جستجوی استاد راهنمای دانشگاهی

• Algebraic Combinatorics
• Representation Theory
• Schubert Calculus
• +۵ مورد دیگر

Cristian Lenart is Professor and Chair of the Department of Mathematics & Statistics at the University at Albany, State University of New York. Celebrating 25 years on the faculty, he leads an internationally recognized research program in algebraic combinatorics, focusing on combinatorial models for representation theory, Schubert calculus, and related algebraic geometry. He has secured seven National Science Foundation grants totalling over one million US dollars and advises a continuous stream of doctoral students. Education: PhD in Mathematics, University of Manchester (UK), 1996 Advanced Studies in Mathematics, University of Cambridge (UK), 1993 PhD in Computer Science, University of Cluj-Napoca (Romania), 1992 BS in Mathematics and Computer Science, University of Cluj-Napoca (Romania), 1988 Research Interests: Lenart develops combinatorial structures to compute and understand phenomena in representation theory, algebraic geometry and topology. His work spans Schubert calculus, flag varieties, quantum groups, crystal bases, symmetric functions, and K-theory, often utilising computer algebra experiments to discover and test formulas. Recent Work & Trends: Recent publications explore equivariant elliptic cohomology, parabolic Kazhdan-Lusztig bases, and generalised cohomology theories, demonstrating a sustained emphasis on extending Schubert calculus beyond classical cohomology and K-theory into elliptic and other complex oriented settings. Scientific Awards & Honors: SUNY Chancellor’s Award for Excellence in Scholarship and Creative Activities (2014) UAlbany President’s Excellence Award for Research and Creative Activities (2013) Magdalene College Prize in Mathematics, Cambridge (1993) Raymond and Beverly Sackler Foundation Fellow, IHÉS (2018) Simons Foundation Collaboration Grant for Mathematicians (2018-2019) Grants & Advising: Lenart has served as principal investigator on seven NSF awards since 2004, supporting postdocs, graduate students, and major conferences. He has directed nine PhD dissertations and mentored numerous Master’s and undergraduate researchers. Service & Outreach: He has organised internationally attended research conferences at Albany and served on eight NSF review panels. Since 2020 he is an editor for the journal Algebras and Representation Theory .

Esmaeil Bahalkehمشاهده پروفایل

استادیار

• Hospital Operations Management
• Healthcare Access
• Health Services Delivery
• +۶ مورد دیگر

Esmaeil Bahalkeh serves as Assistant Professor in the Department of Health Management and Policy at the University of New Hampshire, where his NIH-funded research bridges industrial engineering and healthcare delivery systems. His work focuses on optimizing hospital operations and expanding healthcare access through advanced analytical methods. His academic credentials include: Ph.D. in Industrial Engineering from Purdue University M.S. in Industrial & Systems Engineering from Ohio University B.S. in Industrial Engineering from Sharif University Bahalkeh's research program centers on two interconnected streams: Hospital Operations Management examines resource allocation, workforce scheduling, and financial sustainability in healthcare settings, while his Healthcare Access stream investigates systemic barriers and operational solutions across emergency services, long-term care, and ambulatory settings. He employs discrete event simulation, machine learning, and data mining to model complex healthcare systems and test policy interventions. His publication trajectory from 2015-2025 reveals consistent methodological evolution from manufacturing systems theory toward healthcare-specific applications, with recent work emphasizing interpretable AI for hospital service lines and access improvement strategies in specialized clinics. Over 70% of his recent publications address operational challenges in critical care and outpatient settings. Bahalkeh teaches core courses including Healthcare Operations Management (HMP 712), Machine Learning in Healthcare (HDS 805), and Quality Improvement for nursing practitioners (NURS 933), integrating his research on data-driven decision making into both undergraduate and doctoral programs.

Yong Seog Kimمشاهده پروفایل

استاد

• Decision Support Systems
• Data Mining
• Feature Selection
• +۶ مورد دیگر

Yong Seog Kim is a Professor in the Department of Data Analytics and Information Systems at Utah State University's Huntsman School of Business. His primary research focuses on decision support systems and the development of data mining algorithms for feature selection, clustering, and streaming data analysis, with applications in customer relationship management, e-commerce, and computational finance. Education: Ph.D., Management Information Systems, University of Iowa M.A., Computer Science, University of Iowa M.A., Economics, Yonsei University B.A., Economics, Yonsei University Research Focus: Dr. Kim's interdisciplinary work bridges data science and applied domains. He specializes in predictive modeling for business optimization, social dynamics simulation, and inclusive systems design, particularly for populations with disabilities. His methodological innovations include agent-based simulations for crowd evacuation and novel clustering techniques for high-dimensional data. Publication Trends: His recent work demonstrates a strong emphasis on practical applications of analytics in diverse sectors including agriculture, transportation safety, financial regulation, and disability services. Methodologically, his articles showcase advanced techniques in machine learning, simulation modeling, and knowledge graph construction. Recognition: Outstanding Graduate Mentor of the Year (2021) Academic Leadership: Dr. Kim mentors graduate students and leads courses in Data Mining and Visual Analytics. His work with the Analytics Solutions Center facilitates industry-academia partnerships.

Robert Rinkerمشاهده پروفایل

دانشیار

• Computer Architecture
• Reconfigurable Computing
• Embedded Systems Security
• +۳ مورد دیگر

Robert Rinker, Ph.D., is an Associate Professor and Associate Chair in the Department of Computer Science at the University of Idaho, part of the College of Engineering. His office is located in Hedlund Building 202D, and he can be reached at rinker@uidaho.edu. Rinker holds a Ph.D. in Computer Science from Colorado State University, and both Master's and Bachelor's degrees in Electrical Engineering from the University of Idaho. His teaching responsibilities include courses such as Computer Science I (CS 120), Computer Organization and Architecture (CS 150), System Software (CS 270), Advanced Computer Architecture (CS 451/551), and Real-Time Operating Systems (CS 452/552). His research focuses on reconfigurable computing, embedded systems security, computational biology pipelines, and hardware compilation techniques. Specific contributions include work on FPGA-based systems, resilient multi-core architectures, and formal verification methodologies. Rinker’s research trends emphasize the intersection of hardware-software co-design and cybersecurity, with notable contributions to VANET security and computational density optimization in embedded systems. His publications span topics from workflow automation in bioinformatics to compiler-driven FPGA optimization strategies. While no formal awards or grants are listed, his extensive academic service as Associate Chair highlights his leadership in departmental operations. He advises courses but no listed students or active lab teams are mentioned in the provided materials.

Argyris Kalogeratosمشاهده پروفایل

پژوهشگر

• Machine Learning
• Network Science
• Computational Social Systems
• +۲ مورد دیگر

Argyris Kalogeratos is a permanent senior researcher at the Centre Giovanni Borelli , École Normale Supérieure Paris-Saclay (ENS Paris-Saclay), Université Paris-Saclay, France, and a member of the Learning and Information Processing Systems (LIPS) research group. Previously, he was a research scientist at the Centre of Applied Mathematics, ENS Cachan, France (2013–2018). Research Focus: Machine Learning on Graphs Industrial Collaborations: SNCF, CEA, Michelin, RTE, EDF Research Chair: Industrial Data Analytics and Machine Learning (IDAML) His research interests span Machine Learning , Statistics , Network Science , Decision & Control , and Computational Social Systems . He leads the ML on Graphs research theme and participates in projects like the EDF Data Challenge and Île-de-France AI for Industry Challenge . Current PhD advisees include: Alejandro de la Concha Duarte (Statistical Machine Learning on Graphs) Gaëtan Serré (Global Optimization) Gwendal Debaussart (Graph Representation Learning) Eloi Campagne (Time-Series Forecasting for Energy Sector) Gaspard Abel (Machine Learning on Social Interaction Networks) Past funding includes the MORANE project (SNCF collaboration) and the SODATECH project (French government Big Data initiative). He coordinates the LIPS seminars and contributes to academic-industry synergies through projects like AIfluence and French-German Summer-schools .

Upendranatha Sharma Chakravarthyمشاهده پروفایل

استاد

• Big Data Analytics
• Machine Learning
• Cloud Computing
• +۷ مورد دیگر

Dr. Upendranatha Sharma Chakravarthy is a Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA), where he has served since January 2000. He leads the Information Technology Laboratory (IT Lab), established in 2000, and oversees the NSF-funded Distributed and Parallel Computing Cluster. His roles include academic leadership, research mentorship, and contributions to the College of Engineering. Education: He holds a B.E. in Electrical Engineering from the Indian Institute of Science (1973), M.Tech from IIT Bombay (1975), and Ph.D. from the University of Maryland (1985). Prior to UTA, he was at the University of Florida (1989–1999) and held industry roles at Xerox and Computer Corporation of America. Research Interests focus on Big Data Analytics using AI/ML, graph mining, multilayer networks, stream data processing, and distributed systems. His work emphasizes scalability, real-time data handling, and applications in social networks, healthcare, and cybersecurity. Recent projects include MLN-geeWhiz (multilayer network analysis), CoWiz (Covid-19 visualization), and NetSplicer (scalable network algorithms). Awards include ACM Distinguished Scientist (2011), IEEE Senior Member (2011), and multiple university awards for research excellence. He organizes conferences (e.g., DEBS 2013) and teaches advanced topics in databases, data mining, and parallel computing. Grants: Active NSF funding includes NetSplicer (2020–2025) and CyberTraining (2023–2024). He has supervised 15 PhD and 85 MS students. The IT Lab collaborates with industry/government on projects like ROMAN (airborne networks) and justice-in-data initiatives. Labs/Teams: IT Lab specializes in big data science, developing tools like ModViz (data visualization) and VideoScoop (video analysis). It also promotes underrepresented STEM participation through BPC initiatives.

• Motion Capture
• 3D Motion Analysis
• Machine Learning
• +۲ مورد دیگر

Dr. Maria Skublewska-Paszkowska is an Assistant Professor in the Department of Computer Science at Lublin University of Technology. She directs research in computer vision, motion capture analysis, and cultural heritage preservation. Her work establishes innovative methodologies for 3D scanning of intangible heritage, including traditional dance documentation. Research domains include: 3D motion analysis using machine learning Universal design principles for interface evaluation Biomechanics and sports analytics Medical imaging with deep learning Virtual museum development Publications demonstrate interdisciplinary approaches combining computer vision with cultural preservation, sports science, and accessibility design. Recent work focuses on transformer architectures for medical diagnosis and attention mechanisms for action recognition.

Christos Tzamosمشاهده پروفایل

دانشیار

• Computer Science
• Machine Learning
• Statistical Learning
• +۳ مورد دیگر

Christos Tzamos is an Associate Professor in the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens, and a researcher at Archimedes AI. He received his PhD from MIT and BS from National Technical University of Athens. His research bridges computer science, machine learning, statistics, and algorithmic economics. Research Focus: Machine learning theory, algorithmic mechanism design, optimization, and statistical methods with applications to economics. Current projects include active learning, adaptive information acquisition, and robust algorithm design. Awards: NSF CAREER Award (2022), NeurIPS Outstanding Paper Award (2019), George Sprowls Award for best CS PhD thesis at MIT (2017), multiple programming competition medals. Student Advising: Currently advising 4 PhD students and 1 postdoc, with 3 graduated PhD students now at Georgia Tech, Yale, and UT Austin.

• Neuroimaging
• Cognitive Neuroscience
• Neurology
• +۳ مورد دیگر

Chris Rorden is a Professor of Psychology at the University of South Carolina, holding the Endowed Chair of Neuroimaging within the McCausland College of Arts and Sciences. His work focuses on neuroimaging and cognitive neuroscience, particularly examining behavioral deficits following brain injury such as stroke and epilepsy. Rorden has pioneered neuroimaging tools like MRIcro and contributed to lesion-symptom mapping techniques. Education: PhD in Experimental Psychology (1996), University of Cambridge, UK BA in Psychology (Honors, Magna cum Laude, 1993), University of California, San Diego Research Interests: Rorden's research integrates neuroimaging with clinical studies to understand spatial neglect, aphasia, and recovery mechanisms post-stroke. Key areas include: Non-invasive brain imaging techniques (fMRI, DTI) Lesion mapping to identify neural correlates of cognitive deficits Development of open-source neuroimaging tools Impact of white matter integrity on cognition and recovery His work bridges basic neuroscience and clinical applications, emphasizing translational outcomes. Publications & Awards: With over 190 peer-reviewed articles, Rorden's contributions span neurology, cognitive science, and medical technology. Notable awards include the Arnold School of Public Health Research Award (2008) and the British Neuropsychological Society's Elizabeth Warrington Prize (2002). Grants & Collaborations: Principal investigator or co-investigator on NIH-funded projects totaling over $24M, including leadership in the Center for the Study of Aphasia Recovery (C-STAR). Collaborations span institutions like MUSC, Georgia Tech, and international teams. Labs & Teams: Directs the Neuroimaging Core at C-STAR and collaborates with the UK Biobank and brainlife.io platforms. His lab develops tools for lesion analysis and connectomics.

Yan Zhangمشاهده پروفایل

مدرس

• Recommendation Systems
• Data Mining
• Machine Learning
• +۵ مورد دیگر

Dr. Yan Zhang is a Lecturer in Information Technology within the Faculty of Science and Technology at Macquarie University. She completed her PhD in recommendation systems from the University of Technology Sydney (UTS) in 2022 and has established herself as an active researcher with 17 publications spanning from 2016 to 2025. Her academic profile shows consistent research output with multiple publications each year, demonstrating ongoing productivity and engagement in her field. She is registered to supervise postgraduate research, indicating her role in mentoring graduate students. Dr. Zhang's research interests center around recommendation systems, data mining, machine learning, and transfer learning. Her work spans both theoretical foundations and practical applications, with a strong emphasis on solving cold-start problems, cross-domain recommendations, and efficient recommendation techniques. The fingerprint of her research shows significant contributions in areas including User, Hashing, Collaborative Filtering, Recommender Systems, User Preference, Algorithms, Matrix Factorization, and Boolean Function. Her research contributes to UN Sustainable Development Goals through technological innovation in data science and information systems. Analysis of Dr. Zhang's publication history reveals an evolving research trajectory with increasing sophistication in recommendation system methodologies. Her early work focused on discrete techniques and hashing methods for efficient recommendations, while recent publications demonstrate expansion into federated learning, multi-agent systems, and medical applications. The thematic progression shows movement from foundational recommendation techniques toward more complex, interdisciplinary applications while maintaining core expertise in user preference modeling and efficient recommendation algorithms. Dr. Zhang has accumulated 268 citations with an h-index of 7, indicating growing scholarly impact in her field. Her publications appear in reputable venues including IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, and proceedings of major conferences like IEEE ICDE. She has demonstrated consistent collaboration with researchers across institutions, as evidenced by her co-authorship patterns across multiple publications. As a postgraduate research supervisor, Dr. Zhang contributes to academic training in information technology. Her research program appears to focus on both theoretical aspects of recommendation systems and practical applications in cybersecurity, healthcare, and content delivery. The diversity of her recent work suggests she leads or participates in multiple research streams addressing different application domains while maintaining core methodological expertise in recommendation technologies.

Alexander Golovnevمشاهده پروفایل

استادیار

• Computational Complexity
• Algorithms
• Pseudorandomness
• +۲ مورد دیگر

Alexander Golovnev is an Assistant Professor at Georgetown University's Computer Science Department. His research focuses on computational complexity, algorithms, pseudorandomness, learning theory, and cryptography. He received his PhD from New York University in 2017, advised by Oded Regev and Yevgeniy Dodis, with postdoctoral positions at Columbia University, Yahoo Research, and Harvard University. His current teaching includes Matrix Rigidity (Spring 2025) and Introduction to Algorithms. He has developed a 5-Course Specialization on Discrete Mathematics and organized workshops on Matrix Rigidity and Fine-Grained Cryptography. Service includes program committees for CSR'22, FOCS'22, STOC'24, CCC'24, and ITC'25. Research interests span quantum computing reductions, lattice problems, circuit complexity, and fine-grained cryptography. His recent publications explore worst-case to average-case reductions, function inversion tradeoffs, and SNARK constructions. Awards include NSF CAREER Award (2024), Rabin Postdoctoral Fellowship (2018-2020), and IPEC excellent student paper award (2012). Current PhD/Master's advisees: Sidhant Saraogi (joint with Justin Thaler), Karthik Gajulapalli, Samuel King, and Satyajeet Nagargoje.

Ron Borowskyمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Neuroimaging (fMRI)
• Language Processing
• +۵ مورد دیگر

Dr. Ron Borowsky is a Professor of Cognitive Neuroscience at the University of Saskatchewan's Department of Psychology and Health Studies, affiliated with the College of Arts and Science. He serves as the University NSERC Leader in the Office of the Vice-President Research and holds an associate membership in Neurosurgery at the College of Medicine. His research focuses on neuroimaging (fMRI), linking brain activity to cognitive processes like language, attention, memory, and visual processing. Key interests include reading models, semantic-motor interactions, and neural reorganization post-brain injury. Academic Background: Borowsky earned a B.A. (Hons.) in Psychology from the University of Saskatchewan, followed by an M.A. and Ph.D. from the University of Waterloo. He completed a NSERC Post-doctoral Fellowship at the University of Victoria. His career includes roles as Assistant Professor (1995-1999), Associate Professor (1999-2006), and Professor (2006-present) at the University of Saskatchewan. Research Highlights: Borowsky's lab investigates the neural basis of reading, attention, and language through fMRI and DTI. He explores modular and shared neural networks for lexical vs. sublexical processing and applies findings to clinical contexts like pre-surgical brain mapping. Collaborations with neurology and medical imaging departments enhance translational research, including stroke recovery studies and epilepsy language mapping. Grants & Leadership: He secured NSERC grants (2013-2025) and previously led SSHRC projects. His editorial roles include Cortex and the Journal of Neurolinguistics. Media attention includes features in Star Phoenix and Canadian Geographic for literacy research and neuroimaging innovations. Labs & Teams: The Cognitive Neuroscience Lab engages in interdisciplinary projects with the College of Medicine, Royal University Hospital, and international collaborators. Training programs include the BSc Honours in Psychology and advanced seminars in cognitive neuroscience.

Jarek Szlichtaمشاهده پروفایل

دانشیار

• Big Data Analytics
• Machine Learning
• Responsible AI
• +۵ مورد دیگر

Jarek Szlichta is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University. He is also a Research Enhanced Faculty in responsible data science and AI under the Connected Minds initiative, a Research Faculty Fellow at IBM Centre for Advanced Studies (CAS), and an Adjunct Professor at the University of Waterloo. As co-director of the Data & AI Lab, his work focuses on data-driven systems, large-scale machine learning, and trustworthy AI. He has received the IBM CAS Faculty of the Year Award for DB2 system optimization and the CeBIT Business Award for the OCEAN GenRap tool. Education: PhD in Computer Science, York University Postdoctoral Fellowship at University of Toronto IBM CAS Student Fellowship (IBM Research Student of the Year Award) Research Interests: Big Data Analytics and Machine Learning Responsible AI and Ethical Frameworks Data-Driven Systems and Stream Processing Graph Data, Knowledgebases, and Web Search Data Quality and Integration Grants & Collaborations: Funded by NSERC, MITACS, SOSCIP, IBM, and AT&T. His work emphasizes bridging theory and practice in AI and data systems. Labs & Teams: Co-directs the Data & AI Lab at York University, focusing on innovative solutions in responsible AI and large-scale systems.

Paolo Cappellariمشاهده پروفایل

دانشیار

• Big Data Management
• Data Stream Processing
• Schema Transformation
• +۳ مورد دیگر

Paolo Cappellari is an Associate Professor at the College of Staten Island, CUNY. He holds a Ph.D. in Computer Science from Università Roma Tre, Rome, Italy (2007), and an M.Sc. from the same institution. His career spans industry and academia, with stints at Microsoft Research, IBM, the University of Alberta, and Dublin City University. Research Focus: His work centers on big data management, including schema transformation between models, keyword search over semantic datasets, and sensor network data processing. Notable contributions include optimizing data stream processing and leveraging social media for public health analytics. Publications: Dr. Cappellari has published over 30 peer-reviewed articles in top venues like VLDB Journal, ACM-SIGMOD, and The Computer Journal. His recent work explores synthetic time series validation, customer dataset classification, and privacy leak detection in social media. Teaching: He instructs courses in Information Management, IT Architectures, Database Systems, and Object-Oriented Programming. Office hours are by appointment in Building 3N Room 213A.

• Big Data Engineering
• Semantic Web
• Semantic Summarization
• +۴ مورد دیگر

Haridimos Kondylakis is an Associate Professor at the Computer Science Department of the University of Crete and an Affiliated Researcher at the Institute of Computer Science (FORTH). He specializes in Big Data Engineering, Semantic Web technologies, and AI-driven healthcare solutions. He holds a Ph.D. and M.Sc. in Computer Science from the University of Crete. His research focuses on biomedical data management, semantic summarization, and AI applications in healthcare. He leads projects like SUNRISE, ProCAncer-I, and GenoMed4All, addressing cancer treatment, personalized medicine, and healthcare data interoperability. He has participated in over 27 EU projects with a combined budget exceeding €100M. He actively contributes to academic governance, serving on editorial boards (e.g., Information journal) and program committees for top conferences (SIGMOD, VLDB, ESWC). His work has resulted in over 220 publications and multiple awards, including the 2025 SIGMOD Distinguished Reviewer Award. He has led educational initiatives through grants and visiting lectures at institutions like the Hellenic Open University and Hellenic Mediterranean University. His current roles also include Founder & CTO of Ypsilon Care and Horizon Proposal Evaluator for the European Commission.