Find Academic Supervisors

Lijing WangView profile

Assistant Professor

• Epidemic Forecasting
• Artificial Intelligence
• Machine Learning
• +3 more

Lijing Wang is an Assistant Professor of Data Science at the New Jersey Institute of Technology (NJIT). She specializes in interdisciplinary research at the intersection of artificial intelligence, epidemiology, and public health. Her work emphasizes data-driven approaches to forecasting infectious disease dynamics, integrating machine learning with theoretical epidemiological models. Education: Ph.D. in Computer Science, University of Virginia (2021) M.S. in Computer Science, Chinese Academy of Sciences (2013) B.S. in Software Engineering, Dalian University of Technology (2010) Research focuses on epidemic forecasting using ensemble modeling, graph neural networks, and causal inference. Key topics include: COVID-19 and influenza prediction using mobility data AI-driven disease surveillance systems Policy impact analysis of pharmaceutical/nonpharmaceutical interventions Cross-national epidemic modeling Publications consistently address forecasting challenges through innovative methodological combinations - e.g., Bayesian ensemble techniques, causal graph approaches, and multi-scale human mobility analysis. Recent work emphasizes real-time prediction accuracy improvements for public health decision-making. No scientific awards explicitly noted in text. Active in collaborative research involving public health agencies and international institutions.

John J. CurtinView profile

Professor

• Digital therapeutics
• Machine learning applications in mental health
• Addiction research
• +5 more

John J. Curtin is a Professor in the Department of Psychology at the University of Wisconsin-Madison, where he directs the Addiction Research Center. His work bridges clinical psychology, computer science, and engineering to develop innovative digital solutions for mental health and addiction treatment. Dr. Curtin's research focuses on digital therapeutics and personal sensing technologies for substance use disorders and mental illness. His laboratory develops software applications that provide evidence-based interventions, treatment management tools, and enhanced communication with care providers. He specializes in algorithm development for moment-to-moment psychiatric risk prediction and just-in-time personalized interventions that adapt to both patient characteristics and their current context. His research program is highly interdisciplinary, collaborating with the Center for Health Enhancement Systems Studies, computer science, geography, and electrical and computer engineering departments. Dr. Curtin's work combines machine learning approaches with novel data streams from geolocation, cellular communications, social media activity, and wearable biosensors to create more effective and personalized treatment approaches. Dr. Curtin has secured continuous funding from the National Institutes of Health (NIAAA, NIDA, NCI and NIMH) since 1998. His current research examines machine learning-assisted precision medicine for smoking cessation, contextualized daily prediction of lapse risk in opioid use disorder, and dynamic real-time prediction of alcohol use lapse using mobile health technologies. His laboratory has produced numerous publications advancing the field of digital mental health interventions, with a particular focus on using technology to deliver precisely tailored treatments at the right moment for individuals struggling with substance use disorders.

Steven LivingstoneView profile

Associate Professor

• Data Science
• Affective Computing
• Human Computer Interaction
• +4 more

Steven Livingstone is an Associate Professor in the Department of Computer Science at Ontario Tech University, Faculty of Science. His research focuses on affective data science, applying machine learning and statistical modeling to understand emotion and its disorders. He leads the Affective Data Science Lab (ADSL), specializing in emotion recognition technologies using physiological data like EEG and motion capture. Livingstone holds a PhD from The University of Queensland (2008) and has published over 70 peer-reviewed papers, with over 2,400 citations. His RAVDESS dataset is widely used in speech emotion recognition research. His research interests span data science, affective computing, and music's role in emotion. Recent work emphasizes data provisioning for deep learning applications. Livingstone teaches courses including Scientific Data Analysis and Information Visualization. He actively mentors undergraduate and graduate students in his lab, focusing on research assistantships in emotion technology development. Key contributions include studies on musical tempo’s physiological effects, Parkinson’s disease facial mimicry deficits, and ensemble performance dynamics. His work has been featured in The Atlantic, NBC Today, and on the cover of Informatik Spektrum. The ADSL lab collaborates on projects combining data analytics with human-computer interaction to advance emotion-aware systems.

Rachid GuerraouiView profile

Professor

• Distributed Computing
• Concurrent Computing
• Byzantine Fault Tolerance
• +7 more

Rachid Guerraoui is a Full Professor at the École polytechnique fédérale de Lausanne (EPFL) where he leads the Distributed Computing Laboratory (DCL) within the School of Computer and Communication Sciences. He holds appointments in multiple departments including IC-SSC and IC-SIN for teaching, and serves on the IC Academic Evaluation Committee. A Moroccan/Swiss/French researcher, Guerraoui has previously been affiliated with Commissariat à l'Energie Atomique in Saclay, Hewlett-Packard Labs in Palo Alto, the Massachusetts Institute of Technology in Boston, and Collège de France in Paris. Guerraoui's research focuses on distributed and concurrent computing across various scales, from multiprocessors to wide-area networks. His work spans Byzantine fault tolerance, distributed machine learning, blockchain technologies, transactional memory, and consensus algorithms. His recent publications reveal a strong emphasis on Byzantine-resistant machine learning, decentralized learning systems, and the theoretical foundations of distributed consensus. The research demonstrates significant contributions to making distributed systems more robust, efficient, and secure against adversarial conditions. Guerraoui has received numerous prestigious awards including ACM Fellow (2012), Professor at College de France (2018), Nygaard-Dahl Award (2024), and Barroso Award (2025). His work has earned multiple best paper awards at top conferences including DISC, ICDCS, IPDPS, and ACM Middleware. He serves as Associate Editor of the Journal of the ACM (2010-2025) and has chaired program committees for major conferences such as PODC, DISC, and Middleware. As an educator, Guerraoui supervises numerous doctoral students and has mentored many successful researchers who now work at leading institutions and companies including Meta, Oracle Labs, Chainlink Labs, and Protocol Labs. He teaches courses on Distributed Algorithms and Concurrent Algorithms at EPFL, emphasizing both theoretical foundations and practical implementations. His educational initiatives include Wandida, a library of scientific e-synopses, and Zettabytes, projects aimed at making computer science accessible to broader audiences.

Jiebo LuoView profile

Professor

• Computer Vision
• Natural Language Processing
• Machine Learning
• +7 more

Jiebo Luo is the Albert Arendt Hopeman Professor of Engineering and Professor of Computer Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD and has been affiliated with the Department of Computer Science since 2011, following a 15-year career at Kodak Research. His research spans computer vision, natural language processing, machine learning, data mining, computational social science, and digital health. Luo is an ACM Fellow, AAAI Fellow, IEEE Fellow, SPIE Fellow, and IAPR Fellow. Education: PhD (specific discipline not explicitly stated in text). Research interests include computer vision, machine learning, data mining, social media analysis, biomedical informatics, human-computer interaction, and ubiquitous computing. He co-authored the book Deep Neural Network for Medical Image Computing: Principles and Applications (Elsevier, 2022). His work has led to nearly 600 technical papers and 90+ U.S. patents. Research Trends: Recent work focuses on large language models (LLMs), multimodal systems, AI-driven social media analysis, and healthcare applications. Key areas include bias analysis in political simulations, video understanding, and benchmark development for AI-generated content evaluation. His research bridges theoretical advancements with practical applications in healthcare, social sciences, and multimedia systems. Awards: ACM SIGMM Technical Achievement Award (2021), IEEE Region 1 Technological Innovation Award (2018), Eastman Innovation Award (2004), and multiple best-paper recognitions at top conferences. Service & Leadership: Served as program co-chair for ACM Multimedia 2010, IEEE CVPR 2012, ACM ICMR 2016, and IEEE ICIP 2017. Currently Editor-in-Chief of IEEE Transactions on Multimedia (2020–2022). Editorial board roles include several IEEE Transactions journals and conferences. Labs & Teams: Leads research groups in computer vision and multimodal computing at the University of Rochester, collaborating on projects like UroSAM (kidney stone classification) and computational social science initiatives.

Negin AlemazkoorView profile

Assistant Professor

• Infrastructure Systems
• Artificial Intelligence
• Renewable Energy
• +7 more

Negin Alemazkoor is an Assistant Professor at the University of Virginia's School of Engineering and Applied Science, specializing in interdisciplinary research on infrastructure resilience. Her work focuses on developing AI-driven methodologies for analyzing interconnected systems like power grids, urban flood models, and transportation networks under uncertainty. Key areas include enhancing grid reliability through multi-fidelity modeling, hurricane evacuation equity analysis, and precision-compression techniques for large-scale data. She co-leads a NSF-funded initiative to democratize AI education in high schools. Her research integrates graph neural networks, physics-informed models, and machine learning to address challenges in energy systems, environmental monitoring, and disaster response. Notable projects include hurricane-induced power outage risk analysis under climate change and precision guarantees for smart-meter data analytics. She emphasizes computational efficiency and multi-fidelity approaches to balance accuracy with resource constraints. Recent contributions span AI applications in flood forecasting, renewable energy integration, and infrastructure cybersecurity. Her NSF grant aims to create inclusive AI curricula, reflecting her commitment to education and societal impact. She is affiliated with UVA Engineering’s research initiatives on resilient systems and data-driven decision-making.

Gaye StephensView profile

Assistant Professor

• Health Informatics
• Electronic Health Records
• Patient Engagement
• +4 more

Gaye Stephens serves as Assistant Professor in the School of Computer Science and Statistics at Trinity College Dublin, where she is a core member of the Centre for Health Informatics alongside Dr. Lucy Hederman and Prof. Mary Sharp. She actively contributes to institutional governance as a board member of the Irish Platform for Patient Organisations, Science and Industry (IPPOSI) and serves on the school's research ethics committee. Her research centers on patient-centered health informatics with focus areas including Electronic Health Record design , citizen engagement methodologies (such as Citizen Juries and Think-ins), and scalable information models for healthcare integration. She pioneers approaches that position patients as central stakeholders in health data governance, addressing critical challenges in digital health literacy, informed consent frameworks, and interoperability between legacy and emerging health technologies. Analysis of her recent publications reveals consistent focus on knowledge engineering for healthcare , with particular emphasis on temporal data modeling in knowledge graphs (2024-2025), nurse knowledge elicitation through serious games (2023-2024), and novel methodologies like the WICKED framework for capturing clinical wisdom (2022-2023). Her work consistently bridges technical informatics with human-centered design principles. Stephens actively contributes to national health informatics infrastructure through memberships in: Royal Academy of Medicine of Ireland Council of Clinical Information Officers National Standards Authority of Ireland Health Informatics Group Health Informatics Society of Ireland (including Nursing and Midwifery group) e-Health Ireland ECOSYSTEM working group She supervises across undergraduate, MSc (Health Informatics and Global Health), and PhD programs while leading client-based final year projects. Her teaching portfolio includes Introduction to Health Informatics, Information Modeling, Connected Health, and Electronic Health Record Architectures, with research advising spanning health data governance and patient engagement projects. Stephens co-leads the Knowledge and Data Engineering Group's Health Informatics research stream, focusing on semantic interoperability, adaptive hypermedia systems, and novel engagement models for health data governance. Her current projects emphasize citizen-centered EHR architectures and interdisciplinary approaches to healthcare data integration.

Zixiang XiongView profile

Professor

• Machine Learning
• Image/Video Processing
• Network Information Theory
• +7 more

Zixiang Xiong is a Professor and Associate Department Head in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Robert M. Kennedy '26 Endowed Professorship II. He earned his Ph.D. in Electrical Engineering from the University of Illinois at Urbana-Champaign in 1996. His career includes roles at Princeton University, University of Hawaii, and Texas A&M since 1999. Education: Ph.D., Electrical Engineering, University of Illinois at Urbana-Champaign, 1996 Visiting Research Associate, Princeton University, 1995–1997 University of Hawaii, 1997–1999 Research Interests: Focuses on machine learning, image/video processing, federated learning, network information theory, biomedical engineering, and communications. His work spans distributed source coding, genomic signal processing, and energy-efficient systems. Publications & Awards: Over 200 publications, including seminal works on distributed video coding and network information theory. Notable awards include the NSF Career Award (1999), ONR Young Investigator Award (2001), IEEE Fellow (2006), and the ECE Outstanding Faculty Award (2024). His research has led to patents in video compression and multimedia systems. Grants & Advising: Active in NSF-funded projects on coding theory and energy-delay tradeoffs. Advises numerous PhD and MS students, with over 50 alumni in academia and industry. Collaborates on biomedical imaging, remote sensing, and federated learning initiatives. Labs & Teams: Leads a dynamic research group at Texas A&M, focusing on cutting-edge projects in signal processing and machine learning applications. Collaborates with industry and governmental agencies on applied research.

Frederi G. ViensView profile

Professor

• Probability Theory
• Stochastic Processes
• Quantitative Finance
• +7 more

Frederi G. Viens is a Professor of Statistics at Rice University, where he leads research in probability theory, stochastic processes, and their applications to finance, climate science, and agro-ecology. Previously, he was a full professor at Michigan State University (2016–2022) and Purdue University (2000–2015), serving as Department Chair and Director of Actuarial Science. His work bridges theoretical mathematics with practical problems in agriculture, economics, and nuclear physics. Education: Ph.D. Mathematics, University of California, Irvine (1996) M.S. Mathematics, University of California, Irvine (1991) Maîtrise de Mathématiques Pures, Université de Paris VII, France (1991) Research Interests: Probability Theory & Stochastic Analysis Quantitative Finance & Actuarial Science Climate Science & Bayesian Statistics Agro-ecology & Agricultural Economics His collaborative projects include climate modeling, nuclear physics simulations, and sustainable crop diversity initiatives like the DRIVES network. Awards & Honors: Fellow of the Institute of Mathematical Statistics (2012) Franklin Fellow, U.S. State Department (2010) Purdue College of Science Research Award (2013) Grants & Collaborations: Funded by the NSF, USDA, and private donors, Viens has organized major conferences and serves on editorial boards for journals like Annals of Finance . He advises transnational research groups, including Sustainability Lake Chad , addressing agrarian sustainability in West Africa. Labs & Initiatives: Founding member of the DRIVES agro-ecology collaborative and moderator of the Seminar on Stochastic Processes.

• Cellular Neuroscience
• Systems Neuroscience
• Behavioral Neuroscience
• +3 more

Prof. Laura Busse is a Professor at Ludwig Maximilian University of Munich (LMU), leading the Research Group in the Department of Biology II, Division Neurobiology. She holds roles as a Regular Member of MCN, Full Member of GSN, and Deputy Head of the GSN Examination Board. Her research focuses on cellular and systems neuroscience, particularly investigating how contextual information influences visual perception through neural circuits in mice. Key areas include feedback mechanisms, behavioral state effects, and thalamocortical interactions. Her work employs advanced techniques like high-density extracellular recordings and optogenetics to study active behavior in rodents. Current students include Simon Renner, Gregory Born, and others. Recent research highlights include studies on corticothalamic feedback effects, thalamic spatial integration, and the role of pupil dynamics in neural activity. She leads the Vision Circuits Lab (https://visioncircuitslab.org), exploring how sensory inputs and brain states shape visual processing. Her articles reveal trends in understanding thalamocortical communication, adaptive sensory systems, and the biological basis of neural network models. She coordinates the SPP2411 project on cortico-subcortical loops, emphasizing interdisciplinary neuroscience.

Andreas RichterView profile

Professor

• Creativity and Innovation in Teams
• Team Processes and Dynamics
• Leadership Development
• +5 more

Andreas Richter is a Professor of Organisational Behaviour at Cambridge Judge Business School (University of Cambridge), specialising in creativity and innovation in teams, leadership development, and team effectiveness. His research integrates theoretical insights with practical applications in healthcare, pharmaceutical, and financial sectors. He holds a PhD in Management from Aston University and advanced degrees from Giessen University. He advises multinational corporations and public institutions globally, including TomTom, UNICEF, and the UAE Ministry for Internal Affairs. His work focuses on enhancing team collaboration through psychological safety, leadership training, and resource distribution. Richter serves on editorial boards of top journals like the Academy of Management Journal and Journal of Applied Psychology . Key research themes include: Team processes & creative environments Leadership learning mechanisms Failure recovery systems Temporal dynamics in innovation Notable contributions include frameworks for converting workplace shame into creativity and analyzing team effectiveness through multiplex leadership networks. His 2019 study on learning from failure highlights team resource systems' role in transforming adversities into growth opportunities. Executive education expertise includes designing positive leadership programs addressing empathy and dark triad traits in management. Richter has developed innovative team-building models for organisational change, such as the 'twelve steps to heaven' framework for successful transitions.

Elise LavouéView profile

Professor

• Technology Enhanced Learning
• Human-Computer Interaction
• Gamification
• +12 more

Elise Lavoué is a full Professor in Computer Science at iaelyon School of Management, Jean Moulin Lyon 3 University, and a key researcher at the LIRIS laboratory (CNRS). She leads the SICAL research team and holds leadership roles including Editor-in-Chief of the STICEF journal, member of Labex ASLAN’s management committee, and member of the University of Lyon’s Research Ethics Evaluation Committee (CER-UdL). She is also affiliated with the ATIEF association. Her research focuses on enhancing motivation and engagement in digital learning environments through adaptive gamification, learning analytics, and human-computer interaction. She explores how tailored game elements, emotional awareness tools, and immersive technologies like virtual reality can support self-regulated learning, critical thinking, and skill development in complex digital contexts. Her recent publications span top journals such as IEEE Transactions on Learning Technologies, International Journal of Human-Computer Studies, Computers & Education, and CHI PLAY. These works reflect a strong trend in adaptive and personalized learning technologies, emotion-aware systems, and immersive training environments, particularly in educational and professional settings. Honorable Mention Award at ACM CHI PLAY 2019 (top 4%) Best Industrial Paper award at CSEDU 2020 Elise Lavoué actively supervises PhD students and post-doctoral researchers and leads multiple funded projects including LudiMoodle+, RENFORCE, Lex.gaMe, BODEGA, and Emoviz. These projects involve collaborations with institutions across France and focus on gamification, VR training, emotional dashboards, and vocabulary acquisition. She has secured funding from ANR, Labex ASLAN, CNRS, and other national bodies. Her work emphasizes interdisciplinary collaboration between computer science, education, and social sciences. She is involved in several research teams and labs, primarily the SICAL team within the LIRIS laboratory, a major interdisciplinary research unit in computer science, images, and information systems. Her projects often involve industry partners such as SpeakPlus and Woonoz, and she contributes to both scientific advancement and practical educational innovation.

Lukas EngelmannView profile

Senior Lecturer

• History of Epidemiology
• History of Science and Medicine
• Digital Health
• +7 more

Lukas Engelmann is a Senior Lecturer at the University of Edinburgh , specifically within the Science, Technology and Innovation Studies department under the School of Social and Political Science . His research focuses on the history and sociology of biomedicine , with particular interest in epidemiological reasoning , visual cultures of disease , digital epidemiology , and decolonial approaches to medical history . The Epidemy Lab , which he founded, explores the historical development of epidemiology and its contemporary influence on data-driven public health and pandemic policy-making . Engelmann's work has been funded by prestigious grants including an ERC Starting Grant (2021-2025) for his research on the history of epidemiological reasoning, and support from the Wellcome Trust for projects examining the social dimensions of digital health . His book 'Mapping AIDS' (2018) established him as a leading scholar in medical visualization , while 'Sulphuric Utopias' (2020) with Christos Lynteris explores the technological history of maritime sanitation and its political implications. Recent publications emphasize the visual and data practices that have shaped epidemiology, including works on epidemic modeling during the COVID-19 pandemic , the history of plague mapping , and the ethical implications of digital phenotyping . He has also contributed to interdisciplinary discussions on syndemics , co-infection epistemology , and the commercialization of bacteriology in the early 20th century. His scientific contributions have earned recognition through editorial roles in journals like Big Data and Society , and collaborative projects such as 'Working with Diagrams' (2022) which investigates the epistemological role of visual tools in medical knowledge production. Scientific Awards and Funding: ERC Starting Grant (2021-2025) Wellcome Trust Institutional Support Fund British Academy/Leverhulme Small Research Grant Chancellor's Fellowship (University of Edinburgh) 'Sulphuric Utopias' listed in The Guardian's 30 Books to Understand the World (2020)

Katherine L. MilkmanView profile

Professor

• Behavioral Economics
• Judgment and Decision Making
• Behavior Change
• +2 more

Katherine L. Milkman is the James G. Dinan Endowed Professor at The Wharton School of the University of Pennsylvania, with secondary appointments in the Perelman School of Medicine and School of Arts and Sciences. She co-founded and co-directs the Behavior Change for Good Initiative , focusing on harnessing behavioral economics and psychology for societal benefit. Education: PhD in Computer Science and Business, Harvard University Undergraduate degree (summa cum laude) in Operations Research and American Studies, Princeton University Her research bridges behavioral economics , decision-making , and health psychology , with applications in education, finance, and public health. Recent studies explore quantification fixation , streak incentives , and diversity interventions using megastudy methodologies. Scientific awards include Thinkers50 Top 50 Management Thinker (2021, 2023) APS Fellow (2020) FABBS Early Career Award (2015) Wyss Award (Harvard, 2008) She teaches courses in decision-making and judgment research , including OIDD9950 (Dissertation) and OIDD2990 (Judgment & Decision Making Research). Media outlets like The New York Times and Nature regularly feature her work, while her podcast Choiceology disseminates behavioral economics insights to a broader audience.

Nakul GopalanView profile

Assistant Professor

• Language Grounding
• Robot Learning
• Human-Robot Interaction
• +3 more

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.