جست‌وجوی استاد و پژوهشگر

Minsu Kimمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Deep Learning
• Combinatorial Optimization
• +۷ مورد دیگر

Minsu Kim is a CIFAR AI Safety Post-doc Fellow at KAIST and Mila, collaborating with Prof. Yoshua Bengio, Prof. Sungjin Ahn, and Prof. Sungsoo Ahn. His work bridges System 2 Deep Learning, Bayesian posterior inference, and combinatorial optimization. Ph.D., Industrial Engineering, KAIST (2025) M.S., Electrical Engineering, KAIST (2022) B.S., Mathematics and Computer Science (Dual Degree), KAIST (2020) Kim's research focuses on enabling AI systems to measure uncertainty, represent causality, and perform sequential reasoning for safety-guaranteed planning. His methodology integrates GFlowNets and diffusion models with off-policy amortized inference, targeting applications in scientific discovery , hardware design optimization , and large language model alignment . Recent work explores Bayesian posterior inference through GFlowNets, aiming to unify deep learning with probabilistic reasoning. His 15 most recent publications (2025–2024) reveal a trend of combining combinatorial optimization with generative models for tasks like molecular graph discovery, vehicle routing, and neural architecture search. He also investigates diffusion samplers for Bayesian inverse problems and symmetry-based neural methods (Sym-NCO) to enhance sample efficiency. Jang Yeong Sil Fellowship (2025) KAIST Presidential Best Ph.D. Thesis Award (2025) Qualcomm Innovation Fellowship (2023) DesignCon Best Paper Awards (2021–2022) Kim's collaborations span KAIST's Industrial Engineering department and Mila's AI research groups. He actively contributes to academic peer review for top conferences (NeurIPS, ICML, ICLR) and journals (IEEE TNNLS, TPAMI), emphasizing the intersection of AI safety , uncertainty quantification , and systemic reasoning .

Reba Wissnerمشاهده پروفایل

دانشیار

• Musicology
• Film Music
• Television Music
• +۱۴ مورد دیگر

Dr. Reba Wissner is an Associate Professor of Musicology at the Schwob School of Music, Columbus State University, where she also serves as Interim Associate Dean in the Honors College. She specializes in film, television, and video game music, with a strong commitment to pedagogy and accessibility in higher education. Education: PhD, Musicology, Brandeis University, 2012 MFA, Musicology, Brandeis University, 2008 BA, Music and Italian, Hunter College, CUNY, 2005 Graduate Certificate, Higher Education Administration, Northeastern University, 2019 Graduate Certificate, Instructional Design, University of Wisconsin - Stout, 2020 Levels 1, 2, and 3 UDL Credentials, UDL-IRN Quality Matters Certified Peer Reviewer and Master Reviewer Dr. Wissner’s research centers on music in screen media, particularly Cold War-era television, and the intersection of music with cultural narratives. She is a leading voice in public musicology and Universal Design for Learning (UDL), advocating for inclusive and accessible music education. Her work spans historical analysis, pedagogical innovation, and interdisciplinary media studies. Her recent publications reflect a consistent focus on sonic storytelling, with books on The Twilight Zone , The Outer Limits , David Lynch, and Twin Peaks . She explores themes of nuclear anxiety, identity, and adaptation across media. Her editorial projects include the Oxford Handbook of Public Musicology and a guide on UDL in music history classrooms. Scientific Awards and Recognitions: Scholarship of Teaching and Learning Award, Columbus State University, 2022 Governor’s Teaching Fellow, Louise McBee Institute of Higher Education, 2022 Quality Matters Certified Peer Reviewer and Master Reviewer UDL Levels 1–3 Credentials Dr. Wissner has secured editorial contracts with Routledge and Oxford University Press, reflecting her leadership in the field. She mentors emerging scholars through collaborative editing and public scholarship initiatives. Her work in online learning and accessibility has shaped curriculum development and peer review standards. She leads the Public Musicology Certificate program and contributes to interdisciplinary studies and honors education. She is actively involved in research teams focused on media music and pedagogical innovation, integrating digital tools and inclusive design into music history education.

Don Towsleyمشاهده پروفایل

استاد

• Network Science
• Network Measurement
• Network Modeling
• +۴ مورد دیگر

Don Towsley is a Distinguished University Professor in the Department of Computer Science at the University of Massachusetts Amherst, within the College of Information and Computer Sciences. He has held visiting positions at AT&T Labs, IBM Research, INRIA, Microsoft Research Cambridge, and the University of Paris 6. He earned a B.A. in Physics and a Ph.D. in Computer Science from the University of Texas. Prof. Towsley's research spans network science, measurement, modeling, and analysis, with recent emphasis on quantum networking and wireless security. His work addresses foundational challenges in network tomography, entanglement distribution, and quantum communication protocols, contributing to efficient and secure next-generation networks. Analysis of his 2022-2025 publications reveals a dominant focus on quantum networking—including quantum internet architecture, entanglement distribution, and tomography—alongside continued contributions in classical networking areas such as DDoS detection and edge computing. His exceptional contributions have been recognized with numerous prestigious awards: 2007 IEEE Koji Kobayashi Computer and Communications Award 2007 ACM SIGMETRICS Achievement Award 2008 ACM SIGCOMM Award 2011 INFOCOM Achievement Award 1999 IEEE Communications Society William Bennett Award 2008 ACM SIGCOMM Test of Time Paper Award 2012 ACM SIGMETRICS Test of Time Award 2018 ACM MOBICOM Test of Time Award UMass Award for Outstanding Accomplishments in Research and Creative Activity University of Massachusetts Chancellor's Medal UMass Amherst Distinguished Graduate Mentor Award Outstanding Research Award from the College of Natural Science and Mathematics IBM Faculty Fellowship Award (twice) Fellow of the IEEE Fellow of the ACM Corresponding member of the Brazilian Academy of Sciences Prof. Towsley has mentored numerous graduate students, as evidenced by his Distinguished Graduate Mentor Award, and his research has been funded by significant grants including an NSF NeTS grant for quantum network design. He leads the Gaia research group at UMass Amherst, which has evolved from traditional networking research to pioneering quantum networking initiatives.

• Wireless Communication Systems and Technologies
• Signal Processing
• Full Duplex Communications
• +۳ مورد دیگر

Prof. Xiaojing Huang is a Professor of Information and Communications Technology at the University of Technology Sydney (UTS), serving as Head of Discipline for SEDE Communications and Electronics within the School of Electrical and Data Engineering. He leads the Mobile Sensing and Communications program at the Global Big Data Technologies Centre. With over 30 years of experience, he has authored over 300 publications and 31 patents, focusing on wireless communications, signal processing, and antenna technologies. Education: PhD (Electrical Engineering, Shanghai Jiao Tong University, 1989). Previous roles include Principal Research Scientist at CSIRO (2009-2014), Associate Professor at University of Wollongong (2004-2009), and key industry roles at Motorola and Shanghai Yang Tian Science and Technology Corporation. Research interests include full-duplex wireless systems, millimeter-wave and terahertz communications, massive antenna arrays, and mixed-signal processing platforms. His work on the CSIRO Ngara backhaul system earned multiple awards, including the 2012 CSIRO Chairman's Medal and Australian Engineering Innovation Award. Recent grants include $4.2M (AUD) for projects like 'Radio Frequency Camera for Radar Imaging' (ARC DP220101158) and 'Terabit mm-Wave Backbones for Integrated Space Networks' (ARC DP200101532). He has supervised numerous students in high-speed communication systems and full-duplex technologies. Awards include: 2013 CSIRO Leadership Achievement Award, 2012 Australian Engineering Innovation Award, and IEEE Sumner Award (nominee). Active in IEEE standards (802.11/802.15) and collaborations with institutions like Tsinghua University.

Shungeng Zhangمشاهده پروفایل

استادیار

• Distributed Systems
• Cloud Computing
• Cybersecurity
• +۴ مورد دیگر

Shungeng Zhang serves as an Assistant Professor in the Department of Computer & Cyber Sciences at Augusta University's School of Computer and Cyber Sciences. He holds a Ph.D. in Computer Science from Louisiana State University (2021) and a B.E. in Computer Engineering from Huazhong University of Science and Technology (2014). His educational background includes: Ph.D. in Computer Science, Louisiana State University, 2021 B.E. in Computer Engineering, Huazhong University of Science and Technology, 2014 Dr. Zhang's research spans distributed systems, cloud computing, and cybersecurity with a focus on enhancing performance and scalability of web applications and IoT stream processing in cloud environments. He employs sophisticated timeline analysis and fine-grained monitoring to identify transient bottlenecks causing long-tail latency problems, addressing propagation effects in complex dependency chains among application components. His publication record (2017-2022) reveals consistent contributions to cloud systems performance, particularly in n-tier architectures, concurrency control, and latency optimization. Key themes include stream processing synchronization, fanout query performance, adaptive concurrency for SLO compliance, and mitigation of transient resource contention attacks across distributed systems. No scientific awards were mentioned in the provided information. Dr. Zhang actively seeks graduate students with strong computer systems backgrounds for research collaboration. His departmental service includes Faculty Assembly participation (2021-2022) and faculty interviewing duties. Professionally, he serves as a reviewer for ACM SoCC'23, ACM TOIT, SmartCom 2023, The Journal of Supercomputing, and ACM SoCC'22. Teaching responsibilities include AIST 4720 (Enterprise System Architectures), CSCI 1200 (Introduction to Computers and Programming), and graduate courses such as CSCI 8940 (Dissertation Research) and AIST 3310 (Advanced Networking). No dedicated research labs or teams were specified in the available documentation.

Karl Henrik Johanssonمشاهده پروفایل

استاد

• Networked and distributed control
• Cyber-physical systems
• Cyber-secure control systems
• +۴ مورد دیگر

Karl Henrik Johansson is a Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm, Sweden, where he also serves as the Founding Director of Digital Futures. He is a Fellow of both IEEE and the Royal Swedish Academy of Engineering Sciences, and has held leadership positions including Immediate Past President of the European Control Association and IEEE Control Systems Society Vice President Diversity, Outreach & Development. Dr. Johansson earned his MSc in Electrical Engineering and PhD in Automatic Control from Lund University. His academic journey includes visiting positions at prestigious institutions such as UC Berkeley, Caltech, and NTU. His research focuses on networked control systems and cyber-physical systems with applications in transportation, energy, and automation networks. His work investigates fundamental challenges in connecting physical world systems through communication networks, exploring how wireless communication and sensor technology can enhance system robustness, reliability, energy efficiency, and safety. Current research directions include security of cyber-physical systems, distributed optimization, multi-agent systems, and applications to intelligent transportation and energy networks. Analysis of his recent publications reveals a strong focus on distributed optimization algorithms, secure networked control, multi-agent systems, and applications to transportation and energy networks. His work increasingly integrates machine learning techniques with traditional control theory, addressing challenges in privacy-preserving distributed computation, resilient state estimation, and resource allocation in complex networked systems. IEEE Control Systems Society Hendrik W. Bode Lecture Prize (2024) Swedish Research Council Distinguished Professor (2018-2027) Wallenberg Scholar (2009-2026) IFAC Young Author Prize IEEE CSS Distinguished Lecturer (2017-2019) IFAC Outstanding Service Award IEEE Fellow Dr. Johansson has supervised over 100 postdocs and PhD students, with many now holding prominent positions at institutions worldwide. His research has been supported by significant grants including the Swedish Research Council Distinguished Professor Grant (2018-2027), multiple Wallenberg Foundation grants, and numerous EU and national research projects. He has directed major research centers including ACCESS Linnaeus Centre (2009-2016) and Strategic Research Area ICT TNG (2013-2020). His research group operates within the Digital Futures initiative and maintains strong connections with industry partners through projects like the Integrated Transport Research Lab (supported by Scania and Ericsson) and Smart Mobility Lab. The group actively collaborates with international institutions and participates in major EU-funded projects addressing challenges in cyber-physical systems, transportation, and energy networks.

Ying Caiمشاهده پروفایل

دانشیار

• AI
• Machine Learning
• Data Science
• +۴ مورد دیگر

Ying Cai is an Associate Professor in the Department of Computer Science at Iowa State University, joining in 2003 after earning his Ph.D. in Computer Science from the University of Central Florida (2002). His research focuses on AI, machine learning, data science, cybersecurity, privacy protection, and database systems. He leads projects funded by the Air Force Research Laboratory, including work on authentication data structures for rank-aware queries, requiring U.S. citizenship and expertise in linear algebra/cryptography. Dr. Cai’s work spans cybersecurity (e.g., adversarial example defense, secure secret sharing), spatio-temporal systems (e.g., traffic risk prediction, check-in time modeling), and healthcare AI (e.g., cervical spine diagnosis with transformers). His publications emphasize practical applications of ML in privacy, security, and distributed systems. Professional roles include Associate Editor for Multimedia Tools and Applications (since 2009), Co-chair for COMPSAC TAIN/NCIW symposium (2014–2017), and TPC Chair for Mobilware 2010. His service includes contributions to INFOCOM, ICDCS, and MDM conferences. Current research opportunities exist for graduate students with strong programming/math skills, particularly in cryptography and linear algebra. He emphasizes interdisciplinary work, such as bridging AI with social sciences via large language models.

Michela Becchiمشاهده پروفایل

دانشیار

• Computer Architecture
• Systems Software
• Heterogeneous Computing
• +۵ مورد دیگر

Michela Becchi is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. She specializes in computer architecture, systems software, and applications, with a focus on heterogeneous systems, parallel algorithms, and acceleration techniques for bioinformatics, pattern recognition, and quantum computing. Her work spans multi-core CPUs, GPUs, FPGAs, and distributed clusters, emphasizing the boundary between hardware and software design. Dr. Becchi holds a Ph.D. and Master’s degree in Computer Engineering from Washington University in St. Louis (2009) and a Bachelor’s degree in Computer Engineering from Politecnico di Milano, Italy (2000). Her research has been recognized with prestigious awards, including the NSF CAREER Award (2015) and the University of Missouri System President Award for Early Career Excellence (2016). Her research interests include compiler and runtime techniques for heterogeneous systems, acceleration of bioinformatics algorithms, and high-speed networking applications. She has pioneered frameworks for efficient data transformation, GPU-accelerated compression, and memory-efficient graph algorithms for quantum computing. Her work also explores thread coarsening, mixed-precision auto-tuning, and secure multi-core processor design. Key contributions include the PILOT runtime system for GPU memory management, the GPU-FPtuner auto-tuner for floating-point applications, and innovative approaches to automata processors for genomic analysis. Her publications emphasize reproducible accuracy in scientific simulations and the optimization of irregular applications on many-core platforms.

James Andersonمشاهده پروفایل

استادیار

• Optimal Control Theory
• Robust Control
• Mathematical Programming
• +۴ مورد دیگر

James Anderson is an Assistant Professor in the Department of Electrical Engineering at Columbia University, with affiliations to the Data Science Institute (DSI) and multiple research centers including the Computing Systems for Data-Driven Science and Foundations of Data Science. Prior to Columbia, he was a Senior Research Scientist at Caltech’s Computing + Mathematical Sciences division (2016–2019) and held a Junior Research Fellowship at the University of Oxford’s Department of Engineering Science (pre-2012). He earned his DPhil (PhD) in Engineering Science from Oxford in 2012. His research focuses on optimal/robust control theory, mathematical programming, data privacy, and cyber-physical systems, with applications in smart grids, systems biology, and power systems. Recent work emphasizes energy storage strategies, distributed control algorithms, and cybersecurity in critical infrastructure. His publications span advanced control methodologies (e.g., reinforcement learning for LQR problems), energy market dynamics, and resilient system designs. Notable contributions include frameworks for decision-focused energy storage arbitrage and defenses against false data attacks in power grids. He actively collaborates on federated learning approaches for distributed systems and has pioneered techniques for system-level synthesis in cyber-physical architectures. Anderson’s affiliations include the Data Science Institute (DSI) and specialized centers focused on data-driven science and energy systems. His work bridges theoretical control advancements with real-world applications in energy and healthcare.

Ahmad Afsahiمشاهده پروفایل

استاد

• Parallel Processing
• High-Performance Computing
• Network-Based Computing
• +۵ مورد دیگر

Dr. Ahmad Afsahi is a Professor in the Department of Electrical and Computer Engineering at Queen's University, Canada. He leads the Parallel Processing Research Laboratory (PPRL) and chairs the Graduate Studies committee in ECE. His research focuses on parallel processing, high-performance computing (HPC), and network-based systems, with emphasis on communication runtime systems, accelerated computing, and deep learning infrastructure. Education: Ph.D. (Electrical Engineering, 2000) from University of Victoria; M.Sc. (Computer Engineering, Sharif University of Technology); B.Sc. (Computer Engineering, Shiraz University). Research interests include parallel programming models, MPI optimization, GPU-aware communication, network-aware algorithms, and power-efficient HPC systems. He is a Senior Member of IEEE, ACM member, and licensed Professional Engineer in Ontario. Key Awards: Canada Foundation for Innovation Award, Ontario Innovation Trust Award. Over 50 publications in top venues like SC, EuroMPI, IPDPS, and IEEE journals. Current teaching includes cluster computing and digital systems. Labs/Groups: PPRL, Queen's Collaborative Graduate Specialization in Computational Science and Engineering, Data, Analytics, and Computing (DAC) Research Group.

AI Xinمشاهده پروفایل

مدرس

• Artificial Intelligence
• Multi-Agent Systems & Algorithmic Game Theory
• Machine Learning and Deep Learning
• +۵ مورد دیگر

AI Xin is a Lecturer in the School of Computing at the National University of Singapore (NUS), specializing in Artificial Intelligence and Data Science. She teaches courses such as machine learning, deep learning, and data mining, including advanced modules like CS4225 and CS5425. Education: Ph.D. in Electrical and Computer Engineering from NUS; B.Eng. from Xidian University, China. Her research spans Game Theoretical Modelling , Optimization Methods , Algorithm Design , and Wireless Networks . She has contributed to multi-agent systems, algorithmic game theory, and wireless community networks, focusing on robust and distributed solutions. Her recent publications highlight trends in game theory for wireless networks , distributed coverage algorithms , and optimization for network efficiency , with a strong emphasis on theoretical and practical applications in AI and networking. Scientific Awards: Teaching Excellence Award (NUS, 2024). She has taught courses on Big Data Systems for Data Science and Computational Thinking , bridging academic rigor with industry relevance through her prior experience in risk management, supply chain, and sales at BHP Billiton Marketing Asia.

Christian Bickمشاهده پروفایل

دانشیار

• Dynamical Systems
• Coupled Oscillator Networks
• Network Dynamics
• +۳ مورد دیگر

Christian Bick is an Associate Professor at the Department of Mathematics, Vrije Universiteit Amsterdam (VU Amsterdam). He holds visiting roles as a Visiting Research Fellow at the University of Oxford's Mathematical Institute, Honorary Associate Professor at the University of Exeter, and Visiting Fellow at the Institute for Advanced Study (TUM-IAS), Technische Universität München. His research focuses on dynamical systems and applications, particularly in network dynamics, coupled oscillator networks, and higher-order interactions. He has received prestigious awards such as the Marie Curie Intra-European Fellowship (2015) and the Hans Fischer Fellowship (2019). Education and Career: Bick obtained his PhD from Georg-August-Universität Göttingen (2012) and held postdoctoral positions at Rice University and the University of Exeter. His work bridges theoretical and applied mathematics, with interdisciplinary collaborations in neuroscience, physics, and engineering. Research Interests: Bick explores dynamics of coupled oscillator networks, asynchronous networks, and higher-order interactions. His recent work includes studies on heteroclinic networks, chimera states, and synchronization phenomena in complex systems. He leads projects like BeyondTheEdge (Marie Skłodowska–Curie Doctoral Network) and has contributed to grants such as the EPSRC New Investigator Award (2020–2023). Teaching: He teaches Dynamical Systems at VU Amsterdam and has lectured on Stochastic Processes, Mathematical Methods, and Dynamical Systems and Chaos at the University of Exeter and Oxford. Awards and Grants: His honors include the DAAD Doktorandenstipendien (2010, 2012) and Fulbright support (2008). Active grants include projects on higher-order networks and neurodegenerative disease modeling.

• Wireless Networks
• Internet of Things
• Network Security
• +۷ مورد دیگر

Robin Kravets is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign, where she leads the Mobius research group. She earned her Ph.D. in Computer Science from the Georgia Institute of Technology in August 1999. Her office is located at 3114 Siebel Center for Comp Sci, and she can be contacted via email at rhk@illinois.edu or phone at (217) 244-6026. Courses Taught: CS 438 (ECE 438) - Communication Networks CS 498 WN3 (CS 498 WN4) - Wireless IoT Lab CS 591 PH2 (CS 591 PHD) - PhD Orientation Seminar CS 591 SCH - PhD Job Search Prep CS 591 WN - Wireless Networking Seminar ECE 439 (CS 439) - Wireless Networks ENG 572 - Professional Practicum Research Focus: Dr. Kravets specializes in wireless and mobile systems, with emphasis on IoT security, energy-efficient networking, and privacy-preserving protocols. Her work addresses challenges in dense network environments, vehicular communication, and low-power devices, often leveraging probabilistic models and adaptive protocols to enhance system resilience. Publication Trends: Her recent articles (2016-present) predominantly explore wireless IoT security (e.g., MAC randomization defenses, BLE optimization) and practical applications in environmental sensing/retail. Earlier work (2007-2015) established foundations in vehicular networks, disruption-tolerant communication, and energy-aware protocols. Leadership: She directs the Mobius Group, which develops solutions for mobile and IoT systems, including projects like IVP (Illinois Vehicular Project) and contributions to standards in pervasive computing.

James Aspnesمشاهده پروفایل

استاد

• Distributed Algorithms
• Computer Science
• Randomized Algorithms
• +۵ مورد دیگر

James Aspnes is the Harold W. Cheel Professor of Computer Science at Yale University, specializing in distributed algorithms and randomized methods. He holds a PhD from Carnegie Mellon University and degrees from MIT. His research focuses on distributed systems, peer-to-peer networks, and sensor networks, emphasizing tools for efficient data management and fault-tolerance. Education: PhD (CMU, 1992), SM & SB (MIT, 1987) Affiliations: Yale since 1993, IBM Almaden Research Center (1992–1993) Research interests include distributed algorithms, randomization, and applications in biology and economics. Notable contributions include skip graphs, population protocols, and consensus algorithms. He has received the ACM-EATCS Dijkstra Prize (2020) and Dylan Hixon Prize (2000). Publications span distributed computing, algorithms, and cryptography. Recent work explores consensus protocols and privacy in population models. Grants include NSF awards totaling over $2M. Active in editorial roles (Algorithmica, Distributed Computing) and conference organization (PODC 2005, DCOSS 2007).

Ameer Abdelhadiمشاهده پروفایل

استادیار

• Hardware Acceleration
• Neurotechnology
• Reconfigurable Computing
• +۴ مورد دیگر

Dr. Ameer Abdelhadi is an Assistant Professor in the Department of Electrical and Computer Engineering at McMaster University. His research focuses on application-specific custom-tailored computer architectures, hardware-efficient deep learning, neurotechnology, and reconfigurable computing. He holds a PhD from the University of British Columbia and has held academic positions at the University of Toronto, Imperial College London, and Simon Fraser University, alongside industry experience in semiconductor design. Education: PhD in Computer Engineering (University of British Columbia, 2016). Research Interests: Hardware acceleration for machine learning and neurotechnology Reconfigurable computing and FPGAs/ASICs Asynchronous circuits and synchronization protocols VLSI physical design and CAD algorithms Publications span high-impact venues such as IEEE Journal of Solid-State Circuits, IEEE Hot Chips, and IEEE Micro. Notable achievements include the 2017 Best Paper Award at ASYNC for work on synchronization FIFOs. Teaching includes COMPENG 4DV4 (VLSI System Design) and ELECENG 4OI6B (Engineering Design). His lab focuses on advancing hardware systems for next-generation applications in AI and biomedical engineering.