جست‌وجوی استاد و پژوهشگر

David Zhigang Panمشاهده پروفایل

استاد

• Electronic Design Automation
• Machine Learning
• Hardware Acceleration
• +۵ مورد دیگر

David Zhigang Pan is a Professor in the Department of Electrical & Computer Engineering at The University of Texas at Austin. He also holds the Silicon Laboratories Endowed Chair. Prior to joining UT Austin, he was a Research Staff Member at IBM T. J. Watson Research Center from 2000 to 2003. His academic journey began with a B.S. from Peking University, followed by M.S. and Ph.D. degrees from UCLA. Research Areas: Electronic Design Automation (EDA), Machine Learning Hardware, FPGA Prototyping, Optical Computing, Hardware Security, and CAD for Emerging Technologies Academic Timeline: Assistant Professor (2003-2008), Associate Professor (2008-2013), Full Professor (2013-present) His research focuses on design automation for mixed-signal circuits , GPU-accelerated EDA tools , and hardware-software co-design for AI . Recent work explores FFT-based optical neural networks and deobfuscation techniques for integrated circuits , reflecting his interdisciplinary approach at the intersection of machine learning , computer architecture , and semiconductor manufacturing . Key publication trends reveal expertise in: VLSI design , lithography optimization , and deep learning applications for EDA tools. His work has been recognized with multiple Best Paper Awards at top conferences including DAC , ASP-DAC , and HOST . Awards: IEEE Fellow (2014), SPIE Fellow (2017), ACM SRC Graduate Category Honors for students Patents: 8 U.S. Patents in electronic design and hardware optimization Prof. Pan has mentored 40 PhDs and postdocs who now hold key positions in academia and industry. He leads research initiatives involving GPU acceleration frameworks and optical computing architectures . His lab focuses on vertical integration of architecture, CAD tools, and fabrication technologies for next-generation hardware solutions.

Jeff Zhangمشاهده پروفایل

استادیار

• Deep learning
• Computer architecture
• Embedded systems
• +۵ مورد دیگر

Jeff Zhang is an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. He joined ASU in January 2023 after completing a postdoctoral fellowship at Harvard University. His research spans deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. Education: Ph.D., New York University M.Eng., B.Eng., Hunan University Dr. Zhang's research bridges theoretical machine learning with practical hardware implementation, developing novel architectures that optimize performance, power consumption, and reliability. He has pioneered approaches for hardware acceleration of large language models, efficient sparse matrix operations, and novel memory technologies for AI workloads. His work has received multiple awards including IEEE Top Picks in Test and Reliability (2023) and IEEE Micro Best Paper Award (2022). His recent publications demonstrate a strong trend toward heterogeneous computing, with significant work in chiplet-based AI accelerators, photonic computing for AI, and 2.5D/3D integration techniques. The research spans from high-level compiler frameworks to circuit-level innovations, with a consistent theme of co-designing algorithms and hardware for optimal AI performance. His work on the SODA toolchain has been particularly influential in bridging Python to silicon. Scientific Awards: IEEE Top Picks in Test and Reliability, IEEE ITC, 2023 Best Paper Award, IEEE Micro, 2022 Best Paper Award Candidate, IEEE DATE, 2022 Best Presentation Award Nomination, ACM SIGDA DATE PhD Forum, 2020 Best Paper Award Nomination, IEEE VLSI Test Symposium, 2018 Ernst Weber Ph.D. Fellowship, New York University, 2015, 2016 Dr. Zhang actively mentors a diverse group of graduate and undergraduate students, with several alumni now working at leading technology companies including Apple, TSMC, and Ansys. His research is supported by prestigious grants from NSF, Sandia National Labs, and industry partners. He serves on technical program committees of numerous top conferences and has organized special sessions on emerging topics like Gen AI for Chip Design and LLM-Aided Design. Dr. Zhang leads a vibrant research group that collaborates extensively with industry partners and national laboratories. Current projects focus on next-generation AI hardware, including chiplet-based systems, photonic accelerators, and novel memory technologies for large language models. His group has developed several open-source tools and frameworks, including the SODA toolchain for bridging Python to silicon.

• Energy Efficiency in Communication Networks
• Quality of Service (QoS) and Quality of Experience (QoE)
• Network and Service Management
• +۵ مورد دیگر

Dr. Frank Loh is a researcher at the Department of Computer Science III, University of Würzburg, specializing in energy efficiency, network performance, and Quality of Experience (QoE) in communication networks. His work focuses on optimizing LoRaWAN deployments, serverless computing, and edge-cloud environments, with an emphasis on reducing message collisions and improving resource utilization. He actively contributes to methodologies for gateway placement, traffic modeling, and energy consumption metrics. Research Areas Energy Efficiency in Communication Networks Quality of Service (QoS) and Quality of Experience (QoE) LoRaWAN Network Planning Edge and Serverless Computing Network Resource Analysis Recent Publications 2025: Energy modeling for 6G base stations 2025: Server cluster resilience via Markov models 2024: Serverless computing in edge-cloud environments 2024: LoRaWAN channel access optimization

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +۲ مورد دیگر

Geoffrey Hinton is a Professor in the Department of Computer Science at the University of Toronto , where he has been a pivotal figure in advancing artificial intelligence research. His work focuses on neural networks, deep learning, and machine learning, revolutionizing how machines process information and learn from data. With collaborations spanning institutions like NYU and IIT Mumbai, Hinton’s influence extends beyond academia into public discourse through lectures like the Romanes Lecture (2024) . His research explores Deep Belief Networks , Gradient Methods , Neural Network Architectures , and Probabilistic Models , with recent publications addressing novel algorithms like the Forward-Forward Algorithm and frameworks for Panoptic Segmentation . Though he no longer accepts students, current advisees include Jimmy Lei Ba and Cem Anil. Hinton’s contributions to AI are complemented by media engagements, including CBS 60 Minutes (2023) and CNN Amanpour (2023) , reflecting his role as a thought leader. His technical outputs, such as Nature Deep Learning Review (2015) with Y. LeCun and Y. Bengio, remain foundational texts in the field.

Elad Hazanمشاهده پروفایل

استاد

• Machine Learning
• Mathematical Optimization
• Control Theory
• +۴ مورد دیگر

Elad Hazan is a Professor of Computer Science at Princeton University and co-founder/director of Google AI Princeton. His research focuses on algorithmic foundations of machine learning and optimization, with significant contributions to online learning, nonstochastic control, and adaptive gradient methods. Princeton University (Faculty) Google AI Princeton (Co-founder & Director) His work bridges mathematical optimization, control theory, and computational complexity. Key contributions include the AdaGrad algorithm, sublinear-time optimization methods, and spectral filtering techniques for sequence modeling. Recent research emphasizes efficient neural architectures and provable guarantees in online control. Scientific awards include the Bell Labs Prize, IBM Goldberg Best Paper Award (twice), Google Research Award (twice), European Research Council grant, Marie Curie fellowship, and ACM Fellowship. He has served as program chair for COLT 2015 and on the Association for Computational Learning steering committee. His publications highlight trends in online convex optimization, spectral methods for dynamical systems, and adaptive gradient algorithms. Collaborations span Princeton, Google Brain Research, and interdisciplinary projects in robotics and AI safety.

Omobolanle Ogunseijuمشاهده پروفایل

استادیار

• Workforce Development
• Construction Safety
• Artificial Intelligence
• +۷ مورد دیگر

Omobolanle Ogunseiju is an Assistant Professor in the School of Building Construction at Georgia Institute of Technology . She holds a Ph.D. in Environmental Design and Planning from the Department of Building Construction at Virginia Tech. Education: Ph.D. in Environmental Design and Planning, Virginia Tech Current Role: Assistant Professor, Georgia Tech School of Building Construction Her research focuses on integrating wearable robotics and Artificial Intelligence (via digital twin , cyber-physical systems , and data sensing ) to improve construction workforce safety, health, and well-being . She explores ethical implications of automation in construction, particularly in human-technological dynamics. Key research trends include: Advancing smart communities through robotics and AI Exoskeleton evaluation for ergonomic risk reduction Mixed reality environments for construction education Data analytics for cognitive and physical risk assessment Professional identity development in construction engineering students Industry-academia alignment for sensing technology integration Scientific awards: Outstanding Doctoral Candidate, Myers-Lawson School of Construction Outstanding Doctoral Student, College of Architecture and Urban Studies at Virginia Tech Teaching philosophy emphasizes experiential learning , engagement techniques , and hierarchical assessments . She developed the Construction Cost Management course at Georgia Tech and will lead Construction Technology courses. Previously, she taught Smart Construction , Building Systems Technology , and Wireless Sensing in Construction Management at Virginia Tech.

Juan-Pablo Correa-Baenaمشاهده پروفایل

دانشیار

• Materials Science
• Photovoltaics
• Semiconductor Technology
• +۳ مورد دیگر

Juan-Pablo Correa-Baena is an Associate Professor at the Georgia Institute of Technology , holding the Goizueta Early Career Faculty Chair in the School of Materials Science and Engineering. He leads the Materials for Solar Energy Harvesting and Conversion research initiative at the Institute for Materials (IMat) and Strategic Energy Institute, aiming to consolidate Georgia Tech's expertise in photovoltaics and interdisciplinary energy research. Education: PhD in Environmental Engineering, University of Connecticut (2014) MS in Environmental Engineering, University of Connecticut (2011) BS in Management and Engineering for Manufacturing, University of Connecticut (2008) His research focuses on the chemistry-structure-property relationships of low-cost semiconductors for optoelectronic applications. Key areas include halide perovskites , nanoscale control , and advanced deposition/characterization techniques . He develops atomic layer deposition and synchrotron-based imaging to address metastable material behavior. Recent publications highlight innovations in dimensional control , machine learning for thermal stability , and flexible photovoltaic devices . His work integrates materials synthesis , quantum phenomena , and industrial scalability . Scientific recognition: Highly Cited Researcher (Web of Science, 2019–2021) Nature Index Leading Early Career Researcher in Materials Science (2019) NSF, DoE, and industry-funded projects Students and team: He advises 14 graduate students and postdocs, including Sanggyun Kim, Diana LaFollette, and Leonardo Josué Lugo Salas, fostering interdisciplinary collaboration through workshops and symposia.

Darren Robinsonمشاهده پروفایل

استاد

• Urban Energy Systems
• Building Physics
• Multi-agent Stochastic Simulation
• +۳ مورد دیگر

Professor Darren Robinson holds the Chair in Architectural and Urban Sciences at the University of Sheffield 's School of Architecture and Landscape, where he serves as Director of Research. His work bridges social, building, and urban physics through multiscale modeling approaches. RCUK Innovation Fellowship (2018-2021, £268k) Leverhulme Research Programme Grant (2015-2020, £3.4M) EPSRC grant for Model-Predictive Control in buildings (2016-2019, £541k) His research focuses on statistical modeling of human behavior in buildings, urban energy simulation , and integrated assessment modeling for climate policy. Key contributions include stochastic occupant behavior models and urban metabolism frameworks. Notable awards include the Sustainability Science Best Paper Award (2020) , CIBSE Napier-Shaw Medal (2007), and Fellowships from FIBPSA and the Research Council of Norway. He leads the People, Environments and Performance and Multiscale Simulation research groups.

Daniel J. Sorinمشاهده پروفایل

استاد

• Computer Architecture
• Memory Systems
• Cache Coherence Protocols
• +۷ مورد دیگر

Daniel J. Sorin is a Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where he also serves as Associate Chair of Education. He holds joint appointments in both the Electrical and Computer Engineering department and Computer Science department, and is recognized as a Bass Fellow for his contributions to education and research. His research focuses on computer architecture with specific expertise in memory systems, cache coherence protocols, fault tolerance, and verification-aware design. Dr. Sorin's work bridges theoretical computer architecture with practical implementations, often incorporating coding theory to solve architectural challenges. His research group has made significant contributions to automated protocol generation, hardware acceleration, and robot motion planning systems. Dr. Sorin's publications reveal a consistent focus on memory consistency models, cache coherence protocols, and verification techniques. His recent work has expanded into robot motion planning acceleration, FPGA resource management, and novel error correction techniques for emerging memory technologies. The trend shows increasing interdisciplinary work connecting computer architecture with robotics and machine learning applications. Program Chair of HiPEAC 2017 Co-chair of IEEE Micro's Top Picks selection committee (2016) Lois and John L. Imhoff Distinguished Teaching Award (2011) NSF CAREER Award recipient IEEE Micro Top Pick awards (2011, 2015) ACM Senior Member As an advisor, Dr. Sorin has mentored numerous PhD students who have gone on to successful careers at leading technology companies including Google, Microsoft, Oracle, and Nvidia. His research group maintains strong industry connections and has produced influential work in cache coherence protocols, memory systems, and fault-tolerant architectures. He has also authored the widely-used textbook 'A Primer on Memory Consistency and Cache Coherence' (2nd edition). Dr. Sorin leads an active research laboratory focused on next-generation computer architecture challenges, with ongoing projects in hardware acceleration, memory systems, and robot motion planning. His group collaborates with researchers across multiple disciplines including robotics, coding theory, and semiconductor design.

Lisa Wu Willsمشاهده پروفایل

استادیار

• Computer Architecture
• Hardware-Software Co-design
• Hardware Accelerators
• +۶ مورد دیگر

Lisa Wu Wills is an Assistant Professor in the Department of Computer Science and Electrical and Computer Engineering at Duke University, leading the APEX Lab (Application-driven Programmable Efficient Accelerated Systems Lab). Her research focuses on hardware acceleration for big data analytics in genomics, graphs, and databases to advance healthcare and natural sciences. Education: Ph.D. in Computer Science, Columbia University, 2014 Research Interests: Dr. Wills pioneers computer architecture and hardware-software co-design to create efficient accelerators for emerging applications. Her work targets genomics , graph analytics , and database systems , emphasizing simplified hardware deployment and energy efficiency for scientific breakthroughs in healthcare and AI. Publication Trends: Her 2022-2025 publications reveal a strong focus on open-source frameworks (Beethoven, PyTFHE) for accelerator development, hardware acceleration in privacy-preserving computing, and optimization for large language models. Key themes include transfer learning for EDA, domain-specific architectures for genomics, and energy-efficient image processing. Scientific Awards: Google ML and Systems Junior Faculty Award (2025) Advising and Grants: Dr. Wills mentors three PhD students: Chris Kjellqvist (Beethoven framework architect), Mason Ma (PyTFHE lead for FHE applications), and Mansi Choudhary (COCOSSim simulator creator). Her 2025 Google award funds research on accelerating vector databases and retrieval-augmented generation for LLMs. Labs and Teams: She directs the APEX Lab at Duke, developing tools like Beethoven (open-source accelerator composer) and PyTFHE for hardware-software integration, enabling domain scientists to leverage custom acceleration with minimal hardware expertise.

Alessandro Aliakbargolkarمشاهده پروفایل

استاد

• Federated Satellite Systems
• CubeSat Constellations
• Spacecraft Systems Architecture
• +۳ مورد دیگر

Alessandro Aliakbargolkar is a Professor at the Department of Space Systems Design under the School of Aerospace Engineering at Skolkovo Institute of Science and Technology (Skoltech). His research focuses on Federated Satellite Systems, CubeSat constellations, and Spacecraft Systems Architecture, with applications in Earth observation, messaging services, and networked satellite systems. He has an extensive publication record in these areas, including work on technology roadmapping and digital twin implementation. Key Research Areas: Satellite federation and resource sharing CubeSat constellation design Network performance optimization Integration of systems engineering models with AI Selected Trends: Recent work explores digital twin technologies for CubeSats, federated satellite network analysis, and large language model applications in spacecraft design. Publications often combine theoretical frameworks (e.g., network theory) with practical implementations (e.g., LoRa-based messaging services). ORCID Profile: 0000-0001-5993-2994

Praveen K. Kopalleمشاهده پروفایل

استاد

• Marketing
• Machine Learning
• Artificial Intelligence
• +۱۰ مورد دیگر

Praveen K. Kopalle is the Signal Companies' Professor of Management, professor of marketing, and area chair at the Tuck School of Business, Dartmouth College. He served as associate dean for the MBA program (2015–18) and chairs the marketing area (2012–15, 2022–present). He teaches courses in retail pricing analytics, marketing concepts, and new product development. Education: PhD in Marketing (Columbia University, 1992), MBA (Indian Institute of Management, 1988), BE in Mechanical & Product Engineering (Osmania University, 1986) His research focuses on marketing , machine learning , artificial intelligence , pricing strategy , retail analytics , and new product innovation . Recent work addresses digital customer orientation, loyalty programs in the big data era, and predictive analytics in retailing. He has published in top journals like Journal of Consumer Research , Management Science , and Marketing Science . Praveen serves as departmental editor for Production and Operations Management and associate editor for multiple journals. He has held leadership roles in the INFORMS Society for Marketing Science and contributed to editorial boards of 10+ journals. Scientific Awards: 2018 Lifetime Achievement Award (American Marketing Association’s Retailing and Pricing Special Interest Group) 2015 Core Teaching Excellence Award (Tuck School) 2011 Distinguished Alumni Award (IIM Bangalore) Multiple John Little and Davidson Awards 2022 Distinguished Alumnus Award (Osmania University) His teaching spans MBA core courses ( Marketing , Statistics for Managers ) and electives ( Retail Pricing Strategy , Marketing New Products ). He also leads executive education programs and the Tuck Integrative Experiential Learning (TuckINTEL) initiative.

Daniele Venturiمشاهده پروفایل

استاد

• Stochastic modeling
• Uncertainty quantification
• Numerical analysis
• +۷ مورد دیگر

Daniele Venturi is a Professor of Applied Mathematics at the University of California, Santa Cruz, where he has been faculty since 2015, rising from Assistant Professor to full Professor by 2021. Previously, he was a Research Assistant Professor at Brown University from 2010-2015. His academic journey began at the University of Bologna, where he earned both his combined B.S./Sc.M. in Mechanical Engineering (2002) and Ph.D. in Applied Physics with a focus on thermo-fluid dynamics (2006). University of Bologna: B.S./Sc.M. Mechanical Engineering (2002), Ph.D. Applied Physics (2006) Brown University: Research Assistant Professor (2010-2015) UC Santa Cruz: Assistant to Associate to Full Professor (2015-present) Professor Venturi's research spans multiple cutting-edge areas in computational mathematics. His primary interests include stochastic modeling and uncertainty quantification, numerical tensor methods for high-dimensional PDEs, data-driven modeling approaches, approximation of functional-differential equations, and theoretical/computational fluid dynamics. His work bridges theoretical mathematical frameworks with practical computational implementations, particularly focusing on overcoming the curse of dimensionality in complex systems. His recent research has been heavily focused on hierarchical tensor methods for solving high-dimensional partial differential equations. The analysis of his publication record reveals a strong emphasis on developing computational frameworks that address high-dimensional challenges in uncertainty quantification and model reduction. His work frequently intersects machine learning techniques with traditional numerical methods, particularly in developing physics-informed neural networks and multifidelity modeling approaches. A consistent theme across his publications is the development of mathematical frameworks that maintain computational tractability while preserving physical fidelity in complex systems. Professor Venturi has secured substantial research funding from major agencies including the Air Force Office of Scientific Research (AFOSR), Department of Energy (DoE), National Science Foundation (NSF), Army Research Office (ARO), and Defense Advanced Research Projects Agency (DARPA). His most significant current grant is a 2024-2029 AFOSR MURI award totaling $7.5M as co-PI for 'Tensor Network for simulating kinetic systems.' 2024-2029: AFOSR MURI, $7.5M (co-PI) 2023-2027: DoE, $3.8M (co-PI) 2023-2026: AFOSR, $2.5M (co-PI) 2020-2025: NSF TRIPODS, $2.3M (co-PI) At UC Santa Cruz, Venturi teaches a range of courses including Fundamentals of Uncertainty Quantification, Applied Dynamical Systems, Nonlinear Dynamical Systems, and Numerical Methods for Differential Equations. His teaching spans both undergraduate and graduate levels, reflecting his expertise across theoretical and computational mathematics. His lecture notes for these courses are publicly available and demonstrate his commitment to pedagogical excellence in complex mathematical subjects.

Robert P. Andersonمشاهده پروفایل

استاد

• Biogeography
• Species Distribution Modeling
• Biodiversity Conservation
• +۵ مورد دیگر

Robert P. Anderson is a Professor of Biology in the Division of Science at City College of New York (CCNY), part of the City University of New York (CUNY) system. His research laboratory is located in Marshak Science Building (Room 810), with additional affiliation as a Research Associate at the American Museum of Natural History (AMNH) Mammalogy Department. As a Highly Cited Researcher (2019-2023) and AAAS Fellow (2023), he leads an interdisciplinary biogeography research program focused on modeling species niches and distributions. Dr. Anderson's research spans biodiversity modeling, biogeography, and ecology with specialization in mammals. His lab develops ecological modeling software widely applied in conservation biology, invasive species management, zoonotic disease studies, and climate change impact assessments. Key research themes include: Characterizing spatial configuration of environmental suitability for species Developing machine learning approaches (particularly Maxent) for species distribution modeling Studying climate change effects on biodiversity Conservation applications of biogeographic models Neotropical mammal systematics and ecology His work has resulted in significant software contributions including Wallace, ENMeval, and spThin, with recent publications emphasizing methodological improvements in species distribution modeling and conservation applications. The lab maintains active projects funded by NASA and the National Science Foundation, focusing on small mammals of North and South America. Scientific recognition includes: AAAS Fellow (2023) Web of Science Highly Cited Researcher (2019-2023) Blavatnik Science Scholar (New York Academy of Sciences) Most Downloaded Paper in Ecography (2023-2024) Most Cited Paper in Ecography (2023) Dr. Anderson mentors graduate students through the CUNY Graduate Center and CCNY Master's programs, with recent advisees receiving prestigious awards including the ASM Horner Award and NASA FINESST Fellowship. His lab trains students in environmental biology through interdisciplinary research combining fieldwork, morphology, climatology, remote sensing, physiology, and genetics. Current lab members include Andrew Gaier (NASA Fellow), Mariano Soley-Guardia, and Kass (lead author on highly cited Wallace v2 paper). The Anderson Lab operates from CCNY's Marshak Science Building as part of the university's biodiversity group studying ecology, evolution, and geography of life on Earth. The lab emphasizes software co-design between end-users and developers to enhance conservation utility, with recent work focusing on neighborhood approaches for range estimation and operationalizing expert knowledge in species assessments.

Moinuddin Qureshiمشاهده پروفایل

استاد

• Computer Architecture
• Memory Systems
• Hardware Security
• +۱ مورد دیگر

Moinuddin Qureshi is a Professor of Computer Science at Georgia Institute of Technology, affiliated with the School of Computer Science and involved in the Online Master of Science in Computer Science (OMSCS) program. He holds a Ph.D. and M.S. from the University of Texas at Austin. His research focuses on computer architecture, memory systems, hardware security, and quantum computing, with notable contributions to mitigating rowhammer vulnerabilities and advancing quantum error correction. Previously, he was a Research Staff Member at IBM T.J. Watson Research Center (2007–2011), where he contributed to caching algorithms for Power-7 processors. He has held leadership roles, including Program Chair of MICRO 2015 and Selection Committee Co-Chair of Top Picks 2017. His work has been recognized with prestigious awards, including the 2019 Persistent Impact Prize and multiple best paper awards. Key research areas include secure memory design (e.g., rowhammer mitigation techniques like MINT and Moat), quantum computing (e.g., Flag-Proxy Networks and Élivágar), and hardware vulnerability analysis (e.g., Roguerfm attacks and COAXIAL memory systems). His publications span 2009–2025, addressing topics like error correction, secure tracking, and quantum annealing optimization. Awards include membership in ISCA, MICRO, and HPCA Hall of Fame, alongside contributions to conferences like HiPC and IEEE MICRO. His work bridges theoretical advancements with practical implementations in both classical and quantum domains.