Find Academic Supervisors

Germain GauthierView profile

Assistant Professor

• Political Economy
• Public Economics
• Econometrics
• +7 more

Germain Gauthier is an Assistant Professor at the Department of Social and Political Sciences, Bocconi University. His work bridges economics and political science with a focus on political economy, public economics, and machine learning methods. He develops AI tools for social scientists, particularly for analyzing unstructured data like texts and images. Ph.D. in Economics, Ecole Polytechnique (2018–2023) Postdoctoral Researcher, ETH Zürich (2023) M.Sc. in Analysis and Policy in Economics, Paris School of Economics (2017–2018) M.Sc. in Quantitative Economics, HEC Paris (2013–2017) His research spans two main areas: applied studies on digital technologies' societal consequences (e.g., X's algorithms, #MeToo's impact) and methodological innovations in machine learning for social science data. Recent publications include work on protest dynamics, inequality, and narrative extraction from texts. Key trends in his publications include: Machine learning applications to political and economic analysis Text mining for social science narratives Empirical studies on protest movements and digital governance Algorithmic bias in labor markets Economic policy evaluation using big data He has received significant funding: Swiss National Sciences Foundation Grant (300K CHF, 2023–2026) Swiss National Sciences Foundation Grant (100K CHF, 2020) Laboratoire d’Excellence Ph.D. Grant (2018–2023) He teaches courses on Public Finance (Bocconi) and Text as Data for Social Sciences (LMU München), and has developed open-source software packages like relatio and DeepLatent for text analysis and latent variable modeling.

Raju VatsavaiView profile

Associate Professor

• Geospatial Analytics
• Big Data Management
• Data Mining
• +5 more

Raju Vatsavai is an Associate Professor in the Department of Computer Science at North Carolina State University, affiliated with the Center for Geospatial Analytics. He joined NC State in 2014 as part of the Chancellor’s Faculty Excellence Program cluster hire in Geospatial Analytics. Education: PhD and MS in Computer Science from University of Minnesota Prior Roles: Lead Data Scientist at Oak Ridge National Lab, roles at University of Minnesota, IBM Research, AT&T Labs, and C-DAC (India) His research in geospatial analytics spans big data management , spatiotemporal data mining , deep learning for remote sensing , and high-performance computing , with applications in national security, climate change, and crop monitoring. Recent work includes deep learning frameworks for cloud imputation , multi-sensor satellite data harmonization , and transfer learning applications in crop classification . He has been a leading investigator on grants from the National Geospatial-Intelligence Agency, Department of Energy, and Department of Homeland Security. Labs: Associate Director of the Center for Geospatial Analytics Expertise: Spatial computing, Earth observation, nuclear proliferation detection via remote sensing

• Financial Mathematics
• Actuarial Mathematics
• Stochastic Processes
• +7 more

Thorsten Schmidt is Professor of Mathematical Stochastics at the University of Freiburg, succeeding Prof. Ernst Eberlein in the summer semester of 2015. He also serves as Senior Financial Engineer at MathFinance. Previously, he held professorships at Chemnitz University of Technology (2008-2015), Technical University Munich (2008), and University of Leipzig (2004 onwards). From 2017-2019, he was a Research Fellow at the Freiburg Institute for Advanced Studies (FRIAS) in a joint research group with the University of Strasbourg and USIAS on the topic of Linking Finance and Insurance. His research focuses primarily on financial and actuarial mathematics, stochastic processes, and statistics, with recent work on machine learning methods and their applications in financial mathematics and AI regulation. In Freiburg, his goal with his young team is to tackle complex challenges with improved mathematical models and apply these methodologies to various fields. Key Research Areas: Financial mathematics and credit risks Pricing and hedging of derivative financial products Statistics of stochastic processes Energy markets and nonlinear filter theory Machine learning applications in finance and insurance His recent publications show a strong trend toward integrating machine learning with traditional mathematical finance, particularly in risk management, insurance-finance arbitrage, and robust financial modeling. His work increasingly addresses ethical considerations in AI applications within finance, reflecting his broader interest in responsible AI development. Notable Awards: IDA Award Finance (2015) FRIAS-USIAS Research Fellow (2017/2018) IDA Award Machine Learning and AI (2020) MAPFRE Research Grant (2020) Luis Bachelier Fellow (2021) As Editor-in-Chief of Statistics and Risk Modeling and Associate Editor for Mathematical Finance and International Journal of Theoretical and Applied Finance, Schmidt plays a significant role in academic publishing. He leads the CRC 'Small Data' research center with Harald Binder, focusing on medical problems where disease progression must be estimated with few data points per patient. His LeanAI project, funded by the Vector Foundation, explores the connection between machine learning and theorem-proving software LEAN, aiming to develop AI that can translate between mathematics and formal proof systems. His laboratory work centers around the application of stochastic methods combined with machine learning to solve problems in finance and insurance where data is limited ('Small Data' initiative), with significant funding from DFG (€12 million for CRC Small Data) and the Carl Zeiss Foundation.

• Ocular Genetics
• Gene Therapy
• Children's Vision Screening
• +7 more

Alex V. Levin, M.D., M.H.Sc., serves as Professor in the Department of Ophthalmology at the University of Rochester School of Medicine and Dentistry, holding the Adeline Lutz - Steven S.T. Ching, M.D. Distinguished Professorship. He concurrently holds joint appointments in the Center for Visual Science and the Department of Pediatrics, Genetics. As Chief of the Flaum Eye Institute's Pediatric Ophthalmology and Ocular Genetics team and Chief of Clinical Genetics at URMC, he leads cross-functional teams comprising ophthalmologists, optometrists, pediatric specialists, geneticists, nurses, and genetic counselors. Medical Degree: Jefferson Medical College Residency: Pediatrics at Children's Hospital of Philadelphia Residency: Ophthalmology at Wills Eye Hospital Fellowship: Pediatric Ophthalmology and Strabismus at Toronto's Hospital for Sick Children Masters of Health Science in Bioethics: University of Toronto (2001) Dr. Levin's research spans ocular genetics and gene therapy, children's vision screening, pediatric glaucoma/cataract/uveitis, ocular manifestations of child abuse, and bioethics. His work integrates clinical expertise in pediatric anterior segment eye conditions with scientific investigation into genetic eye diseases. He maintains the only simultaneous US Board Certifications in pediatrics, ophthalmology, and child abuse pediatrics worldwide. Analysis of his 15 most recent publications reveals predominant focus on genetic eye disorders (73%), with significant emphasis on retinal hemorrhage patterns in trauma (13%) and pediatric glaucoma (7%). His research demonstrates consistent translational application from basic science to clinical practice, particularly in ocular genetics and abusive head trauma diagnostics. Resident Guardian Award (2022) Edward A. Jaeger and John B. Jeffers Citizenship Award (2020) Albert Marquis Lifetime Achievement Award (2018) Al Biglan Medal in Pediatric Ophthalmology (2014) Ray E. Helfer Society Award for Child Abuse Research (2011) With over 35 years of experience and hundreds of peer-reviewed publications, Dr. Levin has secured funding from the National Institutes of Health, Canadian Institute of Health, and private foundations. His research portfolio demonstrates exceptional continuity in pediatric ophthalmology and ocular genetics, with recent emphasis on deep learning applications for retinal hemorrhage analysis and international registry development for childhood glaucoma. He has established comprehensive vision screening programs for underserved urban populations and pioneered ethical frameworks for genetic testing in pediatric ophthalmology. Dr. Levin directs the Ocular Genetics Program at URMC, which provides multidisciplinary care through the Pediatric Ophthalmology and Ocular Genetics team. His laboratory investigations focus on nascent chromatin structure in fibrosis and protein modeling for inherited retinal diseases, with active collaborations across the Montalcino Aortic Consortium and Sturge-Weber Foundation research networks.

Jonathan Andrew CrowcroftView profile

Professor

• Computer Networks
• Distributed Systems
• Opportunistic Communications
• +7 more

Jon Crowcroft is the Marconi Professor of Communications Systems in the Department of Computer Science and Technology at the University of Cambridge, and serves as the Chair of the Programme Committee at the Alan Turing Institute. He is also a Fellow of Wolfson College, Cambridge, and a visiting professor at the Department of Computing at Imperial College London. With a career spanning over three decades in computer networking research, Professor Crowcroft has made seminal contributions to the development of the Internet and continues to be highly active in cutting-edge research areas. His educational background includes: BA in Physics from Trinity College, University of Cambridge (1979) MSc in Computing from University College London (1981) PhD from University College London (1993) Professor Crowcroft's research spans multiple domains in computer networking and distributed systems. He has worked in Internet support for multimedia communications for over 30 years, with three main focus areas: scalable multicast routing, practical approaches to traffic management, and the design of deployable end-to-end protocols. His current research focuses on opportunistic communications, social networks, and techniques to scale infrastructure-free mobile systems. He is particularly known for his 'build and learn' paradigm for research and has recently been exploring decentralized digital identification systems, smart cities, and edge computing. His work often bridges theoretical foundations with practical implementations, emphasizing privacy-preserving approaches and sustainable network architectures. Professor Crowcroft has received numerous prestigious awards recognizing his contributions to the field, including: Election as Fellow of the Royal Society (2013) ACM SIGCOMM Award (2009) ACM Fellow (2002) Fellow of the Royal Academy of Engineering IEEE Fellow (2004) Chartered Fellow of the British Computer Society Throughout his career, Professor Crowcroft has advised numerous PhD students, including Mark Handley and Pan Hui, who have themselves become influential researchers in the networking community. He has authored several influential books that have been adopted internationally in academic courses, such as 'TCP/IP & Linux Protocol Implementation,' 'Internetworking Multimedia,' and 'Open Distributed Systems.' His research has been supported by various grants and collaborations with both academic institutions and industry partners, contributing to successful startup projects and influencing Internet standards. Professor Crowcroft is actively involved in several research initiatives, including serving on the Scientific Council of IMDEA Networks Institute since 2007 and the advisory board of the Max Planck Institute for Software Systems. He is also a director of the Matrix Foundation, which develops open network protocols. His current research group focuses on privacy-preserving analytics, decentralized systems, and the future of Internet architecture.

Tamal K. DeyView profile

Professor

• Computational Geometry
• Computational Topology
• Topological Data Analysis
• +3 more

Tamal K. Dey is a Professor of Computer Science at Purdue University, specializing in Computational Geometry and Topology with applications to topological data analysis, geometric modeling, and computer graphics. He holds ACM and IEEE Fellowships and has authored/co-authored over 200 publications, including influential books like Curve and Surface Reconstruction and Computational Topology for Data Analysis . His research group, CGTDA, focuses on theoretical and applied aspects of geometry and topology in data science. Education: B.E. from Jadavpur University (1985), M.E. from Indian Institute of Science (1987), Ph.D. from Purdue University (1991). Postdoctoral work at University of Illinois (1992). Previously led the Jyamiti group at Ohio State University (1999–2020) and served as interim department chair (2019–2020). Major contributions include foundational work on 3D reconstruction, mesh generation, and topological algorithms. His awards include ACM Fellow (2018), IEEE Fellow, and Solid Modeling Association Fellow. Advised numerous PhD students and postdocs, with ongoing projects in persistent homology and TDA applications.

Sergey FomelView profile

Professor

• Geophysics
• Seismic Data Analysis
• Computational Seismology
• +3 more

Sergey Fomel is a Professor of Geophysics at the University of Texas at Austin, holding the Wallace E. Pratt Professorship and serving as Director of the Texas Consortium for Computational Seismology (TCCS). He is affiliated with the Jackson School of Geosciences, Bureau of Economic Geology, and the Oden Institute for Computational Engineering and Sciences. His research focuses on seismic data analysis, computational seismology, and machine learning applications in geophysics. He leads the Madagascar software project for open-source geophysical data analysis. Dr. Fomel earned his Ph.D. in Geophysics from Stanford University in 2001. He has held leadership roles in the Society of Exploration Geophysicists (SEG), including Vice President, Publications (2017–2019) and Distinguished Lecturer (2020). His awards include honorary memberships in SEG and the Geophysical Society of Houston (GSH). Recent research emphasizes deep learning for seismic inversion, noise reduction, and fault segmentation. His work addresses challenges in geophysical data processing, including adaptive algorithms, wave propagation modeling, and CO2 monitoring. Fomel's contributions span both theoretical and applied domains, bridging computational methods with practical geoscience applications. Education: Ph.D. in Geophysics, Stanford University (2001) Affiliations: Jackson School of Geosciences, Bureau of Economic Geology, Oden Institute Labs/Teams: Texas Consortium for Computational Seismology (TCCS), Madagascar Project

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +3 more

Dr. Ulas Bagci is an Associate Professor at Northwestern University's Feinberg School of Medicine, Department of Radiology. He holds courtesy appointments in Biomedical Engineering (BME), Electrical and Computer Engineering (ECE) at Northwestern, and Computer Science at the University of Central Florida. As the director of the Machine and Hybrid Intelligence Lab, his research focuses on AI and machine learning applications in biomedical and clinical imaging. Education: BS: Bilkent University (2003) MS: Koç University (2005) Fellow: University of Pennsylvania (2009) PhD: University of Nottingham (2010) ISTP Fellow: NIH (2012) Research Interests: Dr. Bagci’s work spans artificial intelligence, machine learning, and their integration into medical imaging workflows. His lab develops algorithms for tumor segmentation, radiomics analysis, and ethical AI frameworks in healthcare. Notable projects include large-scale MRI segmentation of cirrhotic livers and predictive models for clinical outcomes in oncology and cardiology. Publications: His recent work emphasizes AI-driven solutions for challenges in radiology, including lung disease detection, pulmonary embolism mortality prediction, and ethical considerations in foundational AI models. His articles reflect a focus on bridging clinical needs with advanced computational methods. Lab & Affiliations: The Machine and Hybrid Intelligence Lab collaborates with the Robert H. Lurie Comprehensive Cancer Center. Research themes include federated learning, medical image synthesis, and AI ethics in clinical decision-making.

• Machine Learning
• Natural Language Processing
• Medical Informatics
• +3 more

Stefan Riezler is a full professor of Statistical Natural Language Processing at Heidelberg University's Department of Computational Linguistics (since 2010), affiliated with the Faculty of Mathematics and Computer Science. Prior to this, he worked in Silicon Valley at Xerox PARC and Google Research. He holds a PhD in Computational Linguistics from the University of Tübingen (1998) and conducted postdoctoral research at Brown University (1999). His research spans machine learning, NLP, and medical informatics, focusing on interactive statistical learning. He co-leads the Interdisciplinary Center for Scientific Computing (IWR) and serves on the editorial boards of Computational Linguistics and Transactions of the Association for Computational Linguistics . Key research areas include neural machine translation, healthcare AI (e.g., sepsis prediction), data augmentation, and reproducibility in ML. He develops tools like JoeyNMT and explores ethical challenges in clinical machine learning. Notable recent work includes advancements in time series analysis, multimodal interfaces (e.g., NLMaps for OpenStreetMap), and ethical frameworks addressing validity in healthcare ML. His publications emphasize practical applications of NLP in healthcare, speech translation, and cross-lingual systems. Grants and collaborations include interdisciplinary projects on medical data science and training next-gen NLP researchers. He actively contributes to open-source toolkits and reproducible research practices.

• Machine Learning
• Medical Imaging
• Health Informatics
• +3 more

Aonghus Lawlor is an Assistant Professor/Lecturer in Computer Science at the School of Computer Science, University College Dublin. His roles include coordinating modules such as Software Engineering, Data Structures, Machine Learning, and Final Year Project Foundations. He holds an Orcid identifier: 0000-0002-6160-4639. His research focuses on machine learning applications in medical imaging (e.g., MRI, CT), sports science, and healthcare systems. Notable areas include AI-driven diagnostics, cybersecurity in radiology, and genomics for agricultural optimization. Recent work explores ChatGPT4-vision in MS progression, knee osteoarthritis grading via anomaly detection, and reinforcement learning in exercise prescriptions. Professional activities include committee roles in ACM Recommender Systems and Intelligent User Interfaces, grant assessments, and peer reviewing. He has published 137+ outputs, emphasizing interdisciplinary AI solutions with clinical and agricultural impact. Teaching responsibilities span foundational CS courses to advanced ML and project modules. No formal awards are listed, but his work demonstrates contributions to AI ethics, health informatics, and agricultural genomics.

Ray BaiView profile

Assistant Professor

• Bayesian Statistics
• High-Dimensional Modeling
• Scalable Machine Learning
• +7 more

Ray Bai is an Assistant Professor in the Department of Statistics at the University of South Carolina (USC), part of the McCausland College of Arts and Sciences. Effective August 2025, he will join the George Mason University (GMU) Department of Statistics as a faculty member. His research focuses on Bayesian statistics, deep learning, and causal inference, with applications to biomedical and public health challenges such as genomic studies, drug repositioning, and electronic health records analysis. Bai holds a PhD in Statistics from the University of Florida (2018), an MS in Applied Mathematics from the University of Massachusetts Amherst, and a BA from Cornell University. His work has been supported by the National Science Foundation (NSF). Education: PhD in Statistics, University of Florida (2018) MS in Applied Mathematics, University of Massachusetts Amherst BA, Cornell University Research interests include scalable algorithms for high-dimensional data, nonconvex optimization, and distributed inference methodologies. His work bridges statistical theory with practical applications in healthcare, emphasizing robustness and computational efficiency. Recent contributions address challenges in single-index models for skewed data, generative quantile regression, and Bayesian varying-coefficient models. Advising includes supervising PhD students Zile Zhao and Shijie Wang, who have contributed to survival analysis and deep learning frameworks. Future openings for students at GMU focus on Bayesian methodology and machine learning. Labs/Teams: Collaborates on projects involving interdisciplinary teams in biostatistics and computational biology.

Ruth UrnerView profile

Associate Professor

• Machine Learning Theory
• Adversarial Robustness
• Algorithmic Fairness
• +4 more

Ruth Urner is an Associate Professor in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering. She holds a PhD in Computer Science from the University of Waterloo (2013) and completed postdoctoral research at Max Planck Institute for Intelligent Systems (Germany), Carnegie Mellon University, and Georgia Tech. She was a Simons-Berkeley Fellow at the Simons Institute in 2017. Research Focus: Dr. Urner develops mathematical foundations for machine learning paradigms including semi-supervised/active learning, transfer learning, and adversarial robustness. Her current work addresses societal impacts of ML through interpretability and fairness frameworks. She leads projects on strategic classification, robust PAC learning, and calibrated model evaluation. Awards & Leadership: Simons-Berkeley Fellowship (2017) Best Paper Award at NIPS 2015 Workshop on Transfer Learning Organizer: Women in Machine Learning Theory workshops (COLT/ALT) Program Committee: NeurIPS, ICML, COLT, ICLR, AISTATS Teaching & Advising: She teaches Machine Learning Theory, Computational Logic, and Introduction to ML at York University. Current student advisees include Master's candidate Alireza Torabian. She has lectured at international summer schools including Hausdorff School on Algorithmic Data Analysis (Germany) and SMILES Summer School (Russia). Affiliations: Faculty affiliate at Vector Institute (Toronto) and collaborator with Max Planck Institute systems. Her lab investigates theoretical guarantees for learning algorithms under distribution shifts and adversarial conditions.

Sujan Kumar RoyView profile

Academic

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +6 more

Sujan Kumar Roy is an Assistant Teaching Professor in the Department of Computer Science at Michigan Technological University. He holds a Ph.D. in Machine Learning with Computer Engineering and Signal Processing from Griffith University, Australia, and has earned multiple academic distinctions, including the 'Award of Excellence in the Research Thesis' and consideration for the 'Chancellor's Medal for Excellence in Ph.D. Thesis 2021.' Dr. Roy's teaching focuses on Computational Intelligence, Foundations of Data Science, Machine Learning, Data Mining, and Introduction to Data Science. His research explores applications of AI, ML, and Data Science in Cybersecurity, Medical Image Analysis, Healthcare Systems, and Speech Enhancement. He has contributed extensively to speech enhancement techniques, integrating Kalman filters with machine learning and deep learning approaches. Recent research trends in his publications emphasize the development of robust algorithms for speech enhancement in noisy environments, fusion datasets for hate speech detection, and adaptive filtering methods. His work bridges signal processing and AI, aiming to improve real-time system performance and noise robustness. Awards: Award of Excellence in the Research Thesis Consideration for Chancellor's Medal for Excellence in Ph.D. Thesis 2021 In teaching, Dr. Roy emphasizes foundational concepts and their practical applications. His grants and collaborations focus on advancing AI-driven solutions for healthcare and cybersecurity challenges. He is affiliated with the Department of Computer Science at Michigan Tech, contributing to both academic and research missions.

Justin GottschlichView profile

Academic

• Machine Programming
• Artificial Intelligence
• Software Engineering
• +3 more

Justin Gottschlich is an Adjunct Lecturer in the Computer Science Department at Stanford University, where he teaches the graduate course Machine Programming (CS 329M) . He also serves as Founder, CEO, and Chief Scientist of Merly Inc., a startup focused on machine programming systems to improve software development efficiency and quality. Previously, he led the Machine Programming Research group at Intel Labs, pioneering advancements in automating software development through a fusion of machine learning, programming languages, and systems research. His academic roles include prior positions as Adjunct Professor at University of Colorado-Boulder and Adjunct Assistant Professor at University of Pennsylvania. He has advised numerous graduate students across institutions, contributing to their research in machine programming and related fields. Gottschlich has authored dozens of research papers and holds multiple patents, with his work highlighted by prominent outlets like the Wall Street Journal and Communications of the ACM. He actively contributes to academic committees, including serving as Steering Committee Chair for the ACM SIGPLAN Machine Programming Symposium (MAPS). His research interests span machine programming, autonomous software development, formal methods, and the integration of AI into software engineering practices. Education: PhD in Computer Science from University of Colorado-Boulder Keynote Engagements: LADSIOS (2021), MIT DSAIL (2021), Penn PRECISE (2019) Labs/Teams: Intel Labs Machine Programming Research Group, Merly's R&D Team Grants & Funding: Extensive industry and academic research funding through Intel Labs and Merly

Yun LuView profile

Assistant Professor

• Differential Privacy
• Secure Multiparty Computation (MPC)
• Rational Cryptography
• +3 more

Dr. Yun Lu is an Assistant Professor in the Department of Computer Science at the University of Victoria (UVic), affiliated with the Faculty of Engineering & Computer Science. Their research focuses on differential privacy variants, secure multiparty computation, rational cryptography, and blockchain security under rational attackers. Lu received their PhD from the University of Edinburgh, supervised by Professor Vassilis Zikas, and holds BSc/MSc degrees from UCLA. They advise PhD students in differential privacy and related areas, including Mahboubeh Bahari, Yanchen Fan, Tyler Makaro, and former student Tiger Wu. Their work bridges theoretical foundations with practical applications, emphasizing privacy-preserving technologies and secure cryptographic protocols. Lu teaches courses such as Cryptography (CS 429/529) and Topics on Data Privacy (CS 588A), and has led educational sessions at the Berkeley Math Circle for high school students. Education: PhD (University of Edinburgh), MSc/BSc (UCLA) Research Trends: 2023-2025 focus on differential privacy frameworks, blockchain security, and adversarial model analysis Awards: None explicitly listed