Find Academic Supervisors

Susanne SchennachView profile

Professor

• Econometrics
• Nonparametric Methods
• Measurement Error Models
• +3 more

Susanne Schennach is a Professor of Economics at Brown University, specializing in econometrics and statistical methodology. She holds a PhD from the Massachusetts Institute of Technology (2000) and a BA from Brandeis University (1996). Her teaching includes courses such as ECON 1630 (Mathematical Econometrics I), ECON 1670 (Advanced Topics in Econometrics), and ECON 2630 (Econometric Theory). Her research focuses on addressing challenges in econometric modeling, particularly measurement errors, latent variables, and nonlinear systems. Key contributions include frameworks for robust inference, estimation methods under nonclassical errors, and the development of hybrid statistical techniques. She explores applications in spatial data, factor analysis, and skill formation technologies. Notable work includes innovations in generalized method of moments (GMM), optimal transport theory, and nonparametric identification. While no specific awards are listed, her extensive publication record reflects significant contributions to econometric theory and methodology. Her advising and grants involve collaborations on topics like nonlinear panel data analysis and model selection. No specific labs or teams are mentioned, though her work often integrates interdisciplinary methods from statistics and machine learning.

Richard BrewsterView profile

Academic

• Quantum Information
• Quantum Communications
• Quantum Optics
• +3 more

Richard Brewster is an Assistant Research Professor at the University of Maryland's Institute for Research in Electronics & Applied Physics. He holds a Ph.D. in Physics from the University of Maryland, Baltimore County (2019) and has been affiliated with the University of Maryland since 2019, collaborating with Professor Yanne Chembo. His research focuses on quantum information, quantum communications, and quantum optics, particularly addressing decoherence in quantum states and fiber-based quantum network simulation frameworks. Education Ph.D. in Physics, University of Maryland, Baltimore County (2019) Research Interests Brewster investigates quantum optical systems, emphasizing the development of robust quantum networks and mitigation of decoherence effects. His work includes modeling nonidealities in fiber-based quantum systems and exploring noiseless amplification techniques to preserve entanglement quality. His methodologies span analytical modeling, numerical simulations, and experimental frameworks for quantum communication protocols. Research Trends His publications (2017–2025) highlight advancements in Bell inequality violation quantification, polarization entanglement degradation analysis, and noiseless attenuation strategies. Recent work emphasizes automated methodologies for entanglement quality estimation and generalized delta function applications in quasi-probability distributions. Awards & Recognition No specific awards mentioned in the provided text. Advising & Grants Brewster collaborates actively with senior faculty (e.g., Yanne Chembo) but no formal advisee list is provided. His research is likely supported by grants focused on quantum communication and photonics. Labs & Teams He is affiliated with the Photonic Systems Laboratory for Aerospace and Communication Engineering , focusing on applied quantum optics and aerospace communication technologies.

• Particle Physics
• Quantum Field Theory
• Lattice QCD
• +3 more

Dr. Ayse Kizilersu is a Grant-Funded Researcher (B) in the Department of Physics at the University of Adelaide. Her work focuses on theoretical and computational physics, with particular emphasis on lattice Quantum Chromodynamics (QCD), particle physics, and statistical distribution analysis. She is affiliated with the Physics division at the North Terrace campus, office 108A. Her research interests span non-perturbative QCD studies, quark-gluon vertex dynamics, and applications of statistical methods to finance and physics. Dr. Kizilersu's recent contributions include investigations into the Weibull distribution's properties and its applications in censored data analysis, alongside lattice QCD simulations exploring quark-gluon interactions. Her articles reflect interdisciplinary strengths in both high-energy physics and econophysics, addressing topics from particle dynamics to market order analysis. Awards and achievements sections remain unspecified in her profile, though her active grant-funded status indicates ongoing research support. No formal advising roles or student supervision details are documented here. Professional activities include contributions to lattice field theory and statistical modeling communities.

• Statistics
• Econometrics
• Risk Management
• +5 more

Ruben Seiberlich is a Lecturer at the Zurich University of Applied Sciences (ZHAW), School of Management and Law, Department of Banking, Finance, and Insurance. He holds a PhD in Econometrics from the University of Konstanz (2013) and has taught courses such as Advanced Quantitative Methods, Risk Management, and Finanzinstrumente & Portfoliotheorie. His research focuses on statistics, econometrics, and risk management, with notable work on real estate bubble risk, gender discrimination in housing, and partisan bias in inflation expectations. Education: Diplom Volkswirt (2009), Dr. rer. pol. Ökonometrie (2013) Current roles: Study Program Leader for MSc Banking and Finance, Model Performance and Backtesting Leader at Credit Suisse AG Research Interests span empirical economics, risk analysis, and applied econometric methods. His recent articles analyze real estate and gold market bubbles, gender bias in rental housing, and political bias in economic expectations. Key methodologies include semi-parametric decomposition, propensity score matching, and shrinkage estimation. Scientific Networks: Member of the German Statistical Society’s committee for Empirical Economics and Applied Econometrics; ORCID ID 0000-0002-5258-5499.

• Machine Learning
• Data Mining
• Computational Biology
• +3 more

Dr. Yijun Zhao is an Associate Professor in the Department of Computer and Information Science at Fordham University . Her research explores Machine Learning methods to address bias and subjectivity in datasets, with applications in Healthcare Analytics and Neuroscience . She has collaborated with Harvard Medical School and NYU Langone Medical Center on projects related to Multiple Sclerosis and Epilepsy . Education: PhD in Computer Science, Tufts University MS in Computer Science, University of Kansas BS in Computer Science, Tianjin University Research Interests The success of supervised machine learning algorithms hinges on the assumption that data are drawn from a single underlying distribution, which often fails in real-world applications involving human judgment. Dr. Zhao has pioneered methods to enhance model performance when learning from biased or subjective datasets, particularly in biomedical domains. Her work includes: Predicting disease progression in Multiple Sclerosis patients Detecting brain abnormalities in neurological disorders Developing non-parametric and probabilistic machine learning techniques Applying transfer learning to mitigate human subjectivity Creating domain-specific Gaussian Process models Using Conditional Random Fields for outlier detection Publications Trends Dr. Zhao's work spans Machine Learning , Healthcare Analytics , and Graph Theory . Her recent publications focus on biomedical applications, including Multiple Sclerosis and Epilepsy prediction models, while her earlier work in 2000 addressed graph algorithms in Computer Science . Teaching Dr. Zhao teaches CISC 2000 Computer Science II and CISC 4631/CISC 6930 Data Mining at Fordham. Her courses emphasize object-oriented programming , data mining techniques , and algorithm implementation with real-world datasets.

• Quantitative Finance
• Stochastic Volatility Models
• Risk Measurement
• +4 more

Erindi Allaj is an Associate Professor at the Department of Economics and Business Sciences of the University of Parma . She teaches General Mathematics and Quantitative Methods for Financial Markets in the Economics and Management bachelor's program. Office hours: Thursdays and Fridays 10:00-12:00 (in-person by appointment) Research focuses on: mathematical finance, volatility modeling, financial instability prediction, ESG integration in portfolio optimization Research Interests: Her work addresses key challenges in quantitative finance, including no-arbitrage valuation frameworks, stochastic volatility modeling, and financial instability prediction. Recent publications explore ESG integration in portfolio optimization, while earlier works examine transaction cost modeling and the Black–Litterman asset allocation framework. Key Publications Trends: Her research spans 2013-2025 with recent focus on ESG factors (2025), high-frequency data applications (2021, 2024), and financial crisis prediction systems (2022-2023). Academic collaborations include Maria Elvira Mancino and Simona Sanfelici. Contact Information: Email: erindi.allaj@unipr.it Office Location: Department of Economics and Business Sciences – Mathematics Section "E. Levi" Address: Via J.F. Kennedy, 6, 43125 Parma, Italy

Sebastian J. GoergView profile

Associate Professor

• Behavioral Economics
• Experimental Economics
• Environmental Economics
• +7 more

Sebastian J. Goerg is an Associate Professor of Economics at Technical University of Munich , affiliated with the Chair of Economics and TUMCS for Biotechnology and Sustainability. He is also a Research Fellow at the TUM School of Management, IZA Institute of Labor Economics, and MPI for Research on Collective Good. His research lies at the intersection of Behavioral Economics and Experimental Economics , with a focus on Environmental Economics , Public Policy , and Game Theory . Recent work examines social norm nudges, behavioral spillovers from norm violations, and the impact of Germany’s public transport policies like the 9-Euro-Ticket. He investigates how incentives, implicit effort costs, and psychological factors shape decision-making in economic contexts. His publication trends highlight empirical studies on cooperation, rationalization of information, consumer behavior for sustainable products, and gender dynamics in unethical behavior. Collaborations with institutions like TUM School of Management and IZA underscore his interdisciplinary approach. Goerg’s work combines lab and field experiments to address questions in Environmental Policy , Transport Economics , and Social Norms . Goerg contributes extensively to methodological discussions, including reproducibility in Management Science and non-parametric statistical testing. His work has appeared in journals like Management Science , European Economic Review , and Games and Economic Behavior .

Niccolò AjroldiView profile

Researcher

• Machine Learning
• Deep Learning
• Optimization
• +4 more

Niccolò Ajroldi is a Researcher at the ELLIS Institute Tübingen, contributing to the Safety & Efficiency-aligned Learning and Deep Models and Optimization groups. His work focuses on enhancing optimization algorithms for neural network training and improving their robustness and efficiency. Master of Science in Mathematical Engineering from Polytechnic University of Milan Former AI Residency Program participant at Meta His research spans machine learning optimization, functional data analysis, conformal prediction, and healthcare applications. Recent projects include intra-document causal masking in language models and algorithmic efficiency benchmarking. Key contributions include weight averaging techniques (ICML 2025), loss landscape characterization (NeurIPS 2024), conformal prediction for functional time series (2023), and acute kidney injury prediction models (PLOS ONE 2023). Articles highlight trends in large-scale optimization, functional statistics, and medical ML. 3rd place in MLCommons Algorithmic Efficiency benchmark (non-industry leader), 2024

Rebecca MarionView profile

Researcher

• Dimensionality Reduction
• Feature Selection
• Nonlinear Embedding
• +3 more

Rebecca Marion is a Researcher at UCLouvain specializing in machine learning, with expertise in dimensionality reduction and feature selection. She holds a Doctor of Science from UCLouvain (awarded 2021) for her work on statistical and machine learning methods for identifying variable clusters. Her education: Doctor of Science, Statistical and Machine Learning Methods for Identifying Clusters of Variables, UCLouvain (2021) Marion's research focuses on nonlinear dimensionality reduction (e.g., t-SNE, UMAP) and feature selection stability. She develops gradient-based explanation methods for complex embeddings and investigates the robustness of machine learning models. Her work bridges theoretical advances with practical data science applications. Recent publications highlight her contributions to explainable AI: a 2024 paper on gradient-based explanations for nonlinear embeddings and a 2023 study improving feature selection stability in regression. These works address critical challenges in interpretability and reliability of machine learning systems. No scientific awards are listed in the available profile information. Information about student advising and research grants is not provided in the current data. She is part of UCLouvain's machine learning research community, though specific lab or team affiliations are not detailed.

Vivek F. FariasView profile

Professor

• Reinforcement Learning
• Dynamic Optimization
• Stochastic Systems
• +4 more

Vivek F. Farias is the Patrick J. McGovern (1959) Professor at the MIT Sloan School of Management , where he is affiliated with the Operations Management group and the Operations Research Center (ORC) . His work bridges high-dimensional optimization, reinforcement learning, and stochastic modeling, with applications in commerce, healthcare, and biology. Education: Ph.D. in Electrical Engineering, Stanford University (Advisor: Prof. Benjamin Van Roy) Research Focus: Vivek’s research spans Reinforcement Learning , Dynamic Optimization , Approximation Algorithms , and Inference in Large-Scale Stochastic Systems . Recent applications include commerce platforms, protein networks, and healthcare operations. He has published extensively in top-tier venues like Management Science , Operations Research , NeurIPS , ICML , and Nature Communications . Editorial & Service Roles: Co-Editor, Data Science Area, Management Science Editorial Board, Operations Research Editorial Board, INFORMS Journal on Optimization Scientific Awards: 2022 RMP Jeff McGill Student Paper Award (First Prize) 2022 Applied Probability Society Student Paper Prize (First Prize) 2016 INFORMS MSOM Best Paper in Management Science 2015 INFORMS Revenue Management and Pricing Section Prize Best Simulation Publication Award, INFORMS Simulation Society (2014) George Nicholson Student Paper Competition, First Place (2017) Advising & Mentorship: Vivek has advised 14+ Ph.D. students and numerous MS students, many now faculty at top institutions (Columbia, NYU, CMU, INSEAD) or leaders in industry (Uber, Facebook, Lyft, McKinsey). His lab focuses on scalable algorithms for real-world decision-making under uncertainty. Industry & Entrepreneurship: He co-founded and served as CTO of Celect (acquired by Nike) and currently advises multiple technology startups in retail, healthcare, and finance.

• Artificial Intelligence
• Spatial Transcriptomics
• Gene Regulatory Networks
• +7 more

Sokratia Georgaka is a Lecturer in Artificial Intelligence at the Division of Informatics, Imaging & Data Sciences. Her research focuses on spatial transcriptomics, gene regulatory networks, and predictive modeling of biological systems, particularly in liver disease and glioblastoma. She holds a PhD in Reduced Order Models for Nuclear Reactor Thermal Hydraulics from Imperial College London (2020), a Master's from the University of Birmingham (2014), and a Bachelor's from the University of Ioannina (2013). Her work contributes to UN Sustainable Development Goals related to health and innovation. Recent projects include developing computational methods for spatial transcriptomics (CellPie) and integrating multi-omic data to study liver fibrosis and glioblastoma progression. Collaborations include the Christabel Pankhurst Institute and institutions in oncology and bioinformatics. Publications emphasize applications of machine learning in medical imaging and molecular biology, with notable contributions to Science Advances , Nucleic Acids Research , and Bioinformatics .

Gregory ValiantView profile

Associate Professor

• Algorithms
• Machine Learning
• Statistics
• +3 more

Gregory Valiant is an Associate Professor of Computer Science at Stanford University , specializing in Algorithms, Machine Learning, Statistics, and Information Theory. He holds a PhD from UC Berkeley and a BA in Mathematics from Harvard University. His research focuses on designing efficient algorithms for inferring information from limited data, addressing challenges in computation, memory, communication, and data quality. He advises multiple PhD students and collaborates with institutions like Microsoft Research New England. Educations: PhD, Computer Science, UC Berkeley (2012) BA, Mathematics, Harvard University (2006) Research Interests: Gregory’s work centers on the interplay between algorithms and statistical inference, particularly in high-dimensional settings. His lab explores topics like distribution learning, sample amplification, and adversarial robustness. Recent projects include developing algorithms for trace reconstruction, matrix completion, and transformer-based in-context learning. His methodologies often combine theoretical rigor with practical applications in machine learning and data science. Key Article Trends: His publications span theoretical foundations (e.g., sample amplification, convex optimization complexity) and applied machine learning (e.g., transformer capabilities, adversarial testing). Notable work includes contributions to distribution testing, statistical estimation under constraints, and algorithmic lower bounds. Advising: Current advisees include Annie Marsden, Steven Cao, and Chirag Pabbaraju. Former students have pursued roles at institutions like Harvard, Berkeley, and companies like Google, Waymo, and Facebook.

• Computational Geometry
• Statistical Inference
• Non-Parametric Methods
• +3 more

Guenther Walther is an Associate Professor in the Department of Statistics at Stanford University. His academic work focuses on statistical applications of geometric duality, bridging computational geometry with statistical inference. Education: PhD in Statistics from Stanford University (1994) Advisor: Warry Millar Research Interests: Geometric duality in statistics High-dimensional data analysis Non-parametric inference Machine learning algorithms Probabilistic modeling

• Non-Parametric Inference
• Kernel Regression
• Biological Applications
• +1 more

Thomas Harder Scheike is an Associate Professor at the University of California, Berkeley, affiliated with the Department of Statistics. He graduated from the Ph.D. Program in Statistics in 1993 under the advisement of Professor David Brillinger. His research focuses on non-parametric statistical methods, particularly tessellations and kernel regression techniques applied to biological problems. This aligns with the department’s emphasis on interdisciplinary research in statistics and medicine. No specific scientific awards, publications, or student advising records were found in the provided information.

• Statistics
• Semiparametric Models
• Non-Parametric Inference
• +2 more

Beong Soo So is a Professor at Ewha Womans University. He received his Ph.D. in Statistics from Berkeley in 1989, with a dissertation titled "Asymptotically Optimal Estimation in the Semiparametric Heteroscedastic Linear Model" under advisor Warry Millar. His research focuses include statistical modeling and non-parametric inference techniques. His work aligns with key areas in modern statistics such as: Semiparametric estimation Heteroscedasticity analysis High-dimensional data modeling Non-parametric methods