Find Academic Supervisors

• Partial Differential Equations
• Stochastic Processes
• Probability Theory
• +3 more

Luigi Amedeo Bianchi is an Associate Professor in the Department of Mathematics at the University of Trento. His expertise spans stochastic processes, partial differential equations, fluid mechanics, and probability theory with a focus on stochastic partial differential equations. He has contributed to areas like turbulence modeling, stochastic fluid dynamics, and mathematical physics. His work bridges theoretical mathematics with applications in cosmology, astrophysics, and education through Olympiad mathematics resources. Research interests include stochastic differential equations under Gaussian noise, fractional processes, and modulation equations in unbounded domains. He has published extensively on topics ranging from stochastic Navier-Stokes equations to logical reasoning in mathematical competitions. His recent work (2023-2024) explores Wagner Framework systematization in graph theory and reinforcement learning, alongside improved cosmological data analysis methods. Notable contributions include textbook authorship on probability theory with exercises, and editorial work honoring prominent stochastic analysts. His publications reflect interdisciplinary engagement with both pure and applied mathematical challenges. Academic career includes teaching and research activities at the University of Trento, though specific educational background details are not provided here. No listed awards or grants are mentioned in the current data.

Wenqing HuView profile

Associate Professor

• Probability
• Data Sciences/Machine Learning/Optimization
• Operations Research
• +2 more

Roles & Affiliations: Wenqing Hu is an Associate Professor of Mathematics at the Department of Mathematics and Statistics, Missouri University of Science and Technology (formerly University of Missouri, Rolla). He holds memberships in professional societies including The Bachelier Finance Society, INFORMS, SIAM, and the Society of Actuaries. Education: Ph.D. in Mathematics, University of Maryland at College Park (2013) B.S. in Mathematics, Peking University (2008) Research Interests: His work spans Probability (stochastic analysis, multiscale problems), Machine Learning (optimization algorithms, neural networks), Operations Research (smart grid systems), Math Biology (Ao's potential function), and Cryptology (zero-knowledge proofs in blockchain). Recent breakthroughs include contributions to zero-knowledge virtual machines and high-dimensional data analysis . Publications Overview: His 15 most recent works (2025-2012) span topics like cryptology (e.g., zkEVM), stochastic differential equations, and reinforcement learning applications in manufacturing. Key themes include stochastic processes, nonlinear optimization, and interdisciplinary applications in energy systems and biology. Awards & Grants: Miner Alumni Teaching Award (2018) Simons Foundation Collaboration Grant (2020-2025) NSF-funded travel support for the 2018 ICM Labs & Collaborations: Active in cross-disciplinary projects such as subspace indexing on Grassmann manifolds and nonlinear optimization in ML .

Jiayi WangView profile

Assistant Professor

• Causal Inference
• Functional Data Analysis
• Reinforcement Learning
• +3 more

Jiayi Wang is an Assistant Professor of Statistics in the Department of Mathematical Sciences at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a PhD in Statistics from Texas A&M University (2022) and a B.S. in Statistics from Zhejiang University (2017). His research focuses on nonparametric statistics and machine learning, particularly in causal inference, functional data analysis, reinforcement learning, low-rank modeling, and matrix completion. He has contributed to methodological advancements in areas such as treatment effect estimation, offline reinforcement learning, and statistical theory for complex data structures. Key achievements include the ASA Section on Nonparametric Statistics Student Paper Award (2020). His work has been published in prestigious journals like the Journal of the American Statistical Association and conferences such as NeurIPS and ICML. He has also developed open-source code for methods like PCATE balancing weights, available on GitHub. Teaching experience includes instructing courses in statistical learning, probability, and applied statistics at both Texas A&M University and UT Dallas. His research group actively explores interdisciplinary applications, including climate science and criminal justice, demonstrating a commitment to bridging statistical theory with real-world problems.

Christian MendlView profile

Assistant Professor

• Quantum Computing
• Computational Physics
• Statistical Physics
• +2 more

Christian Mendl is a Rudolf Mößbauer Tenure Track Assistant Professor at the Technical University of Munich (TUM) , affiliated with the School of Computation, Information and Technology and the Institute for Advanced Study (TUM-IAS) . His career includes a postdoctoral position at Stanford University (2015-2017) under a Feodor Lynen Fellowship from the Alexander von Humboldt Foundation, a Junior Professorship at TU Dresden (2017-2019), and a PhD in Physics from LMU Munich (2012). Education : Physics and Mathematics (TUM) Appointments : Rudolf Mößbauer Assistant Professor (TUM, 2019), Junior Professor (TU Dresden, 2017), Postdoc (Stanford, 2015-2017) Research Focus : Mendl specializes in Quantum Computing , Computational Physics (tensor networks, quantum Monte Carlo, neural-network quantum states), Statistical and Non-Equilibrium Physics , and Numerical Simulation . His work bridges quantum information theory with condensed matter physics, emphasizing efficient quantum algorithms and simulators for complex systems. Scientific Contributions : Recent publications highlight advancements in quantum circuit optimization , tree tensor network simulations , block encoding of operators , and quantum-assisted optimization for problems like the capacitated vehicle routing and Toda lattice dynamics. His methods often integrate machine learning with quantum information to address challenges in Hamiltonian simulation and quantum error analysis . Awards : Alexander von Humboldt Feodor Lynen Fellowship, Boehringer Ingelheim Fonds PhD Fellowship, TopMath Graduate Program, Studienstiftung des deutschen Volkes Grants : Rudolf Mößbauer Tenure Track Fellowship (TUM-IAS), Dieter Schwarz Fellowship

• Medical Image Analysis
• Deep Learning
• Machine Learning
• +7 more

İlkay Öksüz is an Associate Professor in the Department of Computer Engineering at Istanbul Technical University (ITU), where he has been serving since 2021, following his appointment as a Doctoral Academic Staff member in 2020. He previously held research positions at King's College London (2017–2020), The University of Edinburgh (2016), and Yale University (2015–2016). He earned his Ph.D. in Computer, Decision and Systems Science from IMT School for Advanced Studies Lucca, Italy, in 2017, under the supervision of Prof. Sotirios Tsaftaris. B.Sc. in Electronics Engineering, Istanbul Technical University (2010) M.Sc. in Electrical-Electronics Engineering, Bahçeşehir University (2013) Ph.D. in Computer, Decision and Systems Science, IMT School for Advanced Studies Lucca (2017) His research focuses on medical image analysis, particularly in cardiac MRI, with core interests in image segmentation, registration, quality assessment, and reconstruction using deep learning. He also explores electricity price forecasting and explainable AI. His work bridges machine learning with clinical applications, aiming to improve diagnostic accuracy and automation in radiology. The 15 most recent publications highlight a strong trend in applying deep learning to medical imaging, especially cardiac and prostate MRI, mammography, and ECG analysis. A significant emphasis is placed on explainability , optimization , and clinical applicability , with frequent participation in MICCAI challenges and collaborations with radiology departments. Projects also extend into energy forecasting, showing interdisciplinary versatility. Scientific awards include: 2024 ITU Young Scientist Award in Engineering 2024 Parlar Research Incentive Award He leads the Predictive Intelligence and Medical Imaging (PIMI) Lab at ITU, mentoring graduate students and managing multiple funded projects, including the TUBITAK International Fellowship. His lab has achieved top placements in national and international AI competitions such as Teknofest and MICCAI challenges. He serves as principal investigator (PI) on several active grants focused on interpretable deep learning for medical imaging and electricity forecasting. The PIMI Lab, under his leadership, actively participates in high-impact medical AI challenges and has consistently achieved top rankings in competitions related to prostate cancer detection, breast imaging, and cardiac MRI reconstruction, demonstrating strong translational research impact.

Junpei KomiyamaView profile

Assistant Professor

• Machine Learning Methodology
• Decision-making Models
• Algorithmic Bias
• +7 more

Junpei Komiyama is an Assistant Professor in the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University, where he has been a faculty member since September 2019. His research bridges machine learning methodology and business applications, focusing on decision-making, fairness, and reproducibility in data-driven systems. His educational background includes a PhD in Information Science, an MS in Applied Physics, and a BTech, all from the University of Tokyo. Professor Komiyama's research interests center on machine learning with a strong emphasis on ethical and robust methodologies. He investigates decision-making models such as multi-armed bandits, algorithmic fairness , experimental design , and the reproducibility of data science findings. His work aims to redefine how we validate and trust results generated by machine learning systems. The recent articles reflect a strong trend in fairness-aware machine learning , online decision-making , and reproducible research practices . They span applications in business analytics, recommender systems, pricing, and cloud computing, demonstrating both theoretical depth and practical relevance. Presented work at top venues including NeurIPS and KDD Focus on ethical AI and robust scientific methodology He has advised students in machine learning and data science, though specific names are not listed. His research has been supported through academic grants related to AI ethics and data science innovation, though specific grant details are not provided. Professor Komiyama teaches courses in programming and machine learning, contributing to the technological education of business students at Stern. He is actively involved in advancing the integration of rigorous machine learning methods into business decision-making frameworks.

Andrea M. TonelloView profile

Professor

• Power Line Communications
• Wireless Communications
• Embedded Communication Systems
• +12 more

Andrea M. Tonello is a Full Professor at the Institute of Networked and Embedded Systems, University of Klagenfurt, Austria, where he chairs the Embedded Communication Systems Lab. He previously held positions at the University of Udine, Italy, where he was an Associate Professor and founded the Wireless and Power Line Communication Lab (WiPLi Lab). His research spans power line communications, wireless systems, embedded communications, smart grids, and machine learning applications in signal processing. Doctor of Engineering, University of Padova (1996) Doctor of Research, Telecommunications, University of Padova (2003) His research interests focus on next-generation communication systems, including power line and wireless networks, signal processing, machine learning for communications, UAV systems, and smart grid technologies. He has made significant contributions to PLC channel modeling, full-duplex communications, and information-theoretic learning for communication systems. His work integrates theoretical innovation with practical implementation in real-world networks. The most recent publications highlight a strong trend toward integrating machine learning and information theory into communication systems, particularly in power line and wireless networks. Themes include f-divergence based classification, mutual information estimation, neural decoding (MIND), noise-robust receivers, and topology-aware machine learning for PLC quality prediction. There is also a notable focus on UAV control, full-duplex PLC, and digital pre-distortion techniques for high-speed converters. IET 2016 Premium Award Best Paper Award, ISPLC 2016 Best Student Paper Award, ISPLC 2016 Aerospace Best Paper Award, 2018 Best Paper Award, ISPLC 2021 Best PhD Dissertation Award, 2019 IEEE ComSoc Distinguished Lecturer (2018) Two Awards from IEEE ComSoc TC-PLC (2019) University of Klagenfurt Technology Scholarships (2019) Dr. Tonello has supervised numerous PhD and Master’s students, including notable advisees such as Nunzio A. Letizia, Davide Righini, and Babak Salamat. He has led over 10 institutional and multiple industrial research projects with a total funding exceeding 20 million euros. He played a key role in promoting international academic collaboration, including Erasmus agreements, joint PhD programs with INSA Rennes and Ecole Polytechnique de Grenoble, and a joint master’s program with the University of Klagenfurt. He founded and led the WiPLi Lab at the University of Udine, which received around 3 million euros in funding and involved over 60 researchers and students. He also founded WiTiKee s.r.l., a spin-off company specializing in PLC for smart grids. Currently, he chairs the Embedded Communication Systems Lab at the University of Klagenfurt, focusing on next-generation networked and embedded communication technologies.

Pierre-Elouan Mikael RéthoréView profile

Researcher

• Wind Turbine Engineering
• Computational Fluid Dynamics
• Offshore Wind
• +7 more

Pierre-Elouan Mikael Réthoré is a Researcher at the Department of Wind and Energy Systems of the Technical University of Denmark (DTU). His work focuses on wind energy systems, particularly in Wind Turbine Engineering , Computational Fluid Dynamics , and Offshore Wind technologies. He leads the System Engineering and Optimization section within the Wind Energy Systems Division .

• Artificial Intelligence
• Neuroscience
• Machine Learning
• +3 more

Nikola Milosevic is a doctoral researcher at the Max Planck Institute for Human Cognitive and Brain Sciences , affiliated with the International Max Planck Research School NeuroCom and working within the Methods and Development Group Neural Data Science and Statistical Computing . His research spans interdisciplinary topics at the intersection of artificial intelligence, neuroscience, and computational modeling. His work focuses on active matter systems , reinforcement learning architectures , and neural data science methodologies . He has developed the ARC toolbox for artificial speech rhythmicity controls and contributed to explainable AI frameworks. Recent publications highlight his interests in embodied cognition, trust region optimization, and deep learning applications for biomedical imaging. Nikola's methodological expertise includes information processing , representation learning , and algorithmic safety in computational systems. His research combines theoretical approaches with practical implementations in Python and machine learning frameworks.

Nicola GattiView profile

Associate Professor

• Algorithmic Game Theory
• Mechanism Design
• Artificial Intelligence
• +3 more

Nicola Gatti is an Associate Professor in Computer Science and Artificial Intelligence at the Department of Electronics, Information and Bioengineering, Politecnico di Milano. He serves as Co-director of the Observatory Artificial Intelligence of Politecnico di Milano and holds board positions at the Italian National Laboratory of Artificial Intelligence and Intelligent Systems (CINI AIIS) and the Italian Association for Artificial Intelligence (AIxIA). His research spans the intersection of Computer Science, Microeconomics, Optimization, and Machine Learning, with specific focus on Algorithmic Game Theory, Mechanism Design, and Online Learning. He has made significant contributions to areas including negotiation, security games, equilibrium computation, sponsored search auctions, online advertising, election manipulation, and regret minimization in economic problems. Gatti's recent publications demonstrate strong trends in applying game-theoretic and learning approaches to practical problems in advertising, contract design, and constrained decision making. His work frequently appears in top AI venues including AAAI, AAMAS, NeurIPS, and ICML, reflecting his standing as one of the most prolific AI researchers in Italy. He has received notable recognition including being awarded as the best Italian young researcher on Artificial Intelligence in 2011 by AIxIA. His research is supported through competitive projects like PRIN2017 Algadimar and industrial collaborations with companies including lastminute.com, DoveVivo, MMM group, AdsHotel, Analisi e Valore, and the Italian Navy. In addition to his research, Gatti is actively involved in education, teaching courses on Systems Informatics, Economic and Computation, Data Intelligence Application, and specialized industry courses on Online Machine Learning and Algorithmic Game Theory for companies including lastminute.com, Niuma, and Ferrari GES Scuderia F1. Since 2018, he has chaired the Honours Programme in Scientific Research in Information Technology at Politecnico di Milano.

• Adolescent Mental Health
• Substance Use Among Adolescents
• Digital Media and Adolescent Well-being
• +7 more

Dr. Sebastian Kurten serves as an Assistant Professor in the Interdisciplinary Social Science department within the Faculty of Social and Behavioural Sciences at Utrecht University. His academic career spans multiple international institutions including the University of Cambridge, KU Leuven, and the University of Michigan. At Utrecht, he contributes to several research initiatives including DoY - Becoming Adults in a Changing World, DoY - Thriving & Healthy Youth, and Youth Got Talent, while teaching courses in Academic Writing and Risk Behavior and Addiction in Adolescence. Dr. Kurten holds a PhD and has established expertise in applying statistical methods to social work and adolescent development research. His educational background reflects a strong foundation in both quantitative methods and social sciences, enabling his interdisciplinary approach to adolescent mental health research. His research program centers on three interconnected domains: adolescent substance use (particularly vaping and alcohol consumption), digital media's impact on adolescent mental health with attention to socioeconomic disparities, and global mental health interventions. His work demonstrates how digital media exposure interacts with material deprivation to shape adolescent well-being across different cultural contexts. Recent projects include a large-scale survey of 25,000 South African adolescents examining vaping behaviors and a randomized controlled trial in Uganda evaluating mental health interventions for adolescents affected by armed conflict in collaboration with War Child Holland. Analysis of Dr. Kurten's recent publications reveals a consistent focus on methodological innovation in studying adolescent digital behavior and substance use. His work bridges quantitative social science, public health, and digital media studies, with increasing emphasis on global applications and cross-cultural validity of mental health measures. The research demonstrates sophisticated use of mixed methods, large-scale data collection, and advanced statistical techniques to address complex questions about adolescent development in the digital age. Dr. Kurten has received research funding supporting his work on adolescent mental health and digital media, though specific grants aren't detailed in the available information. His collaborations extend to international organizations including UNICEF Innocenti through the Disrupting Harms Project. As a doctoral supervisor, Dr. Kurten mentors PhD candidate Vira Dvoriak whose research focuses on Psychometric Networks for Youth Mental Health. His teaching portfolio includes courses on academic writing and risk behavior and addiction in adolescence, reflecting his commitment to training the next generation of researchers in evidence-based approaches to adolescent health challenges.

Anna RaffertyView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Educational Assessment
• +5 more

Anna Rafferty is an Associate Professor of Computer Science at Carleton College , where she has been teaching since 2014. She earned her Ph.D. from the University of California, Berkeley, under Professor Tom Griffiths, and holds an MS from Stanford University. As the chair of the Computer Science Department, she plays a pivotal leadership role in shaping academic programs. Research Interests : Her work focuses on applying machine learning and artificial intelligence to enhance education. Key projects include: Developing ML models for educational assessment that provide granular insights into student understanding Using inverse reinforcement learning to diagnose algebra misconceptions via Emmy's Workshop Optimizing game-based assessments for efficient knowledge diagnosis Building adaptive platforms for chemistry education with automated feedback systems Exploring bandit algorithms to dynamically improve online educational content Teaching : She teaches foundational CS courses (CS111, CS254) and advanced topics like natural language processing and machine learning . Her commitment to education extends to outreach programs, including teaching at Girls Inc. and volunteering for Expanding Your Horizons and Women in Computer Science and Electrical Engineering at Berkeley.

• statistical learning theory
• nonparametric statistics
• information theory
• +1 more

Dr. Azadeh Khaleghi is a Full Professor of Statistics at École Nationale de la Statistique et de l'Administration Économique (ENSAE) and a permanent member of the Centre de Recherche en Économie et Statistique (CREST). Her research focuses on the mathematical foundations of Statistics and Machine Learning, particularly in devising nonparametric models for long-memory time series and Multi-Armed Bandits problems. Research Interests Statistical learning theory Nonparametric statistics Information theory Long-memory processes Scientific Awards & Grants GRAPH4HEALTH ANR Project (2023) Google Faculty Research Award (2019/20) London Mathematical Society Scheme 4 Grant (2019) Adobe Research Grant (2018) London Mathematical Society Scheme 1 Grant (2017) E.M. Gold Best Paper Award (2013) Teaching Statistics 1 (SE2C1EN) at ENSAE (2022-present) Apprentissage Statistique appliqué (SE308) at ENSAE (2022-present) Machine Learning (MATH336) at Lancaster University (2016-2022) Probability & Stochastic Processes (MATH580/STOR602) at Lancaster University (2015-2021)

Sanjay MadriaView profile

Professor

• Cybersecurity
• Sensor Cloud Computing
• Delay-Tolerant Networks
• +5 more

Sanjay Madria is a Curators’ Distinguished Professor in the Department of Computer Science at Missouri University of Science and Technology. His research spans mobile and sensor computing , machine learning , cloud security , and blockchain , with applications in underground mining safety , UAV systems , and disaster response . PhD: Indian Institute of Technology, Delhi (1995) His work integrates secure sensor cloud architectures , delay-tolerant network optimization , and AI-driven risk assessment . Recent publications focus on UAV swarm detection , reinforcement learning for mining environments , and privacy-preserving social media analytics . Scientific Awards include IEEE Best Paper Awards , NRC Fellowships , and JSPS Fellowship . Funded by NSF , NIST , DOE , Honeywell , and Boeing . He contributes to IEEE/ACM Distinguished Speaker programs and leads the Center for Intelligent Infrastructure at Missouri S&T.

Rui MiaoView profile

Assistant Professor

• Reinforcement Learning
• Causal Inference
• Health AI
• +3 more

Rui Miao serves as an Assistant Professor in the Department of Mathematical Sciences within the School of Natural Sciences and Mathematics at The University of Texas at Dallas. Previously, he worked as a Mathematical Statistician at the Office of Biostatistics Research at NIH/NHLBI and completed postdoctoral training under supervision of Dr. Annie Qu and Dr. Babak Shahbaba. His academic foundation includes a PhD in Statistics from The George Washington University under Dr. Xiaoke Zhang's mentorship. Education: PhD in Statistics, The George Washington University Dr. Miao's research program bridges advanced statistical methodology with critical healthcare applications. His work focuses on Reinforcement Learning for personalized treatment policies, Causal Inference methods addressing unmeasured confounding, Health AI applications, Functional Data Analysis techniques, and computational approaches to Few-shot Learning . His methodological innovations particularly address challenges in heterogeneous medical data and complex decision frameworks. Analyzing his publication trajectory reveals a strong emphasis on developing statistical frameworks for personalized medicine, with increasing interdisciplinary collaboration in cardiology, immunology, and neuroscience. His work spans theoretical statistics in journals like Annals of Statistics and Journal of the American Statistical Association to applied medical research in Science Advances and Journal of the American College of Cardiology . Scientific Recognition: 2021 ICSA Student Paper Award for work on wavelet-based independence testing Dr. Miao actively contributes to the academic community through invited talks at institutions including National Cancer Institute, Duke University, and NIH, presenting on reinforcement learning under heterogeneity and functional data analysis methods. His teaching portfolio at UT Dallas includes STAT 5304 Introduction to Human Health Research, building on previous teaching experience at The George Washington University covering statistical theory and applied courses.