Find Academic Supervisors

Debora NozzaView profile

Assistant Professor

• Natural Language Processing
• Hate Speech Detection
• Ethics in NLP
• +4 more

Debora Nozza is an Assistant Professor in Computing Sciences at Bocconi University , where she leads research on ethics in Natural Language Processing with a focus on hate speech detection and algorithmic bias in multilingual contexts. She was awarded a €1.5 million ERC Starting Grant (2023) for her project PERSONAE , exploring personalized and subjective approaches to NLP. Previously, she secured a €120,000 Fondazione Cariplo Grant (2019) for the MONICA project analyzing Italian responses to the pandemic. Education: PhD in Computer Science (2018), University of Milano Bicocca Her research spans hate speech detection , gender bias in language models , and computational social science . She organizes key workshops like the Workshop on Online Abuse and Harms (WOAH) and Gender Bias in NLP (GeBNLP) at ACL conferences, and co-led shared tasks including HODI (Homotransphobia Detection in Italian) and HatEval (hate speech against immigrants). Recent publications address actionable bias metrics , gender pronoun handling in translation , and large-scale stereotype amplification via text-to-image generation . Her work combines model interpretability , cross-lingual frameworks , and social impact analysis to tackle systemic biases. Scientific Awards: ERC Starting Grant (2023) Fondazione Cariplo Grant (2019) She actively participates in international conferences, including keynotes at ECCV 2024 and WASSA 2024 , and collaborates with groups like MilaNLP and ACL workshops .

Anna KorbaView profile

Assistant Professor

• Machine Learning
• Bayesian Inference
• Sampling Methods
• +4 more

Anna Korba is an Assistant Professor at CREST-ENSAE Paris within the Statistics Department. She holds an ENSAE Engineering degree in Data Science and a Master's in Mathematics, Vision & Learning (MVA) from ENSAE Paris. Her career includes a Ph.D. in Machine Learning at Télécom ParisTech, followed by a postdoctoral position at UCL's Gatsby Unit. Her research focuses on sampling techniques, Bayesian inference, optimal transport, and generative modeling, with recent work on constrained sampling and fairness integration. She contributes to collaborative efforts at the intersection of machine learning, dynamical systems, and PDEs. Notably, she co-presented tutorials on Wasserstein gradient flows at ICML 2022. Her work addresses unsolved challenges in sampling efficiency and fairness constraints. She is actively involved in CREST research initiatives and academic mentorship.

Malaika BrengmanView profile

Senior Lecturer

• Retail Consumer Behavior
• Virtual Retail Environments
• Consumer Trust
• +4 more

Malaika Brengman serves as a Senior Lecturer and postdoctoral researcher at the Free University of Brussels, specializing in consumer behavior within retail contexts. Her academic work bridges Communication Sciences, Business Studies, and Media Innovation through interdisciplinary research on digital retail environments. Her research focuses on consumer decision-making processes in physical and virtual retail spaces, with particular expertise in trust formation, digital signage effectiveness, and the psychological impact of emerging technologies. Key interests include: How virtual worlds influence retail appeal and consumer threat perception The relationship between store environment design and initial trust development Implicit measurement techniques for assessing service experiences Consumer responses to AI-driven personalization in retail settings Analysis of her recent publications reveals a strong emphasis on augmented reality applications, vaccine attitude segmentation, and social media addiction pathways, demonstrating methodological diversity across behavioral experiments and large-scale consumer surveys. Her scientific contributions have been recognized through multiple awards including the Best Conference Paper Award (2010), Best Extended Abstract Award (2020), and a Highly Commended Paper distinction (2016). These accolades highlight her impact in retail technology and consumer psychology research. Brengman actively supervises doctoral candidates and serves on PhD committees, with documented supervision of at least 153 academic works. She frequently engages with media as an expert on consumer finance topics, particularly regarding energy pricing impacts and savings behaviors during economic uncertainty. Her current projects include Brubotics (human-centered robotics) and SHALGO (algorithmic systems research). She maintains significant external engagement through advisory roles in consumer protection initiatives and regular media commentary on retail trends and consumer behavior patterns.

Salar FattahiView profile

Assistant Professor

• Optimization
• Machine Learning
• Robust Matrix Recovery
• +4 more

Salar Fattahi is an Assistant Professor at the University of Michigan, affiliated with the College of Engineering’s Department of Industrial and Operations Engineering. He holds additional appointments with the Michigan Institute for Computational Discovery and Engineering (MICDE), Michigan Institute for Data Science (MIDAS), and the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM). PhD in Industrial Engineering and Operations Research from UC Berkeley M.Sc. in Electrical Engineering from Columbia University B.Sc. in Electrical Engineering from Sharif University of Technology Research Focus: Developing scalable computational methods for structured optimization and machine learning problems by exploiting sparsity, low-rankness, and benign landscape properties. Applications span gene regulatory networks, power systems, and brain connectivity modeling. 2025: Parametric algorithms for MIQPs over trees 2024: Triple Component Matrix Factorization for global/local/noise separation 2023: Robust subspace recovery and dictionary learning Scientific Recognition: NSF CAREER Award (2023) INFORMS Best Paper Awards (2023, 2024) Dean’s MLK Spirit Award (2024) MICDE Catalyst Grant (2021) Academic Service: Associate Editor for INFORMS Journal on Data Science; Area Chair for NeurIPS, ICML, and ICLR. Mentored students including Jianhao Ma (now Tsinghua University), Geyu Liang (Amazon), and Aaresh Bhathena. Research supported by NSF, ONR, MICDE, MIDAS, START, and DEI Faculty grants.

Simone BrugiapagliaView profile

Associate Professor

• Mathematics of Data Science
• Computational Mathematics
• Numerical Analysis
• +5 more

Simone Brugiapaglia is an Associate Professor in the Department of Mathematics and Statistics at Concordia University in Montréal, Canada. His academic journey includes a PhD in Mathematical Models and Methods from Polytechnic University of Milan (2016), an MSc in Mathematics from University of Pisa (2012), and a BSc in Mathematics from University of Pisa (2010), all earned cum laude . Prior to his current role, he held postdoctoral positions at École polytechnique fédérale de Lausanne (2016) and Simon Fraser University (2016-2019). Dr. Brugiapaglia's research bridges mathematics, data science, and computational methods. Key interests include: Foundations of deep learning and neural networks Compressed sensing and sparse recovery algorithms High-dimensional approximation theory Numerical methods for PDEs and diffusion equations Physics-informed machine learning Optimization techniques for large-scale problems His work develops rigorous mathematical frameworks for data-driven algorithms. His publications (30+ including two books) consistently focus on high-dimensional computation , featuring recent advances in neural network theory (e.g., generalization bounds, rank collapse), compressed sensing techniques (e.g., greedy algorithms, unrolled networks), and physics-informed learning. A strong trend involves combining traditional numerical methods with deep learning for PDE solutions. Awards & Fellowships: Concordia Research Fellow (2023) Leslie Fox Prize for Numerical Analysis (2nd place, 2019) PIMS Postdoctoral Fellowship (2016-2018) Multiple INdAM scholarships during graduate/undergraduate studies He has supervised over 20 trainees across postdoctoral, graduate, and undergraduate levels. While specific grants aren't detailed, his fellowship history indicates sustained research funding. No explicit research labs or teams are mentioned, but his supervision record and collaborative publications suggest active leadership in research groups focused on computational mathematics.

• Medical Imaging
• Machine Learning
• Computer Vision
• +3 more

Diana Mateus is a Researcher at Centrale Nantes, affiliated with the LS2N Laboratory. She holds a bachelor's in Electronic Engineering from the University of Javeriana (Colombia), a Master's in Control and Automation from Paul Sabatier University (France), and a PhD in Computer Vision from INRIA Grenoble. After postdoctoral work and eight years as an associate researcher at Technical University of Munich and Helmholtz Zentrum (Germany), she returned to France to join Centrale Nantes via the Connect Talent Program, securing a starting grant for interdisciplinary research in medical image analysis and machine learning. Her research focuses on Milcom —a project integrating multimodal imaging (MRI, CT, ultrasound, PET) with machine learning to advance computational medicine. Key objectives include computer-aided diagnosis, disease knowledge advancement, and improving imaging quality. Collaborations include the hospital's Nuclear Medicine Department, focusing on multiple myeloma, and companies Hera-Mi and Keosys for breast cancer research. She supervises six PhD students and two postdocs, emphasizing synergies between engineering and healthcare. At Centrale Nantes, she benefits from dynamic institutional support, quality students, and complementary team skills. Her lab recently acquired a research-dedicated ultrasound machine to develop machine learning-driven imaging optimization. Living in Nantes, she values its family-friendly environment and proximity to academic and medical networks.

• Geometric Data Processing
• Computational Geometry
• Optimal Transport
• +7 more

Justin Solomon is an Associate Professor in the Department of Electrical Engineering & Computer Science at Massachusetts Institute of Technology, where he serves as Principal Investigator of the Geometric Data Processing Group. He maintains dual affiliations with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Center for Computational Science and Engineering (CCSE), reflecting his interdisciplinary research bridging theoretical mathematics with practical applications in graphics and machine learning. His research interests center around geometric data processing, computational geometry, and optimal transport theory, with significant contributions to computer graphics, machine learning, and computer vision. Solomon's work spans fundamental mathematical theory to practical implementations, particularly in shape analysis, 3D reconstruction, and geometric deep learning. His research demonstrates consistent innovation in developing algorithms that bridge discrete and continuous geometry with applications in graphics, vision, and AI. The publication trends reveal Solomon's evolving research trajectory from foundational work in geometry processing toward increasing integration with modern machine learning techniques. His recent work shows strong emphasis on diffusion models, geometric deep learning, and applications of optimal transport in AI, with significant contributions to SIGGRAPH, NeurIPS, and ICML proceedings. The research demonstrates both mathematical rigor and practical impact, with applications spanning character animation, 3D reconstruction, and generative AI. Amazon Research Award (2017) for Large-Scale Geometrically-Structured Sampling Amazon Research Award (2023) for Lightweight Algorithms for Generative AI Ben Wegbreit Prize for Best Undergraduate Honors Thesis Firestone Medal for Excellence in Undergraduate Research Boothe Prize for Excellence in Writing 2nd place, SGP best paper awards (2010) Solomon has secured substantial research funding through awards like the Amazon Research Awards and maintains active collaborations across academia and industry. His group has produced numerous influential publications with students and collaborators, contributing significantly to both theoretical foundations and practical implementations in geometric data analysis. His textbook "Numerical Algorithms" demonstrates his commitment to education alongside research. As Principal Investigator of the Geometric Data Processing Group, Solomon leads a research team focused on developing mathematical foundations for analyzing and processing geometric data. The group maintains strong connections with both theoretical mathematics and practical applications, working at the intersection of computer graphics, machine learning, and computational geometry. Their work has significant implications for fields ranging from computer animation to medical imaging and scientific computing.

Alexis ShotwellView profile

Professor

• Social and Political Theory
• Ethics
• Complicity
• +9 more

Professor Alexis Shotwell is a distinguished scholar at Carleton University's Department of Sociology and Anthropology, with cross-appointments to the Feminist Institute of Social Transformation and the Department of Philosophy. Based on unceded Algonquin land in Ottawa, she is a prominent feminist theorist of relationality whose work examines the ethical and political implications of our interconnectedness. Her research focuses on social and political theory, particularly exploring complicity and complexity as foundations for ethical action. Key areas of investigation include climate change, disability, gender, ecology, art, sexuality, anarchism, race, and emotions. She serves as co-investigator for an oral history project documenting the history of AIDS activism in Canada. Professor Shotwell is author of the influential books Against Purity: Living Ethically in Compromised Times (2016) and Knowing Otherwise: Race, Gender, and Implicit Understanding , with several current projects including No Higher Purpose: Making Meaning in the Mess about Ursula K. Le Guin's anarchism. Her work challenges individualistic approaches to social change, advocating instead for collective transformation grounded in acknowledging our shared complicity. She has received notable recognition including a Teaching Achievement Award for her innovative work on 'Writing as Thinking' and regularly engages with public audiences through podcasts, workshops, and public speaking. Her 'suffering-free writing' workshops for academics and activists reflect her commitment to making scholarly practice more accessible and sustainable. Teaching Achievement Award recipient Co-investigator, AIDS Activist History Project Author of multiple influential scholarly books Regular contributor to public discourse through podcasts and media Professor Shotwell's scholarship bridges academic theory and practical activism, offering vital frameworks for understanding how we might work collectively toward more just futures despite our imperfect positions within systems of power and oppression.

René VidalView profile

Professor

• Machine Learning
• Computer Vision
• Biomedical Data Science
• +3 more

René Vidal is the Rachleff & Penn Integrates Knowledge (PIK) University Professor at the University of Pennsylvania, with appointments in the Departments of Electrical and Systems Engineering, Radiology, Computer and Information Science, and Statistics and Data Science. He also serves as Director of the Center for Innovation in Data Engineering and Science (IDEAS) and the NSF-Simons Collaboration on the Mathematical Foundations of Deep Learning (THEORINET). A dual faculty member at Johns Hopkins University in Biomedical Engineering, Computer Science, and other departments, Vidal is an Amazon Scholar and Affiliated Chief Scientist at NORCE. PhD, Electrical Engineering and Computer Sciences, University of California, Berkeley (2003) M.S., Electrical Engineering, University of California, Berkeley (2000) B.S. (valedictorian), Electrical Engineering, Pontificia Universidad Catolica de Chile (1997) His research spans the mathematical foundations of deep learning, focusing on non-convex optimization , learning dynamics , and overparametrization . Key contributions include Sparse Subspace Clustering , Kernel GPCA , and Low-Rank Matrix Factorization , with applications in autism diagnosis , robotic surgery , and cardiac imaging . Recent work explores continual learning , adversarial robustness , and trustworthy AI in biomedical contexts. His 15 most recent publications highlight advances in medical imaging , language models , and robust computer vision , spanning topics from chest X-ray analysis to motor imitation tasks in autism . Articles like Geometric Analysis of Nonlinear Manifold Clustering underscore his theoretical contributions, while works on KDA: Knowledge-Distilled Attacker and Conformal Information Pursuit address practical AI safety and interpretability. Scientific accolades include: 2022 ACM Fellow 2021 IEEE McCluskey Technical Achievement Award 2017 Jean D’Alembert Fellowship 2012 J.K. Aggarwal Prize 2009 Sloan Research Fellow 2005 NSF CAREER Award His lab mentors 11 current PhD students across Johns Hopkins and University of Pennsylvania , with alumni contributing to institutions like Meta , Amazon , and GE Research . Vidal’s interdisciplinary work bridges mathematics , engineering , and healthcare , supported by grants from the DoD , NSF , and ONR .

Hachem KadriView profile

Professor

• Machine Learning
• Kernel Methods
• Functional Data Analysis
• +5 more

Hachem Kadri is a Professor of Artificial Intelligence in the Department of Computer Science at Aix-Marseille University and Head of the Data Science Department at LIS Lab since 2023. He was previously an Assistant Professor at the same institution from 2012 to 2019 and conducted postdoctoral research at INRIA Lille and LAGIS. He holds a PhD in Electrical Engineering from the National Engineering School of Tunis (2008) and completed his HDR (Habilitation) at Aix-Marseille University in 2019 on operator-valued kernel methods. His research spans machine learning with core expertise in kernel methods, functional data analysis, statistical learning theory, deep learning, and quantum machine learning, bridging theoretical foundations with applications in signal processing and quantum computing. Recent publications (2022-2024) demonstrate a strategic shift toward quantum machine learning and C*-algebraic frameworks, with significant contributions in implicit regularization for deep tensor factorization, quantum perceptron algorithms, and operator-theoretic kernel extensions. Key works appear in ICML, NeurIPS, and AISTATS, highlighting interdisciplinary innovation at the intersection of algebra, functional analysis, and ML. Professor Kadri leads the ANR Starting Grant QuantML (2019-2024) as Principal Investigator and co-led the Franco-Tunisian OPERA project (2019-2023). He was nominated to the National Universities Council (CNU) for 2024-2027 and elected Head of LIS Data Science Department in 2023, reflecting his leadership in French academia. As a core member of the QARMA machine learning group at LIS Lab, he co-organized the inaugural Quantum Machine Learning workshop at ECML-PKDD 2022 and serves as Vice-president of SSFAM (Société Savante Francophone d’Apprentissage Machine), actively shaping the European ML research landscape.

Silke AdamView profile

Professor

• Political Communication
• Digital Media
• Conspiracy Theories
• +4 more

Professor Silke Adam serves as the Director of the Institute of Communication and Media Studies (IKMB) at the University of Bern, Switzerland. The institute operates within the Department of Social Sciences and focuses on political communication in digital environments. Professor Adam leads research examining how digitization transforms political communication, the influence of algorithms and artificial intelligence, and comparative analyses of communication in democratic and authoritarian contexts. Her primary research interests include political communication in digital environments, conspiracy theories, online information behavior, and right-wing populism. Professor Adam has pioneered research on how conspiracy theories spread during crises, particularly during the COVID-19 pandemic. Her work combines web tracking, survey research, and experimental methods to understand media effects on belief formation. She has made significant contributions to understanding how mainstream media can effectively debunk conspiracy theories without inadvertently reinforcing them. Professor Adam's research portfolio demonstrates a strong focus on methodological innovation in digital communication research, particularly in web tracking and computational content analysis. Her recent publications reveal increasing attention to algorithmic influences on information exposure, the spread of misinformation, and the relationship between political attitudes and selective information consumption. She has developed sophisticated approaches to studying online behavior through combined tracking, survey, and automated text classification methods. ICA Top Faculty-Award of the Mass Communication Division for 'Tracing Knowledge Gaps: Investigating the Influence of Education on News Exposure and Knowledge using Digital Trace Data' Professor Adam actively bridges academic research and practical journalism through workshops connecting media professionals with communication scholars. She has secured funding from the Multidisciplinary Center for Infectious Diseases for pandemic-related communication research. Her work emphasizes the importance of empirical evidence in guiding journalistic practices, particularly regarding how to address conspiracy theories without amplifying them. She supervises research projects examining political communication in digital environments and has mentored numerous researchers in the field. The Institute of Communication and Media Studies under Professor Adam's leadership maintains strong connections with the Multidisciplinary Center for Infectious Diseases and collaborates with media professionals through regular workshops. Her research team employs innovative methodologies including web tracking, algorithmic content analysis, and experimental designs to investigate contemporary challenges in political communication. The institute actively contributes to academic discourse through publications, conferences, and direct engagement with media practitioners to improve evidence-based journalism.

Christophe KervazoView profile

Assistant Professor

• Sparse Blind Source Separation
• Nonnegative Matrix Factorization
• Hyperspectral Imaging
• +4 more

Christophe Kervazo is an Assistant Professor (Maître de Conférences) at Télécom Paris, France, affiliated with the IMAGES group under the Image, Data, Signal (IDS) department . His research focuses on sparse blind source separation, nonnegative matrix factorization, hyperspectral imaging, and optimization techniques for remote sensing and biomedical applications. Education: Engineering degree from Supélec (2015), Master of Science from Georgia Institute of Technology (2016), PhD in Signal and Image Processing from Université Paris Saclay (2019). His work spans deep learning for inverse problems (including deep unrolling techniques), remote sensing (hyperspectral imaging and SAR), and uncertainty quantification . Recent publications address synthetic data training for medical imaging, distributed sparse BSS, and nonlinear component separation. Collaborators include institutions like CEA Saclay, Université de Mons, and ONERA. Current students include PhD candidates working on topics such as digital breast tomosynthesis, hyperspectral unmixing, and SAR image reconstruction. Former students and interns have contributed to projects involving plug-and-play methods, unrolling algorithms, and implicit regularization. He is involved in teaching and research projects, including collaborations with Airbus and ONERA on hyperspectral imaging and spectral band optimization. His lab, LTCI (Information Processing and Communication Laboratory), supports interdisciplinary work in signal and image processing.

Richard RemediosView profile

Associate Professor

• Educational Psychology
• Academic Motivation
• High-stakes Examinations
• +6 more

Richard Remedios is an Associate Professor in the Department of Psychology at Nottingham Trent University (NTU), specializing in educational psychology, academic motivation, and high-stakes examinations. He has served as Chair of the Psychology Teaching and Learning Committee and leads the Psychology suite of modules in Education. His career spans roles at NTU (2021–present), NTU as Senior Lecturer (2019–2021), Northcentral University as Professor (2014–2019), and consultancy work with ICF International (2013–2019). Prior roles include Lecturer at Durham University (2006–2013) and Research Fellow at the University of Stirling (2003–2006). Academic Focus: Motivational messaging in education, fear appeals, academic self-efficacy, achievement goals. Supervision: Mentors UG, PG, and PhD students, including T.C. Hutton’s 2025 thesis on medical clinical reasoning. Research Themes: Teacher and parental motivational strategies, psychological impacts of high-stakes exams, methodological rigor in educational research. Publications: Over 15 articles and book chapters analyzing fear appeals, academic buoyancy, and mixed-methods research in achievement goal theory. Conferences: Regular contributions to Psychology of Education Section Annual Conferences (2022–2025) and Advance HE Teaching and Learning Conferences (2024).

• Theoretical Computer Science
• Logic in Computer Science
• Programming Languages
• +5 more

Dr. Cécilia Pradic is a Lecturer in Computer Science at Swansea University's School of Mathematics and Computer Science. Her research focuses on theoretical computer science, logic, and automata theory with applications in programming languages and database systems. She is affiliated with the Computational Foundry at Bay Campus and holds an ORCID identifier (https://orcid.org/0000-0002-1600-8846). Her recent work emphasizes nested relational query synthesis, implicit automata in λ-calculi, and Weihrauch reducibility for computational problems. She teaches modules including Declarative Programming (CS-205), Software Concepts and Efficiency (CSCM12), and Problem Solving & Software Efficiency (CSCM712), using Java and Python as primary tools. Pradic's publications span topics like proof theory applications to database systems, formal methods for string transformations, and computational complexity analysis. She has supervised multiple graduate research students, including a current PhD candidate and an MRes graduate (2024). Her research bridges foundational theory with practical algorithm design, contributing to advancements in both academic and applied computing domains.

• Relational Machine Learning
• Deep Learning
• Complex Network Science
• +4 more

Dr. Rebekka Burkholz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany, leading the Relational Machine Learning Group . Her research bridges machine learning and complex network science to develop robust, data-efficient models with applications in molecular biology. Previously, she held positions at Harvard T.H. Chan School of Public Health and ETH Zurich. PhD in Systems Design (2016) from ETH Risk Center Mathematics and Physics BSc/MSc from TU Darmstadt Her work focuses on sparse training methods and theoretical deep learning , addressing challenges like computational efficiency and adversarial robustness. Recent publications explore: Sparse training via implicit sparsification GNN optimization through rescaling and rewiring Integration of domain knowledge in biomedical modeling Theoretical guarantees for batch normalization and lottery tickets Scientific awards include: Zurich Dissertation Prize (2016) CSF Best Contribution Award (2016) She actively advises PhD students and collaborates with interdisciplinary teams in biostatistics and systems biology.