جستجوی استاد راهنمای دانشگاهی

Yu Huangمشاهده پروفایل

استادیار

• Software Engineering
• Human-Centered AI
• Human Factors in Programming
• +۵ مورد دیگر

Yu Huang is an Assistant Professor of Computer Science at Vanderbilt University with a secondary appointment in the Department of Teaching and Learning at the Peabody School of Education. She is affiliated with the Institute for Software Integrated Systems, the Frist Center for Autism and Innovation, the Vanderbilt Lab for Immersive AI Translation (VALIANT), and the Vanderbilt LIVE Learning Innovation Incubator. Her research focuses on human-centered AI for software engineering, combining human cognition with machine intelligence to enhance software development processes. Educated at the University of Michigan (PhD, 2021), University of Virginia (MS, 2015), and Harbin Institute of Technology (BS, 2011), her work spans software engineering, human factors, AI, and medical imaging. Key projects include the MIND Lab, studying programmer expertise and cognitive processes, and the HumanAISE workshop on Human-Centered AI for Software Engineering. Huang has received significant recognition, including the 2025 ICPC Vaclav Rajlich Early Career Achievement Award and three ACM SIGSOFT Distinguished Paper Awards. Her research is supported by NSF, GitHub, and Vanderbilt initiatives. She advises numerous graduate and undergraduate students, emphasizing diversity and innovation in programming education.

• Ultra-low power systems
• Microelectronics
• Integrated Circuits
• +۱۱ مورد دیگر

Professor Aniruddha Desai is a Research Professor and Director of the Centre for Technology Infusion (CTI) at La Trobe University. He holds a Bachelor’s in Industrial Electronics, a Master’s in Micro-electronics, and a PhD in Computer Science. His expertise spans microelectronics, AI, IoT, and sensor networks, with a focus on socially impactful applications like transportation, healthcare, and precision agriculture. Research Interests: Ultra-low power systems Micro-nano electronics AI/ML and edge computing IoT and sensor networks Transportation and logistics Major Projects: Led multi-million-dollar R&D programs in areas such as smart cities, energy management, and smart farming. Notable collaborations include the IIT Kanpur - La Trobe University Research Academy and the Asian Smart Cities Research and Innovation Network. Awards: Recipient of the 2016 Vice-Chancellor’s Award for Research Excellence and the 2020 Victorian Tall Poppy Award for Science. Served on advisory panels for the Australian Research Council and provided expert testimony in parliamentary inquiries. Labs/Teams: Directs the CTI, which delivers technology-based innovations to industry and government. Co-founded the Asian Smart Cities network to advance urban technology solutions.

Lillian Chongمشاهده پروفایل

استاد

• Computational Biophysics
• Biomolecular Simulations
• Protein Binding Pathways
• +۳ مورد دیگر

Lillian T. Chong is a Professor in the Department of Chemistry at the University of Pittsburgh, affiliated with the Kenneth P. Dietrich School of Arts and Sciences. She leads the Chong Lab, focusing on computational biophysics and biomolecular simulations. Her research emphasizes developing advanced simulation methods like weighted ensemble (WESTPA) for studying rare events in biomolecules, such as protein folding, binding pathways, and conformational switches. Research Interests: - Development of weighted ensemble algorithms for long-timescale simulations - Protein-protein binding kinetics and unbinding pathways - Design of switchable proteins with enhanced dynamic properties - Integration of experimental data (e.g., NMR, EPR) with simulations Recent Article Trends: Recent work explores ligand unbinding mechanisms, glycan-mediated spike protein dynamics, and force field validation. The lab’s methods are applied to drug discovery, viral entry mechanisms, and enzyme catalysis. Awards & Honors: Gordon Bell Special Prize for HPC-Based COVID-19 Research (2020) NSF CAREER Award (2009-2014) Bellet Teaching Excellence Award (2017) Advising & Grants: Advised students including Darian Yang (PhD 2023) and Jeremy Leung (PhD 2023). Funded by NSF and industry grants, including work on SARS-CoV-2 spike protein dynamics and force field development. Labs & Teams: The Chong Lab collaborates with groups at CMU and NIH, developing open-source tools like WESTPA and LPATH . Research spans Pittsburgh’s computational biophysics community, with interdisciplinary projects in drug design and protein engineering.

• Software Engineering
• Technical Debt Management
• Software Architecture
• +۳ مورد دیگر

Paris Avgeriou is a Professor of Software Engineering at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Bernoulli Institute. He leads the Software Engineering and Architecture research group and serves as Editor-in-Chief of the Journal of Systems and Software . His expertise spans technical debt management, software architecture, self-adaptive systems, and embedded systems design. Avgeriou holds an office at Nijenborgh 9, Groningen, and actively advises academic institutions and funding bodies globally. Research Interests: Avgeriou's work focuses on advancing software architecture principles, technical debt lifecycle management, and the integration of AI in software engineering. His research emphasizes practical solutions for improving software quality, maintainability, and system dependability, particularly in embedded and self-adaptive contexts. Recent Contributions: Recent studies include frameworks for benefit-cost-risk decision-making in self-adaptive systems, automated technical debt management using ML, and tools for tracing architecture-related debt. He collaborates internationally, contributing to standards like the Copenhagen Manifesto for human-centered AI in software engineering. Grants & Awards: While no specific awards are listed, his editorial role and frequent conference contributions reflect recognition in the field. He chairs conference tracks and oversees workshops, fostering early-career researchers and artifact evaluation. Labs & Teams: His group is part of the Bernoulli Institute, working on platforms like SDK4ED for embedded systems and tools such as DebtViz for technical debt monitoring. The team explores intersections between systems engineering and software architecture in complex systems-of-systems.

Damian Arellanesمشاهده پروفایل

مدرس

• Theoretical Computer Science
• Algebraic Composition
• Models of High-Level Computation
• +۵ مورد دیگر

Dr. Damian Arellanes is a Lecturer (Assistant Professor) in Computer Science at Lancaster University, UK, affiliated with the Software Engineering Group and Lancaster Centre for Intelligent, Robotic and Autonomous Systems (LIRA). He holds a PhD from The University of Manchester (2020) and a Postgraduate Certificate in Academic Practice from Lancaster University (2023). His research focuses on theoretical foundations of algebraic composition for high-level computation models, including emergent/self-organising systems and software composition. He has contributed to areas such as category theory, control-flow separation, and compositional programming for IoT systems. Education: PhD in Computer Science, University of Manchester (2020) Postgraduate Certificate in Academic Practice, Lancaster University (2023) MSc in Computer Science, supported by CONACYT (2012–2014) BEng in Computer Engineering, supported by PRONABES (2009–2012) Research Interests: Damian’s work emphasizes algebraic semantics, compositional models for software, and theoretical computer science principles. He explores how abstract mathematical frameworks (e.g., category theory) can formalize computational systems and enable scalable IoT solutions. Publications: Damian has published extensively on algebraic composition, IoT systems, and formal methods. Key themes include compositional programming, self-organizing software, and scalable service architectures. Awards: Official Nominator for the VinFuture Prize (2024) Honourable Mention for Most Outstanding Mexican Student in STEM (2021) Nick Sanders Kickstarter Fund (2019) Outstanding Doctoral Paper Award (2019) Best MSc Thesis in AI (2015) Advising & Grants: Damian supervises PhD students, such as Mina Yavari, and actively reviews for journals like IEEE TSC and conferences like TASE. He has secured scholarships and fellowships from CONACYT and the Mexican government. Labs/Teams: Member of LIRA’s Fundamentals Section and the Software Engineering Group at Lancaster University.

Glen Tianمشاهده پروفایل

استاد

• Big Data Computing
• Cloud Computing
• Computer Networks
• +۱۱ مورد دیگر

Professor Glen Tian is a Professor at the School of Computer Science , Queensland University of Technology . He holds two PhDs: one in computer and software engineering from the University of Sydney (2009) and another in industrial automation from Zhejiang University (1993) . His academic career spans institutions including Hong Kong University of Science and Technology, Curtin University, and the University of Maryland at College Park. Editor-in-Chief of the Handbook of Real-Time Computing (Springer) Associate Editor for Information Sciences (Elsevier) and Asia-Pacific Journal of Chemical Engineering (Wiley) His research focuses on big data computing , cloud computing , computer networks , smart grid communication and control , networked control systems , and cyber-physical system security . Applications include power systems , medical big data , vehicular networks , and transport systems . Recent publications highlight advancements in smart grid communications , distributed optimization , secure multi-agent systems , and medical imaging analysis . He has led QUT's Big Data Lab and served as Leader of QUT's Networks and Communications Discipline . Scientific achievements include Over 20 research grants totaling >$6M 6 Australian Research Council (ARC) grants 1 MRFF-TTRA grant ($745,623) 1 ATN-DAAD Australia-Germany Collaborative Grant 1 DEST International Science Linkage grant He supervises PhD students in big data bioinformatics , smart grid optimization , and cyber-physical security , while mentoring 30+ postdocs and research fellows. Current projects include mitigating cyberattacks on power systems and developing AI-based atheroma diagnostic tools .

Siddharth Gargمشاهده پروفایل

دانشیار

• Machine Learning
• Cybersecurity
• Computer Hardware Design
• +۴ مورد دیگر

Siddharth Garg is the Institute Associate Professor of Electrical and Computer Engineering at NYU Tandon School of Engineering, leading the EnSuRe Research Group. He holds a Ph.D. from Carnegie Mellon University (2009) and a B.Tech. from IIT Madras. His research focuses on secure and energy-efficient computing systems, integrating machine learning, cybersecurity, and hardware design. He previously held roles as Assistant Professor at NYU Tandon (2014-2020) and the University of Waterloo (2010-2014). Key affiliations include NYU Center for Cybersecurity (CCS), NYU Wireless, and the Center for Advanced Technology in Telecommunications. His work has been recognized with prestigious awards like the NSF CAREER Award (2015) and inclusion in Popular Science’s 'Brilliant 10' (2016). Notable research includes private inference optimization, secure hardware IP protection, and adversarial machine learning defenses. Publications highlight advancements in zero-knowledge proofs, AI-driven chip design, and mitigating backdoor attacks in neural networks. His grants include funding from NYU Wireless and NSF initiatives like the Chips4All project. The EnSuRe group emphasizes bridging software and hardware design gaps using AI and fostering cybersecurity education.

Rajeev Sahayمشاهده پروفایل

استاد آموزشی

• Machine Learning
• Wireless Communications
• Networking
• +۴ مورد دیگر

Rajeev Sahay is an Assistant Teaching Professor in the Department of Electrical and Computer Engineering at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. He holds a Ph.D. and M.S. from Purdue University (2022 and 2021) and a B.S. from the University of Utah (2018). Prior to UCSD, he worked as a Senior Machine Learning Software Engineer at Saab, Inc., and taught courses at Purdue, earning the Purdue Engineering Dean’s Teaching Fellow award in 2021. His research bridges machine learning and networking, focusing on social learning networks (SLNs) for personalized education and robust wireless communications in adversarial environments. Key areas include federated learning for privacy preservation, adversarial attack mitigation, and next-generation 6G wireless systems. His work emphasizes data-driven methodologies to enhance learning outcomes and secure communication networks against vulnerabilities. Dr. Sahay’s teaching spans undergraduate and graduate courses in machine learning, data science, and programming languages (C/C++/Python), with a passion for tailoring curricula to student needs. His publications reflect expertise in federated learning, adversarial defense mechanisms, and signal processing, with recent contributions to AI-assisted education and robust wireless systems. Scientific Awards: Purdue Engineering Dean’s Teaching Fellow (2021) Teaching and Research Interests: Machine Learning Pedagogy Adversarial Robustness in ML Systems Federated Learning Applications Social Learning Network Analysis 6G Wireless Communication Security

Pablo Aragónمشاهده پروفایل

استاد مدعو

• Computational Social Science
• Social Computing
• Wikipedia Studies
• +۴ مورد دیگر

Pablo Aragón is a Research Scientist at the Wikimedia Foundation and an Adjunct Professor at Universitat Pompeu Fabra. His work bridges computational social science, civic technology, and technopolitics, with a focus on Wikipedia's governance, digital democracy tools, and participatory systems. He co-founded the Democratic Innovation Lab in Barcelona and the DatAnalysis15M research network. Key research interests include analyzing knowledge integrity in Wikipedia, configuring digital participatory budgeting systems, and studying platform effects in civic technologies. He has led projects like DECODE (decentralized citizen engagement) and contributed to platforms like Decidim, which empower participatory democracy in cities like Barcelona. Recent conference engagements include KDD 2024 (data mining), ICWSM 2024 (social media analysis), and Wikimedia CEE Meeting 2024. His work emphasizes cross-cultural collaboration, with studies published in ACM Transactions on Computer-Human Interaction and peer-reviewed conferences like CIKM and ACM SIGKDD. Professional affiliations include the Decidim association, Amnistía Internacional España, and the open knowledge advocacy group Civio. His research often intersects with open science, free culture movements, and gender equity in urban mobility.

Xi Zhangمشاهده پروفایل

استاد

• Wireless Networks
• Quality of Service (QoS) Theory
• 6G/Next-Generation Mobile Networks
• +۷ مورد دیگر

Xi Zhang is a Full Professor in the Department of Electrical and Computer Engineering at Texas A&M University . He is also the Founding Director of the Networking and Information Systems Laboratory. His academic career includes research fellowships at the University of Technology Sydney and James Cook University, as well as prior roles at AT&T Bell Laboratories and AT&T Laboratories Research. Education: B.S. and M.S. in Electrical Engineering & Computer Science, Xidian University, China M.S. in Electrical Engineering & Computer Science, Lehigh University, USA Ph.D. in Electrical Engineering-Systems, University of Michigan, USA Research Interests: His work focuses on Quality-of-Service (QoS) theory, 6G/Next-Generation Wireless Networks , Massive MIMO , Integrated Sensing and Communications (ISAC) , and Network Function Virtualization (NFV) . He has pioneered advancements in statistical delay/error-rate bounded QoS , AI-driven 6G architectures , and mURLLC (massive ultra-reliable low-latency communications) . Awards & Honors: IEEE Fellow (2014) for contributions to QoS theory in mobile wireless networks NSF Early Career Award (2004) Multiple Best Paper Awards (IEEE GLOBECOM, WCNC, ICC) Outstanding Faculty Award from Texas A&M (2020) Leadership Roles: He has held key positions as Technical Program Committee (TPC) Chair for major conferences (e.g., IEEE GLOBECOM 2011, IEEE ICDCS 2026) and serves as Editor for top-tier journals like IEEE Transactions on Communications and IEEE Transactions on Wireless Communications . Labs & Teams: He leads the Networking and Information Systems Laboratory , focusing on 6G mobile networks, ISAC systems, and AI-driven network architectures.

Sandeep Kumarمشاهده پروفایل

دانشیار

• Computer Security
• Computer Networking
• Operating Systems
• +۳ مورد دیگر

Sandeep Kumar is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University, College Station. He holds a PhD in Computer Science from Purdue University (1995) and a B.Tech in Electrical Engineering from the Indian Institute of Technology, New Delhi (1985). His research focuses on computer security, networking, and system-level programming. Prior to academia, he worked in industry roles including at VMware in Palo Alto, CA. He currently teaches courses such as CSCE 313 (Introduction to Computer Systems) and CSCE 222 (Discrete Mathematics), emphasizing system software, networking, and cybersecurity. His teaching philosophy incorporates modern tools like GCP and Docker for practical learning. He advises students on technical projects but notes his non-tenure track role limits formal research supervision. Professional interests include curriculum design, educational technology, and bridging industry-academia gaps in cybersecurity. Education: Ph.D., Computer Science, Purdue University, 1995 M.S., Computer Science, University of Tennessee, 1987 B.Tech, Electrical Engineering, IIT Delhi, 1985 Research Interests: Computer Security, Networking, Operating Systems Teaching: CSCE 313 (Computer Systems), CSCE 222 (Discrete Math), CSCE 111 (Java Programming) Industry Experience: VMware (Networking & Security), Former Googler Awards: Hagler Fellow (2023), Google GCP Educational Grants Dr. Kumar’s work emphasizes practical system-level programming and security, with contributions to intrusion detection systems and secure enterprise networks. His courses integrate modern tools like RustRover and Docker, reflecting industry standards. He actively engages with educational technology, including LaTeX-based lecture materials and Gradescope integration.

Paolo Rotaمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Multimodal AI
• +۹ مورد دیگر

Paolo Rota is a tenure-track Assistant Professor at the University of Trento, affiliated with the Department of Information Engineering and Computer Science (DISI) and the Center for Mind/Brain Sciences (CIMeC). His research lies at the intersection of computer vision, machine learning, and multimodal AI, with a strong emphasis on vision-language models and activity recognition. His research interests include zero-shot action recognition, temporal action localization, open-world recognition, and person image synthesis. He explores how large multimodal models can be leveraged for practical applications in video analytics and industrial AI, often developing training-free or source-free adaptation methods that improve model generalization. Recent publications show a consistent trend in utilizing large vision-language models (e.g., CLIP, LMMs) for tasks such as image classification, domain adaptation, and action recognition, emphasizing simplicity, zero-shot capabilities, and real-world applicability. His work frequently appears in top venues including CVPR, NeurIPS, ICCV, and ICIAP. He actively mentors PhD students including Benedetta Liberatori, Jiaqi Liu, Yan Shu, Shiyao Xu, and Alessandro Conti, often co-advising with faculty such as Elisa Ricci and Nicu Sebe. He also contributes to teaching, including delivering lectures on machine learning for the MSc in Data Science program. He co-founded Mountain Maps, a startup using AI to enhance outdoor navigation and mountain exploration. His work bridges academic research and practical innovation, aiming to increase the real-world impact of AI systems.

• Control Theory
• Distributed Control
• Networked Systems
• +۱۲ مورد دیگر

Emma Tegling is a Senior Lecturer (Associate Professor) at the Department of Automatic Control, Faculty of Engineering (LTH), Lund University, Sweden. She joined the department in January 2021 and holds a prestigious WASP (Wallenberg AI, Autonomous Systems and Software Program) professorship. Her research focuses on the analysis and control of large-scale networked systems, with applications in distributed electric power networks and socio-epidemiological networks. She is actively involved in multiple research projects, supervises several PhD students, and contributes to major academic events in control theory. Education: Ph.D. in Electrical Engineering, KTH Royal Institute of Technology (2019) M.Sc. in Engineering Physics, KTH Royal Institute of Technology (2013) B.Sc. in Engineering Physics, KTH Royal Institute of Technology (2011) Emma Tegling's research centers on the fundamental limitations of distributed control, particularly in large-scale and non-normal network systems. Her work addresses critical challenges in vehicular formations, power grids, and social networks. She develops scalable control designs, consensus protocols, and optimal control strategies for complex networked environments. Her recent publications highlight breakthroughs in string stability, transient performance, and distributed optimization. The trend in her articles shows a strong focus on mathematical control theory, network dynamics, and real-world applications in socio-technical systems. Scientific Awards: WASP professorship (Wallenberg AI, Autonomous Systems and Software Program) Emma Tegling leads and co-leads several significant research grants, including WASP NEST: Learning in Networks and Dynamics of Complex Socio-Technological Network Systems. She actively supervises PhD students such as Jonas Hansson and David Ohlin, whose work has led to novel consensus protocols and optimal control formulations. Her academic leadership extends to organizing the European Control Conference and co-organizing interdisciplinary workshops on power and democracy in modern societies. She is also involved in public engagement and academic service through supervision and project coordination. Emma Tegling is a key member of the Department of Automatic Control at Lund University, contributing to research teams focused on networked systems, control theory, and AI integration. She collaborates extensively within ELLIIT (the Linköping-Lund initiative on IT and mobile communication) and participates in cross-disciplinary labs working on AI, digitalization, and natural/artificial cognition. Her work is aligned with UN Sustainable Development Goals related to sustainable energy and resilient infrastructure.

• Applied Conversational AI
• Human-Computer Interaction (HCI)
• Web Systems and Services
• +۴ مورد دیگر

Dr. Jue (Grace) Xie is a Lecturer in the Department of Human Centred Computing at Monash University's Faculty of Information Technology. She is also a Senior Software Engineer and Research Fellow at Action Lab, with over 15 years of experience in applied research spanning software architectures, web systems, consumer health informatics, and conversational AI. Grace holds a PhD from Monash Faculty of Information Technology (2012) and has contributed to diverse interdisciplinary projects, including collaborations with Microsoft Research Asia and Monash research centers. Her teaching roles include Chief Examiner for units like FIT5183 Mobile and Distributed Computing Systems and Lecturer for Programming for Distributed Systems and Service-Oriented Computing . Research Interests Applied Conversational AI Human-Computer Interaction (HCI) Web Systems & Social Networks Knowledge Engineering Recent publications focus on AI-driven healthcare interventions, mental health support via chatbots, and stakeholder-inclusive design in aged care. Her work aligns with UN Sustainable Development Goals 3 (Good Health) and 9 (Innovation). Awards include the Future Women Leaders Conference 2019 . She is a Fellow of the Computer Society (IEEE) and the Association for Information Systems .

Parisa Kordjamshidiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۷ مورد دیگر

Parisa Kordjamshidi is an Associate Professor of Computer Science and Engineering at Michigan State University (MSU), leading the Heterogeneous Learning and Reasoning (HLR) Lab. Her research focuses on Neuro-Symbolic AI, spatial language understanding, and structured learning, with notable contributions to frameworks like Saul for declarative programming. She joined MSU in 2019 after roles at Tulane University and the Florida Institute for Human and Machine Cognition. Education: Ph.D. in Computer Science from KU Leuven (2013), postdoctoral research at UIUC's Cognitive Computation Group, and work in the KnowEng project. Research Interests: Artificial Intelligence, Machine Learning, Natural Language Processing, Neuro-Symbolic systems, spatial semantics extraction, structured output learning, and multimodal reasoning. Key projects include NSF CAREER awards for spatial language understanding and ONR grants for integrating domain knowledge into AI. Awards: NSF CAREER (2019), Amazon Faculty Research Award (2022), Fulbright Scholar (2025), and Rising Stars at MIT EECS (2015). Grants: Active projects on Neuro-Symbolic compositional generalization (ONR), spatial language learning (NSF), and collaborations with the Department of Media and Information for health misinformation management. Professional Activities: Editorial roles at JAIR, TACL, and Frontiers journals; service on program committees for ACL, EMNLP, and AAAI; organization of workshops like Spatial Language Understanding (SpLU) and CLeaR. Lab and Software: HLR Lab develops Saul (declarative learning-based programming framework) and tools for spatial role labeling. Her team emphasizes mentoring, with structured weekly meetings, reading groups, and conference participation for students.