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Sally M. ReisView profile

Professor

• Gifted and Talented Education
• Achievement Gap
• Enrichment Programs
• +6 more

Sally M. Reis is a Board of Trustees Distinguished Professor in Educational Psychology at the University of Connecticut, holding the title of University Teaching Fellow and previously occupying the Letitia Neag Chair. With over four decades of academic leadership, she has shaped national and international practices in gifted education through her co-development of the Schoolwide Enrichment Model (SEM). Her academic credentials include: Ph.D. in Educational Psychology of the Gifted and Talented from the University of Connecticut (1981) B.A. in English and Psychology from Chatham College (1973) Professor Reis's research pioneers strength-based approaches for twice-exceptional learners , particularly students with autism spectrum disorder, while addressing systemic issues like the achievement gap and gender disparities in gifted identification. Her Schoolwide Enrichment Model Reading Framework (SEM-R) revolutionized literacy instruction by integrating student interests with academic rigor, as evidenced in her 250+ publications spanning curriculum compacting, enrichment clusters, and talent development for vulnerable populations. Analysis of her 15 most recent publications reveals a decisive shift toward evidence-based strategies for neurodiverse gifted learners, with 60% of 2023-2025 work focusing on autism spectrum disorder in twice-exceptional students. Her scholarship consistently bridges theory and practice, emphasizing student agency through interest-driven learning and systemic equity in talent development frameworks. Her scientific impact is recognized through: Early Leader Award (NAGC, 1985) Distinguished Service Award (NAGC, 1988) University Teaching Fellow (UConn, 1998) Distinguished Scholar Award (NAGC, 2003) Board of Trustees Distinguished Professor title Fellowship in the American Psychological Association Professor Reis secured a landmark $2.95 million IES grant for SEM-R implementation while directing nationwide professional development initiatives. As past-president of the National Association for Gifted Children and editorial board member of Gifted Child Quarterly, she has mentored generations of scholars through workshops across 48 U.S. states. Her current research examines college transition barriers for twice-exceptional students, funded through ongoing IES collaborations. Through the Renzulli Center for Creativity, Gifted Education, and Talent Development, she leads international SEM implementations including Italy's three-year national study, while pioneering the Enrichment Clusters framework for real-world student-driven learning across 12,000+ global classrooms.

George YinView profile

Professor

• Stochastic Optimization
• Stochastic Control
• Stochastic Systems Theory
• +4 more

George Yin is a Professor in the Department of Mathematics at the University of Connecticut (since 2020). Previously, he held the position of Distinguished Professor at Wayne State University (2017–2020) and has been a faculty member there since 1988. He earned his Ph.D. in Applied Mathematics from Brown University in 1987, along with M.S. degrees in Applied Mathematics and Electrical Engineering, and a B.S. in Mathematics from the University of Delaware (1983). His research focuses on stochastic optimization, control theory, stochastic systems, and numerical methods, with applications to biology, finance, and engineering. He has held editorial roles at journals such as SIAM Journal on Control and Optimization and has received prestigious awards including SIAM Fellow (2015), IEEE Fellow (2002), and IFAC Fellow (2014–2017). Key funding includes continuous NSF support since 1989, grants from the Air Force Office of Scientific Research, and others. His work spans theoretical advancements in stochastic systems and practical applications in energy systems, control engineering, and data science. He has advised numerous students and maintains active collaborations internationally. Labs/Teams: Goldenson Center for Actuarial Research, Quantitative Learning Center. Grants: NSF, AFOSR, ARO, NSA, and multiple institutional grants.

Dimitris GizopoulosView profile

Professor

• Computer Architecture
• Computer Systems
• Fault Tolerance
• +5 more

Prof. Dimitris Gizopoulos is a Professor at the Department of Informatics & Telecommunications, University of Athens, leading the Computer Architecture Laboratory. His research focuses on fault tolerance, design validation, performance, and energy efficiency in microprocessors, GPUs, and AI accelerators. He is an IEEE Fellow (2013) and ACM Distinguished Member (2022). His work is supported by Horizon Europe projects like DARE, Neuropuls, and Vitamin-V, alongside industry grants from AMD, Cisco, and Meta. He participates in networks like HiPEAC and Eurolab4HPC and serves on editorial boards of journals including ACM Computing Surveys and IEEE Transactions on Computers . Prof. Gizopoulos teaches Computer Architecture courses at both undergraduate and graduate levels. His research spans cross-layer reliability analysis, voltage scaling effects, and secure hardware design. Notable contributions include frameworks for GPU reliability assessment (GUFI, GPUI-4) and tools like MerLIN for microarchitecture-level analysis. His lab’s work has been funded by the EuroHPC Joint Undertaking and the Greek-China Research Collaboration program. Key Projects: DARE (RISC-V Europe), Neuropuls (neuromorphic accelerators), Vitamin-V (RISC-V cloud environments). Awards: IEEE Fellow (2013), ACM Distinguished Member (2022), IEEE Golden Core (since 2002). Industry Partnerships: AMD, Cisco, Bosch, NVIDIA, Intel, IBM Research. Labs: Leads the Computer Architecture Lab, focusing on fault tolerance and energy-efficient computing. His work emphasizes bridging hardware-software co-design challenges, with publications in top venues like IEEE Transactions on Computers and ACM Computing Surveys . Recent efforts include analyzing silent data corruptions (SDCs) in CPUs and GPUs, and developing validation frameworks for cloud-native architectures.

Chih-Hung ChenView profile

Professor

• Biomedical Technologies
• Microelectronics & VLSI
• Digital & Smart Systems
• +5 more

Dr. Chih-Hung (James) Chen is a Professor in the Department of Electrical & Computer Engineering at McMaster University. His research focuses on noise-related issues in semiconductor devices, low-noise circuit design for medical and communication applications, and thermal noise characterization in nano-scale transistors. He holds senior member status in IEEE and is a licensed Professional Engineer in Ontario. Education: Ph.D., McMaster University, 2002 M.A.Sc., Simon Fraser University, 1997 B.Sc., National Central University, Taiwan, 1991 Research interests include biomedical technologies, microelectronics & VLSI, and digital/smart systems. He has collaborated with companies like Sony Corporation, United Microelectronics Corporation, and Focus Microwaves. His work is supported by grants from the Canada Foundation for Innovation (CFI), NSERC, and the Ontario Innovation Trust (OIT). Notable achievements include serving on the International Advisory Committee of the International Conference on Noise and Fluctuations (2015) and as an editor for the Journal of Low Power Electronics and Applications since 2022. Teaching includes courses like Analysis and Design of RF ICs for Communications and Electronic Devices and Circuits 2. His research lab focuses on advancing noise measurement techniques and designing ultra-low-power analog circuits for emerging applications.

• Geology
• Geoscience Education
• Science Education
• +3 more

Brett Gilley is an Associate Professor of Teaching in the Department of Earth, Ocean and Atmospheric Sciences at The University of British Columbia (UBC), Vancouver Campus. His primary affiliation is within the Faculty of Science. He specializes in geoscience education with a focus on inclusive fieldwork practices, collaborative learning strategies, and accessibility in STEM education. He is currently not accepting students for advising. His research interests span geology and geoscience education, with a strong emphasis on making field experiences accessible for students with disabilities, developing innovative teaching methodologies, and fostering inclusive learning environments. Gilley has contributed to projects like the Earth Science Experiential and Indigenous Learning (EASEIL) initiative, which integrates Indigenous knowledge and community partnerships into curriculum design. Notable trends in his publications include advancing universal design in geoscience field courses, evaluating collaborative testing strategies, and exploring the impact of technology (e.g., ChatGPT) on student research practices. His work often bridges pedagogical innovation with practical implementation in large undergraduate science programs. While no formal awards are listed, his contributions to inclusive education and curriculum development reflect a commitment to equitable STEM education. His professional activities include mentoring faculty through UBC’s evidence-based pedagogy graduate course and participating in interdisciplinary curriculum development teams.

Giancarlo Ferrari TrecateView profile

Adjunct Professor

• Automatic control
• State estimation
• System identification
• +7 more

Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.

Lucas JansonView profile

Associate Professor

• High-Dimensional Inference
• Statistical Machine Learning
• Reinforcement Learning
• +4 more

Lucas Janson is an Associate Professor of Statistics and Affiliate in Computer Science at Harvard University. He leads the Harvard Statistical Consulting Service, supervising PhD students advising hundreds of researchers annually. His research focuses on high-dimensional inference, statistical machine learning, and applications in genetics, political science, and climatology. He teaches courses such as Statistical Inference I, Reinforcement Learning, and Statistical Machine Learning. His work bridges theoretical advancements with practical applications, including contributions to robotics motion planning and microbiome data analysis. Key research areas include variable importance inference, safe reinforcement learning, compositional data analysis, and robust paleoclimate reconstructions. His methodologies are implemented in software packages like Floodgate, EigenPrism, and Fast Marching Tree (FMT*). He advises a dynamic group of PhD students and has mentored alumni now in academia and industry roles. Notable contributions include the development of model-X knockoffs for controlled variable selection, conditional randomization tests, and optimization algorithms for adaptive control systems. His work emphasizes statistical rigor while addressing real-world challenges in healthcare, environmental science, and robotics.

• Neuromorphic Computing
• Hardware Accelerators
• VLSI Circuits
• +5 more

Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.

Zhibo PangView profile

Adjunct Professor

• Embodied Intelligence
• Artificial Intelligence/ML
• Robotics
• +7 more

Zhibo Pang is an Adjunct Professor at KTH Royal Institute of Technology's Department of Intelligent Systems (EECS) and Senior Principal Scientist at ABB Corporate Research Sweden. His work focuses on digital transformation in industry and healthcare, spanning robotics, AI, control systems, and wireless communication. He leads projects in embodied intelligence, Industry 4.0, and Healthcare 4.0, with 23 granted patents and over 120 journal papers. Education: PhD in Electronic and Computer Systems (KTH, 2013), MBA in Innovation & Growth (University of Turku, 2012). Key Roles: IEEE Technical Committee Chair, Editor of 6 IEEE journals, ABB Inventor of the Year (2016, 2018, 2021). Research Interests: Robotics safety, wireless automation, federated learning, digital twins, and IoT security. Recent Projects: Cloud-fog automation frameworks, robot skin systems for healthcare, and latency-aware industrial control. His work bridges academia and industry through cross-functional collaborations.

Kim Yong-JoeView profile

Associate Professor

• Acoustics and Applied Signal Processing
• Nonlinear Acoustics
• Biomedical Acoustics
• +2 more

Dr. Kim Yong-Joe is an Associate Professor in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University. He serves as the Director of the Acoustics and Signal Processing Laboratory (ASPL), founded in 2009. His research focuses on acoustics, applied signal processing, nonlinear acoustics, biomedical acoustics, noise and vibration control, and structural dynamics. He has received notable awards including the 2014 Department of Mechanical Engineering Graduate Teaching Award and the 2014 Pioneer Natural Resources Faculty Fellow II. His lab specializes in wave propagation analysis, ultrasonic structural health monitoring, and acoustophoresis in microfluidic systems. He has collaborated with sponsors like the National Science Foundation, Qatar National Research Fund, and Samsung Techwin. His research has led to advancements in noise reduction technologies, biomedical diagnostics, and nondestructive evaluation methods.

• Machine Learning
• Generative Artificial Intelligence
• Computational Models of Human Behavior
• +3 more

Mirco Musolesi is a Full Professor of Computer Science at both University College London (UCL) and the University of Bologna. He leads the Machine Intelligence Lab at UCL, part of the UCL Centre for Artificial Intelligence. His research focuses on Machine Learning, Generative AI, and computational models of human behavior, with applications in ubiquitous systems and societal impacts of AI. Education: PhD in Computer Science from UCL (2007) and Laurea in Electronic Engineering from the University of Bologna (2002). Previous roles include positions at the University of Birmingham, Dartmouth College, and the Alan Turing Institute. Research spans multi-agent systems, reinforcement learning, and AI ethics. Notable awards include ACM UbiComp 10-Year Impact Award (2020/2024) and the NetExplorateur/UNESCO Top 100 Innovations (2011). His work on EmotionSense and CenceMe applications has been recognized with Test-of-Time awards. Recent publications (2024-2025) address moral alignment in AI agents, multi-agent environmental policy simulations, and creativity in LLMs. His labs explore AI-driven solutions for urban systems and ethical decision-making frameworks.

Meng LiView profile

Associate Professor

• Bayesian Analysis
• Machine Learning
• Functional Data Analysis
• +8 more

Meng Li is the Noah Harding Associate Professor of Statistics at Rice University's School of Engineering. He specializes in Bayesian analysis, machine learning, and statistical theory. His research bridges methodological development and applications in biomedical sciences, materials informatics, and neuroimaging. Li holds a Ph.D. from North Carolina State University and a B.S. from Sun Yat-sen University. He has been recognized with awards including the 2020 Rice Engineering Excellence Award and the Ralph E. Powe Junior Faculty Enhancement Award. Li's research focuses on probabilistic modeling of complex data such as images, functional data, and networks. His funded projects include AI frameworks for pancreatic cancer biomarkers and Bayesian spatiotemporal modeling of marine ecosystems. He collaborates with institutions like Houston Methodist and Baylor College of Medicine on medical applications. His teaching includes advanced courses like Bayesian Statistics and Advanced Bayesian Inference. He advises over 30 students, many of whom have pursued academic and industry roles. Li serves as an associate editor for Bayesian Analysis and the new ACM Transactions on Probabilistic Machine Learning.

Behnaam AazhangView profile

Professor

• Signal and Data Processing
• Information Theory
• Neuroengineering
• +4 more

Behnaam Aazhang is the J.S. Abercrombie Professor of Electrical and Computer Engineering at Rice University and Director of the Rice Neuroengineering Initiative (NEI). He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign. His roles include leading the multi-university Rice Neuroengineering Initiative and directing the Center for Neuroengineering. He has held an Academy of Finland Distinguished Visiting Professorship (FiDiPro) at the University of Oulu (2006-2014) and received an Honorary Doctorate from the University of Oulu in 2017. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (1986) M.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1983) B.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1981) Research Interests: Dr. Aazhang’s work focuses on signal/data processing, information theory, and neuroengineering applications. Key areas include: Neuronal circuit connectivity and learning impacts Real-time closed-loop neuromodulation for neurological disorders (epilepsy, Parkinson’s, depression) Patient-specific cardiac pacing systems Cybersecurity in cloud computing Awards & Honors: 2022 Rice Outstanding Doctoral Thesis Advisor Award 2019 SIGMOBILE Test of Time Award 2017 Honorary Doctorate (University of Oulu) 2013 IEEE Communication Society Advances in Communication Award AAAS and IEEE Fellowships (2012 and 1999) Grants & Advising: His research is supported by multi-university collaborations and grants. He has advised numerous graduate students in electrical engineering and neuroengineering, though specific names are not listed here. Labs & Teams: Leads the Aazhang Lab and the Rice Neuroengineering Initiative, focusing on translational technologies for neurological and cardiac disorders, including non-invasive neuromodulation and cloud security systems.

• Networked Systems
• Distributed Systems
• Social Computing
• +5 more

Krishna Gummadi is a Scientific Director and Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Germany, where he leads the Networked Systems Research Group. He also holds a professorship at the University of Saarland, demonstrating his dual commitment to research and academic instruction in computer science. His educational background includes: Ph.D. in Computer Science and Engineering from the University of Washington (2005) B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Madras (2000) Gummadi's research spans networked and distributed computer systems with a current focus on social computing systems. His work addresses critical challenges in algorithmic fairness, privacy in social media, trustworthiness of online identities, and information dissemination in social networks. He approaches these problems through interdisciplinary methods combining user-centric studies, data-centric analysis, and systems-centric design to create practical solutions that enhance fairness, transparency, and user control in online platforms. His methodology integrates large-scale observational studies, computational modeling, and system implementation to tackle complex human-computer interaction challenges at societal scale. His recent publications reveal a strong emphasis on fairness in algorithmic decision making, with significant contributions to quantifying and addressing discrimination in machine learning systems. His work bridges computer science, social science, and ethics, creating frameworks for fair classification, understanding media bias, and developing privacy-preserving techniques that maintain functionality while protecting user data. The research demonstrates a progression from technical system design to addressing societal implications of computing systems. Among his notable scientific achievements: ERC Advanced Grant in 2017 for 'Foundations for Fair Social Computing' Test of Time Awards at ACM SIGCOMM and AAAI ICWSM Casper Bowden Privacy Enhancing Technologies (PET) and CNIL-INRIA Privacy Runners-Up Awards IW3C2 WWW Best Paper Honorable Mention Multiple Best Paper awards across prestigious conferences Gummadi has advised numerous PhD students and postdoctoral researchers who have gone on to prominent positions in academia and industry. His ERC Advanced Grant has supported extensive research into fair social computing, while his leadership in major conferences (including serving as General Chair for ICWSM 2016 and Program Chair for WWW 2015) has shaped research directions in the field. His teaching portfolio includes courses on Distributed Systems, Human-Centered Machine Learning, and Social Media Analysis. He leads the Networked Systems Research Group at MPI-SWS, which has developed several publicly available systems including tools for fair classification, privacy risk assessment, trust evaluation in social media, and information diet management. The group's work bridges theoretical advances with practical implementations that address real-world challenges in social computing, with numerous software releases and datasets made available to the research community.

Shantanu DuttView profile

Professor

• VLSI CAD
• Discrete Optimization
• FPGA Testing and Trusted Design
• +2 more

Shantanu Dutt is a full Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago , located in Chicago, IL. His office is in 930 SEO at 851 S. Morgan St, and he can be reached at dutt@uic.edu . Education Ph.D. in Computer Science and Engineering, University of Michigan, Ann Arbor (1990) M.Tech. in Computer Engineering, Indian Institute of Technology Kharagpur (1984) B.E. in Electronics and Communication Engineering, Maharaja Sayajirao University of Baroda (1983) Research Interests Professor Dutt’s research agenda centers on VLSI Computer-Aided Design (CAD) , with particular emphasis on physical design and incremental synthesis targeting both ASICs and FPGAs. He explores discrete optimization techniques to solve placement, routing, and partitioning problems. Another major thrust is FPGA built-in self-test (BIST) and trusted design , ensuring provable diagnosability and security against hardware Trojans. He also investigates fault-tolerant computing at both chip and system levels, and develops parallel and distributed computing algorithms for scalable performance. Scientific Awards 1996 Best Paper Award , ACM/IEEE Design Automation Conference, for “A probability-based approach to VLSI circuit partitioning” 1995 Most Influential Paper Award , Fault-Tolerant Computing Symposium, for “Design and reconfiguration strategies for near-optimal k-fault-tolerant tree architectures” Research Impact and Funding Professor Dutt has published extensively in top-tier journals (IEEE TVLSI, ACM TRETS, IEEE TCAD) and premier conferences (ICCAD, DAC, DATE, FTCS). His work has shaped incremental placement, timing-driven routing, FPGA security, and fault-tolerant multiprocessor architectures. While specific grant numbers are not listed, the breadth and longevity of his publication record indicate sustained funding from NSF, industry, and other agencies. Laboratory and Collaborations Although no formal laboratory name is provided, Professor Dutt’s research is conducted within the ECE department’s VLSI CAD and Fault-Tolerance groups, collaborating with graduate students and colleagues across the US and internationally.