جست‌وجوی استاد و پژوهشگر

• Human-Computer Interaction
• Participatory Design
• Sustainable Computing
• +۷ مورد دیگر

Aurélien Tabard is an Associate Professor in the Department of Computer Science at Université Claude Bernard Lyon 1, affiliated with the LIRIS laboratory and CNRS. His research focuses on Human-Computer Interaction (HCI), participatory design, and sustainable computing, with a strong emphasis on digital sufficiency, software obsolescence, and resilient digital infrastructures. His research interests include: Convivial informatics and participatory design Digital sustainability and software obsolescence Activity-centric computing and classroom orchestration Data physicalization and situated visualization Design for maintenance, autonomy, and durability in digital systems His recent publications reflect a consistent focus on sustainability in computing, user-centered design, and the socio-technical aspects of digital platforms. Themes include understanding obsolescence paths in smartphones, supporting digital skill development, and designing participatory infrastructures resilient to breakdowns. His work often involves field studies, participatory methods, and prototyping. Scientific awards include: Honorable Mention, CHI 2018 Honorable Mention, ICT4S 2023 Best Paper Award, ITS 2011 He advises several students, including Léa Mosesso, Edlira Nano, and Maëva Calmettes, and collaborates with researchers at Inria Lille, University of Victoria, and other institutions. He is currently co-leading the Limites Numériques and PC4-Congrats projects within the PEPR eNSEMBLE initiative. He spent a sabbatical year in 2023 at Inria Lille, working with the Loki and Spirals teams. He has been involved in numerous research projects, such as: PLACED (ERA-NET): Redesigning library services around activities REPI (FUI): Activity-based computing for high school education Épisteme (ANR): Interdisciplinary epistemology of digital technologies Jenlab (ANR): Epistemic digital games for learning

Zhuowen Tuمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Deep Learning
• +۹ مورد دیگر

Zhuowen Tu is a Professor in the Department of Cognitive Science at the University of California, San Diego (UCSD), with an affiliate appointment in the Department of Computer Science and Engineering. He leads the Machine Learning, Perception, and Cognition Lab (mlPC), where his research lies at the intersection of computer vision, machine learning, deep learning, natural language processing, and neural computation, focusing on statistical models for structured, large-scale, and multi-modal data. He received his Ph.D. from The Ohio State University and held faculty positions at UCLA before joining UCSD in 2013. He also served as a Lead Researcher at Microsoft Research Asia (2011–2013) and was an Amazon Scholar (2021–2022). His academic trajectory includes progression from Assistant to Associate and then Full Professor at UCSD. His research interests span computer vision , deep learning , generative modeling , vision-language models , diffusion models , and structured prediction . He has made seminal contributions to image parsing, auto-context models, introspective neural networks, and holistically-nested edge detection. His recent work emphasizes Bayesian diffusion models, panoptic 3D parsing, and multimodal learning. The analysis of his recent publications shows a strong trend toward diffusion-based generative modeling , particularly in 3D vision, image restoration, and vision-language tasks. He also continues to advance work in multimodal understanding, continual learning, and efficient transformers. His lab actively publishes in top venues such as CVPR, ICCV, NeurIPS, and TPAMI. Selected Scientific Awards and Honors: IEEE Fellow David Marr Prize (2003) David Marr Prize Honorable Mention (2015) NSF CAREER Award (2009) Test-of-Time Award, AISTATS 2025 (for Deeply-Supervised Nets) First Prize, MICCAI Grand Challenge on Caudate Segmentation (2007) Talbert Abrams Award Honorable Mention (2003) Advising and Grants: He has advised numerous PhD students who are now faculty at NYU, CMU, and Stanford, or research scientists at Apple, Microsoft, Intel, and Facebook. His lab has been supported by significant grants from the National Science Foundation (NSF) , Office of Naval Research (ONR) , Intel , Qualcomm , Samsung , and Northrop Grumman . Current and recent grants include NSF IIS-2433768 on Bayesian Diffusion Models and NSF IIS-2127544 on Panoptic 3D Parsing. Laboratories and Teams: He leads the Machine Learning, Perception, and Cognition Lab (mlPC) at UCSD, which brings together students and researchers working on fundamental and applied problems in AI, vision, and cognition. The lab has strong collaborations with industry and other academic institutions.

Syed Fawad Hussainمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Multi-View Learning
• +۷ مورد دیگر

Dr. Syed Fawad Hussain is an Associate Professor in the School of Computer Science at the University of Birmingham, Dubai campus. He is actively engaged in research and academic leadership, contributing significantly to the fields of artificial intelligence and machine learning. Education: PhD in Computer Science, Grenoble Alpes University, France (2010) MSc in Computer Science, Sorbonne University (Pierre and Marie Curie), Paris, France (2006) BS in Computer Science (2003) Dr. Hussain's research focuses on developing advanced machine learning algorithms for high-dimensional data, with specializations in multi-view learning, co-clustering, and semantic kernels. His work spans interdisciplinary domains including mobile healthcare, social media analysis, and cultural heritage preservation. He applies AI to classify ECG/EEG signals, detect diseases like schizophrenia and NAFLD, restore ancient manuscripts, and improve wireless communication systems. His recent publications reveal a strong trend in applying deep learning and ensemble methods to real-world problems across healthcare, defense, transportation, and digital humanities. He frequently collaborates on projects involving signal processing, image classification, and data fusion. Dr. Hussain has supervised numerous graduate students and co-authored with researchers across multiple disciplines. He has contributed to national-level academic development in Pakistan as co-chair of the Higher Education Commission's curriculum revision committee. Scientific Contributions: Published in high-impact journals such as Scientometrics , Information Fusion , Expert Systems with Applications , and Knowledge-Based Systems . Active in both theoretical and applied AI research. Contributor to national curriculum development in computer science. He is affiliated with the University of Birmingham’s Dubai campus and previously served as a faculty member and dean at GIK Institute, Pakistan. His work continues to bridge theory and application in artificial intelligence.

Sebastian Baderمشاهده پروفایل

پژوهشگر

• Neuro-Symbolic Artificial Intelligence
• Human Activity Recognition
• Assistive Systems for Dementia
• +۷ مورد دیگر

Sebastian Bader is a research scientist at the Institute for Visual & Analytic Computing at the University of Rostock, where he contributes to the Chair of Mobile Multimedia Information Systems. His work bridges artificial intelligence, human-computer interaction, and healthcare applications, particularly in assistive technologies. Doctoral Degree: Dr. rer. nat. in Neural Symbolic Integration, TU Dresden, 2009 (summa cum laude) His research interests focus on neuro-symbolic AI, human activity recognition, and intelligent assistive systems for dementia care and neurorehabilitation. He investigates how AI can support clinical interventions, improve patient monitoring, and enhance explainability in medical contexts. His work emphasizes ethical considerations, real-world usability, and integration with sensor and robotic systems. The recent publications highlight a strong trend in applying AI to healthcare, particularly in modeling patient behavior, evaluating robotic assistance, and developing explainable and ethical AI systems. Topics span from underwater robotics to Alzheimer’s diagnostics, showcasing interdisciplinary expertise. He supervises Bachelor's and Master's theses on topics such as visualizing uncertainty in ML, semi-automatic test generation using LLMs, and building probabilistic digital shadows of the Port of Rostock. Sebastian Bader actively collaborates with medical researchers and industry partners, and his projects include "SAMi" (sensor-based activity management for dementia) and "E-BRAiN" (robot-assisted neurorehabilitation). He contributes to both technical AI development and real-world deployment in healthcare environments.

Joanna Malinowskaمشاهده پروفایل

استادیار

• Sculpture
• Performance Art
• Video Art
• +۱۱ مورد دیگر

Joanna Malinowska is an Assistant Professor of the Practice and Director of Graduate Studies in Creative Visual Arts at Cornell University's College of Architecture, Art, and Planning (AAP), Department of Art. She is a Polish-born, New York-based contemporary artist whose interdisciplinary practice includes sculpture, video, sound, and performance, often exploring themes of national identity, cultural paradoxes, and experimental anthropology. Education: MFA, Yale University (2001); BFA, Rutgers University (1998) Institution: Cornell University, College of Architecture, Art, and Planning, Department of Art Representation: CANADA Gallery, New York Malinowska’s research interests center on collaborative practice, drawing, film/video/sound, installation art, interdisciplinary art, performance art, public art, sculpture, and technology in art. Her work critically engages with historical narratives, myth, and cultural exchange, often through collaborations with C.T. Jasper. Projects like Halka/Haiti 18°48'05"N 72°23'01"W (2015), which represented Poland at the Venice Biennale, exemplify her exploration of transnational identity and postcolonial discourse. Her recent exhibitions, such as Holy Philip Guston, Pray For Us (2024), delve into monumentality, religious symbolism, and the ethical implications of material use. Her artistic output spans over two decades and includes diverse media—from ceramic sculptures and watercolors to multi-channel sound installations and reconstructed historical sets. The recurring themes in her work include human-animal relationships, myth-making, frontier imaginaries, and the absurdity of cultural constructs. Her pieces often combine humor with existential critique, drawing from literature, music, and philosophy. Joanna Malinowska has been recognized with numerous awards, including the Guggenheim Fellowship (2009), Pollock-Krasner Foundation Grant (2013), and NYFA Fellowship (2011). Her work is held in major collections such as the Saatchi Gallery, Zachęta National Gallery of Art, and the Hirshhorn Museum. She has exhibited internationally at venues including the Whitney Museum, Sculpture Center, and Centre Pompidou-Metz. John Simon Guggenheim Memorial Fellowship (2009) Pollock-Krasner Foundation Grant (2013) New York Foundation for the Arts Fellowship (2011) Polish Ministry of Culture and National Heritage (2013) Smack Mellon Grant and Studio Program (2008) Malinowska is actively involved in teaching and mentoring at Cornell, where she leads foundational courses in sculpture and drawing. While specific advisees are not listed, her role as Director of Graduate Studies indicates significant engagement with student development. She has not received grants explicitly mentioned beyond awards and fellowships. Her collaborative work with C.T. Jasper forms a central lab-like partnership, driving major projects and exhibitions. Future works continue to explore architectural fantasy, cultural memory, and the contradictions of monument-building in polarized societies.

Jürgen Brauerمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Robotics
• +۴ مورد دیگر

Prof. Dr.-Ing. Jürgen Brauer is a professor at the Kempten University of Applied Sciences , holding positions in both the Faculty of Computer Science and Faculty of Electrical Engineering . He chairs the AI and Computer Vision and Driver Assistance Systems programs for Master’s students. His research spans Computer Vision, Machine Learning, Robotics, Deep Learning , and Sensor Data Processing , with a focus on applications in autonomous systems and medical imaging. Education : Doctoral Thesis (2014) at Karlsruhe Institute of Technology (KIT). Recent Research Trends : His work emphasizes unsupervised representation learning, addressing objective function mismatches in pretext-target task relationships, and advancing generative models for 3D human pose estimation. Publications : He has contributed to journals like Neural Computing and Applications and conferences such as IEEE CVW , ICMLA , and RoboCup , often in collaboration with industry partners like AGCO Fendt and academic institutions. Scientific Awards : Best Paper Award at ACM ARTEMIS 2010. Advising : Supervised over 40 theses (2016–2025), including topics in autonomous driving, reinforcement learning, and robotics. Collaborations : Partnered with Fraunhofer IOSB, DLR, Bosch, and others on projects involving sensor systems and industrial applications.

Felipe Werndl Trevizanمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Automated Planning and Scheduling
• Reasoning under Uncertainty
• +۹ مورد دیگر

Felipe Werndl Trevizan is a Senior Lecturer in the School of Computing at the Australian National University (ANU), where he conducts research at the intersection of Artificial Intelligence, Operations Research, and Machine Learning. His work focuses on automated planning and scheduling, reasoning under uncertainty, heuristic search, and machine learning for planning. He is actively involved in research projects related to uncertainty-centric pilot assistance systems and probabilistic planning. His research interests include: Automated Planning and Scheduling Probabilistic and Multi-Objective Planning Heuristic Search and Admissible Heuristics Deep Learning for Generalized Planning Constraint-Based and Risk-Aware Planning Learning Heuristics from Data His recent publications demonstrate a strong trend toward integrating machine learning with classical planning techniques, particularly in learning reliable, domain-independent heuristics using both deep and classical machine learning methods. He has developed novel algorithms for constrained and stochastic shortest path problems, multi-objective probabilistic planning, and generalized planning using neural networks. His scientific awards include: Best Paper Award at ICAPS 2016 Best Paper Award at ICAPS 2017 2016 Kikuchi-Karlaftis Best Paper Award from the Transport Research Board Felipe Werndl Trevizan is registered to supervise research students and has advised several successful projects in AI planning. He has secured research grants for projects such as 'Integrated Planning for Uncertainty-Centric Pilot Assistance Systems' and 'Chance-Constrained POMDP for Pilot Assistance Systems' at ANU. He is also organizing key community events including the 'Reliability In Planning and Learning (RIPL)' workshop and a tutorial on 'Learning for Planning' at ICAPS 2025. He is affiliated with the School of Computing at ANU and previously worked as a Senior Research Scientist at NICTA and Data61/CSIRO. His research is supported by active collaborations with leading researchers such as Sylvie Thiébaux, Hanna Kurniawati, and others.

Kenneth Brydenمشاهده پروفایل

استاد

• Integrated Computational Environments
• Engineering Decision Making
• Energy Systems
• +۸ مورد دیگر

Dr. Kenneth (Mark) Bryden is a Professor in the Department of Mechanical Engineering at Iowa State University and serves as the Program Director for Simulation, Modeling, and Decision Science at the U.S. Department of Energy’s Ames Laboratory. He is the founding director of these programs and has dedicated his career to integrating computational models, data, and information to enhance engineering decision-making for complex systems. Education: PhD in Mechanical Engineering, University of Wisconsin-Madison (1998) MS in Mechanical Engineering, University of Wisconsin-Madison (1993) BS in General Engineering, Idaho State University (1977) Dr. Bryden’s research spans integrated computational environments, energy systems, and sustainability. His work focuses on model federation across engineering, human, and natural systems, with applications in bioenergy, cookstove optimization, and decision science. He has developed open-source frameworks for CAD/analysis software integration and tools for assessing energy interventions in developing regions. His publications include over 180 peer-reviewed articles and the textbook Combustion Engineering . Key research themes include multiscale simulation, thermal modeling of biomass systems, and computational methods for sustainable development. His work has addressed CO₂ sequestration, microgrid energy systems, and environmental impacts of agricultural residue harvesting. Scientific Awards: ASME Melville Medal (2013) R&D 100 Awards (2010, 2009, 2006) Excellence in Technology Transfer Awards (National and Regional) Best Paper Awards (ANNIE, AIAA, ISES) Dr. Bryden’s practical contributions include founding two startups and the nonprofit ETHOS, which mobilizes 150+ researchers to provide clean energy solutions to off-grid communities. His fieldwork in Mali led to scalable household lighting systems using locally adapted technology. He holds three patents and has secured significant grants for energy systems research.

Negin Forouzeshمشاهده پروفایل

دانشیار

• Computer Science
• Life Sciences
• Drug Discovery
• +۶ مورد دیگر

Negin Forouzesh is a tenure-track Associate Professor in the Department of Computer Science at California State University, Los Angeles, with a joint appointment in the Biomedical Engineering (BME) Minor Program. She co-directs the NSF-PREC program and contributes to AMBER, an open-source molecular dynamics software. Ph.D. in Computer Science from Virginia Tech (2020) M.Sc. and B.Sc. from Amirkabir University of Technology (Tehran Polytechnic) Her research focuses on computational sciences for drug discovery, combining machine learning , scalable optimization algorithms , and high-performance computing to model biomolecular systems. She pioneered physics-informed neural networks for binding free energy calculations and developed novel geometric metrics for protein pocket analysis. Recent work trends show expertise in deep generative models for ligand discovery, virtual reality applications in molecular docking, and hybrid physics-ML approaches for predictive modeling in biomolecular systems. As a principal investigator for NIH-R16 and co-director of NSF-PREC programs, she leads interdisciplinary projects spanning computational biology, medical imaging, and software development for molecular simulations. Her lab, COMB Lab, maintains active GitHub repositories and collaborates with institutions like Stanford Center for Genomics and MolSSI.

• Computer Vision
• Machine Learning
• Healthcare AI
• +۷ مورد دیگر

Jianbo Jiao is an Associate Professor in Computer Vision and Machine Learning at the School of Computer Science, University of Birmingham, where he leads the MIx (Machine Intelligence + x) research group. Previously, he was a Postdoctoral Researcher in the Biomedical Image Analysis (BioMedIA) group and Visual Geometry Group (VGG) at the University of Oxford, working on the SeeBiByte and VisualAI projects. MSc from Peking University (2015) PhD in Computer Science from City University of Hong Kong (2018), supported by Hong Kong PhD Fellowship Scheme Visiting scholar at Beckman Institute, University of Illinois at Urbana-Champaign (2017-2018) Dr. Jiao's research focuses on Computer Vision, Machine Learning, Healthcare AI, and AI for Science. His work primarily investigates learning representations in open-world scenarios by exploring auxiliary information from multiple modalities (image/video, speech/audio, text/NLP, depth/stereo, gaze/saliency, motion) and reducing dependency on supervision through self-supervised, weakly-supervised, and semi-supervised learning approaches. His group has made significant contributions to multimodal learning, medical image analysis, and 3D vision. His recent publications demonstrate strong trends in multimodal learning, medical AI applications, and 3D computer vision. The research spans fundamental computer vision tasks like segmentation and scene understanding while addressing practical challenges in healthcare and scientific domains. Notably, there's significant focus on making vision systems more robust to distribution shifts and requiring less labeled data. Amazon Research Award recipient (2024) Royal Society Short Industry Fellow Best Paper Award at MICAD 2024 Best Paper Award at MICCAI 2024 ASMUS Workshop Best Presentation (Runner-Up) Award at MICCAI 2024 ASMUS Workshop Best Paper Award at ECCV 2022 Medical Computer Vision Workshop Multiple Outstanding Reviewer Awards from top conferences Dr. Jiao serves as Associate Editor for The Transactions on Machine Learning Research (TMLR) and IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), and has been actively contributing to the community as Area Chair for NeurIPS, ICML, ICLR, and ACM MM. His MIx group has secured multiple research grants including the Amazon Research Award for the PCo3D project on Physically Plausible Controllable 3D Generative Models. Current PhD students include Isaac Akintaro, Hao Ai, and Kangning Zhang. The MIx group maintains active collaborations with industry partners including Amazon and Meta, and has developed notable datasets such as 360+x for panoptic multi-modal scene understanding and DyMVHumans for dynamic human modeling. Their research bridges fundamental computer vision with practical applications in healthcare, physics, chemistry, and other scientific domains.

• Computer Vision
• Computer Audition
• Machine Learning
• +۷ مورد دیگر

Dr. Yapeng Tian is an Assistant Professor in the Computer Science Department within the Erik Jonsson School of Engineering and Computer Science at The University of Texas at Dallas. He leads the Computer Vision and Multimodal Computing (CVMC) Lab, where his research focuses on solving core problems in computer vision, computer audition, and machine learning with applications to multisensory perception, computational photography, AR/VR, accessibility, and healthcare. Dr. Tian received his PhD in Computer Science from the University of Rochester in 2022, Master's in Electronic Engineering from Tsinghua University in 2017, and Bachelor's in Electronic Engineering from Xidian University in 2013. Prior to joining UT Dallas, he completed research internships at Facebook and Adobe Research, and served as a Research Assistant at both University of Rochester and Tsinghua University. His research interests span audio-visual scene understanding, audio-visual scene generation, AI for accessibility and healthcare, and image/video processing. He has made significant contributions to multimodal learning, particularly in audio-visual integration, where his work has been recognized with numerous awards including the Amazon Research Award, Cisco Faculty Research Award, and AAAI New Faculty Highlights. Analysis of his recent publications reveals a strong focus on diffusion models for multimodal generation, audio-visual learning, accessibility applications, and efficient multimodal processing. His work increasingly integrates healthcare applications, particularly in autism assessment and assistive technologies for people with disabilities. Key awards and recognitions: Amazon Research Award [2024] UIST Belonging & Inclusion Best Paper Award [2024] ACCV Best Paper Honorable Mention Award [2024] IEEE ISMAR IDEATExR workshop Best Paper Award [2024] Cisco Faculty Research Award [2023] AAAI New Faculty Highlights [2023] Dr. Tian actively mentors students at multiple levels, including PhD candidates, undergraduate researchers, and even K-12 students through outreach programs. His lab has secured significant grant funding including an NIH R01 grant and an Amazon Research Award. Current research directions include developing AI/AR-assisted vision for people with low vision and creating tools for kitchen accessibility through wearable AR.

• Reinforcement Learning
• Artificial Intelligence
• Computer Vision
• +۶ مورد دیگر

C. H. T. Child is an Associate Professor in the Department of Computer Science at City, University of London, with a distinguished research career spanning over two decades. The academic maintains an active research profile with publications extending through 2025, demonstrating continued scholarly contributions across multiple domains of computer science and artificial intelligence. Child's primary research interests center around Reinforcement Learning , Artificial Intelligence , and Computer Vision , with specialized expertise in non-Euclidean geometry applications for gaming environments, medical informatics, and human-computer interaction. The research portfolio reveals a consistent trajectory from foundational work in reinforcement learning algorithms to applied research in game AI, medical informatics, and scene representation learning. A notable research thread involves the application of non-Euclidean geometry to create novel gaming experiences, while another significant strand focuses on developing advanced AI systems for non-player characters with personality modeling capabilities. Analysis of the most recent publications (2019-2025) shows an evolution toward more sophisticated deep learning architectures applied to complex problems in medical informatics and scene representation. The work has increasingly incorporated transformer networks, attention mechanisms, and advanced feature extraction techniques, reflecting broader trends in the field while maintaining the researcher's distinctive focus on reinforcement learning fundamentals. The publication record demonstrates successful translation of theoretical AI concepts into practical applications across gaming, healthcare, and computer vision domains. Child has supervised multiple doctoral students and research collaborators, with several co-authors appearing consistently across publications as first authors on significant works. The research has been supported through university resources and likely external funding, though specific grant details are not provided in the available materials. The academic maintains an active presence in both theoretical and applied research communities, with publications spanning top conferences and journals in computer science and AI. The researcher leads work in the Extreme AI Personality Engine project and contributes to computer vision research with applications in hand pose estimation and medical imaging. These research streams are well-integrated, with reinforcement learning principles providing a unifying theoretical framework across diverse application domains.

Carol MARTINEZ LUNAمشاهده پروفایل

پژوهشگر

• Space Robotics
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Dr. Carol Martinez Luna is a Research Scientist and Deputy Head of the Space Robotics group (SpaceR) at the SnT Interdisciplinary Centre, University of Luxembourg. She holds a PhD in Robotics and Automation from Universidad Politécnica de Madrid (2013) and previously served as an Assistant Professor at Pontificia Universidad Javeriana, Colombia. Her research spans Space Robotics , Computer Vision , and Machine Learning , with current focus areas including: Multi-purpose manipulation for planetary/orbital robots Perception systems for autonomous space operations Domain adaptation to bridge simulation-to-reality gaps On-ground testing of orbital technologies Recent publications (2022-2025) predominantly address space debris removal , autonomous grasping , and robotic perception , with heavy emphasis on machine learning techniques and high-fidelity simulation validation. Notable trends include generative models for manipulation and adaptive control under uncertainty. She actively advises 7 PhD students and 9 Master's candidates on topics ranging from space manipulation to vision-based safety systems. Her lab develops experimental platforms for orbital scenario emulation and collaborates on ESA-funded debris removal initiatives.

Albert Crokerمشاهده پروفایل

استاد

• Database Management Systems
• Relational Database Theory
• Temporal Database Theory
• +۳ مورد دیگر

Albert Croker is a Professor in the Paul H. Chook Department of Information Systems and Statistics at Baruch College's Zicklin School of Business. With a PhD in Computer Science from SUNY Stony Brook (1984), he specializes in database systems and object-oriented programming. BS in Mathematics (SUNY Stony Brook, 1972) MS in Computer Science (SUNY Stony Brook, 1978) PhD in Computer Science (SUNY Stony Brook, 1984) His research focuses on database management systems , particularly relational database theory , temporal database theory , and object-oriented programming applications. He has developed innovative approaches to constraint satisfaction problems and temporal data representation. Professor Croker's publications highlight his expertise in temporal databases and object-oriented programming . His work explores time-variant data modeling, constraint satisfaction frameworks, and concurrency control mechanisms. Scientific Awards: Zicklin School of Business Teaching Excellence Award (1997) Golden Key National Honor Society Honorary Member (1997) He serves as Course Coordinator for multiple CIS courses including 9310/9410, 3100/4100, and 3650/9365, while maintaining active roles in curriculum development and faculty recruitment.

Pai-chun Maمشاهده پروفایل

دانشیار

• Data Warehouse and OLAP
• Work Flow Management Systems
• Information Retrieval
• +۲ مورد دیگر

Pai-chun Ma is an Associate Professor at the Paul H. Chook Department of Information Systems and Statistics within Baruch College's Zicklin School of Business (City University of New York). He holds a PhD in Information Systems from New York University (1988), an MBA from SUNY Buffalo (1982), and a BS from National Chiao-Tung University (1978). His academic roles include serving as the department's Undergraduate Internship Coordinator and membership on key committees like the Graduate Committee on Academic Standing. Ma's research focuses on decision support systems , information retrieval , and computational optimization . His expertise spans Data Warehousing, Workflow Management, Negotiation Support Systems, and Mathematical Programming. His publications frequently integrate operations research with information systems design. Analysis of his 15 most recent publications reveals a strong emphasis on algorithmic decision-making and system design , with applications ranging from healthcare technology to energy markets. Key themes include optimization modeling, human-computer interaction, and multi-agent systems. His honors include: Best Paper Award at HICSS (1996) Nomination for Presidential Excellence in Teaching (1999) Beta Gamma Sigma Honor Fraternity induction (1985) Ma has extensive teaching experience, regularly instructing courses in Object-Oriented Programming, Information Systems, and Business Internships. He maintains active professional involvement with INFORMS, ACM, and IEEE, and serves on editorial boards for journals including Management Science and Decision Support Systems .