جست‌وجوی استاد و پژوهشگر

Ke Wuمشاهده پروفایل

استادیار

• Theoretical Cryptography
• Game Theory
• Decentralized Protocols
• +۲ مورد دیگر

Ke Wu is an Assistant Professor at the University of Michigan within the Theory of Computation Lab of the EECS Department. He focuses on theoretical cryptography and game theory, designing incentive-compatible decentralized protocols, with applications in blockchain security and error correction codes. He actively seeks PhD students for the CSE graduate program. University: University of Michigan Department: Electrical Engineering and Computer Science (EECS) Academic Rank: Assistant Professor Research Interests: Ke Wu explores the intersection of cryptography and game theory, emphasizing provably secure decentralized systems. His work includes mechanisms for distributed consensus, fair coin toss protocols, and applications of error correction in cryptographic primitives. He has contributed to transaction fee design, anonymous routing, and synchronization string construction. Recent Publications: His research spans topics such as game-theoretically fair leader election, platform-assisted auctions, and error correction in blockchain contexts. Key venues include CRYPTO, ITCS, and FOCS. Scientific Awards: Stellar Academic Research Grant (2024) CMU CyLab Presidential Fellowship (2022–2023) JP Morgan Chase AI Research PhD Fellowship (2023) Education: Ph.D. from Carnegie Mellon University (advised by Elaine Shi), Master’s from Johns Hopkins University (advised by Xin Li), and B.S. from Fudan University in Information and Computing Science.

Charles Joseph Argueمشاهده پروفایل

مدرس

• Algorithms
• Combinatorics
• Optimization
• +۴ مورد دیگر

Charles Joseph Argue is a Lecturer in Mathematics at Yale University since July 2022, specializing in Algorithms, Combinatorics, and Optimization. He received his PhD in Mathematics from Carnegie Mellon University, where he was advised by Anupam Gupta. Specialties: Online algorithms, convex geometry, convex optimization, combinatorial optimization Current research focuses on information-theoretic questions in algorithms, convex optimization, and their applications to theoretical computer science. The trends in his publications emphasize algorithmic design for convex and combinatorial optimization problems, with significant contributions to competitive algorithms for convex body chasing and packing integer programs. His work spans both theoretical foundations and practical applications. Scientific awards include: SODA 2020 Best Paper SODA 2019 Best Paper At Carnegie Mellon University, he taught courses in multivariate analysis, matrix algebra, discrete mathematics, and calculus. Since 2016, he has coached the Western Pennsylvania ARML Team, mentoring high school and middle school students in mathematics competitions.

Slobodan Vuceticمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Science
• Applied Machine Learning
• +۳ مورد دیگر

Slobodan Vucetic is a Professor in the Department of Computer and Information Sciences at Temple University's College of Science and Technology and serves as Director of the Center for Hybrid Intelligence (CHI), formerly the Center for Cognitive Computing. His work bridges theoretical machine learning advancements with practical applications across public health, biomedicine, and social sciences, focusing on enhancing human capabilities through intelligent software systems. His research spans Human Factors in Artificial Intelligence, Data Science, Applied Machine Learning, Biomedical Informatics, and Bioinformatics. He develops novel algorithms to solve real-world data science problems in diverse domains including healthcare, education, traffic engineering, and social sciences. His work emphasizes human-AI collaboration, particularly in assistive technologies for neurodiverse populations and biomedical applications requiring interpretability. Analysis of his recent publications reveals dominant themes in AI-driven healthcare solutions (medical coding, protein structure prediction), human-in-the-loop systems for neurodiverse individuals, and sports analytics. His work consistently integrates domain-specific constraints with machine learning innovation, showing increasing focus on ethical AI applications for social good and accessibility. His scientific achievements include: CAREER award from National Science Foundation Outstanding paper at IJCAI 2013 (AI and Computational Sustainability Track) Leadership in top-performing CAFA teams across multiple assessment cycles Best protein disorder predictor at CASP competitions 5-7 Vucetic has advised 20 graduate students (11 current, 9 graduated), with alumni securing positions at Google, Facebook, Fox Chase Cancer Center, and academia. His extensive research portfolio includes 12 major NSF-funded projects, NIH grants for medical claims analysis, and industry collaborations with Yahoo! and ExxonMobil, totaling over $15M in funding for initiatives ranging from autism therapy customization to wireless localization security. He directs Temple's Center for Hybrid Intelligence, which pioneers human-AI collaboration frameworks. Current initiatives include NSF's Future of Work project for neurodiverse IT careers and ONR-funded additive technology research, with active workshops like KDD's Data Science with Human-in-the-Loop fostering community innovation.

Claire L. Streibertمشاهده پروفایل

استادیار

• Breast Imaging
• Diagnostic Radiology
• Breast Cancer Screening
• +۲ مورد دیگر

Claire L. Streibert, MD, is an Assistant Professor of Clinical Radiology at Temple University's Lewis Katz School of Medicine and serves as Site Chief for Breast Imaging. She holds appointments across multiple Temple Health facilities including Fox Chase Cancer Center, Temple University Hospital's Main Campus, and Jeanes Campus. Her clinical focus centers on breast cancer risk assessment and advanced diagnostic imaging techniques. Education & Training: MD from George Washington University School of Medicine Breast and Body Imaging Fellowship at Johns Hopkins University Hospital Diagnostic Radiology Residency at Johns Hopkins University Hospital Research Focus: Her work spans breast cancer screening methodologies, optimization of MRI applications in oncology, and technological innovations in mammography. She maintains active research in pediatric diagnostic imaging, particularly appendicitis evaluation protocols. Awards & Honors: Top Doctors recognition in Diagnostic Radiology by Philadelphia Magazine (2024-2025) Affiliations: Member of Alpha Omega Alpha Honor Medical Society, Society of Breast Imaging, American College of Radiology, and Radiological Society of North America.

Hui Fangمشاهده پروفایل

استاد

• Information Retrieval
• Text Mining
• Bioinformatics
• +۶ مورد دیگر

Prof. Hui Fang is a Professor in the Department of Electrical and Computer Engineering at the University of Delaware's College of Engineering. Her research focuses on overcoming information overload through advancements in text information management, including search engine technology, text mining, and bioinformatics. She has pioneered task-aware information retrieval frameworks and collaborates with domain experts in journalism and biology. Education : PhD from the University of Illinois at Urbana-Champaign. Research Interests : Information Retrieval (Search Engine Technology) Text Mining and Natural Language Processing Algorithmic Fairness and Bias Mitigation Conversational AI and Dialogue Systems Medical and Scientific Domain Retrieval Awards & Honors : JP Morgan Chase Interim Fellow/Scholar (2017-Present) David L. and Beverley J. C. Mills Career Development Chair (2016-2020) 2013 College of Engineering Excellence in Teaching Award ACM SIGIR 2004 Best Paper Award Advising & Lab Work : Leads the UDInfoLab, focusing on fair web search and conversational systems. Her work integrates interdisciplinary approaches to tackle challenges in scalable information access and ethical AI systems.

Yonggang Luمشاهده پروفایل

دانشیار

• Business Analytics
• Bayesian Modeling
• Predictive Analytics
• +۵ مورد دیگر

Dr. Yonggang Lu is the Harold Alfond Associate Professor of Business Analytics at the University of Maine’s Maine Business School. Previously, he held an associate professorship at the University of Alaska’s College of Business and Public Policy. Prior to academia, he worked at JPMorgan Chase as a customer analytics specialist, focusing on large-scale financial data mining and predictive modeling in mortgage, home equity, and student loan markets. He is FRM-certified by the Global Association of Risk Professionals. Education: Ph.D. Information Systems and Quantitative Sciences, Texas Tech University M.S. Applied Mathematics M.A. Economics M.S. Finance B.E. Chemical Process Equipment and Control Engineering, Xi’an Jiaotong University B.A. Financial Economics His research focuses on Transparent, Reliable, Efficient, and Effective (TREE) Bayesian models for business decision-making, emphasizing Bayesian inference algorithms and social network analysis. He develops methodologies for integrating subjective prior knowledge into machine learning and statistical models, with applications in inventory control, consumer behavior analysis, and risk management. Key Research Contributions: Pioneered Bayesian approaches for predictor contribution analysis in logistic regression Advanced causal inference frameworks using Bayesian networks Explored online information diffusion dynamics via social networks Awards & Recognition: Excellence in Research Award (Maine Business School, 2022) Faculty Mentor Impact Award (University of Maine, 2021) Nominated for Teaching Excellence (Maine Business School, 2021-2022) Recipient of The C Oswald George Prize (Royal Statistical Society, 2013) His teaching portfolio includes foundational courses in business analytics, data visualization, and decision analysis. He actively bridges academic research with industry applications, particularly in financial risk management and data-driven decision systems. His work has been published in journals like European Journal of Operational Research , Journal of Applied Statistics , and American Statistician .

Vijay Pappuمشاهده پروفایل

دانشیار مدعو

• Machine Learning
• Optimization
• Engineering Applications
• +۳ مورد دیگر

Dr. Vijay Pappu is an Adjunct Associate Professor of Computer Science at Columbia Engineering. He leads personalization efforts at Peloton Interactive Inc., focusing on scalable Machine Learning techniques for digital experiences. His research bridges Machine Learning, Optimization, and Engineering applications in domains like digital acquisition and product personalization. He teaches Applied Machine Learning courses at Columbia and has extensive industry experience at firms including Spotify, JP Morgan Chase, and American Express. Dr. Pappu holds a Ph.D. in Machine Learning from the University of Florida (2013), alongside advanced degrees in Management, Industrial & Systems Engineering, Mechanical & Aerospace Engineering, and a Bachelor’s from IIT Madras. His work includes over 300 citations, with publications in peer-reviewed journals/conferences. He co-edited Springer volumes and organized conferences on Biomedicine and Smart Grids. His research emphasizes high-dimensional data classification, feature selection, and optimization in areas like biomedical diagnostics and smart grid security. He has managed teams building large-scale recommender systems, demonstrating expertise in both academic research and industrial application.

Ambrose Kofi Laingمشاهده پروفایل

عضو هیئت علمی

• Parallel and Distributed Computing
• Networking
• Algorithms
• +۲ مورد دیگر

Ambrose Kofi Laing is an Associate Teaching Professor at Iowa State University's Department of Computer Science. He holds a Ph.D. (1999) and M.S.E. (1994) in Computer Science from The Johns Hopkins University, and a B.Sc. in Mathematics and Computer Science from the University of Ghana (1991). His research focuses on Parallel and Distributed Computing, with expertise in routing algorithms, network topologies, and theoretical computer science. Prior to his academic role, he worked at institutions like Tufts University and industry leaders such as Google, JP Morgan Chase, and Nuance Communications. His publications span foundational work in routing protocols, compact data structures, and distributed systems, appearing in journals like Networks , SIAM Journal on Discrete Mathematics , and Information Processing Letters . His research emphasizes optimizing network performance through algorithmic innovation, with contributions to name-independent routing, hypercube networks, and deadlock-free systems. Collaborations include work with Lenore Cowen, Rajmohan Rajaraman, and others on compact routing and topology-independent systems.

Ron Kanielمشاهده پروفایل

استاد

• Asset Pricing
• Financial Intermediation
• Investments
• +۶ مورد دیگر

Ron Kaniel is the Jay S. and Jeanne P. Benet Professor of Finance at the Simon Business School, University of Rochester. He holds honorary visiting professorships at Gothenburg University and Fudan University, and is a Research Fellow at the Center for Economic Policy Research (CEPR). His research focuses on asset pricing, mutual fund behavior, and financial intermediation, with an emphasis on how institutional investors impact security prices and market dynamics. Education: PhD in Finance (1999, Wharton School, University of Pennsylvania); M.Sc. in Computer Science (1994, Hebrew University of Jerusalem); B.Sc. in Mathematics & Computer Science (1992, Hebrew University of Jerusalem). Research Interests : Analysis of mutual fund investment strategies and their market consequences Endogenous community effects in investment decisions Predictive power of trading volume and investor order flow Machine learning applications in finance Contract design in financial networks Awards & Recognition : European Research Council Starting Grant Simon School Dean’s Teaching Honor Roll (multiple years) Editorial Roles: Co-Editor of Journal of Financial Economics, Founding Editor of Finance Theory Insights President of the Finance Theory Group Grants & Impact : Recipient of ERC funding for research on financial markets. His work has been cited in policy portals like VOX CEPR and the Harvard Law School Forum. Teaches advanced courses in corporate finance, capital budgeting, and PhD-level applied economics workshops. Professional Contributions : Editorial leadership in top journals, advisory roles in academic and policy institutions, and active participation in global finance networks.

Anouk Michelle Oudshoornمشاهده پروفایل

پژوهشگر

• Semantic Web Technologies
• Ontology Engineering
• Description Logics
• +۳ مورد دیگر

Anouk Michelle Oudshoorn is a PhD candidate and researcher at the Department of Knowledge-Based Systems, Institute of Logic and Computation, Technical University of Vienna. Her research focuses on integrating SHACL constraints with Description Logics and Ontology Engineering. She holds an MSc in Logic from the University of Amsterdam (ILLC) and dual BSc/BA degrees in Mathematics and Philosophy from Radboud University, Nijmegen. Education: PhD Candidate (2022–present), Technical University of Vienna MSc in Logic (2020–2022), University of Amsterdam BSc Mathematics & BA Philosophy (2017–2020), Radboud University Her research explores formal validation mechanisms for semantic technologies, emphasizing interoperability between SHACL and OWL ontologies. She has conducted research visits at Umeå University (2023) and TU Dresden (2024). Notable contributions include work on SHACL-Description Logic combinations and Vossian antonomasia generation for Wikidata. Awards: Netidee Stipend (2023) 2nd Prize Best Poster Award (ISWC 2023) Teaching: She has served as a teaching assistant for courses including 'Logic and Reasoning in Computer Science', 'Description Logics and Ontologies', and 'Knowledge-Based Systems' at TU Wien. Additional experience includes mathematics tutoring and logic instruction. Sustainability: Prioritizes train travel for international conferences to reduce environmental impact, undertaking extensive rail journeys across Europe.

Ming Maمشاهده پروفایل

استادیار

• Artificial Intelligence
• Medical Imaging
• Large Language Model
• +۳ مورد دیگر

Ming Ma is an Assistant Professor in the Department of Graduate Computer Science and Engineering at the Katz School of Science and Health, Yeshiva University. He leads the Medical Imaging and Artificial Intelligence lab. He holds a Ph.D. in Computer Science from Stony Brook University and completed a postdoctoral fellowship at Stanford University. His research focuses on integrating artificial intelligence with medical imaging, particularly in areas like medical image segmentation, deep learning applications in healthcare, and geometric modeling for biomedical systems. Education: Ph.D. in Computer Science, Stony Brook University, USA Postdoctoral Scholar, Stanford University, USA Research Interests: His work spans Artificial Intelligence , Medical Imaging , Large Language Models , Geometric Modeling , and Computer Vision . He develops novel algorithms for medical image analysis, including techniques for dose prediction in radiation therapy, pulmonary nodule detection, and segmentation of vascular structures. Recent projects emphasize the application of graph convolutional networks and optimal transport methods in low-dose CT imaging. Conference Contributions: He serves on program committees for conferences like AAAI 2022, ICONIP 2024, and CHASE 2025, reflecting his leadership in advancing AI and biomedical engineering research. Labs/Teams: His lab at Yeshiva University focuses on interdisciplinary research at the intersection of AI, medical imaging, and computational anatomy, with a commitment to translating algorithmic innovations into clinical applications.

David Carralمشاهده پروفایل

پژوهشگر

• Knowledge Representation
• Database Theory
• Artificial Intelligence
• +۳ مورد دیگر

Dr. David Carral is a former Research Associate at the International Center for Computational Logic (ICCL) within the Faculty of Computer Science at TU Dresden. His work focuses on Knowledge Representation , Database Theory , and Computational Logic , particularly in the context of ontology-based data access, existential rules, and chase termination analysis. He contributed to projects like CFAED and collaborated on systems such as VLog and ROWL . David supervised theses including "Don’t Repeat Yourself: Termination of the Skolem Chase on Disjunctive Existential Rules" by Lukas Gerlach. His research bridges formal methods with practical applications in AI and database systems. Research interests include ontology design patterns , query rewritability , and efficient reasoning algorithms . He actively participated in conferences like KR and IJCAI, contributing to advancements in nonmonotonic reasoning and hyperproperties. His work integrates description logics, answer set programming, and semantic web technologies to address challenges in knowledge-based systems.

Philipp Hanischمشاهده پروفایل

پژوهشگر

• Knowledge Representation and Reasoning
• Answer Set Programming
• Artificial Intelligence
• +۲ مورد دیگر

Philipp Hanisch is a Researcher at the Technische Universität Dresden within the Faculty of Computer Science , specifically in the International Center for Computational Logic (ICCL) . He serves as the scientific coordinator of the School of Embedded Composite Artificial Intelligence (SECAI) , overseeing its development. Hanisch holds a diploma in Computer Science from TU Dresden (2021) and previously worked as a Young Graduate Trainee at the European Space Agency before returning to academia. His research focuses on Knowledge Representation and Reasoning , Answer Set Programming , and broader aspects of Artificial Intelligence . Recent publications explore topics like chase termination in database systems and set simulation techniques in logic programming. He has contributed to projects such as indexing for Datalog materialization and collaborative efforts within the SECAI initiative. As part of the Knowledge-Based Systems research group led by Prof. Markus Krötzsch, Hanisch bridges theoretical advancements with practical applications in computational logic. His work reflects interdisciplinary engagement between formal methods and AI systems design.

Piotr Ostropolski-Nalewajaمشاهده پروفایل

عضو هیئت علمی

• Computational Logic
• Database Theory
• Knowledge Representation
• +۲ مورد دیگر

Piotr Ostropolski-Nalewaja is a Research Associate in Computational Logic at Technische Universität Dresden's Faculty of Computer Science. His research focuses on computational logic, database theory, and knowledge representation, with particular emphasis on decidability problems and formal methods in computer science. Research interests include: Decidability in formal systems Query processing and optimization Knowledge representation formalisms Automated reasoning Ontology-based data access Recent publications investigate decidability boundaries for various logical formalisms, developing novel approaches to query answering under existential rules. His work bridges theoretical computer science and practical applications in database systems. Awards: No awards listed He contributes to multiple collaborative projects exploring the theoretical foundations of knowledge representation and reasoning systems.

Marek Eliasمشاهده پروفایل

استادیار

• Algorithms
• Online Algorithms
• Differential Privacy
• +۹ مورد دیگر

Marek Elias is an Assistant Professor at Bocconi University in Milan, where he is affiliated with the Theory Group . Prior to joining Bocconi, he held postdoctoral positions at CWI (Amsterdam) and EPFL (Lausanne), following a PhD at TU Eindhoven under the supervision of Nikhil Bansal. Earlier, he studied at Charles University in Prague with advisor Jiří Matoušek. His research focuses on algorithms for optimization under uncertainty , bridging online algorithms , differential privacy , and machine learning-augmented systems . Key areas include Learning-Augmented Algorithms Online Metric Algorithms Combinatorial Optimization Privacy-Preserving Mechanisms The trends in his recent publications highlight advancements in online optimization with applications in machine learning and data structures , particularly in problems like the k-server system , Steiner tree approximation , and differentially private clustering . Papers often explore the interplay between algorithmic robustness and predictive modeling to enhance efficiency in uncertain environments. He has contributed to major conferences including ICML, NeurIPS, SODA, and FOCS, with a consistent emphasis on theoretical guarantees and practical applications . While specific awards or grants are not detailed in the provided texts, his work is supported through institutional affiliations and collaborative research networks. Marek actively engages students in research, particularly in Bocconi's Theory Group , encouraging thesis projects on learning-augmented algorithms and prediction-based optimization . His laboratory environment fosters interdisciplinary collaboration between theoretical computer science and machine learning communities.