Find Academic Supervisors

• Integrated Circuit Design
• Analog Circuit Design
• CAD for Integrated Circuits
• +7 more

Günhan Dündar is a Professor at the Department of Electrical and Electronics Engineering, Boğaziçi University. His primary research focuses on integrated circuit design with an emphasis on analog circuits, CAD tools for IC design, and biomedical sensor systems. He specializes in low-power and radiation-hardened analog circuits, RF front-ends, and machine learning applications in analog design. Key projects include embedded ocular pressure sensors in contact lenses, super-regenerative receivers for biomedical implants, and yield-aware analog circuit synthesis tools using AI. His work spans from nanoscale CMOS processes (65nm/40nm) to MEMS sensor systems for medical applications. He has published extensively on topics like SAR ADCs, IR-UWB transmitters, and multi-objective optimization frameworks. His design tools (e.g., DATA-IP, Radispice) address challenges in analog IP development and radiation effects mitigation. His research bridges theoretical circuit design with practical biomedical and aerospace applications.

• Electromagnetic Wave Propagation
• Electromagnetic Compatibility (EMC)
• Biomedical Effects of Radiation
• +3 more

Selim Şeker is a part-time Professor at Bogazici University. His research focuses on electromagnetic wave propagation, electromagnetic compatibility (EMC), biomedical effects of radiation, and remote sensing. He has contributed to interdisciplinary studies spanning neurology, telecommunications, and environmental science. Research interests include advanced MRI techniques for glial tumors, neuroimaging for hydrocephalus diagnostics, and modeling electromagnetic radiation effects in biological systems. His work bridges theoretical electromagnetics with practical applications in healthcare and telecommunications infrastructure. Key publications span neuroimaging case studies, tumor recurrence analysis, and propagation models for wireless systems. Notable contributions include curve-fitting models for energy consumption forecasting and analysis of rare medical cases involving cranial trauma and metastatic carcinomas.

• Algebraic Geometry
• Mathematical Biology
• Phylogenetics
• +3 more

Jesús Fernández Sánchez is a Researcher at the Department of Mathematics, Polytechnic University of Catalonia (UPC), associated with the GEOMVAP (Geometry of Varieties and Applications) research group. He holds an affiliation with the Faculty of Mathematics and Statistics (FME) and the Institute of Mathematics of UPC-BarcelonaTech (IMTech). His work focuses on algebraic geometry, mathematical phylogenetics, and computational algebra, with applications to evolutionary biology and stochastic processes. Research Interests: Fernández Sánchez's research bridges algebraic geometry with biological applications, particularly in phylogenetics. He explores topics such as Markov matrix embeddability, phylogenetic invariants, equivariant models, and algebraic methods for evolutionary analysis. His work includes developing semi-algebraic methods for quartet reconstruction and analyzing rank conditions in phylogenetic networks. Grants & Projects: He has led or contributed to several competitive research projects, including 'GEOMETRIA DE VARIETATS I APLICACIONS' (GEOMVAP group), the María de Maeztu Excellence Program, and collaborations on phylogenetic model development. His projects emphasize multidisciplinary approaches in geometry, algebra, and topology. Advising: He has supervised doctoral students like Marina Garrote-López and Jordi Roca-Lacostena in topics such as phylogenetic reconstruction methods and Markov matrix analysis. His lab investigates geometric and algebraic tools for evolutionary biology, with a focus on model validation and computational techniques. Affiliations: Besides his primary role at UPC, he collaborates with international research networks in algebraic phylogenetics and stochastic processes. His work integrates pure mathematics with applied problems in biology and statistics.

• Reinforcement Learning
• Stochastic Dynamic Optimization
• Sequential Learning
• +6 more

Somayeh Moazeni is an Associate Professor (Tenured) at the School of Business, Stevens Institute of Technology since September 2021, and also holds a Visiting Associate Professor position at the Department of Industrial Engineering & Management Sciences, Northwestern University since September 2023. Her academic journey includes previous positions as Assistant Professor at Stevens Institute of Technology in both the School of Business (2017-2021) and School of Systems and Enterprises (2014-2016), as well as a Postdoctoral Research Associate at Princeton University (2012-2014). She earned her PhD in Computer Science from the University of Waterloo in 2012, supervised by Professors Thomas F. Coleman and Yuying Li. Her educational background also includes an MMath in Combinatorics and Optimization (2006), a Certificate in University Teaching (2010), and a BSc and MSc in Mathematics (2005) from Amirkabir University of Technology in Iran. Research Interests: Dr. Moazeni's research focuses on Reinforcement Learning , Stochastic Dynamic Optimization , Sequential Learning and Information Collection , Bayesian Optimization , and Mathematical Optimization . Her work spans various application areas including Inventory Management , Logistics , Algorithmic Trading , and Energy Markets . Her recent publications demonstrate a strong trajectory in applying optimization techniques to complex decision-making problems under uncertainty across multiple domains. Her research has been supported by significant funding from organizations including the National Science Foundation (NSF), Natural Sciences and Engineering Research Council of Canada (NSERC), PSEG Foundation, and Accenture. She is an affiliated faculty member of the Stevens Institute for Artificial Intelligence (SIAI) and a faculty member of the Stevens Energy Efficiency cluster. IEEE Senior Member (2019) Vice-provost for Academics Teaching Excellence commendation (2017) Anita Borg Institute GHC Faculty Scholar (2017) Ranked First for NSERC Postdoctoral Fellowship-Mathematical Sciences in Canada (2013) INFORMS Teaching Effectiveness Colloquium (2012) First Place at MITACS Annual Conference Poster Presentation Competition (2009) Dr. Moazeni has successfully supervised multiple PhD and Master's students, including Ruijing Yang (PhD in progress), Rodrigo Andrade (PhD graduated 2020), and several Master's students who completed theses on topics related to energy markets and financial engineering. She serves as a Dissertation Advisory Committee Member for numerous PhD candidates across multiple departments at Stevens. Her industry experience includes roles as Senior Risk Analyst at Royal Bank of Canada and Risk Analyst at Bank of Montreal, providing practical grounding for her academic research.

Anirban DasGuptaView profile

Professor

• Mathematical Statistics
• Asymptotics
• Applied Probability
• +3 more

Anirban DasGupta is a Professor of Statistics at Purdue University's Department of Statistics within the College of Science. He received his B.S. (1977), M.S. (1978), and Ph.D. (1984) degrees from the Indian Statistical Institute. His office is located in MATH 528, and he can be contacted at dasgupta@purdue.edu or by phone at 765-494-6033. His research focuses on mathematical statistics, asymptotics, applied probability, analytic number theory, probability inequalities, and connections between mathematical disciplines. Recent publications emphasize high-dimensional inference, distribution theory, and applications in statistical methodology. Professor DasGupta has received significant recognition including: Fellow of the Institute of Mathematical Statistics (1993) Top Ten Outstanding Teachers in College of Science (1987, 1997) Member of IMS Council (2008-09, 2014-16) He has advised numerous PhD students including Sanjib Basu, Saurabh Mukhopadhyay, and Mei-Mei Zen. He has held editorial positions for major statistical journals including Annals of Statistics, Bernoulli, and Journal of Statistical Planning and Inference.

Jeffrey PaiView profile

Professor

• Risk management
• Derivatives statistical modeling
• Term structure of interest rates
• +3 more

Jeffrey Pai is a Professor at the Warren Centre for Actuarial Studies and Research within the Asper School of Business at the University of Manitoba. With over 30 years of academic experience spanning multiple countries, he specializes in actuarial science, risk modeling, and financial derivatives. His research examines livestock/crop insurance, weather derivatives, and Bayesian statistical methods for risk assessment. Research encompasses quantitative risk management frameworks, stochastic modeling of insurance products, and econometric analysis of financial instruments. Recent work focuses on parametric insurance for natural disasters, mortality catastrophe modeling, and agricultural risk transfer mechanisms using advanced statistical approaches. Publications demonstrate consistent focus on practical applications of actuarial science, particularly in agricultural insurance and weather-related financial instruments. Recent trends show increased emphasis on Bayesian methods, catastrophe modeling, and developing market applications. Awards: Students' Teacher Recognition Awards (2021, 2002) CAS Best Paper Award (2016) Literati Network Outstanding Paper Award (2011) Featured in MACLEAN's Guide to Canadian Universities (2004) Extensive teaching includes courses in life contingencies, risk theory, and actuarial modeling. Secured research funding from international agencies including SSHRC (Canada), Society of Actuaries (USA), and UK Development Partnerships.

George KarabatsosView profile

Professor

• Bayesian Statistics
• Statistical Modeling
• Psychometrics
• +6 more

George Karabatsos is a Professor at the University of Illinois at Chicago (UIC), affiliated with the Department of Educational Psychology. His research focuses on developing and applying Bayesian statistical models, computational methods, and psychometric frameworks for complex data analysis. He has contributed to areas such as Bayesian nonparametrics, meta-analysis, causal inference, and educational measurement. His work has been supported by grants from the National Science Foundation, the Spencer Foundation, and the National Institutes of Health. Karabatsos has authored a menu-based statistical software package for analyzing data using over 100 models and served as an Associate Editor for journals like Psychometrika and Computational Statistics and Data Analysis . Education: PhD in Measurement, Evaluation, and Statistical Analysis (MESA) from the University of Chicago, 1998. Research interests include Bayesian modeling, computational statistics, psychometrics, and applications in education and biostatistics. His recent work emphasizes Bayesian methods for addressing issues like hidden bias, sensitivity analysis, and local dependence in statistical inference. Publications span topics such as nonparametric mixture models, meta-analytic techniques, and software development. His articles reflect a focus on methodological advancements and their practical implementation in real-world problems. Grants and advising: Karabatsos has led numerous research projects funded by major institutions. While specific student advisees are not listed, his software contributions indicate involvement in training researchers through accessible tools. Labs/Teams: He is the lead developer of a Bayesian statistical software package, fostering collaboration in applied statistical research.

Pica PierreView profile

Visiting Professor

• Linguistics
• Cognitive Science
• Numerical Cognition
• +3 more

Pierre Pica is a Visiting Professor at the Brain Institute (Instituto do Cérebro) of the Federal University of Rio Grande do Norte (Brazil) and an Associate Researcher at the Department of Linguistics of Leiden University (Netherlands). His career includes roles as a Research Fellow at the National Center for Scientific Research (CNRS, France) and Visiting Professorships at institutions like MIT (USA) and Federal University of Rio de Janeiro (Brazil). He holds a Doctorat 3ième cycle (1988) and MA (1974) in linguistics from Paris universities, with postdoctoral training at MIT. His research focuses on linguistic theory (syntax, binding theory, parametric variation), numerical cognition, and cross-cultural studies of indigenous populations like the Mundurucu of Brazil. Notable contributions include demonstrating universal geometric intuitions and non-verbal numerical systems through fieldwork with the Mundurucu, challenging linguistic relativism. He has conducted over 30 months of fieldwork in the Amazon region. Awarded prizes include the 2012 La Recherche Magazine Prize in Neuroscience and the 2004 Discovery Prize (Ministry of Research). He has edited major linguistic series including the Language Faculty and Beyond (John Benjamins) and founded the Linguistic Variation Yearbook . His work bridges linguistics, cognitive science, and anthropology, emphasizing core knowledge systems' interaction with language.

• Statistics
• Machine Learning
• Simulation
• +5 more

Motonobu Kanagawa is an Assistant Professor (Maitre de Conférences) in the Data Science Department at EURECOM in France since 2019. Previously, he held research positions at the University of Tübingen and the Max Planck Institute for Intelligent Systems in Germany under Prof. Philipp Hennig. He earned his PhD in 2016 from the Institute of Statistical Mathematics in Tokyo under Prof. Kenji Fukumizu. His research focuses on statistical methodologies for complex systems simulation, including reliability validation of simulators, Gaussian processes, Bayesian optimization, and kernel methods. Recent work explores variable selection in distribution comparison and covariate shift adaptation. Key achievements include organizing the inaugural ProbNum 2025 conference, receiving the Chris Daykin Prize (2023) for pension system analysis with Bayesian optimization, and securing a chair position at the 3IA Côte d'Azur AI institute (2021). He reviews for leading journals like JMLR and conferences such as NeurIPS and ICML. Publications span topics like k-NN regression optimization, Gaussian process scale parameter estimation, and counterfactual mean embeddings. His work bridges theoretical statistics and applied machine learning, emphasizing computational efficiency and uncertainty quantification.

• Network Science
• Graphical Models
• Causality
• +3 more

Veronica Vinciotti is an Associate Professor in the Department of Mathematics at the University of Trento. She holds a PhD in Statistics from Imperial College London (2002) and a First Degree in Mathematics from the University of Perugia (1999). Her academic career includes roles at Brunel University London as Lecturer (2007-2012), Senior Lecturer (2012-2017), and Reader (2017-2020) before joining the University of Trento in 2020. Her research focuses on Network Science, Graphical Models, Causality, and Biostatistics. Key contributions include advancements in sparse inference, causal generalized linear models, and applications to microbiome interactions and healthcare quality evaluation. She leads the PRIN 2022 project and has coordinated initiatives like COSTNET (2016-2020). Editorial roles include Editor of Statistica Neerlandica and guest editorships for special issues on Network Science. She has advised six PhD students and contributed to R packages like cglasso and sglasso . Her work bridges statistical methodology with applications in biology, healthcare, and sociology. Recent grants include funding from the Research Council of Finland and Swedish Research Council. She is a member of the Bernoulli Society and Royal Statistical Society, and has organized conferences such as the International Workshop on Statistical Modelling (2024) and sessions at the Isaac Newton Institute (2016).

Benjamin AkersView profile

Professor

• Nonlinear Waves
• Fluid Dynamics
• Applied Mathematics
• +3 more

Benjamin Akers is a Professor of Mathematics at the Air Force Institute of Technology (AFIT), specializing in nonlinear waves and fluid dynamics. He holds a PhD from the University of Wisconsin-Madison (2008), an MA from the same institution (2005), and a BS from Pennsylvania State University (2003). His research focuses on mathematical modeling of water waves, thermal blooming in high-energy lasers, solar magnetic field dynamics, and numerical methods for nonlinear PDEs. Dr. Akers has received numerous teaching awards, including the AFIT Dean's Distinguished Teaching Professorship (2020) and multiple SOCHE Excellence in Teaching accolades (2012, 2019). His work bridges applied mathematics with engineering challenges, with applications in laser propagation, solar physics, and environmental modeling. Key research themes include: Wave dynamics (gravity-capillary waves, Wilton ripples) Laser thermal blooming and atmospheric effects Computational methods (radial basis functions, machine learning) Solar flare magnetic field analysis Recent publications emphasize high-order resonance phenomena in nonlinear systems and machine learning-enhanced numerical simulations. He collaborates widely with institutions like NASA and the National Solar Observatory.

Paweł WachelView profile

Researcher

• Control Theory
• System Identification
• Signal Processing
• +5 more

Paweł Wachel is a researcher at the Department of Control Systems and Mechatronics within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. He is actively engaged in research and academic activities, with recent publications spanning from 2022 to 2024. Research Interests: His primary research areas include control theory, system identification, signal processing, and nonlinear systems. He specializes in the modelling and identification of complex systems such as Wiener, Hammerstein, and infinite memory nonlinear systems, employing advanced techniques like kernel-based methods, aggregative modelling, and exponential excitations. The recent publications indicate a strong focus on both theoretical and applied aspects of control systems, with increasing integration of learning-based and data-driven approaches. Topics such as safe learning, decentralized diffusion, and dual averaging algorithms reflect modern trends in adaptive and robust control. The interdisciplinary reach extends into neural networks and materials science, particularly in using fractal analysis for material hardness estimation. Scientific Awards: No awards or fellowships are mentioned in the provided text. Advising and Grants: While no specific students or grant funding are listed, Paweł Wachel collaborates extensively with researchers such as Krzysztof R Kowalczyk, Cristian R Rojas, Koen Tiels, and others, suggesting active participation in research teams and potential supervision roles. His consistent publication output indicates sustained research activity, likely supported by institutional or project-based funding. Labs and Teams: As a member of the Department of Control Systems and Mechatronics, he is likely involved in research laboratories focused on control systems, system identification, and mechatronic applications, though specific lab names or team structures are not detailed in the provided content.

Michael AntolovichView profile

Senior Lecturer

• Drones
• 3D printing
• AI and robotic systems
• +2 more

Michael Antolovich is a Senior Lecturer and Course Director in Computer Science at Charles Sturt University (CSU), affiliated with the School of Computing and Mathematics and the Machine Vision and Digital Health (MaViDH) Research Group. He has held roles such as Associate Head of School and Acting Head of the Environment Studies Unit (ESU) at CSU. Doctor of Philosophy, University of New South Wales (1988) Bachelor of Science, University of New South Wales (1983) His research focuses on drones, 3D printing, AI, robotic systems, sensor networks, and data analysis. Recent work explores parametric classification of Bingham distributions, low-rank matrix approximation algorithms, and sports highlight generation using audio-visual features. Key trends in his publications include advancements in image processing , machine learning , and data visualization applied to video analysis, sensor networks, and business communication. Antolovich has served as a registered supervisor at CSU and coordinated the Apple University Consortium Development Fund (AUCDF). He is a Member of the Australian Computer Society (MACS) and a Practicing Computer Professional (PCP).

Yuhua ZhuView profile

Assistant Professor

• Machine Learning
• Reinforcement Learning
• Optimization Algorithms
• +4 more

Yuhua Zhu is a tenure-track Assistant Professor in the Department of Statistics and Data Science at the University of California, Los Angeles (UCLA). Her research focuses on the intersection of partial differential equations (PDEs) and machine learning, with particular emphasis on using differential equations to analyze and design efficient reinforcement learning and optimization algorithms. Education: Ph.D. in Mathematics, University of Wisconsin-Madison (2019), advised by Shi Jin M.S. in Mathematics, University of Wisconsin-Madison (2015) B.S. in Mathematics, Shanghai Jiao Tong University (2014) Postdoc at Stanford University (2019–2022), mentored by Lexing Ying Research Interests: Her work spans sequential decision-making (e.g., continuous-time reinforcement learning, multi-armed bandits), optimization (gradient-free methods, asynchronous-SGD, convergence analysis), and uncertainty quantification in kinetic equations (sensitivity analysis, high-dimensional uncertainty). She develops PDE-based frameworks for model-free reinforcement learning and consensus-based optimization methods for distributed learning systems. Recent Work Trends: Her publications emphasize PDE-driven approaches to reinforcement learning, consensus-based optimization in federated learning, and uncertainty quantification in kinetic equations. Recent topics include addressing adversarial attacks in federated learning, high-order discretization in policy evaluation, and stabilization of kinetic equations with reflective boundary conditions. Awards & Grants: No specific awards mentioned in the text. Current recruiting focuses on graduate students in reinforcement learning and optimization for machine learning problems. Labs & Collaborations: Collaborations include work with Jose Carrillo, Shi Jin, Lexing Ying, and others on consensus-based methods and kinetic theory applications in machine learning.

Anna Rozanova-PierratView profile

Researcher

• Mathematical Analysis
• Nonlinear Acoustics
• Partial Differential Equations
• +5 more

Anna Rozanova-Pierrat is a researcher affiliated with the Laboratory Mathematics and Computer Science for Complexity and Systems , focusing on advanced mathematical analysis, partial differential equations, and their applications in acoustics and engineering. Her work emphasizes boundary value problems, fractal geometries, and optimization techniques for irregular domains. Key research areas include: Nonlinear acoustics and wave propagation Optimal shape design for acoustic absorption Fractal boundary analysis in heat diffusion and PDEs Inverse problems and microwave imaging Her recent studies (2023–2025) explore: Mathematical foundations of Kuznetsov and Khokhlov-Zabolotskaya-Kuznetsov equations Stability and compactness in non-Lipschitz domains Poincaré-Steklov operator applications to fractal interfaces Publications highlight collaborations with institutions like the University of Pisa (Italy) and the University of Bremen (Germany), addressing both theoretical and applied aspects of complex systems. She has contributed to over 30 peer-reviewed articles and co-authored works on fractal engineering and optimal acoustic design.