جست‌وجوی استاد و پژوهشگر

Sujith Vijayanمشاهده پروفایل

دانشیار

• Computational modeling
• Brain oscillatory dynamics in health and disease
• Development of stimulation and pharmacological therapies for brain disorders
• +۷ مورد دیگر

Sujith Vijayan is an Associate Professor at the School of Neuroscience , part of the Virginia Tech College of Science . His research focuses on neural dynamics during sleep and active behavior , leveraging computational modeling , signal processing , and invasive/non-invasive recording to explore memory consolidation, emotional regulation, and therapeutic applications for neurological disorders. Research Interests include: Sleep-mediated memory processing Brain-computer interface (BMI) learning Pathological brain rhythms in PTSD and Parkinson’s Neural stimulation therapy development REM sleep emotional memory mechanisms His 15 most recent publications reveal a trajectory from anesthetic-induced neural oscillations (2013), through schizophrenia network analysis (2015), to targeted memory reactivation during sleep (2017). Key scientific contributions include the 2022 NSF CAREER award for BCI learning enhancement via sleep manipulation . Lab members include graduate students in Biomedical Engineering, TBMH, and Neuroscience programs, with alumni now at institutions like Barrow Neurological Institute and University of Arizona’s Sleep Lab.

Haobin Niمشاهده پروفایل

پژوهشگر ارشد

• Programming Languages
• Software Engineering
• Formal Verification
• +۵ مورد دیگر

Haobin Ni is a Postdoctoral Scholar at the University of Washington in the Programming Language and Software Engineering (PLSE) group, advised by Professor Zachary Tatlock. His research bridges theoretical formal methods with practical systems development in programming languages and security. Education: Ph.D. in Computer Science, Cornell University (2024). Dissertation: "Formal Modeling Languages for High-assurance Domain-specific Systems." Advisors: Greg Morrisett and Robbert van Renesse. Ni's research focuses on language design, program analysis, and compiler optimization with emphasis on formal verification of distributed systems, concurrent programs, and parsers. He pioneers secure smart contract languages using information flow control type systems and develops novel protocols for distributed systems. His work consistently targets high-assurance systems where correctness and security are critical, spanning blockchain, binary parsing, and state machine replication. Analysis of his 11 publications (2019-2024) reveals three dominant research thrusts: compositional security frameworks for smart contracts (especially against reentrancy attacks), provably correct implementations of critical infrastructure like ASN.1 parsers, and modular abstractions for distributed ledger technologies. His methodology combines deep theoretical formalization with practical implementation, resulting in tools and protocols adopted in real-world systems. Scientific awards: Best Paper Award, IEEE Symposium on Security and Privacy (2021) ICPC World Finals Gold Medal (2016) ICPC World Finals Silver Medal (2014) Ni actively mentors through competitive programming: he coached Cornell's ICPC team (2018-2024), leading them to World Finals qualifications in 2019 and 2023, and volunteered for high school programming contests. Currently, he leads the PLSE Programming Languages Reading Group (PLRG) at the University of Washington, fostering community engagement in PL research. His work shows strong industry collaboration, particularly with Microsoft Research on blockchain and security projects. As a core member of UW's PLSE group, Ni contributes to one of academia's leading programming languages research teams. His current leadership of the PLRG demonstrates active community building, while his technical work on Charlotte and ASN1★ positions him at the forefront of secure distributed systems research.

Dollas Apostolosمشاهده پروفایل

استاد

• Reconfigurable Computing
• Hardware Architecture
• Embedded Systems
• +۴ مورد دیگر

Professor Dollas Apostolos serves as a Professor in the School of Electrical and Computer Engineering at the Technical University of Crete (TUC), where he has held leadership roles such as Department Chairman. He directs the Microprocessor and Hardware Laboratory, focusing on reconfigurable computing, embedded systems, and high-performance digital systems. His work emphasizes rapid prototyping and real-world implementation of computational solutions. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign (1987) M.Sc., Computer Science, University of Illinois at Urbana-Champaign (1984) B.Sc., Computer Science, University of Illinois at Urbana-Champaign (1982) Research Interests: Reconfigurable computing architectures FPGA-based acceleration for bioinformatics and genomics Embedded systems and real-time processing Hardware-software co-design for high-performance computing His research bridges theoretical innovation with practical applications, such as FPGA implementations for genome assembly and aquaculture monitoring systems. Publications: Recent work highlights FPGA-based solutions for bioinformatics (e.g., genome assembly acceleration), real-time embedded systems (e.g., fish cage net monitoring), and scalable data processing frameworks. His articles often explore the intersection of FPGA technology with computational biology, embedded vision, and distributed systems. Awards and Affiliations: Senior Member, IEEE and IEEE Computer Society Recipient of IEEE Computer Society Golden Core and Meritorious Service Awards Twice honored with the University of Illinois Teaching Excellence Award He is a co-founder of IEEE conferences like FCCM and RSP, reflecting his leadership in the reconfigurable computing community. Teaching and Labs: Teaches courses on computer architecture, logic design, and VLSI design. The Microprocessor and Hardware Lab under his direction drives advancements in FPGA-based systems, with projects ranging from bioinformatics hardware accelerators to embedded vision systems.

Melanie Mitchellمشاهده پروفایل

استاد

• Artificial Intelligence
• Cognitive Science
• Complex Systems
• +۴ مورد دیگر

Melanie Mitchell is a Professor at the Santa Fe Institute, where she conducts research at the intersection of artificial intelligence, cognitive science, and complex systems. Her work focuses on conceptual abstraction and analogy-making in artificial intelligence systems, seeking to understand the mechanisms that enable both human and machine intelligence. Mitchell received her PhD in Computer Science from the University of Michigan in 1990 and has held positions at numerous institutions including the University of Michigan, Los Alamos National Laboratory, the Oregon Graduate Institute, and Portland State University. Her research bridges the gap between theoretical understanding of intelligence and practical AI development. Her recent publications explore fundamental questions about AI understanding, reasoning capabilities, and the relationship between language models and world knowledge. She has developed novel evaluation frameworks for assessing analogical reasoning and abstraction in AI systems, challenging assumptions about what current AI can truly comprehend. Her work often examines the limitations of large language models while proposing pathways for more robust and human-like artificial intelligence. 2010 Phi Beta Kappa Science Book Award for "Complexity: A Guided Tour" Finalist for the 2023 Cosmos Prize for Scientific Writing for "Artificial Intelligence: A Guide for Thinking Humans" Senior Scientific Award from the Complex Systems Society Distinguished Cognitive Scientist Award from UC Merced Herbert A. Simon Award of the International Conference on Complex Systems Mitchell actively advises numerous PhD students and postdoctoral researchers, fostering the next generation of researchers in AI and complex systems. She has also developed educational resources including the popular online course "Introduction to Complexity" on Complexity Explorer. Her public outreach includes a Substack newsletter "AI: A Guide for Thinking Humans," Science Magazine columns, and the podcast series "The Nature of Intelligence."

Houria Oudghiriمشاهده پروفایل

استاد آموزشی

• Computer Science
• Big Data
• Computer Architecture
• +۷ مورد دیگر

Houria Oudghiri is a Teaching Professor in the Department of Computer Science & Engineering at Lehigh University. She earned her PhD in Computer Engineering from McGill University in 1999 and has held academic positions at Ecole Supérieure d’Informatique (Algiers, Algeria), Effat University (Jeddah, Saudi Arabia), and SUNY Polytechnic Institute (Utica, NY). Her career spans 20+ years in academia, with expertise in computer architecture, programming, and data structures. PhD, Computer Engineering, McGill University, 1999 Her research focuses on Big Data discretization techniques, hardware-software co-design for digital systems, and educational assessment frameworks. Her work on threshold analysis and scheduling algorithms reflects interdisciplinary applications in computational theory and system optimization. Current research trends include high-level synthesis of digital systems, data preprocessing for machine learning, and pedagogical evaluation methodologies. She has contributed to understanding design space exploration in embedded systems and weighted scheduling algorithms for resource allocation. Houria works in Packard Lab at Lehigh University, where she teaches foundational computing courses. Her academic journey reflects a global perspective with experiences in Algeria, Saudi Arabia, and the United States.

• Artificial Intelligence
• Robotics
• Manufacturing Systems
• +۵ مورد دیگر

Professor Will Browne is the Chair in Manufacturing Robotics at QUT, collaborating with the ARM Hub and CSIRO to advance robotics in manufacturing, healthcare, and industry. He holds a Doctorate in Engineering from the University of Wales and has over 30 years of expertise in AI, robotics, and Learning Classifier Systems (LCS). His research focuses on transparent AI systems like LCS for Explainable AI (XAI), human-robot collaboration, and advanced manufacturing solutions. He co-leads the $5M SfTI Robotics Spearhead project and is internationally recognized for LCS contributions, including co-authoring the first LCS textbook with Ryan Urbanowicz. Publications emphasize emotion recognition, embodied AI, and scalable machine learning. His work bridges academia and industry through the ARM Hub, fostering innovation in robotics and design-led manufacturing. Key roles include co-track chair at GECCO (2011–2025) and editorial board memberships. Research spans evolutionary computation, robotics control, and energy systems optimization.

Akshay Krishnamurthyمشاهده پروفایل

استادیار مدعو

• Machine Learning
• Statistics
• Reinforcement Learning
• +۴ مورد دیگر

Akshay Krishnamurthy is an Adjunct Assistant Professor at the University of Massachusetts Amherst and a Senior Principal Research Manager at Microsoft Research NYC. His research focuses on machine learning and statistics, particularly interactive learning frameworks, reinforcement learning, and generative AI. He holds a PhD from Carnegie Mellon University and a B.Sc. from UC Berkeley. Education: PhD in Computer Science, Carnegie Mellon University (2015) B.Sc. in Electrical Engineering and Computer Science, UC Berkeley (2010) Research Interests: His work emphasizes feedback-driven data collection in machine learning, including contextual bandits, reinforcement learning, and their applications to language models and generative AI. Recent focus areas include sample-efficient exploration and decision-making under limited feedback. Awards: National Science Foundation Graduate Research Fellowship (2010) Best Student Paper Award at Asilomar Conference (2013) Grants & Collaborations: Active in interdisciplinary collaborations, including organizing workshops on foundations of post-training in generative AI. His research bridges theoretical guarantees with practical applications in reinforcement learning and decision-making systems. Labs/Teams: Leads research initiatives at Microsoft on generative AI, reinforcement learning, and scalable machine learning systems.

Adlen KSENTINIمشاهده پروفایل

استاد

• Mobile and Wireless Networks
• Software Defined Networking (SDN)
• Mobile Edge Computing (MEC)
• +۷ مورد دیگر

Adlen KSENTINI is a Professor at EURECOM's Communication Systems department, specializing in advanced networking technologies. His research focuses on Mobile and Wireless Networks, Software Defined Networking (SDN), Mobile Edge Computing (MEC), Network Function Virtualization (NFV), and Content Delivery Networks (CDN), with an emphasis on performance evaluation and network virtualization. He has contributed to projects like AC3 and 6G-BRICKS, exploring cloud-edge continuum integration and 6G infrastructure. Key research interests include virtualized mobile core networks, carrier cloud systems, and AI-driven network management. He has received Best Paper Awards at IEEE WCNC 2018 and IWCMC 2016 for works on network slicing and LTE modeling accuracy. His work often integrates machine learning for optimization, sustainability, and security in 5G/6G networks. Distinctions: Two Best Paper Awards Labs/Teams: Involved in EU-funded projects like AC3 and 6G-BRICKS Grants: Not explicitly listed, but active in collaborative research initiatives

Ryan Leducمشاهده پروفایل

دانشیار

• Discrete-Event Systems
• Supervisory Control
• Fault Tolerance
• +۵ مورد دیگر

Dr. Ryan Leduc is an Associate Professor at the Department of Computing and Software within McMaster University's Faculty of Engineering . He holds degrees of B.Eng (Victoria) , M.A.Sc. (Toronto) , and Ph.D. (Toronto) . His research focuses on Discrete-Event Systems (DES) , particularly in Supervisory Control , Hierarchical Structures , Concurrency , and Formal Verification of software and hardware. He has developed the DESpot software tool for hierarchical DES research.

Kate Smithمشاهده پروفایل

استادیار

• Quantum Computing
• Computer Architecture
• Quantum Compilation
• +۵ مورد دیگر

Kate Smith is an Assistant Professor of Computer Science at Northwestern University, affiliated with the McCormick School of Engineering. She holds a PhD in Electrical Engineering from Southern Methodist University (SMU), along with MS and BS degrees in Electrical Engineering and Mathematics from the same institution. Her research focuses on quantum computing, specifically in system architecture, optimized compilation, error mitigation, and security. Prior to joining Northwestern in 2024, she worked at Infleqtion managing the Superstaq compiler team and as a postdoctoral scholar at the University of Chicago under the CQE/IBM program. She has contributed to over 25 peer-reviewed publications and served on technical committees for major conferences like MICRO, ISCA, and DAC. Education: SMU (PhD 2019, MS 2015, BS in EE/Math 2014). Professional experience includes roles at EPFL (Switzerland), Texas Instruments, and the Darwin Deason Institute for Cyber Security. Her honors include the 2022 HPCA Best Paper Award, MIT EECS Rising Star (2021), and the IEEE TC-MVL Early Career Award (2021). Research interests span quantum compilation, distributed systems, qudit processing, and quantum security. She co-organized the 2023 CCC Workshop on Next Steps in Quantum Computing and chaired the 2022 ISMVL conference. Her work emphasizes bridging hardware-software gaps to enable scalable quantum systems. Grants and collaborations include the EPiQC group at the University of Chicago and projects funded by the Swiss NSF. Teaching experience includes courses on quantum computing fundamentals and digital design at SMU, University of Chicago, and adjunct roles.

• Information Visualization
• Visual Analytics
• Human-Computer Interaction
• +۸ مورد دیگر

Paolo Buono is Associate Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. He holds a PhD in Computer Science with specialization in Visual Data Analysis. His research focuses on Information Visualization, Visual Analytics, Human-Computer Interaction, and Mobile Applications. Co-founder and CEO of LARE (2010), a university spinoff providing real-time surgical support through audio-video telestration Member (since 2002) and computer science coordinator at METEA Research Center for environmental protection Visiting scientist at AVIZ (France), University of Maryland (USA), and Fraunhofer IPSI (Germany) His work spans multiple application domains including: Cultural Heritage through interactive exploration systems Healthcare with smart therapeutic devices Environmental Monitoring via CET system IoT-based Smart Interactive Experiences He has contributed to: Dynamic hypergraph visualization techniques End-User Development frameworks (EUDroid) Usability evaluation methodologies Mobile health applications As project leader, he has coordinated: EU-funded VisMaster Coordination Action (2008-2010) Italian Ministry-funded LOGIN project (2014-2015) Apulia Region environmental projects His professional engagements include: Co-chair roles at INTERACT, AVI, IS-EUD conferences Program committee participation in VIS series and HCI conferences Member of ACM, IEEE, and SIGCHI Italy

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Ahmed AbuRa'ed is a Researcher at the Department of Information and Communication Technologies (DTIC) at Universitat Pompeu Fabra (UPF), Barcelona. He is affiliated with the TALN research group and the Large-Scale Text Understanding Systems Lab. His work focuses on advancing knowledge in scientific text summarization, information extraction, and machine learning. Education: PhD in Computer Science (2020), UPF, Barcelona, Spain M.Sc. in Computer Science (2015), University of Trento, Italy B.Sc. in Computer Information Systems (2007), An-Najah University, Nablus, Palestine Research Interests: Natural Language Processing (NLP), Machine Learning/Deep Learning, Semantic Web, Information Extraction, Data Mining, and Scientific Document Summarization. His projects include developing systems for automatic generation of state-of-the-art reports, scientific text summarization, and cross-document relation discovery. Publications Focus: His 15 most recent articles (2016–2021) emphasize advancements in scientific literature analysis, including citation detection, text simplification, and cross-document summarization. Notable works involve systems like LaSTUS/TALN for scientific text processing and OlloBot for Arabic health dialogue agents. Labs & Teams: Active member of the TALN research group and the Large-Scale Text Understanding Systems Lab at UPF's DTIC department. Open to collaborations in NLP, Machine Learning, and related fields via email or Skype.

Howard Schwartzمشاهده پروفایل

استاد

• Machine Learning
• Adaptive Control
• Multi-Agent Machine Learning
• +۳ مورد دیگر

Professor Howard Schwartz is a distinguished academic in Electrical and Computer Engineering at Carleton University , with a career spanning academia, industry, and robotics research. He earned his BEng in Civil Engineering from McGill University (1982), followed by MSc (1984) and PhD (1986) in Aerospace and Mechanical Engineering from MIT. His academic leadership includes serving as Department Chairman (2009-2013) and authoring the seminal text Multi-Agent Machine Learning: A Reinforcement Approach (Wiley, 2014). As an Associate Editor for IEEE Transactions on Cybernetics , he shapes discourse in cybernetic systems and machine learning. Research Focus Professor Schwartz's research bridges theoretical and applied domains: Machine learning for robotics and autonomous systems Adaptive control theory and multi-agent systems Reinforcement learning algorithms with fuzzy logic integration Real-time video analytics and GPS receiver development Nonlinear control systems for UAVs and wind energy optimization Industry Experience His career includes impactful industry engagements: Early development of high-performance GPS receivers at Canadian Marconi Co. (1982-1984) Sabbatical at March Networks (2001-2002) for video analytics Software quality control at CMC Electronics (2008) Software development for TV set-top boxes at Espial Inc. (2014-2015)

• Neuroscience
• Cognitive Science
• Computational Neuroscience
• +۵ مورد دیگر

Jonathan D. Cohen is a Robert Bendheim and Lynn Bendheim Thoman Professor in Neuroscience and Professor of Psychology and Neuroscience at Princeton University. He leads the Natural and Artificial Minds Initiative as Associate Director, bridging computational neuroscience, cognitive science, and machine learning. His research explores the interplay between cognition, neural dynamics, and AI systems. Research interests include Computational models of cognitive control and decision-making Cerebellar-cortical interactions in neurodevelopment and aging Neural-symbolic integration in AI architectures Quantum computing applications for cognitive modeling Medical applications of machine learning Recent publications demonstrate expertise in artificial neural networks, autism neurobiology, semantic cognition, and interdisciplinary approaches to modeling human and machine intelligence. His work spans theoretical neuroscience, AI interpretability, and translational medicine applications. Scientific contributions include Advancing hybrid mamba-transformer architectures for efficient reasoning Elucidating cerebellar roles in cognitive aging and disease Innovations in quantum models of cognition Developing frameworks for causal analysis in neural networks Exploring neurochemical diversity in psychedelic-producing fungi

• Software Verification
• Formal Methods
• Program Synthesis
• +۴ مورد دیگر

Madhusudan Parthasarathy is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign, College of Engineering. His research focuses on software verification, formal methods, and logic in computer science, with significant contributions to trustworthy AI systems, program synthesis, and security. Ph.D. in Theoretical Computer Science (2002), Institute of Mathematical Sciences, University of Madras Research interests include automating software verification, building correct-by-design systems, and exploring synergies between machine learning and program synthesis. He pioneered visibly pushdown languages , impacting XML processing and program verification. His tools like VEX and Strand advanced security and heap reasoning. Recent articles focus on blockchain verification, timed automata, and learning logics from data. His work has been widely cited, with the visibly pushdown language paper alone generating over 940 scholarly entries. Best Paper Award, 19th USENIX Security Symposium (2010) He has advised numerous students and postdocs, with former advisees now at institutions like Purdue University and Google. His outreach initiatives include the ConTraIL privacy-preserving contact tracing project and the MASSIVELY EMPOWERED CLASSROOMS MOOC platform for Indian undergraduates. He teaches courses like CS 521: Advanced Topics in Programming Systems and CS 474: Logic in Computer Science , while actively serving on program committees for top-tier conferences such as POPL and PLDI.