.umbc.edu | Office: 336 Information Technology and Engineering (ITE) Building

Mark J. HillView profile

Lecturer

• Digital Humanities
• Computational Social Science
• Intellectual History
• +6 more

Dr Mark J. Hill is a Lecturer in Cultural Computation at King's College London's Department of Digital Humanities within the Faculty of Arts & Humanities. He holds a DPhil from the University of Oxford, an M.Sc. in Political Theory from the London School of Economics, and a B.A. in Political Science from Concordia University. His interdisciplinary work bridges digital humanities, computational social science, and intellectual history. Research Focus: Social network analysis, public discourse analysis via large datasets, quantitative text analysis, and critical evaluation of digital methods. Current Projects: Investigating discourse patterns across historical and contemporary contexts, including Early Modern Nonconformist networks and digital discourse around football fandom. He collaborates with institutions like the University of Helsinki and engages with public sectors on digital research projects. Teaching includes digital research methods and critical thinking in the digital age. His affiliations include the Computational Humanities Research Group and the Centre for Digital Culture at King's College London.

Brian KulisView profile

Associate Professor

• Machine Learning
• Statistics
• Large-scale Data Analysis
• +5 more

Brian Kulis is an Associate Professor at Boston University with appointments in the Department of Electrical and Computer Engineering, Computer Science, Systems Engineering, and the Faculty of Computing and Data Sciences. He holds the Peter J. Levine Career Development Professorship and has previously been an Amazon Scholar at Alexa AI (2019–2023) and an assistant professor at Ohio State University (2012–2015). His research focuses on machine learning, including large-scale optimization, metric learning, deep learning, Bayesian methods, and applications in audio and visual data analysis. He earned his PhD in Computer Science from the University of Texas at Austin (2008) and a BS in Computer Science and Mathematics from Cornell University. Key awards include the NSF CAREER Award (2015), CVPR Best Student Paper (2008), and ICML Best Student Paper (2007, 2005). His work spans publications in top venues like CVPR, NeurIPS, ICML, and ECCV, emphasizing scalable algorithms and domain adaptation. Current research explores metric learning, adversarial audio augmentation, and HPC anomaly detection. He advises multiple PhD students and collaborates on grants such as the NSF Traineeship for Sustainable Energy Solutions (2024). He teaches advanced courses in machine learning, deep learning, and data structures. His lab focuses on foundational and applied ML challenges, with affiliations in the Intelligent, Autonomous & Secure Systems group. Recent service includes senior area chair roles at AAAI, NeurIPS, and ICML.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Min Peng is a Professor at Wuhan University's School of Computer Science. His research focuses on artificial intelligence, machine learning, natural language processing, and knowledge graphs. He has collaborated extensively with institutions like Hefei University of Technology and the University of Chinese Academy of Sciences. His work bridges theoretical advancements in AI with practical applications in finance, social media analysis, and network optimization. Recent contributions include neural-symbolic reasoning frameworks, contrastive learning for knowledge graphs, and financial benchmarking with large language models. Research interests emphasize scalable machine learning models for complex reasoning tasks, explainable AI, and domain-specific applications in finance and social networks. Over 100 publications span venues like WWW, ACL, and NeurIPS, highlighting interdisciplinary impact. Notable projects include SymAgent (neural-symbolic agent frameworks), PIXIU (financial LLM benchmark), and DTC (commonsense machine comprehension). Key technical trends include integrating large language models with structured data, temporal knowledge graph reasoning, and transfer learning across domains. His work often addresses real-world challenges in data efficiency, interpretability, and cross-domain scalability. Current efforts explore financial LLMs, agent-based reasoning systems, and multimodal applications. While no specific grants or awards are listed in the provided data, his prolific publication record indicates sustained research excellence. Collaboration networks include teams in computer science, electrical engineering, and finance disciplines.

• Large-Scale Data Management
• Data Systems
• Big Data Analysis
• +5 more

Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto. His research focuses on large-scale data management, integrating machine learning into data systems, and developing efficient query processing techniques for unstructured and streaming data. He holds a PhD from the University of Toronto, an MSc from the University of Maryland at College Park, and a Bachelor's from the University of Patras in Greece. Research interests include data systems, big data analysis, video query processing, and natural language interfaces for databases. He leads projects like ReDD (Relational Deep Dive), SVQ (Streaming Video Queries), and Reliable Text-to-SQL, aiming to bridge human-readable queries with database execution. His work emphasizes scalability, intelligence, and real-world applicability. Recipient of the University of Toronto's Inventor of the Year Award (2011), he translates research into startups like Sysomos, Aislelabs, and Workorb. His contributions span over 200 publications in top venues such as SIGMOD, VLDB, and ICDE. Courses taught include advanced data systems, database design, and system internals. Current projects explore schema extraction from unstructured data, video query optimization, and cost-effective machine learning pipelines. Collaborations with industry and academic partners drive innovations in both theory and practical applications.

• Artificial Intelligence
• Machine Learning
• Fairness in AI
• +5 more

Sanmi Koyejo is an Assistant Professor of Computer Science at Stanford University and holds an adjunct position as Associate Professor at the University of Illinois at Urbana-Champaign. He leads the Stanford Trustworthy AI Research (STAIR) group, focusing on fairness, robustness, and healthcare applications in machine learning. His work bridges theoretical foundations with practical systems, emphasizing ethical AI and clinical informatics. Affiliations: SAIL, HAI, CRFM, AIMI, AI Safety, and the Machine Learning Group. Research Interests: His expertise spans trustworthy AI, federated learning, and neuroimaging. He actively addresses challenges in algorithmic fairness, particularly in healthcare, where he collaborates with institutions like OSF Healthcare on projects like federated learning for clinical data. Key Contributions: Co-developed frameworks for unlearning in large language models, evaluated AI systems' societal impacts, and advanced benchmarks for medical applications. His work has been featured in venues like NeurIPS, ICML, and AAAI. Awards: NSF CAREER Award, Alfred P. Sloan Fellowship, and Terman Faculty Fellowship. Grants & Teams: Leads NSF-funded projects on domain adaptation and fairness in breast cancer risk scoring. Collaborates with interdisciplinary teams on NIH's MIDRC and NSF's AIFARMS initiative for agricultural sustainability. Labs/Teams: STAIR lab drives interdisciplinary research in ethical AI, with emphasis on real-world deployment and policy implications.

Lingming ZhangView profile

Associate Professor

• Software Engineering
• Programming Languages
• Machine Learning
• +6 more

Lingming Zhang is an Associate Professor at the Department of Computer Science, University of Illinois Urbana-Champaign, affiliated with the Grainger College of Engineering. His research focuses on the intersection of Software Engineering, Programming Languages, and Machine Learning, with a particular emphasis on automated program repair, compiler testing, and large language model (LLM) applications in software engineering. He has published over 100 papers, achieving an h-index of 50+, and holds an ACM Distinguished Member status. Research Interests: LLM-based software testing, repair, and synthesis Fuzzing of deep-learning libraries and compilers Open-source code LLMs (e.g., StarCoder2, Magicoder) with over 1M downloads Automated program repair systems (e.g., AlphaRepair, ChatRepair, Agentless) Recent Contributions: Developed TitanFuzz for coverage-guided compiler fuzzing Released Agentless , an LLM-based coding tool adopted by OpenAI and DeepSeek Proposed SWE-RL to enhance LLM reasoning via reinforcement learning Service Roles: Program Co-Chair for ASE 2025 and LLM4Code 2025 Associate Chair for OOPSLA 2024 and Area Chair for ICSE 2025/2026 Recipient of NSF CAREER Award and ACM SIGSOFT Early Career Award Lab/Teams: Develops open-source tools like UniAPR for efficient patch validation Active in releasing industry-adopted LLM-based software engineering tools

Zachary McDowellView profile

Assistant Professor

• Digital Media Studies
• Information Literacy
• Wikipedia and Knowledge Production
• +5 more

Zachary McDowell is an Assistant Professor of Communication at the University of Illinois Chicago (UIC), affiliated with the College of Liberal Arts and Sciences and the Department of Communication. His research focuses on access, advocacy, and equity in digital spaces, particularly regarding knowledge production in platforms like Wikipedia and the ethical implications of AI. He holds a PhD in Communication from the University of Massachusetts, Amherst. Dr. McDowell’s research explores how technology shapes cultural production, digital literacy, and equity. Key areas include AI’s impact on knowledge systems, ethics of data commons (e.g., Wikidata), and participatory media’s role in marginalized communities. He co-founded the open-access journal communication +1 , emphasizing innovation in communication scholarship. His teaching philosophy integrates critical engagement with technology and equity, using embodied practice to bridge theory and application. He volunteers in Chicago’s McKinley Park neighborhood and advocates for systemic equity in education and knowledge systems. Notable collaborations include work with the Wiki Education Foundation on student learning outcomes through Wikipedia editing. Education: PhD in Communication, University of Massachusetts Amherst Key Roles: Co-founder, communication +1 Journal; Research Fellow, Wiki Education Foundation Awards: None explicitly listed in provided texts

• Machine Learning
• Artificial Intelligence
• Genomics
• +4 more

Haohan Wang serves as Assistant Professor at the School of Information Sciences, University of Illinois Urbana-Champaign, with additional appointments as Affiliate at the Carl R. Woese Institute for Genomic Biology and Assistant Professor at the National Center for Supercomputing Applications (NCSA). His interdisciplinary work bridges machine learning, genomics, and AI security, focusing on trustworthy systems for biomedical applications and foundational AI research. Wang's research centers on robust and secure artificial intelligence, with emphasis on large language model vulnerabilities (jailbreaking, safety evaluation), federated learning personalization, and genomic data analysis. He develops techniques for privacy-preserving dataset distillation, confounding factor correction in genome-wide studies, and multi-agent frameworks for scientific discovery. His fingerprint highlights expertise in Machine Learning (94%), Linear Mixed Models (87%), and Confounding Factor Correction (41%), reflecting his focus on methodological rigor in complex data environments. Analysis of his 2025 publications reveals dominant trends in AI security (jailbreak evaluation frameworks like GuardVal, adversarial attacks such as InfoFlood), biomedical AI (transcriptomic analysis, wearable data privacy), and foundational methods (federated learning optimization, synthetic data generation). These works consistently address real-world challenges in model trustworthiness while advancing computational techniques for genomics and healthcare. Through NCSA's high-performance computing resources and the Institute for Genomic Biology's collaborative ecosystem, Wang integrates supercomputing capabilities with biological research to tackle data-intensive problems in disease modeling and AI safety testing, as evidenced by media coverage of his team's AI security testing methods.