Find Academic Supervisors

• Multivariate Analysis
• Fuzzy Clustering
• Probabilistic Graphical Models
• +3 more

Vincenzina Vitale serves as a Tenure-Track Assistant Professor of Statistics within the Department of Social and Economic Sciences at Sapienza University of Rome. Her academic profile centers on advanced statistical methodologies with applications spanning economics, public policy, and sustainability initiatives. She teaches core courses including Statistics and Data Science for Sustainability and Statistical Methods and Models for Economics and Public Policy, maintaining regular office hours on Tuesdays from 12:30 to 14:30 by email appointment. Her research program focuses on multivariate analysis, specializing in innovative fuzzy clustering techniques for complex data structures such as time series, spatial data, and mixed data types. She extensively employs probabilistic graphical models, particularly Bayesian networks, for data integration and modeling challenges. This work bridges theoretical statistics with practical applications in electoral analysis, financial volatility, sports analytics, and public health domains including COVID-19 pandemic response. Analysis of her 15 most recent publications reveals a dominant trend toward developing spatially-aware and robust fuzzy clustering algorithms. These methods increasingly incorporate regularization techniques, entropy principles, and copula models to handle interval-valued data, count data, and tail dependencies. Key application areas include regional competitiveness measurement (NUTS2/NUTS3 frameworks), electoral studies, sports performance analytics, and pandemic modeling, demonstrating consistent contributions to top-tier statistical journals. No scientific awards or fellowships were documented in the available materials. While her publication record indicates significant research productivity, specific details regarding graduate student advising, research grants, or collaborative projects were not explicitly mentioned in the provided texts. Similarly, information about laboratory facilities or dedicated research teams remains undocumented in the current sources.

Emrah AhiView profile

Assistant Professor

• Fixed Income Securities
• Derivatives
• Computational Finance
• +7 more

Dr. Emrah Ahi is an Assistant Professor of Finance at Özyeğin University's Faculty of Business Administration and a researcher at the Center for Financial Engineering. With over 15 years of experience in finance, he previously served as a fund manager at HSBC Global Asset Management. PhD in Finance (Özyeğin University, 2010-2016) MS in Computational Science and Engineering (Koç University, 2004-2007) BSc in Mathematics (Middle East Technical University, 2000-2004) His research spans fixed income securities, derivatives, asset pricing , and machine learning , with cross-disciplinary contributions to peer-to-peer network protocols and distributed systems . Key themes include risk modeling, volatility analysis, and robust computational methods in finance, alongside technical innovations in gossip-based data dissemination. Recent publications focus on volatility smile modeling in currency options, term structure estimation in emerging markets, and applying machine learning to credit risk analysis. Earlier work on buffer management in epidemic algorithms demonstrates methodological rigor in distributed computing. He teaches courses in Applied Financial Economics, Derivatives, Fixed Income Markets, and Investments. His affiliations include Özyeğin University's Center for Financial Engineering and collaborations in computational finance research.

Paolo PalmieriView profile

Senior Lecturer

• Cryptography
• Privacy Enhancing Technologies
• Internet of Things Security
• +4 more

Dr. Paolo Palmieri is a Senior Lecturer in Cyber Security at the University College Cork (UCC) , affiliated with the School of Computer Science & IT . He serves as a Funded Investigator in Insight and CONNECT research centers, Supervisor in ADVANCE and AI Centres for Research Training, and co-Investigator in DTIF-Holistics and SFI-Spoke on Blended Autonomous Vehicles . PhD in Cryptography (2013), Université catholique de Louvain Prior positions: Lecturer at Cranfield University and Bournemouth University (UK), Post-Doctoral Researcher at Delft University of Technology (Netherlands) Research spans cryptography, privacy, and security , with focus on: Secure multi-party computation Homomorphic encryption Location privacy mechanisms Spatial Bloom filters Smart city security Blockchain applications in IoT Recent publications highlight advancements in blockchain-based healthcare authentication , privacy-preserving vehicular networks , and homomorphic encryption for sentiment analysis . He chairs Malicious Software and Hardware in Internet of Things (Mal-IoT) and Location Privacy Workshop (LPW) . Active in PhD supervision , he currently advises students in cryptographic protocols for location-based services. Collaborates with Smart City Lab at the University of Bologna and participates in international research networks .

Claudia CalifanoView profile

Associate Professor

• Nonlinear Systems Analysis
• Time-Delay Systems
• Digital Control
• +5 more

Claudia Califano is an Associate Professor at the Department of Computer, Control and Management Engineering, University of Rome "La Sapienza". Her work focuses on nonlinear systems, time-delay systems, and digital control with applications in biomedical systems and multi-agent networks. She has held editorial roles at the IMA Mathematical Control and Information Journal and IFAC World Congress, and served as Vice Chair for Education in IFAC Technical Committee Nonlinear Systems. B.Sc. and Ph.D. in Systems Engineering from University of Rome "La Sapienza" Research Interests: Her research spans geometric control theory for nonlinear systems, dynamic feedback linearization, and time-delay systems. Recent work includes deep learning applications for non-invasive medical diagnostics and consensus protocols for multi-agent systems with communication delays. Scientific Contributions: Her publications emphasize geometric methods for nonlinear control, integrability conditions, and observer design. Key themes include accessibility-observability duality, canonical forms, and applications to diabetic gastroparesis modeling. Best Paper Presentation Award at ACC 2010 Finalist for Best Poster at IFAC'96 Senior Member of Control Systems Society Editorial board member for IEEE and IFAC Teaching Roles: • Teaching Coordinator of Master's Degree in Emergency Engineering (2001-2013) • Courses taught: Systems Theory, Fundamentals of Automatic Control, Digital Control (in English)

Tatjana PavlenkoView profile

Professor

• high-dimensional statistical inference
• statistical machine learning
• sparse signal detection
• +4 more

Tatjana Pavlenko is a Professor in Statistics at Uppsala University, affiliated with the Department of Statistics. Her research bridges mathematical statistics, probability theory, and computational methods, focusing on high-dimensional data analysis in biomedical and machine learning contexts. Key research areas include: High-dimensional statistical inference and Bayesian graph structure learning Sparse signal detection and adaptive thresholding methods Statistical machine learning with applications to biomedical datasets Her recent publications demonstrate expertise in: Bayesian model averaging and junction tree sampling Asymptotic theory for high-dimensional classifiers Testing independence and covariance structures L2-type statistics and empirical process theory She supervises PhD students working on: High-dimensional causal inference in media Bayesian graphical models Sparse classification algorithms Currently active in Uppsala University's AI4Research initiative, Pavlenko develops adaptive data-driven procedures for statistical learning problems with complex sparsity patterns.

• Computational Biology
• Bioinformatics
• Data Compression
• +7 more

Raffaele Giancarlo is a Full Professor in the Department of Mathematics and Computer Science at the University of Palermo. His office hours are held on Mondays and Thursdays from 2:30 PM to 5:30 PM in Room 106 of the DMI (Dipartimento di Matematica e Informatica) or via TEAMS. He can be reached at raffaele.giancarlo@unipa.it. Research Focus: Computational Biology, Bioinformatics, Data Compression, and Algorithm Design. Key Contributions: Development of agile platforms for genomic data analysis, optimization of Bloom filters, and innovative text indexing techniques.

• Data Mining
• Machine Learning
• Information Theory
• +4 more

Manabu Kobayashi is a Professor at the Center for Data Science of Waseda University, with previous faculty roles at Shonan Institute of Technology (1998-2014). His research spans data mining, machine learning, and information theory, with a focus on applications in document classification, ECOC frameworks, and latent structural models. Current Affiliation: Waseda University (2018-present) Previous: Shonan Institute of Technology (2002-2014) Research Interests: Specializes in distance metric learning, latent class modeling, and fault diagnosis systems using probabilistic frameworks. Key areas include: ECOC (Error-Correcting Output Codes) optimization Collaborative filtering for recommendation systems Reconfigurable logic circuits using DG-CNTFETs Privacy-preserving distributed linear regression Scientific Awards: PC Conference Best Paper Award (2025) World CIST'18 Best Paper Award IEICE Achievement Award (2017) 74th Information Processing Society of Japan Conference Excellence Award (2012) Article Trends: Recent work focuses on latent structure analysis for relational data (2025), ECOC performance with noisy classifiers (2023), and flipped classroom effectiveness through log-based grouping (2022-2025). Earlier contributions include reconfigurable logic circuits (2013-2014) and probabilistic fault diagnosis (2011-2012).

• Bioinformatics
• Machine Learning
• Neural Networks
• +5 more

Ole Winther is a Professor at the Department of Biology , University of Copenhagen , and a Professor at the Section for Cognitive Systems , DTU Compute , Technical University of Denmark . He specializes in high-dimensional biological data analysis, machine learning, and data science. Academic Affiliations: University of Copenhagen (Computational and RNA Biology), DTU Compute (Cognitive Systems) Leadership Roles: Chief Research Officer at raffle.ai, CTO at FindZebra, Head of ELLIS Unit Copenhagen, co-PI of Machine Learning for Life Science (MLLS) Center Research Interests Bioinformatics: Protein sequence analysis (SignalP, DeepLoc, DeepTMHMM), single-cell gene expression, RNA/DNA level regulation Latent Variable Models: Variational autoencoders, diffusion models, deep generative models, structured mean-field, matrix factorization Natural Language Processing: Enterprise search applications, medical context LLMs, joint retrieval/generation models AI for Science: Surrogate models for physical models (density functional theory), fast scientific dataset generation Publication Trends His research focuses on deep learning methods for bioinformatics and AI applications, with a strong emphasis on variational inference, generative models, and probabilistic architectures. Publications span top venues like Bioinformatics , NeurIPS , ICML , and JMLR , addressing both theoretical and applied challenges. Teaching He teaches courses on deep learning at both institutions ( 02456 Deep Learning at DTU and NDAK24002U Deep Learning at University of Copenhagen). Research Group His current group includes PhD/postdoc researchers like Panagiotis Antoniadis, Felix G. Teufel, and Irene R. Rodriguez. Former advisees include Casper Kaae Sønderby, Marco Fraccaro, and Lars Maaløe, many of whom now hold prominent industry/academic positions.

• Plant Biology
• Microbiology
• Virology
• +7 more

Jarkko Salojärvi is a University Researcher at the University of Helsinki, affiliated with the Viikki Plant Science Centre (ViPS) and the Organismal and Evolutionary Biology Research Program . He serves as a supervisor in the Doctoral Programme in Plant Sciences and contributes to bioinformatics research for molecular biology and genomics. His research spans plant genomics , evolutionary biology , and bioinformatics , with a focus on silver birch , pathogen interactions , and plant stress responses . Recent work includes genomic analyses of Taphrina infections , betulin biosynthesis in fungi, and adaptive landscapes in forest trees. He has published extensively on plant-microbe interactions , genetic diversity , and computational biology , with funded projects such as TreeBio: Centre of Excellence in Tree Biology and Future Forests . His activities include peer review , conference presentations , and thesis supervision .

Qianru GuoView profile

Adjunct Professor

• Structural Fire Engineering
• Performance-Based Fire Protection Design
• Reliability Analysis
• +3 more

Qianru Guo serves as an Adjunct Professor in the Department of Civil and Urban Engineering at NYU's Tandon School of Engineering, while maintaining her primary role as Senior Consulting Engineer at Simpson Gumpertz & Heger Inc. (SGH) with over 10 years of specialized experience in performance-based fire protection design and structural fire engineering. Her academic credentials include: Ph.D. in Structural Engineering from University of Michigan, Ann Arbor M.S. in Civil Engineering and Mechanical Engineering from University of Michigan, Ann Arbor B.S. in Civil Engineering from Tongji University, Shanghai, China Dr. Guo's research pioneers the integration of probabilistic methodologies with structural fire engineering, focusing on reliability analysis of fire-exposed structures through advanced computational techniques. Her work develops inverse modeling for heat release rate determination, stochastic finite element methods for fire resistance evaluation, and performance-based design frameworks that redefine safety standards. She addresses critical gaps in predicting structural behavior under fire by quantifying uncertainties in material properties, fire scenarios, and structural responses. Analysis of her publication trajectory reveals consistent methodological evolution from fundamental reliability theory (2011-2013) toward practical performance-based design applications (2014-2018). Her research consistently bridges computational mechanics with fire safety engineering, emphasizing probabilistic safety assessment and code-compliant reliability calibration across residential and commercial structures. Based at 6 MetroTech Center, 4th Floor, Brooklyn, NY 11201, her professional activities integrate academic research with real-world engineering practice to advance fire-resilient infrastructure design.

Matthew WickerView profile

Assistant Professor

• Trustworthy Machine Learning
• Formal Verification
• Adversarial Robustness
• +3 more

Matthew Wicker is an Assistant Professor (Lecturer) at Imperial College London and a Research Associate at The Alan Turing Institute. His research focuses on developing formally verified, trustworthy machine learning systems with provable guarantees for safety, robustness, and fairness. He collaborates extensively with academic and industry partners, including Accenture. Research Interests: Formal verification of ML systems (e.g., neural network robustness/fairness) Bayesian neural networks and uncertainty quantification Adversarial attack/defense strategies Causal structure learning with probabilistic methods Certifiable guarantees for model explanations His recent publications (ICLR, ICML, NeurIPS) converge on theoretical and practical frameworks for trustworthy AI, emphasizing adversarial robustness in Bayesian models, global fairness certification, and scalable uncertainty methods. A consistent theme is bridging formal methods with real-world ML deployment challenges. Awards: Best Paper at TPM 2022 for causal structure learning research He maintains the deepbayes repository, enabling reproducibility of his PhD work on Bayesian neural network robustness.

Cameron FreerView profile

Research Professor

• Probabilistic computing
• Foundations of probabilistic programming
• Random structures
• +5 more

Cameron Freer is a Research Scientist in the MIT Probabilistic Computing Project , with prior roles including Instructor in Pure Mathematics at MIT, Postdoctoral Fellow at CSAIL, and Project Associate Professor at Keio University. His work bridges probabilistic computing, logic, and theoretical computer science. Education PhD in Mathematics, Harvard University, 2008 (Thesis: Models with High Scott Rank ) Research Interests Freer's research explores the deep interplay between randomness and computation , focusing on: Foundations of probabilistic programming languages and systems Efficient samplers for discrete and continuous distributions Mathematics of random structures like graphons and exchangeable processes Computability in measure theory and probabilistic inference Publications Overview His recent work (2020–2024) advances probabilistic programming systems (e.g., GenSQL), theoretical frameworks for random graphs via Markov categories, and computable approaches to PAC learning. Earlier contributions include exact sampling algorithms, computable exchangeability, and algorithmic barriers in conditional probability. Academic Service Steering Committee Member, LAFI (formerly PPS) workshop series (2017–2025) Program Committee Chair/Co-chair, PPS 2017–2018 Session Chair, POPL 2017 (PPS track) Industry & Visiting Roles Chief Scientist, Remine (2017–2018) Research Scientist, Gamalon Labs (2013–2016) Lyric Labs Visiting Fellow, Analog Devices (2013–2014) Project Associate Professor, Keio University (2021–2024) Labs & Collaborations Freer collaborates extensively with the MIT Probabilistic Computing Project, Harvard Logic Group, and international partners in Oxford, CMU, and Keio University. His work integrates theoretical insights with practical systems in AI and probabilistic inference.

Patrick BasView profile

Research Professor

• Steganography
• Steganalysis
• Digital Watermarking
• +2 more

Patrick Bas serves as Research Director and Thematic Group Facilitator for the Data Intelligence Group (DatInG) at the University of Lille, France, where he leads the Signal and Images team within the CRISTAL laboratory. His office is located at ESPRIT in Lille's Scientific City, and he holds membership on the institution's Scientific Council. Bas earned his Electrical Engineering degree (1997) and Ph.D. in Signal and Image Processing (2000) from the Institut National Polytechnique de Grenoble. His academic journey includes postdoctoral work at Université Catholique de Louvain, CNRS research at Gipsa-Lab (2001-2009), and a visiting position at Helsinki University of Technology (2005-2008). His primary research centers on steganography, steganalysis, and digital watermarking, with significant contributions to image manipulation detection and neural network applications in multimedia security. He has organized major initiatives including the BOSS steganalysis contest (2010) and served as co-coordinator for the Ecrypt European NoE's watermarking virtual lab (2004-2008). While his Google Scholar profile indicates recent publications in graph theory and combinatorics (2021-2025), these appear inconsistent with his documented research focus on multimedia security as evidenced by his thesis supervision and professional activities. Bas has supervised seven doctoral theses including Rony Abecidan's work on robust image manipulation detection (2024) and Solène Bernard's research on neural network steganography (2021). He has held editorial roles for the Eurasip Journal on Information Security (since 2011) and IEEE Transactions on Information Forensics and Security (2013-2016), and co-organized the International Workshop on Information Hiding (IH07).

Benjamin TrabingView profile

Researcher

• tropical meteorology
• tropical cyclones
• lightning applications
• +4 more

Benjamin Trabing serves as an Associate Scientist at the University Corporation for Atmospheric Research (UCAR) working remotely with the National Hurricane Center (NHC), focusing on advancing tropical cyclone forecasting capabilities and hazard communication strategies. His research directly impacts operational forecasting practices at one of the world's leading hurricane prediction centers. His academic foundation includes: B.S. in Meteorology from the University of Oklahoma M.S. and Ph.D. in Atmospheric Science from Colorado State University Dr. Trabing's research program centers on critical challenges in tropical cyclone prediction with particular emphasis on: Improving intensity and structure forecasting through advanced modeling techniques Developing and validating probabilistic forecast products for public communication Applying lightning observations to tropical cyclone monitoring Investigating radiation effects on tropical cyclone dynamics Enhancing understanding of diurnal cycles in tropical systems Translating research findings into operational forecasting improvements Analysis of his publication record reveals a strategic research trajectory that increasingly bridges meteorological science with social science considerations in risk communication. His recent work demonstrates growing emphasis on probabilistic forecasting methods and effective communication of forecast uncertainty to diverse stakeholders. Trabing's research consistently addresses operational forecasting challenges identified by the National Hurricane Center, ensuring practical applications for improving public safety during tropical cyclone events. Through his GitLab contributions and collaborative projects like the Landfall-Distribution-Product (LDP) and Intensity Change Bias and Uncertainty Scheme (IBUS), Dr. Trabing actively develops tools that directly inform operational forecasting practices at the National Hurricane Center, strengthening the research-to-operations pipeline in tropical meteorology.

Saikat DuttaView profile

Assistant Professor

• Software Engineering
• Machine Learning
• Program Analysis
• +7 more

Saikat Dutta is an Assistant Professor in the Department of Computer Science at Cornell University, where he joined in August 2024. His research sits at the intersection of Software Engineering and Machine Learning, with a focus on improving the reliability of machine learning systems and leveraging machine learning techniques to solve challenging software engineering problems. His research interests span several key areas including automated test generation and debugging of ML/DL libraries , using AI/ML for automated software engineering , improving performance and effectiveness of regression tests in ML libraries , and static and dynamic analyses for probabilistic programming . His work bridges theoretical foundations with practical applications in real-world systems. Dutta has developed multiple influential frameworks and tools including BugsInDLLs (a database of reproducible bugs in deep learning libraries), FLEX (for fixing flaky tests in ML projects), and TERA (for optimizing stochastic regression tests). His research has been published in top-tier venues including ICSE, FSE, ISSTA, PLDI, and ICLR. Amazon Research Award 2025 Meta AI LLM Evaluation Research Grant 2025 Mavis Future Faculty Fellowship 2022-23 Facebook PhD Fellowship 2020-22 3M Foundation Fellowship 2019-2020 Dutta actively mentors PhD students including Yingao (Elaine) Yao, Shinhae (Joseph) Kim, and Junkai Huang. He has served on program committees for major conferences including ASE, ISSTA, and ICSE. His teaching includes courses on Software Engineering in the Era of ML/AI.