جست‌وجوی استاد و پژوهشگر

Daniel Royمشاهده پروفایل

استاد

• Machine Learning
• Statistical Learning Theory
• Foundations of Statistics
• +۱۱ مورد دیگر

Daniel Roy is a Full Professor at the University of Toronto, holding cross-appointments in the Department of Statistical Sciences, Computer Science, Electrical and Computer Engineering, and the Department of Computer and Mathematical Sciences at UTSC. He is also a Canada CIFAR AI Chair and Research Director at the Vector Institute, reflecting his leadership in AI and machine learning research. His educational background includes a PhD, MEng, and BSc in Computer Science from MIT, where his doctoral work earned the MIT EECS Sprowls Award. Prior to joining Toronto, he was a Newton International Fellow at the Royal Society and a Research Fellow at Emmanuel College, University of Cambridge. His research centers on foundational principles in machine learning, statistics, and probabilistic reasoning. Key interests include statistical learning theory, Bayesian nonparametrics, probabilistic programming, and information-theoretic generalization. His work bridges theoretical computer science, mathematical logic, and applied probability. His recent publications, appearing in ICML, NeurIPS, COLT, and JMLR, reflect a strong focus on theoretical advances in generalization, online learning, and stochastic optimization. Themes include minimax rates, conditional mutual information, and the role of data in PAC-Bayes bounds. His group has made foundational contributions to probabilistic programming, including work on Church and the computability of conditional probability. NSERC Discovery Accelerator Supplement Ontario Early Researcher Award Google Faculty Research Award Newton International Fellowship MIT EECS Sprowls Award Daniel Roy advises numerous PhD students and postdoctoral researchers, many of whom have gone on to prestigious positions in academia and industry. His group actively collaborates with leading researchers in machine learning and statistics. He is also an Action Editor for the Journal of Machine Learning Research and Transactions of Machine Learning Research, underscoring his role in shaping the field. He leads a vibrant research group focused on theoretical machine learning and probabilistic modeling, and maintains active collaborations with institutions such as MIT, Cambridge, and the Vector Institute. He is also the founder and maintainer of the probabilistic-programming.org wiki, a key resource in the community.

Sabrina Karimمشاهده پروفایل

دانشیار

• Conflict and Peace Processes
• Post-Conflict State Building
• International Security Assistance
• +۳ مورد دیگر

Sabrina Karim is an Associate Professor in the Department of Government at Cornell University and serves as the Associate Director of the Reppy Institute for Peace and Conflict Studies. Her research focuses on conflict and peace processes, particularly state-building in post-civil war contexts, with a geographic emphasis on Sub-Saharan Africa and Bangladesh. She examines international security assistance to post-conflict states and gender reforms in peacekeeping, using experimental methods to explore topics like policing reforms and public perceptions of security sectors. Karim holds affiliations with the Institute for African Development, South Asia Program, and the Reppy Institute’s core faculty roles. Her work bridges academic rigor and policy relevance, addressing issues such as election violence prevention in Liberia, the impact of epidemics on marginalized populations, and the economic consequences of peacekeeping withdrawal. She has conducted extensive fieldwork in Liberia, Bangladesh, and other post-conflict regions, producing evidence-based insights on gendered security sector reforms and democratic transitions. Karim’s research often integrates experimental designs with qualitative analysis to assess policy interventions, such as gender-balancing measures in police forces and UN peacekeeping mandates. Roles: Faculty Associate at Einaudi Center, PACS Steering Committee Member, SAP Faculty Associate Research Themes: Peacebuilding, Gender in Security, Post-Conflict Governance, Experimental Methods Geographic Regions: Liberia, Bangladesh, Sub-Saharan Africa Karim’s publications emphasize the interplay between gender equality and conflict dynamics, arguing that women’s inclusion in security sectors and peace processes can reduce violence and improve governance. Her experimental studies in Liberia, such as testing police reforms’ impact on public trust, demonstrate practical applications of her scholarship.

Shahin Kamaliمشاهده پروفایل

دانشیار

• Algorithms Design
• Algorithms Analysis
• Online Algorithms
• +۹ مورد دیگر

Shahin Kamali is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. Previously, he served as an Assistant Professor at York University (2022-2024) and the University of Manitoba (2017-2022), where he continues to hold an Adjunct Professor position. He completed his Ph.D. in Computer Science at the University of Waterloo in 2014 and was a postdoctoral fellow at MIT's CSAIL lab from 2015-2017. Dr. Kamali received his B.Sc. from the University of Tehran and his M.Sc. from Concordia University, both in Computer Science. He has earned teaching certifications including the Kaufman Teaching Certificate Program from MIT and the Certificate in University Teaching from the University of Waterloo. His research focuses on algorithms' design, analysis, and limitations, with particular emphasis on online problems such as bin packing, paging, list update, and k-Server. His work extends to big-data applications of algorithms in data compression, graph partitioning, resource allocation in the cloud, and algorithmic aspects of blockchain technology. His recent publications (2023-2025) demonstrate a strong focus on learning-augmented algorithms, online computation with predictions, and novel applications in blockchain and graph theory. His research has been supported by multiple NSERC grants since 2010, with recent projects investigating models, applications, and limitations of online algorithms. He has successfully supervised numerous graduate students and serves on various committees related to algorithms conferences and academic governance. University of Waterloo Doctoral Thesis Completion Award (2014) University of Waterloo Mathematics Graduate Experience Award (2008) Dr. Kamali teaches advanced courses in algorithms and data structures and is scheduled to teach Design and Analysis of Algorithms (EECS 3101) in Fall 2025 and Advanced Data Structures (EECS 4101/5101) in Winter 2026. He is actively involved in organizing major conferences, including CCCG and WADS 2025 at York University.

• Life Mastery
• Learning environment
• Mental health
• +۵ مورد دیگر

Eli Vibeke Eriksen serves as an Associate Professor in the Department of Teacher Education and Pedagogy at UiT The Arctic University of Norway. She is actively engaged in research and teaching at the Tromsø campus, with contact information listing her email and phone number for professional inquiries. Her research interests focus on critical aspects of educational psychology and student development, including Life Mastery, Learning environment, Mental health, Social and emotional competencies, Motivation, and Involvement. Through her work, she examines how these factors interact within educational settings to influence student outcomes, particularly in secondary school contexts. Dr. Eriksen's recent publications demonstrate a consistent research trajectory examining social competencies, classroom relationships, and academic engagement using sophisticated quantitative methods like latent change score modeling. Her work primarily focuses on lower secondary school students, investigating how social-emotional factors impact educational experiences and outcomes during critical developmental periods. She is an active member of the Participation and communication (PAC) research group and participates in the project 'Bruk av VR-briller som praksisnær studentaktivitet' (Use of VR glasses as practice-oriented student activity), indicating her interest in innovative educational technologies and methodologies. Her teaching expertise spans pedagogical psychology, pedagogical sociology, quantitative and qualitative research methods, and philosophy of science, providing students with comprehensive training in educational research methodologies.

Maxim Raginskyمشاهده پروفایل

استاد

• Probability and Stochastic Processes
• Control Theory
• Machine Learning
• +۵ مورد دیگر

Maxim Raginsky is a Professor at the University of Illinois at Urbana-Champaign, holding appointments in the Department of Electrical and Computer Engineering, Coordinated Science Laboratory, and a courtesy appointment in Computer Science. His work bridges probability, stochastic processes, control theory, machine learning, optimization, and information theory , focusing on modeling, learning, and simulation of nonlinear dynamical systems with applications to advanced electronics, autonomy, and artificial intelligence. Research Interests Nonlinear dynamical systems in machine learning and control Statistical machine learning theory Information-theoretic methods in learning Stochastic control and filtering Scientific Contributions Co-author of foundational monographs on concentration inequalities and generalization bounds Recipient of the NSF CAREER Award (2013) , IEEE Fellow (2025) , and Roberto Tempo Best CDC Paper Award (2024) Editorial roles in Foundations and Trends in Machine Learning , Journal of Machine Learning Research , and SIAM Journal on Mathematics of Data Science Academic Leadership Advising 15+ graduate students and postdocs including Joshua Hanson, Belinda Tzen, and Tanya Veeravalli Teaching core graduate courses: Control of Stochastic Systems , Statistical Learning Theory , Optimization by Vector Space Methods

Kasper Green Larsenمشاهده پروفایل

استاد

• Machine Learning
• Theoretical Computer Science
• Algorithms and Data Structures
• +۳ مورد دیگر

Kasper Green Larsen is a Professor in the Department of Computer Science at Aarhus University. His research focuses on theoretical computer science, machine learning, algorithms, and data structures. He has made significant contributions to boosting algorithms, PAC learning theory, and computational geometry. His work often bridges algorithm design with complexity theory, addressing challenges in optimization, memory efficiency, and lower bounds analysis. Key research areas include: Algorithmic Learning Theory (e.g., boosting, bagging, and PAC learners) Data Structure Design (e.g., invertible Bloom tables, succinct representations) Computational Complexity (e.g., lower bounds for dynamic and oblivious algorithms) Geometric Algorithms (e.g., hierarchical searching, range queries) Recent publications emphasize foundational advancements in learning theory (e.g., optimal weak-to-strong learning) and data efficiency (e.g., memory-reduced Bloom filters). His work frequently appears in top conferences like IJCAI, ICALP, and SODA, reflecting rigorous theoretical contributions with practical implications.

• Differential Algebra
• Model Theory
• Number Theory
• +۷ مورد دیگر

James Freitag is a Professor in the Department of Mathematics, Statistics, and Computer Science at the University of Illinois at Chicago (UIC). He earned his PhD from UIC in 2012, specializing in Model Theory and Differential Algebraic Geometry . Prior to his current role, he held postdoctoral positions at UC Berkeley and UCLA and served as a research member at the Mathematical Sciences Research Institute (MSRI) and Fields Institute. His research integrates model theory , differential algebra , and number theory , with recent emphasis on functional transcendence, geometric stability theory, and applications to machine learning. Key themes include: Strong minimality in differential equations and stability theory Ax-Lindemann-Weierstrass theorems for Fuchsian groups Combinatorial and algorithmic aspects of query learning His publications (2017–2025) predominantly explore differential-algebraic geometry, model-theoretic classification, and computational learning. Recurring topics include Painlevé equations, Littlestone dimension, nonminimality bounds, and geometric invariants in differential systems. He advises doctoral students in differential algebra and machine learning and directs the Young Scholar Program —a summer camp for Chicago high school students. His research is funded by NSF grants, including CAREER #1945251 and #2452197.

Mathukumalli Vidyasagarمشاهده پروفایل

استاد

• Machine Learning
• Systems and Control Theory
• Computational Biology
• +۴ مورد دیگر

Mathukumalli Vidyasagar is a Distinguished Professor at the Indian Institute of Technology Hyderabad and previously held the SERB National Science Chair and Cecil & Ida Green Chair in Systems Biology Science at the University of Texas at Dallas. He earned his Ph.D. from the University of Wisconsin, Madison, and has authored 13 books and over 160 peer-reviewed papers.

• Ultrasound
• Radiology
• Doppler Ultrasound
• +۳ مورد دیگر

Edward G. Grant, MD, serves as a Clinical Professor of Radiology (Part-Time) at the University of Southern California (USC), where he was Chair of the Department of Radiology from 2002 to 2019. Prior to USC, he chaired the Imaging Department at Greater Los Angeles VA Healthcare System and held professorial and vice-chair positions at UCLA. His career spans over three decades in academic radiology with a focus on clinical ultrasound innovation and leadership. Dr. Grant is an internationally recognized authority in ultrasound, having pioneered neonatal cranial sonography early in his career. His research encompasses Doppler ultrasound for vascular disease diagnosis, clinical investigation of ultrasound contrast agents, PACS, and advancements in thyroid and liver imaging. He has authored four books and numerous peer-reviewed articles, establishing himself as a leading figure in diagnostic radiology. Analysis of his 15 most recent publications (2021-2025) reveals a strong emphasis on contrast-enhanced ultrasound applications across multiple organ systems, particularly in thyroid nodule characterization (TI-RADS), liver disease quantification, and renal transplant evaluation. His work also addresses emerging areas like AI integration in ultrasound reporting and environmental impacts on fetal development through the MADRES cohort study. His notable awards include: Pasadena Magazine Top Doctor (2011) Dr. Grant has served as an examiner for the American Board of Radiology for 13 consecutive years and remains an active lecturer at national and international conferences. While the provided text does not detail specific grants or student advising, his extensive publication record and leadership roles indicate significant contributions to radiology education and clinical practice advancement.

Akshay Krishnamurthyمشاهده پروفایل

استادیار مدعو

• Machine Learning
• Statistics
• Reinforcement Learning
• +۴ مورد دیگر

Akshay Krishnamurthy is an Adjunct Assistant Professor at the University of Massachusetts Amherst and a Senior Principal Research Manager at Microsoft Research NYC. His research focuses on machine learning and statistics, particularly interactive learning frameworks, reinforcement learning, and generative AI. He holds a PhD from Carnegie Mellon University and a B.Sc. from UC Berkeley. Education: PhD in Computer Science, Carnegie Mellon University (2015) B.Sc. in Electrical Engineering and Computer Science, UC Berkeley (2010) Research Interests: His work emphasizes feedback-driven data collection in machine learning, including contextual bandits, reinforcement learning, and their applications to language models and generative AI. Recent focus areas include sample-efficient exploration and decision-making under limited feedback. Awards: National Science Foundation Graduate Research Fellowship (2010) Best Student Paper Award at Asilomar Conference (2013) Grants & Collaborations: Active in interdisciplinary collaborations, including organizing workshops on foundations of post-training in generative AI. His research bridges theoretical guarantees with practical applications in reinforcement learning and decision-making systems. Labs/Teams: Leads research initiatives at Microsoft on generative AI, reinforcement learning, and scalable machine learning systems.

Zeynep Erson Omayمشاهده پروفایل

استادیار

• Computational Biology
• Genomics
• Epigenomics
• +۶ مورد دیگر

Dr. Zeynep Erson Omay serves as an Assistant Professor in the Department of Neurosurgery and Biomedical Informatics & Data Science at Yale School of Medicine. Her work bridges computational biology with neurosurgical oncology, focusing on precision medicine applications for brain tumors, with particular emphasis on understanding tumor heterogeneity and molecular mechanisms of CNS tumors. Dr. Erson Omay's educational background includes: PhD in Computer Science from Case Western Reserve University (2011) MS in Computer Science from Bilkent University (2005) BS in Computer Science from Bilkent University (2003) Her research focuses on computational analysis of multi-omic datasets to understand tumor heterogeneity, particularly in central nervous system tumors. Dr. Erson Omay specializes in genomic, transcriptomic, and epigenetic profiling of brain tumors, with emphasis on meningiomas, glioblastomas, and rare CNS tumor subtypes. She leads the Erson Lab, which develops bioinformatics approaches to study large datasets and reveal molecular mechanisms in tumor formation, progression, and clinical outlier subgroups. Her work in precision medicine aims to decipher the molecular architecture of individual tumors to guide personalized treatment approaches, with significant contributions to understanding tumor ecosystems and evolutionary patterns in brain cancers. Dr. Erson Omay's scientific contributions span neuro-oncology, computational biology, and precision medicine, with a strong emphasis on translating genomic findings into clinical applications. Her publications demonstrate consistent innovation in applying computational methods to complex neurosurgical problems, with particular focus on tumor heterogeneity, molecular classification, and racial disparities in tumor genomics. Her notable scientific awards include: 10x Genomics 2021 Pilot Award (2022) Mission Bio Tapestri Grant (2022) Case Western Reserve University, Research ShowCASE-Best Poster Award (2007) Dr. Erson Omay actively mentors students and researchers at various levels, including undergraduate students, graduate students, postdocs, and postgraduate associates. She collaborates extensively within Yale's neurosurgery department and across disciplines to advance computational approaches to brain tumor research. Her work is supported by various grants that enable the development of novel bioinformatics platforms for tumor genomic characterization. She leads the Erson Lab, which focuses on three major research areas: Tumor Ecosystem (studying interactions among tumor and immune cells), Tumor Evolution and Heterogeneity (understanding temporal and spatial tumor evolution), and Precision Medicine (applying genomic techniques to personalize brain tumor treatment). The lab employs diverse omics technologies to explore brain tumor biology and develop computational methods for precision medicine applications.

• Machine Learning
• Statistical Learning
• Bayesian Methods
• +۵ مورد دیگر

Pierre ALQUIER is a Professor at ESSEC Business School (Singapore) since 2023, specializing in statistical learning and machine learning. Previously, he held professorships at ENSAE Paris (2014–2019) and the University of Dublin (2012–2014). He earned his PhD in Mathematical Statistics from Pierre and Marie Curie University in 2006, with a focus on advanced statistical methodologies. His research centers on Bayesian methods, PAC-Bayes bounds, high-dimensional data analysis, and robust estimation, with applications in quantum computing and time series. He has authored over 60 peer-reviewed articles, including influential works on kernel mean embeddings and meta-learning. Alquier has received the 2019 Best Paper Award at the Asian Conference on Machine Learning. He actively contributes to academic leadership, serving as an associate editor for leading journals like the Journal of Machine Learning Research and organizing international workshops. His educational contributions include co-supervising multiple doctoral theses on topics like robust Bayesian inference and non-negative matrix factorization. Education: PhD in Mathematical Statistics (2006), Pierre and Marie Curie University MSc in Probability Theory and Statistics (2003), Pierre and Marie Curie University Diploma in Statistician-Economist (2003), ENSAE Research Focus: Machine learning theory, PAC-Bayes bounds, Bayesian computation, high-dimensional statistics, quantum tomography, and time series forecasting. Grants & Activities: Member of key academic societies (IMS, SFdS), reviewer for top conferences (NeurIPS, ICML), and organizer of workshops on approximate Bayesian inference and high-dimensional data analysis. His recent work emphasizes robust regression, meta-learning, and the theoretical foundations of deep learning, often addressing challenges in dependent data and model misspecification. He has developed R packages like regMMD for robust statistical estimation.

• Ancient History
• Neo-Assyrian Empire
• Imperial Administration
• +۳ مورد دیگر

Dr. Christopher W. Jones serves as an Assistant Professor of History at Union University, where he joined the faculty in 2023. His academic specialization centers on the history of the ancient world, with particular expertise in the Neo-Assyrian empire (930-612 BC). Prior to Union, he taught at Warren Wilson College in Asheville, North Carolina, and held a postdoctoral position at the Centre of Excellence in Ancient Near Eastern Empires (ANEE) at the University of Helsinki in Finland. His academic credentials include: Ph.D. in History from Columbia University M.A. in Biblical Archaeology from Wheaton College B.A. in Peace, War, & Defense and History from the University of North Carolina Dr. Jones's research focuses on imperial administration, cultural responses to living under empire, trade connections, and the influence of the Neo-Assyrian empire on Biblical history. His current book project, The Structure of the Late Assyrian State , examines the changing political structure of the Assyrian Empire during its final century. His methodology increasingly incorporates social network analysis to understand court dynamics, while his work bridges ancient history with contemporary issues through analyses of cultural heritage destruction by groups like ISIS. His scholarly output demonstrates a clear trajectory toward examining power structures within the Neo-Assyrian Empire, with recent publications showing sophisticated interdisciplinary approaches that combine historical analysis with political science and archaeology. His work maintains relevance to modern geopolitical challenges through examinations of cultural heritage destruction and national identity formation. Dr. Jones received the International Association for Assyriology Prize for Best First Article in 2024 for his work on Sennacherib's assassination. His publications appear in respected journals including the Journal of Ancient Near Eastern History, Journal of Biblical Literature, and Avar: An Interdisciplinary Journal of Life and Society in the Ancient Near East. While specific advising details aren't extensively documented, his active publication record and conference presentations at major venues like the American Society of Overseas Research and Society of Biblical Literature Annual Meetings indicate significant scholarly engagement. His work bridges academic history with public discourse through contributions to platforms like HyperAllergic. Based in PAC J-9H at Union University, Dr. Jones contributes to the College of Arts & Sciences' mission of integrating Christ-centered academic excellence with historical scholarship, serving as part of a learning community that combines top-tier academics with Christian faith across numerous programs of study.

Thomas Gärtnerمشاهده پروفایل

استاد

• Machine Learning
• Graph Neural Networks
• Geometric Deep Learning
• +۳ مورد دیگر

Thomas Gärtner is a Professor at the Institute of Logic and Computation within the Faculty of Informatics at Vienna University of Technology, leading the Machine Learning research group (E194-06). His work bridges theoretical machine learning with practical applications in chemistry, biology, and network analysis. His primary research focuses on graph neural networks (GNNs) and geometric deep learning, with significant contributions to GNN expressivity, graph transformations, and kernel methods for structured data. He explores fundamental questions about the limitations of message-passing architectures while developing practical enhancements like path-based extensions and expectation-complete representations. His chemical informatics work applies these techniques to binding affinity prediction, reaction classification, and solvent selection, demonstrating real-world impact in computational chemistry. Analysis of his 15 most recent publications (2023-2025) reveals three dominant research thrusts: theoretical GNN advancements (35% of articles), chemical informatics applications (40%), and novel learning frameworks (25%). The theoretical work increasingly addresses expressivity limitations through graph transformations and path-based approaches, while chemical applications show growing sophistication in molecular representation. Recent publications also indicate expanding interest in foundation models for graphs and robustness verification. He actively supervises master's students including Fabian Traxler (binding affinity prediction), Maximilian Plattner (SGD optimization), Fabian Jogl (graph transformations), and Thomas Schmied (reinforcement learning). His research is conducted through the Network Lab at TU Wien, where he serves as Principal Investigator for the Structured Data Learning with Generalized Similarities project.

• Machine Learning
• Computer Science
• Artificial Intelligence
• +۳ مورد دیگر

Amauri Holanda De Souza Junior is a Postdoctoral Researcher affiliated with the Department of Computer Science , focusing on Probabilistic Machine Learning . His work bridges theoretical advancements with practical applications in graph-based models. Active research areas include Graph Neural Networks , Persistent Homology , and Simulation-based Inference . His recent publications highlight innovations in: Topological data analysis for graph representations Robust statistical methods under model misspecification Scalable Bayesian inference frameworks Equivariant architectures for graph learning