جستجوی استاد راهنمای دانشگاهی

Joseph Eremondiمشاهده پروفایل

استادیار

• Programming Languages
• Type Theory
• Dependent Types
• +۱۲ مورد دیگر

Joseph Eremondi is an Assistant Professor in the Department of Computer Science at the University of Regina, Faculty of Science, Canada. He began his tenure in 2024 after serving as a Royal Society Newton International Fellow at the University of Edinburgh, where he conducted postdoctoral research with Ohad Kammar in the Laboratory for Foundations of Computer Science. He earned his PhD from the University of British Columbia (UBC) under the supervision of Ron Garcia at the UBC Software Practices Laboratory. His research is centered on programming languages theory, with a strong focus on type systems that enhance software reliability and usability. He is particularly known for his work in dependent types, gradual typing, and the integration of both paradigms. His research interests include: Dependent pattern matching and its semantic foundations Gradual dependent types and approximate normalization Error message generation and usability in dependently typed languages Static analysis using set constraints and SMT solvers Theoretical properties of reversal-bounded counter automata and shuffle operations His recent publications, appearing in premier venues like POPL, ICFP, and CPP, reflect a consistent trajectory toward making advanced type systems more accessible and practical. Key themes include coverage semantics for dependent pattern matching, formal models of gradual dependent typing, and improving the developer experience through better tooling and error diagnostics. Notable scientific recognitions include the NSERC Discovery Grant (awarded in 2025) and the prestigious Royal Society Newton International Fellowship. These awards underscore the impact and promise of his research program on the usability of dependently typed programming languages. Joseph is actively mentoring and recruiting graduate students, particularly in areas such as dependently typed programming (Lean, Agda, Idris, Coq), gradual typing, live programming environments, and static analysis. He emphasizes close collaboration within a small, focused research group. He has also served on program committees, including for TyDe and POPL Artifact Evaluation, demonstrating active engagement in the programming languages community. His work bridges theoretical rigor with practical implementation, evident in his artifact releases on GitHub and integration with tools like Ott and DrRacket. He maintains a personal website and open-source repositories that support reproducibility and community involvement.

Waldemar Kolanusمشاهده پروفایل

استاد

• Molecular Immunology
• Cell Signaling
• Signal Transduction Pathways
• +۴ مورد دیگر

Prof. Waldemar Kolanus leads the Molecular Immunology and Cell Biology department at the University of Bonn's Life & Medical Sciences Institute (LIMES) . His research bridges immunoregulation , stem cell dynamics , and metabolic stress responses in immune cells. Unit 2 member at LIMES Principal investigator in SFB 704 and ImmunoSensation Cluster Leads a multidisciplinary lab with postdocs, PhD students, and technical staff His work focuses on intracellular signaling pathways connecting immune activation to tissue homeostasis, particularly through: Cytohesin proteins in integrin-mediated adhesion and migration TRIM71 in stem cell regulation and congenital hydrocephalus High-salt environments affecting macrophage function Publication trends show expertise in immune cell migration , genetic models , and chemical inhibition , with frequent use of mice and zebrafish for in vivo studies. Key articles explore: TRIM71's dual role in auditory development and germ cell maintenance Cytohesin family's Golgi regulation and insulin signaling Ruxolitinib's off-target migration inhibition of dendritic cells Contact details: Address: LIMES Institute, Carl-Troll-Straße 31, Bonn Email: kolanus.sekretariat@uni-bonn.de Phone: +49 228 73-62788

Juan Zhaiمشاهده پروفایل

استادیار

• Software Engineering
• Program Languages
• Natural Language Processing
• +۶ مورد دیگر

Juan Zhai is an Assistant Professor in the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. She co-directs the Laboratory for Advanced Software Engineering Research (LASER) and is a member of the UMass NLP group. Her research advances software engineering through automated techniques for building high-quality systems with emphasis on behavioral specifications, AI safety, and trustworthy AI. Her work addresses the fundamental challenge of aligning software behavior with intended specifications through two main directions: automated specification synthesis (translating natural language comments to formal specifications via tools like C2S and LLMCup) and defect detection/repair (developing frameworks for AI system testing, bias mitigation, and training diagnostics). Her vision integrates these into end-to-end assurance systems that continuously validate, repair, and audit evolving software in dynamic environments. Recent publications (2024-2025) reveal dominant trends at the software engineering/AI intersection: formal specification synthesis for IoT and code generation, comment maintenance using LLMs, deep learning framework testing (DevMuT, Citadel), bias detection in LLMs, and automated training repair (AutoTrainer, DREAM). These contributions appear in top venues including ICSE, FSE, ASE, ISSTA, and ACL. Professor Zhai currently advises PhD student Gehao Zhang (focusing on Software Engineering and AI Safety) and actively recruits new PhD/Master's students. Her LASER lab develops practical tools for specification inference, LLM-driven synthesis, and trustworthy AI, while collaborating with the UMass NLP group on language-centric software analysis. The LASER lab, co-directed by Zhai, pioneers techniques for behavioral specification enforcement across traditional and AI-powered systems. Key projects include CPC for bidirectional code-comment analysis, ModelMeta for deep learning framework testing, and frameworks for bias mitigation across the ML lifecycle. The lab emphasizes practical, scalable tools that enhance correctness, robustness, and fairness in critical AI applications.

Juliet Bennettمشاهده پروفایل

پژوهشگر ارشد

• Social and Ecological Justice
• Climate Change
• Global Inequality
• +۸ مورد دیگر

Dr Juliet Bennett is a Research Fellow at the Sydney Centre for Healthy Societies (SCHS) and Charles Perkins Centre, University of Sydney. Her work bridges process philosophy with systemic interventions for social and ecological wellbeing. She holds a PhD in Peace and Conflict Studies, focusing on paradigm shifts from static to process thinking. Previously, she managed collaborative research at Sydney Policy Lab and directed the Sydney Peace Foundation. Education: PhD (Peace and Conflict Studies), MPhil/MA (Peace and Conflict Studies). Teaching includes units on peace studies, narratology, and structural violence at the University of Sydney and Lenoir Rhyne University. Research interests center on mitigating social/ecological injustice, structural violence, and climate change through critical sociology, contextual economics, and participatory methods. Current projects examine food systems and ultra-processed foods via critical social theory. Awards: 2020 Excellence in Teaching for 'Key Issues in Peace and Conflict Studies'. Memberships include Sydney Policy Lab, New Economic Networks Australia, and the International Peace Research Association. Publications span process philosophy applications, climate policy, and participatory research methods. Her work advocates for systemic transformations toward ecological civilization through interdisciplinary collaboration.

• Computer Hardware
• FPGA Acceleration
• Artificial Intelligence
• +۸ مورد دیگر

Professor Wayne Luk is a Professor of Computer Engineering at the Department of Computing, Faculty of Engineering, Imperial College London. He leads the Programming Languages and Systems Section and the Custom Computing Research Group, and directs the EPSRC Centre for Doctoral Training in High-performance Embedded and Distributed Systems and the Centre for Advanced Financial Engineering. He previously served as a Visiting Professor at Stanford University from 2006 to 2009. His research spans FPGA acceleration, quantum computing, deep learning optimization, and algorithm-hardware co-design, with affiliations to the CRUK Convergence Science Centre and the Engineering Secure Software Systems group. His research interests include computational modeling for particle physics, causal discovery in agent-based systems, and high-throughput digital electronics. Notable contributions include FPGA-accelerated algorithms for neural networks, quantum circuit simulation, and Bayesian optimization frameworks. His work emphasizes practical applications of reconfigurable hardware in fields like medical imaging, high-energy physics, and financial systems. Professor Luk is a Fellow of the Royal Academy of Engineering, IEEE, and BCS. His publications focus on advancing hardware-aware machine learning, FPGA-based acceleration techniques, and scalable design methodologies. His research bridges theoretical computer science with applied engineering, addressing challenges in real-time systems, embedded computing, and next-generation computing architectures. His academic leadership includes directing interdisciplinary centers and training programs, fostering collaboration across computing, engineering, and physics. Current projects explore quantum computing tools, causal inference systems, and high-performance graph neural networks for particle physics applications.

Daniel Varroمشاهده پروفایل

استاد

• Model-Based Systems Engineering
• Cyber-Physical Systems
• Software Engineering
• +۱۲ مورد دیگر

Daniel Varro is a Professor affiliated with McGill University (Faculty of Engineering, School of Computer Science), with strong ties to Budapest University of Technology and Economics and Linköping University. He is a leading researcher in model-driven engineering, cyber-physical systems, and software engineering, actively contributing to top-tier conferences such as MODELS, ICSE, and ASE. His research focuses on model-based systems engineering (MBSE) , automated model generation , model transformations , and constraint-based consistency checking . Recently, his work has expanded into integrating large language models (LLMs) and machine learning into modeling workflows, including model querying, domain modeling, and bug detection. The recent publications reveal a strong trend toward AI-augmented modeling, logic-based solvers (e.g., Refinery), and safety assurance of autonomous systems (e.g., COLREGs compliance). His work bridges formal methods with practical software engineering challenges in industrial and safety-critical domains. Scientific Awards: No specific awards mentioned in the text. Advising and Grants: While no explicit list of students or grants is provided, his mentorship in the Doctoral Symposium and repeated leadership roles suggest active supervision and likely grant funding. He has led projects on automated model generation, model quality, and AI integration in modeling. Labs and Teams: Daniel Varro is associated with research groups focused on model-driven engineering and software evolution, likely leading or co-leading teams working on the VIATRA and Refinery frameworks for model transformation and solving.

• Network Security
• Android Security
• Malware Analysis
• +۶ مورد دیگر

Vijay Laxmi is a Professor in the Department of Computer Science and Engineering at Malaviya National Institute of Technology (MNIT) Jaipur, India. With over a decade of active research publication from 2014-2024, Dr. Laxmi has established themselves as a prominent researcher in network security, Android security systems, and Network-on-Chip architectures. Their work demonstrates consistent collaboration with Manoj Singh Gaur and numerous doctoral students at MNIT Jaipur. Dr. Laxmi's research interests span Network Security, Android Security, Malware Analysis, Network-on-Chip Architectures, Routing Protocols, Side-Channel Attacks, Wireless Networks, and Mobile Security. Their work bridges theoretical security frameworks with practical implementations, particularly in mobile and embedded systems. Recent publications indicate a growing focus on AI-based security approaches including GAN applications for fuzzing and deep learning for image dehazing. The research trajectory shows increasing sophistication in security analysis techniques, evolving from basic malware detection to advanced side-channel attack analysis and sophisticated network security protocols. Recent publications demonstrate expertise in both theoretical frameworks and practical implementations with applications in real-world security challenges. Dr. Laxmi has mentored numerous graduate students including Vineeta Jain, Anugrah Jain, Sonal Yadav, Mohit Singh, and Gaurav Singal, who appear as co-authors across multiple publications. Their collaborative network extends to researchers at international institutions, indicating strong academic connections beyond their home institution.

Robin Laycockمشاهده پروفایل

مدرس ارشد

• Cognitive Neuroscience
• Visual Neuroscience
• Social Perception
• +۴ مورد دیگر

Dr. Robin Laycock is a Senior Lecturer in the Department of Health and Biomedical Sciences at RMIT University. He leads the Social and Cognitive Neuroscience (SoCoNeuro) Lab, focusing on behavioral and neural mechanisms of social perception in neurotypical and neurodivergent populations, including autism spectrum disorders and the neurocognitive effects of concussion. His research integrates methodologies such as eye-tracking, EEG, and fNIRS to study visual perception, face processing, and the impact of stress/anxiety on cognition. Dr. Laycock holds a PhD from La Trobe University, where he investigated visual processing pathways. His work also includes the BabyFace study, exploring social perception in pre-term infants using fNIRS. He is affiliated with RMIT’s Healthy Foundations Research Group and supervises research projects on topics like concussion neurocognition and social media’s neurobiological effects. Research interests span visual neuroscience, social neuroscience, and clinical applications of neuroimaging. He teaches undergraduate courses in Biological Psychology and supervises postgraduate research in vision science, neuropsychology, and affective neuroscience. His lab’s recent studies examine sex differences in sports-related concussion neuroimaging and the role of deepfakes in emotion perception research. He actively collaborates on projects addressing autism traits, stress effects on visual processing, and neuroimaging advancements.

Yangruibo Dingمشاهده پروفایل

استادیار

• Software Engineering
• Large Language Models
• Agentic Systems
• +۷ مورد دیگر

Yangruibo Ding is an incoming Assistant Professor in the Department of Computer Science at the University of California, Los Angeles (UCLA), and currently serves as a Postdoctoral Scientist at AWS Agentic AI. He has held significant research positions at Google DeepMind, Amazon AWS AI Labs, and IBM Research, establishing himself as a leading researcher in software engineering with a focus on large language models for code. His research focuses on developing large language models (LLMs) and agentic systems for software engineering. He specializes in training LLMs with advanced symbolic reasoning capabilities for debugging, testing, program analysis, and verification. His work aims to build efficient, collaborative agentic systems for complex software development and maintenance tasks, with particular emphasis on code generation, vulnerability detection, and execution-aware pre-training techniques. Dr. Ding's publication record reveals a strong trajectory toward enhancing code intelligence through comprehensive semantics reasoning and self-refinement approaches. His research spans multiple dimensions of software engineering including code completion, vulnerability detection, model evaluation, and cross-file context understanding, with applications across various programming languages and development environments. Dr. Ding has received numerous prestigious awards recognizing his contributions to the field: IBM Ph.D. Fellowship Award (2022-2024) ACM SIGSOFT Distinguished Paper Award (2023) IEEE TSE Best Paper Award Runner-up (2022) Ph.D. Service Award, Columbia CS (2025) NSF Student Travel Award for ESEC/FSE'23 (2023) ACM SIGSOFT CAPS Travel Grant (2023) NSF Travel Award for ICSE'22 (2022) As he establishes his research group at UCLA, Dr. Ding is actively seeking students with strong coding skills and experience in large language models, program analysis, verification, or security. He serves on program committees for major conferences including ICSE (2026), ASE (2024, 2025), and ESEC/FSE Artifacts Track (2023), and regularly reviews for top-tier conferences and journals in AI and software engineering. His research is conducted through collaborations with leading industry teams including AWS Agentic AI, Google DeepMind's Learning4Code team, and IBM Research's AI for Code team, creating a robust network of industry-academia partnerships that drive innovation in software engineering research.

Steven H. H. Dingمشاهده پروفایل

استادیار

• Cybersecurity
• Machine Learning
• Data Mining
• +۷ مورد دیگر

Dr. Steven H. H. Ding is an Assistant Professor at McGill University's School of Information Studies, specializing in cybersecurity, machine learning, and data mining. His research focuses on AI-driven solutions for malware detection, software vulnerability analysis, and reverse engineering. He holds a PhD from McGill University and has been supported by BlackBerry Cylance and DRDC. His work bridges theoretical advancements with practical applications in military systems and avionics cybersecurity. Dr. Ding earned his PhD in 2019 with notable awards including the FRQNT Doctoral Research Scholarship and McGill's Dean’s Graduate Award. His educational background includes degrees from McGill, Concordia University, and the University of Shanghai for Science and Technology. His research interests span cybersecurity domains such as zero-day malware identification, code obfuscation countermeasures, authorship verification for digital forensics, and AI applications in avionics anomaly detection. He actively contributes to open-source tools like the Kam1n0 MapReduce-based assembly clone search system. Recent work emphasizes adversarial machine learning for evasive malware generation, transformer-based anomaly detection in avionics, and automated SBOM generation for firmware analysis. His publications reflect a focus on real-world cybersecurity challenges in both civilian and defense sectors. Dr. Ding leads the L1NNA Lab and collaborates with industry partners on cutting-edge projects. His contributions include novel techniques for phishing detection leveraging large language models and innovative approaches to reverse engineering software composition in JavaScript applications.

Alexander Pretschnerمشاهده پروفایل

استاد

• Software Engineering
• Testing
• Information Security
• +۵ مورد دیگر

Prof. Alexander Pretschner is a Professor of Software & Systems Engineering at the Technical University of Munich (TUM) and Founding Director of the Bavarian Research Institute for Digital Transformation (bidt). He also serves as Scientific Director of fortiss, a Bavarian research institute for software-intensive systems. His research focuses on software engineering, testing, information security, and ethical software development. Pretschner holds a PhD from TUM and has held academic positions at Karlsruhe Institute of Technology (KIT) and TU Kaiserslautern. He is a co-editor of several prestigious journals, including IEEE Transactions on Reliability and the Journal of Software Testing, Verification and Reliability. Education: PhD in Computer Science, Technical University of Munich MSc in Computer Science, University of Kansas (on Fulbright Scholarship) Diplom in Computer Science, RWTH Aachen University Research Interests: His work spans testing methodologies, secure software design, and ethical considerations in agile development. Notable contributions include frameworks for metamorphic testing, distributed data usage control, and accountability mechanisms for cyber-physical systems. Awards: IBM Faculty Award (2012, 2013) Google Focused Research Award (2011, 2012) EARTO Innovation Prize (2014) 2nd Platz Supervisory Award (2020) Advising & Grants: Pretschner has supervised numerous PhD and Master’s students, contributing to over 200 publications. He leads projects like EDAP (Ethical Deliberation in Agile Processes) and collaborates with industry partners on cybersecurity and AI ethics initiatives. Labs & Teams: His work is anchored in bidt, fortiss, and TUM’s Chair of Software & Systems Engineering, focusing on societal impacts of digitalization and trustworthy AI systems.

David Lieمشاهده پروفایل

استاد

• Computer Systems Security
• Trusted Execution Environments
• Mobile Platform Security
• +۷ مورد دیگر

David Lie is a Professor at the University of Toronto, jointly appointed in the Edward S. Rogers Department of Electrical and Computer Engineering, Department of Computer Science, and Faculty of Law. He directs the Schwartz Reisman Institute for Technology and Society, co-founded the IT3 Lab, and serves as Associate Director at the Data Sciences Institute. His research focuses on securing computer systems through operating systems, architecture, and formal verification approaches. B.A.Sc (University of Toronto, 1998) M.S. (Stanford, 2001) Ph.D. (Stanford, 2004) His research emphasizes building secure systems for mobile platforms and cloud computing, with significant contributions to trusted execution environments (XOM architecture precursor to Intel SGX/ARM TrustZone) and Android permission mapping (PScout tool). Recent work spans cryptographic side-channels, web tracking detection, and AI safety. Key honors include SOSP 2003 Best Paper, Ontario MRI Early Researcher Award (2008), Connaught Global Challenge Award (2017), and Canada Research Chairs (Tier 2 2013-2018, Tier 1 current). He has secured over $30M in research funding and served as General Chair for CCS 2018. Lie leads the IT3 Lab (Technology and Policy Integration), collaborates with industry leaders (Google, VMware, Telus), and mentors graduate students working on practical security implementations. He co-teaches ECE1724: Privacy Problems with Lisa Austin from the Faculty of Law, reflecting his technology-policy interests.

Westley Weimerمشاهده پروفایل

استاد

• Automated Program Repair
• Software Engineering
• Programming Languages
• +۱۸ مورد دیگر

Westley Weimer is a Professor in the Department of Electrical Engineering and Computer Science (EECS) at the University of Michigan, College of Engineering. He teaches advanced courses such as EECS 590 (Advanced Programming Languages) and EECS 481 (Software Engineering), and has previously taught at the University of Virginia. His research integrates software engineering, programming languages, and cognitive science, focusing on automated program repair, program analysis, and the neuroscience of code comprehension. University: University of Michigan School: College of Engineering Department: Department of Electrical Engineering and Computer Science Academic Rank: Professor His research interests include automated program repair (e.g., GenProg), software quality, cognitive modeling of programming, neuroimaging studies of code review, and the application of medical imaging to software engineering. He explores deep questions at the intersection of consciousness, time, and computation, advocating for interdisciplinary approaches to understanding the mind through programming behavior. The most recent publications reflect a trend toward empirical and cognitive studies in software engineering, combining automated repair with human factors, neuroimaging (fMRI, TMS), and real-world software challenges. Themes include bias in code review, programming under cognitive influences, and the neurological basis of code comprehension. His work increasingly bridges computer science with psychology, neuroscience, and social science. Scientific awards include multiple Distinguished Paper Awards at ICSE, FSE, and ESEC/FSE, Best Paper and Runner-up awards, and several 10-Year Most Influential Paper Awards from ASE, GECCO, POPL, and ASPLOS, recognizing the lasting impact of his contributions to automated software repair and program analysis. He has advised numerous PhD and Master’s students, many of whom have gone on to faculty positions or industry research roles. He contributes to academic service through organizing diversity and inclusion initiatives, maintaining graduate career resources, and promoting ethical and inclusive practices in computing. He leads a vibrant research group focused on improving software quality through both technical and human-centered innovations, with ongoing projects in automated repair, cognitive modeling, and secure systems.

Jun Liuمشاهده پروفایل

استادیار

• Advanced energy materials
• Nano/micro-mechanics
• Self-powered electronics and systems
• +۲ مورد دیگر

Jun Liu is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the School of Engineering and Applied Sciences, University at Buffalo. His research focuses on advanced energy materials, nano/micro-mechanics, and self-powered systems, with applications in triboelectric energy harvesting and scanning probe microscopy. Education: PhD, Materials Engineering, University of Alberta (2018) MS, Materials Science, Shanghai University (2015) BE, Materials Science and Engineering, Nanchang University (2012) Research Interests: Development of tribovoltaic and triboelectric systems for self-powered electronics Mechanical energy harvesting via dynamic heterojunctions and Schottky contacts 3D-printed hydrogel structures for energy absorption and flexible electronics Nanoscale characterization using atomic force microscopy Design of nanocomposite sensors and catalytic materials Publication Trends: His work emphasizes triboelectricity, nanoscale energy conversion, and sustainable materials. Recent articles explore bionic tactile sensing, tunable hydrogels, and quantum dynamics in sliding interfaces. Awards: SONY Faculty Innovation Award (2021) Nature Springer MINE Young Scientist Award (2020) International Contest of Applications in Nano/Micro Technology Prize (2013) Laboratory: Advanced Energy Materials and Nanomechanics Lab at University at Buffalo.

• Materials Handling
• Logistics
• Crane Dynamics
• +۳ مورد دیگر

Dr. Michael Kleeberger is a Researcher at the Chair of Materials Handling, Material Flow, Logistics (FML) at the Technical University of Munich, based at Boltzmannstr. 15 in Garching. He collaborates closely with Prof. Johannes Fottner and maintains an active research profile in crane dynamics and mechanical systems simulation. His research specializes in Materials Handling and Logistics with emphasis on Crane Dynamics, Flexible Multibody Systems, and Control Systems. He develops advanced models for hydraulic actuated cranes, focusing on dynamic behavior during hoisting, slewing, and trajectory operations using port-Hamiltonian formulations and geometrically exact beam theory. His work bridges theoretical mechanics with industrial applications in heavy machinery. Analysis of his 15 most recent publications reveals consistent focus on numerical methods for flexible crane structures, with growing emphasis on optimal control strategies (2020-2025). Key trends include port-Hamiltonian system applications, lunar crane feasibility studies, and vibration mitigation techniques for lattice boom and knuckle boom configurations across diverse operational scenarios. As part of FML, Dr. Kleeberger contributes to TUM's leadership in logistics engineering through industry-collaborative projects and fundamental research in material flow systems, maintaining the chair's reputation for excellence in mechanical dynamics and practical engineering solutions.