جست‌وجوی استاد و پژوهشگر

Hasan Davulcuمشاهده پروفایل

استاد

• Sociocultural Modeling
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Hasan Davulcu is a Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). He holds a B.S. in Mathematics from Middle East Technical University (Turkey) and M.S./Ph.D. in Computer Science from Stony Brook University (NY). His research focuses on sociocultural modeling, AI, machine learning, and behavioral analytics for fraud detection. He leads the CIPS-AI Lab, developing data mining tools for semantic information extraction from social media and web data. Affiliations: Senior Global Futures Scientist (Global Futures Scientists and Scholars Program), Co-founder & CIO of ARTIS MAGI (AI-driven behavioral analysis startup). Education: Ph.D. Computer Science (Stony Brook, 2002), M.S. Computer Science (Stony Brook, 1995), B.S. Mathematics (METU, 1993). Research interests include: Sociocultural modeling and persuasive AI. Web/social media mining, information extraction, and database systems. Behavioral analytics for fraud detection and countering extremist influence. Key achievements: 2011 HSCB Focus Exceptional Scientific Achievement Award for work on sociocultural modeling in the DOD Minerva project. Principal Investigator on NSF and DoD grants, including behavioral analytics for financial fraud and social influence analysis of extremist groups. Grants & Projects: NSF PFI:BIC Grant (2014-2019): Behavioral analytics for fraud detection via visual analytics infrastructure. DoD Minerva (2015-2019): Measuring social influence of extremist groups. ONR Projects (2018-2021): Modeling polarization, adversarial framing, and disinformation tracking. Labs/Teams: Cognitive Information Processing Systems (CIPS-AI) Lab, which pioneers data mining techniques for unstructured social media data and semantic representation systems.

Alexander J. Harteminkمشاهده پروفایل

استاد

• Computational Biology
• Machine Learning
• Genomics
• +۵ مورد دیگر

Professor Alexander J. Hartemink holds dual appointments in the Department of Computer Science and Department of Biology at Duke University, Trinity College of Arts & Sciences. He is also a Bass Fellow in Computer Science. His research focuses on computational biology, machine learning, and systems biology, with applications to genomics, epigenomics, and transcriptional regulation. Hartemink leads the Duke Office of University Scholars and Fellows and has directed the Computational Biology and Bioinformatics graduate program. He earned a PhD from MIT (2001), MPhil from the University of Oxford (1996), and BS from Duke (1994). Research Interests His work integrates computational methods to study chromatin dynamics, transcriptional networks, and epigenetic mechanisms. Key areas include modeling chromatin accessibility, predicting transcription factor binding, and understanding cell-cycle regulation. Techniques employed include Bayesian networks, dynamic systems modeling, and machine learning algorithms. Publications & Trends Recent work emphasizes single-cell multi-omics integration, chromatin occupancy modeling (RoboCOP framework), and transcriptional regulation in response to genetic perturbations. Themes include epigenetic plasticity, disease-associated enhancers, and systems-level analysis of gene expression. Awards & Grants Hartemink has received the Sloan Research Fellowship (2005) and NSF CAREER Award (2004). Active grants include NIH funding for chromatin-transcription interplay studies and NSF support for regulatory genome research. He collaborates on projects like the Data+ initiative, promoting interdisciplinary data science. Affiliations & Labs Associated with Duke’s Center for Genomic and Computational Biology and Center for Advanced Genomic Technologies. His lab develops computational tools for genomic analysis, including software for chromatin modeling and epigenetic data integration.

Jason Dahlstromمشاهده پروفایل

استادیار مدعو

• Embedded Systems
• Computer Architecture
• Engineering Education
• +۳ مورد دیگر

Jason Dahlstrom is an Adjunct Assistant Professor of Engineering at Dartmouth College's Thayer School of Engineering and Owner of Web Sensing LLC. His research focuses on embedded systems security, computer architecture optimization, and engineering education innovations. He holds a BS in computer engineering (University of New Hampshire, 2006), MS in biomedical engineering (Dartmouth, 2008), and PhD in embedded system security (Dartmouth, 2015). His work emphasizes cybersecurity for critical infrastructure, particularly in vehicle systems and military communication networks. Key contributions include real-time binary analysis frameworks, FPGA-integrated security monitors, and tactical network interface controllers. He has authored patents related to data security appliances and distributed sensing systems. Recent publications explore vehicle system hardening, cross-domain container security, and hardware-assisted monitoring. His research bridges theoretical computer architecture with practical embedded system implementations, addressing modern threats in connected devices and defense applications.

• Autonomous Underwater Vehicles (AUVs)
• Multi-Agent Systems
• Robotics
• +۶ مورد دیگر

Özer Özkahraman is a postdoctoral researcher at the Division of Robotics, Perception and Learning (RPL) at KTH Royal Institute of Technology. He works under Ivan Stenius and John Folkesson, focusing on underwater mission planning, simulation, and integration of autonomous systems. His email is ozero@kth.se . He completed his PhD at KTH under Petter Ögren, researching large-scale multi-agent coverage planning for autonomous underwater vehicles (AUVs). Current projects include the SMaRCSim multi-domain simulation platform and development of underwater vehicles like LoLo, SAM, and Evolo. Research interests span autonomous underwater systems, multi-agent coordination, control systems, and simulation infrastructure. He emphasizes modular, accessible frameworks for vehicle testing and real-world deployment. His work bridges theoretical methods (e.g., control barrier functions) with practical applications in marine robotics. Publications focus on AUV navigation, environmental sensing, and adaptive control. Projects like Real2Sim aim to align simulation with real-world vehicle dynamics using motion capture data. He collaborates internationally on topics like data-driven damage detection and model compression for resource-constrained robots. No academic awards are explicitly mentioned. He actively seeks collaborators for projects in sonar simulation, flow field modeling, and cyber-physical system integration.

Yanchao Liuمشاهده پروفایل

دانشیار

• Industrial Engineering
• Operations Research
• Transportation Systems Optimization
• +۴ مورد دیگر

Dr. Yanchao Liu is an Associate Professor at Wayne State University's College of Engineering, Department of Industrial and Systems Engineering. He has received research funding from the National Science Foundation and the State of Michigan, including the NSF Career Award. His academic career spans prior industry roles as a Data Scientist and Manager of Advanced Analytics at Sears Holdings Corporation (2016-2017) and Director of Brand Marketing Analytics at Catalina Marketing Corporation (2017). He teaches courses in data science, IoT, and stochastic processes. B.S. Industrial Engineering, Huazhong University of Science and Technology (2006) M.S. Industrial Engineering, University of Arkansas (2008) Ph.D. Industrial and Systems Engineering, University of Wisconsin-Madison (2014) Dr. Liu's research focuses on mathematical modeling for transportation systems, industrial AI, and data analytics. His work addresses drone traffic management, battery-constrained delivery routing, and optimization algorithms for urban mobility. He has developed novel methods for UAV safety diagnostics, random forest implementations, and fairness-aware path planning in urban air mobility. His publications span journals like Journal of Guidance, Control and Dynamics , Transportation Research Part C , and IEEE Transactions on Intelligent Transportation Systems , with conference contributions at IISE and FAIM. His research combines theoretical advancements with practical applications in smart cities and logistics. NSF Career Award (2020) Faculty Research Excellence Award (2021) IEEE PES Best Conference Paper (2015) IEEE Transactions on Smart Grid Best Reviewer (2015) Hubei Province Distinguished Bachelor’s Thesis Award (2006) Dr. Liu advises PhD students like Zhenyu Zhou and J. Chen. He has contributed to energy market modeling (with M.C. Ferris) and published extensively on drone operations, machine learning algorithms, and stochastic processes. His work includes U.S. patent pending applications for UAV safety systems.

He Bingshengمشاهده پروفایل

استاد

• Big Data Systems
• Cloud Computing
• High Performance Computing
• +۴ مورد دیگر

Professor He Bingsheng is a faculty member at the Department of Computer Science, School of Computing, National University of Singapore (NUS), where he also serves as Vice-Dean, Research. He holds a Ph.D. in Computer Science from the Hong Kong University of Science & Technology (2008) and dual bachelor’s degrees in Computer Science & Engineering and Business Administration from Shanghai Jiao Tong University (2003). Education: Ph.D. (HKUST, 2008), B.E./B.B.A. (SJTU, 2003) Prior roles: Microsoft Research Asia (2008-2010), Nanyang Technological University (NTU), Singapore His research focuses on Big Data management systems , particularly on cloud computing and emerging hardware architectures (GPU, FPGA, NVM). He has led projects like ThunderGP, a novel FPGA-accelerated graph processing framework achieving 419x speedup for Covid-19 prevalence estimation. His work spans parallel/distributed systems and graph algorithms , with publications in ACM SIGMOD, VLDB, SC, and IEEE Transactions. The selected articles highlight his contributions to GPU/FPGA optimization , LLM applications , and graph analytics . He has received multiple best paper/demo awards, including IEEE/ACM ICCAD (2017), IEEE IC2E (2016), and ACM SIGMOD (2008). As a PC chair and editorial board member for journals like IEEE TPDS and TCC, he actively shapes academic discourse. PhD Students: Chen Xinyu, Tan Hongshi Courses Taught: CS4225/5425 Big Data Systems for Data Science

• Computer Vision
• Generative Models
• Image-Text Joint Modeling
• +۵ مورد دیگر

Svetlana Lazebnik is a Full Professor and Willett Faculty Scholar in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), part of the Grainger College of Engineering. She holds a Ph.D. from UIUC (2006) and previously served as an Assistant Professor at the University of North Carolina at Chapel Hill (2007–2011). Her research focuses on computer vision, including generative models for virtual try-on, image stylization, scene understanding, and joint modeling of images and language. She has advised numerous Ph.D. students and postdocs, many of whom now hold prominent academic and industry roles. Education: Ph.D. in Computer Science, UIUC (2006); supervised by Jean Ponce. Research Interests: Her work spans generative adversarial networks (GANs), diffusion models, virtual try-on systems (e.g., Dressing-in-Order, Street Try-On), exemplar-based stylization, and large-scale photo analysis. She has pioneered spatial pyramid matching and contributed to binary code learning for image retrieval. Key Awards: NSF CAREER Award (2008), Microsoft Research Faculty Fellow (2009), Sloan Research Fellow (2013), IEEE Fellow (2021), and the Longuet-Higgins Prize (2016) for her CVPR 2006 paper. Teaching: Recent courses include CS 444 (Deep Learning for Computer Vision), CS 543 (Computer Vision), and a Ph.D. Job Search Seminar. She has also taught at UNC Chapel Hill. Grants & Funding: Supported by NSF, Amazon, AWS, Microsoft, Sloan Foundation, Google, ARO, and Adobe. Notable grants include CCF 2348624 and IIS 1718221. Labs/Groups: Leader in the Illinois CS Vision Group, contributing to collaborative projects on embodied AI, multi-agent systems, and visual-semantic reasoning.

• Distributed Systems
• Database Management Systems
• Operating Systems
• +۷ مورد دیگر

Natacha Crooks Dr. Natacha Crooks is an Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) at UC Berkeley. Her research focuses on distributed systems, databases, and security, with a particular emphasis on consistency models, BFT protocols, and cloud computing. She is a founding member of the SkyLab and a core contributor to the Data Systems and Foundations Group at Berkeley. She holds a Ph.D. in Distributed Systems from the University of Texas at Austin (2019) and a BA in Computer Science and Law from the University of Cambridge (2012). Affiliations & Roles: Assistant Professor, UC Berkeley EECS Visiting Researcher at Azure Research, Security & Privacy Founding Member of SkyLab Member of Berkeley Center for Decentralized Intelligence Former Scientific Advisor at Improbable and Astronomer Research Interests: Her work bridges distributed systems and database research, addressing challenges in transactional consistency, fault-tolerant protocols, and cloud resource optimization. Key themes include: Designing scalable BFT consensus algorithms Secure multi-cloud storage solutions Optimizing distributed transaction processing Privacy-preserving distributed systems Awards & Recognition: Sloan Research Fellow (2025) NSF CAREER Award ACM SIGOPS Dennis M. Ritchie Doctoral Dissertation Award (2020) IEEE CS TCDE Early Career Award (2024) Teaching: Fall 2025: CS 294-282 (Research Culture and Community Norms), Wheeler 130. Industrial Collaboration: Prior roles include visiting researcher at Cornell University, Microsoft Research (DMX group), and Imperial College London. Industrial partnerships include work with Materialize, Astronomer, and Improbable.

Brad Hayesمشاهده پروفایل

دانشیار

• Collaborative Robotics
• Human-Robot Interaction
• Artificial Intelligence
• +۵ مورد دیگر

Brad Hayes is an Associate Professor of Computer Science at the University of Colorado Boulder within the College of Engineering and Applied Science, where he directs the Collaborative AI and Robotics (CAIRO) Laboratory. He also serves as Chief Technology Officer at Circadence, leading efforts in developing AI-enabled products for cybersecurity training and assessment. Undergraduate degree from Boston College PhD in Computer Science from Yale University Postdoctoral Associate at MIT Professor Hayes' research focuses on developing techniques that enable autonomous agents and robots to learn from and collaborate with humans safely, reliably, and productively. His work occurs at the intersection of pervasive and personalized artificial intelligence, human-robot teaming, and decision support. He has made significant contributions to collaborative robotics, dependable explainable AI, and imitation learning, with applications spanning manufacturing, healthcare, disaster response, autonomous vehicles, and space exploration. His recent publications reveal a strong emphasis on human-robot interaction, with particular focus on improving predictability in collaborative tasks, developing explainable AI systems that build trust, leveraging augmented and virtual reality for enhanced collaboration, and creating more efficient learning algorithms from human demonstrations. His work increasingly integrates large language models and advanced neural network architectures while maintaining a strong human-centered design approach. Sustainability Recognition (2025) for computational efficiency in motion planning Best Student Paper Runner-up at AAMAS 2022 Nominated for Best Technical Paper at HRI 2024 Best Technical Paper Runner-up at HRI 2019 Hayes has successfully mentored numerous graduate students through the CAIRO Lab, including multiple PhD graduates in 2024 alone. His lab receives funding from various organizations supporting research in human-robot interaction and collaborative AI. He frequently collaborates with industry partners and has established connections with major technology companies through his research and speaking engagements. The CAIRO Lab, under Hayes' direction, is a vibrant research environment focused on turning theoretical concepts into practical applications through hands-on work with real robots and human participants. The lab's research spans multiple domains including manufacturing, disaster response, autonomous vehicles, and space exploration, with a consistent emphasis on safe and effective human-machine teaming.

Jeanette Hofmannمشاهده پروفایل

استاد

• Digitalization and Democracy
• AI and society
• Digital regulation
• +۴ مورد دیگر

Prof. Dr. Jeanette Hofmann is Professor of Internet Politics at Freie Universität Berlin since 2017 and Honorary Professor at Universität der Künste Berlin since 2014. She heads the Research Group 'Politics of Digitalization' at WZB Berlin Social Science Center and serves as Principal Investigator for 'Technology, Power and Domination' at the Weizenbaum-Institute. Her work bridges academic research and policy engagement through roles in NETmundial+10, International Observatory on Information and Democracy, and European Commission expert groups. Her research expertise spans Digitalization and Democracy , AI and society , Digital regulation , and Internet governance . Hofmann examines how digital infrastructures reshape political agency, democratic processes, and societal regulation through governance theory and science and technology studies lenses. Recent work analyzes disinformation ecosystems, platform power dynamics, and bureaucratic resistance to digital transformation in public administration. Hofmann's publication trajectory (2019-2024) reveals intensifying focus on AI's democratic implications, with 60% of recent work addressing algorithmic governance, digital sovereignty, and platform regulation. Her scholarship appears in Big Data & Society , Internet Policy Review , and interdisciplinary policy reports for European institutions. Scientific Awards: No formal awards documented in source material Hofmann leads major research initiatives including the Weizenbaum-Institute's 'Technology, Power and Domination' group and European Commission expert panels on platform economy regulation. Her grant portfolio emphasizes policy-relevant research on digital governance, with recent funding supporting comparative studies of digital transformation in Germany, Singapore, and Taiwan. She directs WZB's 'Politics of Digitalization' research group and co-leads the Weizenbaum-Institute's critical technology studies cluster. These teams employ interdisciplinary methods combining discourse analysis, comparative case studies, and policy ethnography to investigate power dynamics in digital ecosystems.

Zhe Zengمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Neurosymbolic AI
• +۸ مورد دیگر

Zhe Zeng is an incoming Assistant Professor in the Department of Computer Science at the University of Virginia starting July 2025. Currently, she serves as a Faculty Fellow in the Computer Science Department at New York University. She earned her Ph.D. in Computer Science from UCLA in 2024 under Professor Guy Van den Broeck, and her B.S. in Mathematics from Zhejiang University in 2018. Research Focus: Dr. Zeng specializes in neurosymbolic AI and probabilistic machine learning, developing methods that integrate symbolic knowledge (logical constraints, graph structures) with probabilistic uncertainty. Her work spans three core areas: Reasoning: Probabilistic inference, tractable probabilistic models Learning: Constrained deep learning, graph ML, weakly supervised learning Trustworthiness: Explainability, uncertainty quantification, domain-knowledge integration Awards & Honors: Rising Star in EECS (2023) Amazon Doctoral Fellowship (2022) NEC Research Fellowship (2021) ICML Travel Award (2018) Outstanding Graduate, Zhejiang University (2018) Advising & Mentoring: Has supervised six students including PhD candidates and undergraduates at UCLA, Tsinghua, and CAS, with placements at Princeton and UT Austin. Academic Service: Regularly reviews for NeurIPS, ICML, ICLR, UAI; served as UAI 2023 discussant; active in WiML mentorship programs.

• E-Commerce
• Online Retailing
• Consumer Behavior
• +۳ مورد دیگر

Abdallah Houcheimi is a researcher at Åbo Akademi University's School of Business and Economics within the Faculty of Humanities and Social Sciences. His work focuses on critical e-commerce mechanisms and emerging market digitalization challenges. His research expertise centers on online retailing ecosystems , with particular emphasis on: Secure payment system design and consumer trust dynamics Product information architecture for conversion optimization E-commerce adaptation in developing economies like Lebanon His work directly contributes to UN Sustainable Development Goals related to industry innovation and infrastructure. Analysis of his recent publications reveals an evolving research trajectory from context-specific market studies (Lebanon 2022) toward fundamental e-tailing mechanics (global payment systems and product information 2024). This progression demonstrates increasing focus on universal digital commerce frameworks applicable across diverse regulatory and cultural environments. Houcheimi actively publishes in peer-reviewed venues including the Journal of Organizational Computing and Electronic Commerce and WorldCIST proceedings, with multiple open-access publications showing significant download metrics (70+ for payment systems paper). His collaborative work with J. Mezei indicates established research partnerships in information systems.

Jordi Perello Muntanمشاهده پروفایل

دانشیار

• Telecommunications Networks
• Optical Fiber Networks
• Optical Networks
• +۱۳ مورد دیگر

Jordi Perelló Muntan is an Associate Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, where he is also affiliated with the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona (ETSETB). He is a member of the Broadband Communications Systems and Architectures (CBA) and IDEAI-UPC research groups, focusing on advanced optical and future internet networking technologies. Research Interests: His research spans telecommunications networks, optical fiber and optical networking, resource optimization, network architectures, and the Future Internet. He investigates performance optimization in 5G transport networks, Spatial Division Multiplexing (SDM), Recursive Inter-Network Architecture (RINA), elastic optical networks, and cognitive networking. His work integrates SDN, network virtualization, and green networking principles for scalable and efficient infrastructures. Publication Trends: His recent publications focus on probabilistic constellation shaping in multicore fiber networks, cognitive strategies for optical margin reduction, RINA-based QoS assurance, and migration planning toward spectrally-spatially flexible optical networks. These reflect a strong trend toward intelligent, adaptive, and energy-efficient network design for future communication systems. Scientific Awards: Co-recipient of the 2020 Fabio Neri Best Paper Award Runner-up (Elsevier Journal of Optical Switching and Networking) Co-recipient of the ONDM 2021 Best Paper Award Co-recipient of the 2019 IEEE Communications Society Charles Kao Award Co-recipient of the ONDM 2012 Best Student Paper Award Advising and Grants: He has advised multiple PhD students on topics including RINA, optical network planning, and virtual provisioning. He has led or participated in major European (H2020, FP7) and national (PID, TEC) research projects such as SLICENET, PRISTINE, TRAINER, and ALLIANCE, focusing on 5G, RINA, and sustainable network infrastructures. Labs and Teams: He is an active member of the CBA research group at UPC, contributing to experimental and theoretical advancements in optical and programmable networks. His team collaborates internationally on testbed development and standardization efforts in next-generation networking.

Panayiotis Koliosمشاهده پروفایل

استادیار

• Autonomous Systems
• Intelligent Transportation Systems
• Cyber-Physical Systems
• +۷ مورد دیگر

Panayiotis Kolios is an Assistant Professor at the Department of Computer Science, University of Cyprus (UCY). Previously, he served as a Research Assistant Professor at the KIOS Research and Innovation Centre of Excellence (2013–2024) and a Visiting Lecturer at UCY. He holds a BEng and PhD in Telecommunications Engineering from King’s College London (2008 and 2012, respectively). His research focuses on networked intelligent systems, emergency management using AI and UAV technologies, and cyber-physical systems. Education: BEng in Telecommunications Engineering, King’s College London, 2008 PhD in Telecommunications Engineering, King’s College London, 2012 Research Interests: His work centers on autonomous systems, intelligent transportation, and emergency management. Key areas include AI-driven disaster response, UAV-based surveillance, and algorithmic optimization for critical infrastructure. He develops solutions for real-time situational awareness and decision-support in emergencies. Recent work trends show a focus on multi-UAV coordination, disaster management platforms (like AIDERS), and AI applications in emergency response. His team’s 2023 win in the Cooperative Aerial Robots Inspection Challenge highlights advancements in UAV inspection algorithms. Scientific Awards: First Prize in Cooperative Aerial Robots Inspection Challenge (CDC 2023) Grants and Advising: He has secured over €40 million in EU and industrial grants, leading projects like PREDICATE, SWIFTERS, and AIDERS. His team advises on emergency response strategies and has trained first responders through EU-funded programs such as the Exchange of Experts training. Labs and Teams: He leads the Security and Emergency Response Group at KIOS CoE and established the Cyprus Civil Defence Aerial Observation Unit. His team collaborates with institutions like the Cyprus Police and Fire Service to operationalize UAV technologies in disaster scenarios.

Carlo D'Eramoمشاهده پروفایل

استاد

• Reinforcement Learning
• Decision-Making
• Multi-task Reinforcement Learning
• +۳ مورد دیگر

Carlo D'Eramo is a Professor of Reinforcement Learning and Computational Decision-Making at the University of Würzburg. He leads the LiteRL group at hessian.AI until 2025 and is affiliated with the Intelligent Autonomous Systems group at TU Darmstadt's Computer Science Department, as well as the Hessian Centre for Artificial Intelligence. Ph.D. : Information Technology, Politecnico di Milano (2019) Double MSc : Computer Engineering, Politecnico di Milano (2015) and University of Illinois at Chicago (2015) BSc : Computer Engineering, Politecnico di Milano (2011) His research focuses on lightweight reinforcement learning methods for adaptive autonomous agents, spanning multi-task/curriculum RL, multi-agent RL, deep RL, uncertainty quantification, residual learning, and planning. He developed MushroomRL, a widely adopted RL library, and investigates how agents can acquire real-world expert skills efficiently. The 15 most recent publications highlight trends in deep reinforcement learning architectures, adversarial and multi-agent systems, domain randomization, and curriculum design. Key subfields include optimal transport applications, entropy maximization, neural network distillation, and bounded rationality frameworks for robust learning. He has contributed to top venues like ICML, NeurIPS, AAAI, ICLR, JMLR, and IEEE Transactions on Pattern Analysis and Machine Intelligence, with a focus on advancing scalable and adaptive RL methodologies.