Find Academic Supervisors

• Biostatistics
• Cancer Genomics
• Statistical Methods for High-Dimensional Data
• +4 more

Yuan Huang is an Assistant Professor in the Department of Biostatistics at the Yale School of Public Health . Her research focuses on statistical methods for high-dimensional data, motivated by challenges in cancer genomics and neurodegenerative diseases. She develops approaches for biomarker identification, network structure estimation, and gene-environment interaction analysis, with applications in Alzheimer’s, Huntington’s, and Parkinson’s diseases. Key Affiliations: Yale Cancer Center, Center for Brain & Mind Health, Yale Center for Analytical Sciences (YCAS) Her methodological work emphasizes integrative analysis across multiple datasets to improve reproducibility and discovery. Recent collaborations span clinical trials, genetics, and epidemiology, with a focus on addressing heterogeneity and nonlinearity in complex biomedical data. Her publications include Bayesian finite mixture models, precision matrix estimation, and advanced techniques for high-dimensional causal mediation analysis. She actively engages in translational research, linking statistical innovation to clinical and public health challenges.

• Data Science and Data Analytics
• Machine Learning
• Recommender Systems
• +7 more

Guandong Xu is a Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he has been employed since 2012. He also serves as the Director of the UTS-Providence Smart Future Research Centre, which focuses on disruptive technology for sustainability, and leads the Data Science and Machine Intelligence Lab dedicated to research excellence and industry innovation in data science and artificial intelligence. Dr. Xu holds a PhD in Computer Science from Victoria University, Australia, along with MSc and BSc degrees in Computer Science and Engineering. After holding various research positions at European and Australian universities, he joined UTS in 2012 and was promoted to Associate Professor in January 2017, then to Professor in January 2019. His research spans data mining, machine learning, social computing, recommender systems, text mining, predictive analytics, and user behavior modeling. He has published over 240 papers in these areas with increasing citations from academia. His recent work demonstrates a strong focus on integrating large language models with recommendation systems, causal inference in recommendation, multimodal learning, and fairness in AI systems. His publications reveal sophisticated graph-based approaches and addressing challenges in dynamic recommendation scenarios, particularly through temporal modeling and hypergraph structures. Dr. Xu has received numerous prestigious awards including the Digital Disruptors Winner for ICT Research Project of the Year (2021), eBay's Leaders' Choice Award (2021), and was elected Fellow of Institution of Engineering and Technology (IET), UK (2021) and Fellow of Australian Computer Society (ACS) (2022). He has shown strong academic leadership as founding Editor-in-Chief of Human-centric Intelligent Systems Journal, Assistant Editor-in-Chief of World Wide Web Journal, and founding Steering Committee Chair of the International Conference of Behavioural and Social Computing Conference. He has supervised over 25 high degree research students and secured over $8 million in research funding from ARC, government, and industry sources, including projects like 'Smart Personalized Privacy Preserved Information Sharing in Social Networks' and 'A Secured Smart Sensing and Industry Analytics Facility for Industry 4.0.' Dr. Xu directs the Data Science and Machine Intelligence Lab at UTS, which aligns with UTS research priority areas in data science and artificial intelligence. The lab focuses on research excellence and industry innovation across academia and industry, with particular emphasis on developing advanced techniques for recommendation systems, knowledge graphs, and multimodal learning applications.

Markus JänttiView profile

Professor

• Income Distribution
• Wealth Distribution
• Poverty
• +4 more

Markus Jäntti serves as Professor at Stockholm University's Institute for Social Research (SOFI), where his research centers on income and wealth distribution, poverty dynamics, and socio-economic mobility through comparative international lenses. He specializes in quantifying family background's influence on economic outcomes and leads pivotal projects including MapIneq (life-course inequality trends), PrecaNord (Nordic precarious work analysis), and TITA (austerity-era inequality assessment). His research portfolio spans income distribution , wealth inequality , intergenerational mobility , and comparative social policy , employing advanced econometric techniques and cross-national datasets like the Luxembourg Income Study. Work within SOFI's Labor Market Economics (AME) group extends to education, health, taxation, and gender equality, reflecting labor economics' interdisciplinary nature. Analysis of his 2020-2025 publications reveals dual emphases: methodological innovation (e.g., grouped-data inequality measurement) and empirical exploration of family-background effects, labor policies, and social transfers. His influential 2020 framework synthesizing four mobility approaches underscores conceptual rigor, while recent Nordic-focused studies address contemporary challenges like migrant labor exploitation. Scientific awards: No awards were documented in source materials. Major grant-funded projects include: MapIneq : Tracking intergenerational, educational, labor market, and health inequality drivers across lifespans PrecaNord : Multi-level analysis of precarious/informal work in Finland, Norway, and Sweden TITA : Consortium study of austerity's impacts on financial, health, and opportunity inequalities As core faculty in SOFI's AME group, he contributes to research spanning labor market outcomes (wages, employment), social transfers, crime, and political economy—operating within Stockholm University's broader social policy ecosystem while maintaining independent research leadership.

Marco L. Della VedovaView profile

Senior Lecturer

• Artificial Intelligence and Machine Learning
• Interpretable and Explainable AI
• Geospatial AI
• +7 more

Marco L. Della Vedova is a Senior Lecturer in Applied Artificial Intelligence at Chalmers University of Technology, Sweden. He works in the Vehicle Engineering and Autonomous Systems division within the Department of Mechanics and Maritime Sciences, as part of Prof. Mattias Wahde's research group. Since 2025, he has served as Director of the Data Science and AI master's programme (MPDSC) at Chalmers, where he teaches courses including Introduction to Artificial Intelligence and Digitalization in Sports. Dr. Della Vedova earned his academic foundation at the University of Pavia, Italy, where he completed his BSc (2006), MSc (2009), and PhD (2013) in Computer Engineering. His doctoral research focused on "Real-Time Physical Systems and Electric Load Scheduling" under Prof. Tullio Facchinetti. During his PhD studies, he spent a year at U.C. Berkeley hosted by Prof. Francesco Borrelli at the Model Based Predictive and Distributed Control Lab. His research spans multiple AI domains with a strong emphasis on interpretability. Dr. Della Vedova develops interpretable methods for conversational AI, naturalness evaluation of forests using canopy height models, and geospatial applications. His work bridges theoretical AI with practical societal benefits, particularly in environmental monitoring, transportation systems, and orienteering. He has previously contributed to cloud computing, hate speech detection, and cyber-physical energy systems, demonstrating his interdisciplinary approach to AI research. Dr. Della Vedova's publication record reveals a consistent trajectory of impactful research across multiple domains of artificial intelligence. His recent work shows a strong focus on interpretability in AI systems, with significant contributions to natural language processing, geospatial analysis, and causal inference. The research demonstrates both theoretical depth and practical applications, particularly in environmental monitoring and social media analysis. His methodology often combines traditional machine learning approaches with novel interpretability techniques, creating bridges between complex AI systems and human understanding. Dr. Della Vedova has received several prestigious recognitions for his work: Best PhD thesis award from the Order of the Engineers of Bergamo (2013) Italian champion of Il Cervellone (2012) Top Italian performer in IEEEXtreme 6.0 programming competition (148th overall globally, 2012) Premio Arturo Schena award from Fondazione Credito Valtellinese (2010) With over 50 students supervised through bachelor's and master's theses, Dr. Della Vedova has established himself as a dedicated mentor in the AI community. His current PhD students include Minerva Suvanto working on interpretable NLP and Vivien Lacorre developing AI for railway infrastructure inspection. His supervision spans diverse topics from forest naturalness evaluation to hate speech detection and transportation optimization. Beyond formal supervision, he actively contributes to educational initiatives including serving as Director of Chalmers' Data Science and AI master's program and developing innovative teaching methods that connect theoretical concepts with real-world applications. Dr. Della Vedova is deeply embedded in both academic and professional communities. He leads the Applied Artificial Intelligence research group at Chalmers while maintaining strong connections with European research networks through projects like the ERASMUS+ EUrienteering initiative. His interdisciplinary approach is reflected in collaborations across computer science, environmental science, and social sciences. Notably, he applies his AI expertise to orienteering both as a researcher developing localization methods and as a licensed Event Advisor for the International Orienteering Federation, demonstrating how his professional and personal interests converge in innovative ways.

Jamal AtifView profile

Professor

• Artificial Intelligence
• Responsible AI
• Privacy Preservation in Machine Learning
• +4 more

Jamal Atif is a Professor at Paris-Dauphine University and holds multiple significant leadership positions including Project Manager for 'Data Science and Artificial Intelligence' at the Institute of Information Sciences and their Interactions (INS2I) of the CNRS, Deputy Scientific Director of 3IA PRAIRIE, Head of the MILES team/project at LAMSADE (UMR CNRS-Université Paris-Dauphine), Co-leader of the Transverse Artificial Intelligence Program at PSL University, and Director of the Dauphine Numérique program. Professor Atif's primary research focuses on the foundations of responsible artificial intelligence, with specific expertise in privacy preservation in machine learning, robustness of deep learning algorithms to malicious attacks, causality, and explainability. His work bridges theoretical foundations with practical applications in security and reliability of AI systems. He has developed innovative approaches to address adversarial vulnerabilities in machine learning models and has made significant contributions to privacy-preserving techniques in data analysis. His publication record demonstrates a consistent focus on robust and trustworthy AI systems, with recent work exploring differential privacy in clustering, adversarial robustness, and explainable AI. The research spans theoretical foundations in logic and knowledge representation to practical applications in finance, healthcare, and computer vision. His publications appear in top-tier venues including Machine Learning journal, Neural Information Processing Systems, and International Joint Conferences on Artificial Intelligence. Scientific Awards: Recipient of two awards from the North American Society of Radiology for his thesis work Professor Atif has co-supervised or is currently supervising around fifteen doctoral students, demonstrating his commitment to mentoring the next generation of AI researchers. His leadership extends to directing major institutional programs including Dauphine Numérique and the Transverse Artificial Intelligence Program at PSL University, where he shapes strategic research directions in AI. He leads the MILES team/project at LAMSADE, which focuses on foundational aspects of machine learning and artificial intelligence. The team's research spans theoretical aspects of learning algorithms to practical applications requiring robust and reliable AI systems, with particular emphasis on security and privacy considerations in modern machine learning deployments.

• Bioinformatics
• Computational Biology
• Pharmaceutical Drug Discovery & Development
• +13 more

Leonard Wesley is an Associate Professor in the Department of Computer Science at the College Of Science, San Jose State University. His research spans interdisciplinary domains at the intersection of Bioinformatics , Computational Biology , and Machine Learning , with specific applications in Pharmaceutical Drug Discovery , Genomic Data Analysis , and Autonomous Robotics . Education: Ph.D. in Computer Science, University of Massachusetts M.S. in Computer Science, University of Massachusetts B.A. in Physics and Math, Northeastern University Research Interests include Approximate Reasoning (probabilistic, evidential, and fuzzy logic), Agent-Oriented Systems , and Sensor Fusion . His work applies these methodologies to Drug Portfolio Management , Protein Structure Scoring , and Medical Diagnostics . Publication Trends show a consistent focus on Computational Biology , Robotics , and Uncertainty Quantification over four decades. Early work in Computer Vision evolved into modern applications in Pharmaceutical Analytics and AI in Aerospace . Key Projects include SVM-based drug affinity prediction, evidence-driven decision support systems for biopharma, and real-time agent development frameworks like ROADS. He has contributed to CFD code control and Mobile Network Congestion solutions. Collaborations with institutions like NASA, Los Alamos National Laboratory, and international conferences (WMSCI, ICINCO, AIAA) highlight his cross-disciplinary impact. His teaching includes Artificial Intelligence and Bioinformatics courses.

Ayesha AliView profile

Professor

• Graphical Markov models
• Pollination networks
• Multivariate statistics
• +7 more

Ayesha Ali is a Professor of Statistics and Director of the Master of Data Science program at the University of Guelph. She holds a PhD in Statistics from the University of Washington (2002) and has expertise in statistical methods for complex high-dimensional systems, including ecological networks, causal inference, and bioinformatics. Her research integrates graphical Markov models, machine learning, and statistical computing to address challenges in plant-pollinator networks, livestock genetics, and disease risk modeling. Education: B.Sc. Honours in Statistics and Actuarial Science, University of Western Ontario (1996) M.Sc. in Statistics, University of Toronto (1998) Ph.D. in Statistics, University of Washington (2002) Research Interests: Graphical Markov models and ecological networks Causal inference and longitudinal data analysis Machine learning and high-dimensional predictive modeling Statistical methods for livestock genetics and animal health Computational statistics and bioinformatics Articles Trends: Her recent work spans interdisciplinary applications, including veterinary oncology biomarker discovery, remote sensing for agricultural suitability, and pipeline development for cross-species transcriptomics. She emphasizes graphical structure exploitation in regression and predictive modeling, with contributions to both theoretical and applied statistical methodologies. Awards: Canadian Journal of Statistics Award (2020) for groundbreaking work on doubly sparse regression NSERC Discovery Grant (2018) NSERC Collaborative Research and Development Grant (2015) Advising & Grants: She has supervised numerous graduate and undergraduate students on projects ranging from plant-pollinator network analysis to bioinformatics. Her grants include NSERC-funded research on milk fatty acid genetics and statistical methods for clustered data. Labs/Teams: Involved in the Bioinformatics program at the University of Guelph, contributing to interdisciplinary research collaborations in ecology and animal science.

• Artificial Intelligence and Law
• Computational Argumentation
• Legal Reasoning
• +5 more

Professor Henry Prakken is a leading researcher in Artificial Intelligence and Law at Utrecht University's Department of Computer Science. He holds a prominent position in the Responsible AI research group and has been instrumental in advancing computational models of legal argumentation. With an extensive publication record spanning over three decades, Prakken has established himself as a central figure in the intersection of law and artificial intelligence. Prakken's research interests focus on computational models of argumentation, legal reasoning systems, and the application of AI to legal domains. His work bridges theoretical foundations with practical applications, particularly in case-based reasoning, Bayesian networks for legal evidence, and formal argumentation frameworks. He has made significant contributions to understanding how computational models can represent and analyze legal reasoning processes, with particular attention to precedent-based reasoning and argument strength. His publication record demonstrates consistent research productivity with numerous high-impact papers in venues like Artificial Intelligence, Artificial Intelligence and Law, and proceedings of major conferences including ICAIL and COMMA. Recent work has expanded into evaluating generative AI's legal reasoning capabilities and developing more sophisticated models of argument strength and acceptability. Past president of the International Association for AI & Law (IAAIL) Past president of the JURIX Foundation for Legal Knowledge-Based Systems Past president of the steering committee of the COMMA conferences on Computational Models of Argument Editorial board member of Artificial Intelligence and Law Associate editor of Artificial Intelligence (2017-2022) Prakken has played a significant role in mentoring and shaping the field through his leadership positions and editorial work. His research has influenced both theoretical developments in computational argumentation and practical applications in legal technology, contributing to the growing field of responsible AI in legal contexts.

• Developmental Psychopathology
• Antisocial Behavior
• Behavioral Genetics
• +3 more

Terrie Moffitt is a Professor in the field of Social Behavior & Development at the Social Genetic & Developmental Psychiatry department in the UK. Her work focuses on developmental psychopathology, antisocial behavior trajectories, and longitudinal research methodologies. She has contributed to over 400 research outputs and leads major studies like the Dunedin Longitudinal Study and E-Risk Twin Study. Research Interests: Natural history of antisocial behavior across lifespans Behavioral genetics and neuropsychological assessment Longitudinal methodologies for mental health studies Childhood adversity and developmental outcomes Recent Projects: Midlife Aging in the Dunedin Study Phase 52 (2023-2028) E-Risk @30: Longitudinal Twin Study (2023-2027) Childhood Maltreatment Resilience Study (2017-2019) Grant funding includes Medical Research Council (MRC) and Economic and Social Research Council (ESRC) awards. Her research contributes to UN Sustainable Development Goals related to good health and well-being (SDG 3).

Nikil DuttView profile

Professor

• Embedded Systems
• Electronic Design Automation
• Computer Architecture
• +6 more

Nikil Dutt is a Chancellor’s Professor at the University of California, Irvine (UCI), with academic appointments in Computer Science, Electrical Engineering and Computer Science (EECS), and Cognitive Sciences. He is affiliated with UCI's Center for Embedded Computer Systems (CECS), Center for Cognitive Neuroscience and Engineering (CENCE), Calit2, CPCC, and LUCI. His research focuses on embedded systems, electronic design automation, computer architecture, and healthcare IoT. Dutt has authored/co-authored seven books and holds IEEE Fellow and ACM Distinguished Scientist titles. Education: B.E. (Mechanical Engineering) from Birla Institute of Technology and Science (Pilani, India), 1980; M.S. (Computer Science) from Pennsylvania State University, 1983; Ph.D. (Computer Science) from University of Illinois at Urbana-Champaign, 1989. Research Interests: Embedded systems, brain-inspired architectures, neuromorphic computing, and healthcare IoT. Current projects include the Information Processing Factory (IPF) for autonomous systems and CareDex for disaster resilience in aging communities. Awards: Multiple Best Paper Awards, NSF grants, and fellowships. Serves as editor for ACM TECS, IEEE TVLSI, and former Editor-in-Chief of ACM TODAES. Active in academic service, including ESWEEK Steering Committee roles. Grants: NSF IPF, UNITE, CareDex, and industry partnerships (e.g., Facebook). Research addresses energy-efficient data centers, autonomous driving systems, and wearable health technologies. Labs/Teams: Leads the Dutt Research Group (DRG), focusing on self-aware systems, edge computing, and neuromorphic architectures.

Leong Shu MinView profile

Lecturer

• Face Recognition
• Emotion Recognition
• Face Analysis
• +3 more

Leong Shu Min is a Lecturer in the School of Information Technology at Monash University Malaysia. She holds a Ph.D. in IT from Monash University Malaysia (2023), focusing on privacy-preserving and emotional understanding of human faces using machine learning. She earned her Master of Engineering Science (2020) and B.Eng. (Hons) in Electronics with Computer specialization (2018) from Multimedia University. Her research emphasizes face analysis, emotion recognition, and security-related image processing. Education Ph.D., IT, Monash University Malaysia (2019–2023) M.Eng.Sc., Multimedia University (2018–2020) B.Eng., Multimedia University (2014–2018) Research Interests Her work centers on facial recognition systems, emotion analysis, and privacy-preserving techniques. She explores Local Binary Pattern algorithms and micro-expression recognition, aiming to enhance security and ethical AI applications. Recent projects include detecting synthetic music and uncovering biases in video-based emotion recognition systems. Projects Chief Investigator in the Æinstein: Adversarial AI amongst Materials Discovery Domains project (2024–2026), focusing on AI-driven material discovery and ethical AI challenges. Advising She has been accepting PhD students since 2020, mentoring research in facial analysis and machine learning applications.

Patryk PerkowskiView profile

Assistant Professor

• Information Technology
• Human Resource Management
• Causal Inference
• +3 more

Patryk Perkowski is an Assistant Professor of Strategy and Entrepreneurship at the Sy Syms School of Business, Yeshiva University. He holds a PhD from Columbia Business School and completed a postdoctoral fellowship in Business, AI, and Democracy at Columbia. His research focuses on the intersection of information technology, human resource management, and causal inference, examining how technological advancements like AI reshape talent competition and organizational strategies. He also develops methodological tools for empirical business research. Education: PhD and Postdoctoral Scholar at Columbia Business School. Research interests include AI's impact on workplaces, internal talent markets, labor discrimination trends, and algorithmic hiring. He publishes in top journals such as the Strategic Management Journal and Management Science. His work analyzes modern workforce dynamics, including automation effects, team coordination, and central banking AI applications. Notable studies address gender representation in hiring algorithms and long-term discrimination trajectories through meta-analyses. Advising: No advisee list provided. Grants and funding details not specified. His research bridges theory and practice, offering insights for firms to leverage human capital strategically in tech-driven environments.

Max SchaubView profile

Assistant Professor

• Violence Studies
• Migration Studies
• Political Science
• +3 more

Prof. Max Schaub is an Assistant Professor of Political Science at the University of Hamburg and a WZB Fellow at the Berlin Social Science Research Center. His research focuses on violence, migration, health, and their societal impacts, employing experimental and mixed-methods approaches. He holds a Ph.D. from the European University Institute (2016), an M.Sc. from Oxford, and a B.A. from Dresden. Prior roles include postdoctoral positions at WZB and Bocconi University. Education: Ph.D. in Political and Social Sciences, European University Institute (2016) M.Sc. in African Studies, University of Oxford B.A. in International Relations, University of Dresden Research Interests: Violence dynamics and conflict consequences Migration determinants and societal integration Health outcomes in conflict zones and policy responses Political behavior under economic stress Current Projects: WarGen (DFG-funded): Examines electoral turnovers and health costs during crises ViVac (GLOHRA-funded): Analyzes civilian targeting in interstate wars Cultural roots of prejudice via antisemitism studies Teaching Roles: University of Hamburg: Courses on peacebuilding, political violence, global health Hertie School: Causal inference Free University of Berlin: Historical political economy Labs/Teams: Leads WarGen and ViVac projects, collaborating with institutions in Germany, Italy, and Senegal.

Sriraam NatarajanView profile

Professor

• Artificial Intelligence
• Machine Learning
• Statistical Relational Learning
• +5 more

Sriraam Natarajan is a Professor and Director of the Center for Machine Learning at the Erik Jonsson School of Engineering & Computer Science, University of Texas at Dallas. He previously served as an Associate Professor at Indiana University (on leave since 2017) and Wake Forest School of Medicine. His research focuses on artificial intelligence, machine learning, and their biomedical applications, particularly in relational learning, reinforcement learning, and graphical models. He leads the StaRLing Lab and holds fellowships from hessian.AI and RBCDSAI. Education: PhD in Computer Science from Oregon State University (2007), advised by Prasad Tadepalli. Postdoctoral research at University of Wisconsin-Madison under Jude Shavlik and David Page. Research interests span statistical relational AI, causal inference, and healthcare applications. Notable awards include AAAI Fellow (2025), UTD Outstanding Graduate Teaching Award, and roles as AAAI Program Co-Chair and CODS-COMAD 2024 co-chair. Students supervised include over 20 PhD/MS graduates and current advisees in AI and machine learning. Active in editorial roles for JAIR, Machine Learning Journal, and conference PCs (ICML, AAAI, NIPS).

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

Susan Wei is an Associate Professor in the Department of Econometrics & Business Statistics at Monash University. Her research focuses on machine learning theory, fairness in AI, causal inference, and Bayesian methods. She leads two active research projects: 'Deep Learning Theory' funded by Google (Singapore) and 'Making Machine Learning Fair(er)' supported by the Australian Research Council (ARC). Her work contributes to UN Sustainable Development Goals through ethical AI applications. Key collaborations include international institutions like the ARC and top universities globally. Wei's research explores the mathematical foundations of deep learning, addressing challenges in optimization, fairness, and interpretability. Notable contributions include developing variance reduction techniques in variational inference and advancing interventional fairness frameworks using causal graphs. Her projects aim to improve algorithmic transparency and ethical AI deployment across industries. Her grants include a 2020-2025 ARC grant (Making Machine Learning Fair(er)) and a Google-funded initiative starting in 2024. Though no formal advisees are listed, she collaborates with researchers like Zuo, Gong, and Lau on high-impact papers. Her work is published in top venues like IEEE Transactions and the International Conference on Learning Representations (ICLR).