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Fred FeinbergView profile

Professor

• Discrete choice models
• Bayesian statistics
• Marketing analytics
• +7 more

Fred Feinberg is the Joseph and Sally Handleman Professor of Marketing and Professor of Statistics (by courtesy) at the University of Michigan, where he is also an Affiliated Faculty member of the Center for the Study of Complex Systems. His work integrates advanced Bayesian methods with large-scale marketing data to illuminate how people make choices under uncertainty. Education Ph.D., Sloan School of Management, Massachusetts Institute of Technology (1989) Doctoral program in Mathematics, Cornell University (1983–84) S.B. Mathematics & S.B. Philosophy, Massachusetts Institute of Technology (1983) Research Focus Feinberg’s scholarship centers on discrete choice models that leverage real-world decisions to infer latent attributes such as demographics, product appeal, and socioeconomic status. Methodologically, he employs Hierarchical Bayes (HB) models and cutting-edge MCMC algorithms to handle massive data sets, while theoretically he advances dyadic utility theory and optimal search under uncertainty. Applications span click-through behavior, menu-based choice, online dating preferences, spatial marketing, and consumer reactions to intangible or aesthetic product features. Recent empirical studies explore the wearout versus weariness effects of online advertising, the impact of data breaches on consumer behavior, and dynamic pricing for digital media subscriptions. Across these projects, Feinberg couples rigorous statistical innovation with actionable managerial insights, bridging marketing science, operations, and engineering. Scientific Awards & Leadership Joseph and Sally Handleman Endowed Professorship Past President, INFORMS Society for Marketing Science Departmental Editor, Production and Operations Management Former Co-Editor, Marketing Science Co-author (with T. Kinnear & J. Taylor) of the textbook Modern Marketing Research: Concepts, Methods, and Cases Grants & Collaborations While explicit grant lists are not provided, Feinberg’s prolific publication record in top-tier journals (e.g., Journal of Marketing Research , Marketing Science , Management Science ) and editorial board service imply sustained external funding and interdisciplinary partnerships, particularly with operations, engineering, and computer-science groups. Laboratories & Teams Feinberg is formally affiliated with the Center for the Study of Complex Systems (CSCS) at the University of Michigan, where he collaborates on network-based choice frameworks and large-scale behavioral data analytics. He maintains active ties to the Ross Marketing faculty and the Department of Statistics, fostering joint workshops and doctoral training initiatives.

Serdar ÖzoğuzView profile

Professor

• Active RC Filters
• Nonlinear Electronic Circuits
• Analog IC Design
• +3 more

Professor Serdar Özoğuz is a full faculty member at the Department of Electronics and Communication Engineering , Istanbul Technical University . Holding a Ph.D. from ITU (2000) and a M.Sc. from ITU (1993) , he has taught courses like Active Network Synthesis , Basics of Electrical Circuits , and Scientific Research Ethics since 2014. His research focuses on Active RC filters Nonlinear electronic circuits Analog integrated circuit design Network synthesis . His recent publications emphasize machine learning applications in RF/microwave design , quantum computing for CAD tools , and emerging memory devices . The department's Devreler ve Sistemler Laboratuvarı Çok Geniş Ölçekli Tümdevre (VLSI) Tasarımı Laboratuvarı likely support his work. Despite no explicit awards listed, his 15+ recent articles in high-impact journals underscore his technical contributions.

• DNA Nanotechnology
• Molecular Robotics
• Self-Assembling Nanostructures
• +4 more

Dr. Shelley Wickham is an Associate Professor and ARC DECRA Fellow at the University of Sydney, holding joint appointments in the Schools of Chemistry and Physics. She serves as a Westpac Research Fellow and leads the DNA Nanotechnology Group at the Sydney Nano Institute. Dr. Wickham is also co-Champion of the Sydney Nano Institute Grand Challenge project in Molecular Nanorobotics for Health, co-lead of the School of Physics Grand Challenge on Nanoscale brain navigation for targeted drug delivery, and faculty mentor of the University of Sydney BIOMOD team. Bachelor of Science and Master of Science in Physics from University of Sydney PhD in Condensed Matter Physics from University of Oxford Postdoctoral Fellow at Harvard Medical School, Dana-Farber Cancer Institute, and Wyss Institute Dr. Wickham's research focuses on self-assembling nanotechnology and molecular robotics, particularly in the design and assembly of programmable nanostructures out of DNA. Her work spans applications in cell biology, materials science, and nanomedicine. Current research projects include design and synthesis of self-assembling DNA nanostructures, proto-cells made of DNA gels that move under flow, new plasma fabrication methods for biomolecule micropatterning, and DNA computation circuits for navigating the brain using machine learning. Her research aligns with the Faculty of Science Research Strengths in Molecules to Materials, Preventing and Treating Disease & Disorder, and Next Generation Materials. Analysis of Dr. Wickham's recent publications reveals a consistent focus on DNA nanotechnology with increasing sophistication in structural complexity and biological applications. Her work has evolved from fundamental DNA origami structures to increasingly complex multi-component systems with practical applications in nanomedicine and biomimetic engineering. Recent publications show strong interdisciplinary collaboration across chemistry, physics, biology, and engineering disciplines, with emphasis on real-world applications including drug delivery systems and biomolecular sensors. ARC DECRA Fellow Westpac Research Fellow BIOMOD World Champions (2019) Dr. Wickham actively mentors PhD students and postdoctoral researchers in her DNA nanotechnology group. She has secured significant research funding including ARC Discovery Projects, Westpac Scholarships, and NSW Health grants. Her current grants support projects such as '3D Bio-Nanomaterial Displays with Designer Architectures and Functions' and 'RNA aptamer sensing devices for rapid detection of blood clotting.' Dr. Wickham encourages applications from diverse backgrounds and maintains active collaborations with researchers at Harvard, Oxford, and other international institutions. Dr. Wickham leads the DNA Nanotechnology Group at the University of Sydney, which is part of the Sydney Nano Institute. Her lab focuses on building tools from DNA origami - including tweezers, spanners, wrenches and springs - to better understand biological processes at the nanoscale. The group has achieved notable success with the BIOMOD team winning world championships in 2019, and continues to develop innovative approaches to molecular robotics for healthcare applications.

Angshuman KarmakarView profile

Assistant Professor

• Post-Quantum Cryptography
• Computation On Encrypted Data
• Side-Channel Analysis
• +4 more

Angshuman Karmakar is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur, India. His research focuses primarily on Post-Quantum Cryptography (PQC) and Computation On Encrypted Data (COED), which are critical areas in modern cryptography and computer security. Dr. Karmakar received his Ph.D. from Katholieke Universiteit Leuven (KU Leuven), Belgium, where he worked under Prof. Ingrid Verbauwhede in the COSIC research group. He was awarded the prestigious Erasmus Mundus fellowship for his doctoral studies and the FWO (Fonds voor Wetenschappelijk Onderzoek – Vlaanderen) fellowship for his post-doctoral research at KU Leuven. His research spans theoretical development of cryptographic schemes, implementation algorithms, side-channel and fault attack analysis, and countermeasure development. Dr. Karmakar has established extensive international collaborations with researchers and engineers worldwide to address complex challenges in cryptography and security. Recent publications demonstrate a strong focus on practical post-quantum cryptographic implementations with particular attention to hardware and software efficiency, side-channel resistance, and novel attack methodologies. His work bridges theoretical cryptography with real-world implementation challenges across diverse platforms from IoT devices to high-performance computing systems. Erasmus Mundus fellowship for doctoral studies at KU Leuven FWO fellowship for post-doctoral study at KU Leuven Google India Research Award for work on practical transition to post-quantum cryptography Dr. Karmakar is actively seeking graduate students and postdoctoral researchers to collaborate on cutting-edge research in cryptography and computer security. His work has significant implications for securing future communication systems against quantum computing threats, with applications spanning blockchain technologies, IoT security, and general-purpose computing systems.

• Human-Computer Interaction
• Virtual Reality
• Extended Reality
• +4 more

Teresa Hirzle is a Tenure Track Assistant Professor at the Department of Computer Science , University of Copenhagen , specializing in Human-Centred Computing . Her research focuses on Human-Computer Interaction (HCI) , Virtual Reality (VR) , Extended Reality (XR) , and Gaze-Based Interaction . Research Interests: Designing interaction techniques for immersive environments Eye movement analysis for educational applications Addressing digital eye strain in interactive systems Evaluating user experience in VR/AR Recent Research Trends: Her recent publications examine AI representation in VR co-creation, VR sickness in locomotion, eye strain in gaze-driven systems, and hybrid applications of comics/AR. She also explores pedagogical implications of eye tracking in remote learning. Contact: Email: tehi@di.ku.dk Office: Sigurdsgade 41, 2200 København N.

• Distributed Computing
• Applied Economics
• Policy and Administration Law
• +2 more

Naranker Dulay is a Professor in the Department of Computing at Imperial College London, part of the Faculty of Engineering. He holds affiliations with the Centre for Cryptocurrency Research and Engineering, Centre for Smart Connected Futures, and the Distributed Software Engineering group. His research focuses on Distributed Computing, Applied Economics, Policy and Administration Law, Computer Software, and Information Systems. His work explores blockchain technologies, smart contracts, consensus algorithms, and distributed systems. Recent research includes optimizing post-trade processing using distributed ledgers and developing adaptive protocols for dispute resolution in smart contracts. He is also involved in cybersecurity and privacy-preserving technologies for data management. Key contributions include frameworks like Chainlog for logic-based smart contracts and FADE for self-destructing data. His articles span over a decade, emphasizing blockchain scalability, energy-efficient neural networks, and decentralized macro-programming in wireless sensor networks. Dr. Dulay collaborates across interdisciplinary domains, blending technical innovation with socio-technical challenges. His affiliations reflect a commitment to advancing smart connected futures through cutting-edge research.

Andreas LööwView profile

Lecturer

• Hardware Verification
• Software Verification
• Symbolic Execution
• +5 more

Andreas Lööw is a Lecturer at Royal Holloway, University of London , focusing on hardware and software verification. Previously, he was a postdoctoral researcher at Imperial College London under Philippa Gardner , contributing to the Gillian Platform . He completed his PhD at Chalmers University of Technology under Magnus Myreen , specializing in interactive theorem proving and hardware verification. His research explores symbolic execution, separation logic, and formal verification of hardware/software systems. Key projects include Betterlog (Verilog semantics reformulation) and foundational work on the Gillian Platform . 2025 : Compositional Symbolic Execution for Memory Models 2025 : Simulation Semantics of Synthesisable Verilog 2024 : Compositional Symbolic Execution for Correctness/Incorrectness 2023 : Exact Separation Logic (Distinguished Paper at ECOOP'24) 2023 : Hardware Verification of Pipelined Processors 2022 : Verilog Concurrency Analysis 2021 : Verified Verilog Compiler (Lutsig) Scientific Awards : Distinguished Paper at ECOOP 2024 He maintains the vv Verilog visualization tool and collaborates on the Gillian Platform . Contact: andreas.loow@rhul.ac.uk

Geoffrey NelissenView profile

Assistant Professor

• Real-Time Systems
• Schedulability Analysis
• Multicore Platforms
• +3 more

Geoffrey Nelissen is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. He holds additional roles as a Research Scientist and Investigador Auxiliar at INESC TEC (Portugal), contributing to interdisciplinary research in real-time systems. His work focuses on schedulability analysis, parallel task execution, and real-time communication protocols, with applications in embedded systems and multicore architectures. Research Interests: Real-Time Scheduling Response Time Analysis Multi-core and Parallel Systems Time-Sensitive Networking (TSN) Formal Verification Recent work emphasizes schedule abstraction frameworks, memory contention analysis, and deterministic communication protocols. His research has received recognition through multiple best paper awards including ICESS 2021 and RTAS 2022. He actively contributes to conferences like RTSS and ECRTS while teaching courses on Real-Time Systems and Operating Systems. Collaborations span European institutions with focus on embedded systems, autonomous driving, and safety-critical applications. Current projects explore holistic approaches to WCRT analysis and resilient real-time communication architectures.

Wenfeng ZhaoView profile

Assistant Professor

• Neural Engineering
• Compressed Sensing
• Ultra-low Power VLSI
• +3 more

Wenfeng Zhao is an Assistant Professor in the Department of Electrical and Computer Engineering at Binghamton University. He holds a PhD from the National University of Singapore (2014) and BS/MS degrees from Huazhong University of Science and Technology (2007-2009). Prior to this role, he conducted postdoctoral research at the University of Minnesota's Biomedical Engineering Department. His research focuses on neural engineering, compressed sensing, ultra-low-power VLSI systems, and in-memory computing. Key areas include hardware security, biomedical signal processing, and energy-efficient computing architectures. His work spans applications in neural interfaces, cryptographic hardware, and IoT edge devices. Recent publications highlight advancements in block-cipher-in-memory architectures, emotion recognition via EEG analysis, and energy-efficient FPGA accelerators for neural networks. His research also addresses challenges in cryogenic memory systems and MRI-compatible neural recording devices. Zhao's contributions emphasize interdisciplinary approaches at the intersection of hardware design, signal processing, and cybersecurity. His lab develops novel solutions for low-power embedded systems and trustworthy IoT infrastructure.

Marco SerafiniView profile

Assistant Professor

• Database Systems
• Distributed Systems
• Data Science
• +3 more

Marco Serafini is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences (CICS). He leads the DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and is part of the Center for Data Science. Prior to UMass, Serafini worked as a Senior Scientist at the Qatar Computing Research Institute (QCRI) and held a postdoctoral fellowship at Yahoo! Research in Barcelona. He earned his PhD in Computer Science from TU Darmstadt (Germany), where his thesis was recognized through nominations for best thesis awards across German, Swiss, and Austrian computer science societies. His research focuses on the intersection of database systems, distributed systems, and data science, emphasizing scalable architectures for big data analytics and machine learning. Key areas include computation pushdown in cloud DBMSs, graph neural network training systems, and efficient graph pattern matching. His work addresses challenges in tail latency mitigation, resource optimization, and transparent scaling of ML models. Serafini has contributed to influential systems like Arabesque (for distributed graph mining), E-Store (elastic partitioning), and Aion (event-time stream processing). He has been awarded an NSF CNS Core grant to advance scalable GNN training. His publications span top venues such as ACM SIGOPS, VLDB, and ICDE, reflecting his expertise in both theoretical foundations and practical system implementations. Professional recognition includes thesis nominations from major computer science societies and sustained contributions to open-source projects in distributed computing. Serafini advises students through the DREAM Lab, focusing on preparing the next generation of data systems researchers.

• Instrumentation Electronics
• Memristor Modelling
• Signal Integrity
• +6 more

Dr. Christos Papavassiliou is an Associate Professor in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. His research focuses on instrumentation electronics, memristor modeling, signal integrity, and novel device technologies such as SiGe devices, RF MEMS, and ReRAM. He leads the Space Lab and collaborates with the National Centre for Scientific Research in Athens. He holds senior membership in IEEE and is a member of the IET. Education: Ph.D. in Applied Physics, Yale University (1983–1989) MPhil in Applied Physics, Yale University (1983–1988) MS in Applied Physics, Yale University (1983–1985) B.S. in Physics, MIT (1979–1983) Research Interests: Memristor-based neuromorphic computing and stochastic systems High-performance instrumentation hardware and data acquisition Multi-state memristive memory and selectorless arrays Integration of memristors with CMOS for hybrid circuits Applications in biomedical wearables and edge AI deployment Key Contributions: Developed novel memristor models for circuit simulation Pioneered work on memristor-based true random number generators Advanced understanding of resistive drift and energy-constrained storage Designed FPGA-based systems for analog circuit emulation Labs & Teams: Active in the Space Lab at Imperial College, focusing on interdisciplinary research in electronics and space applications.

Garvesh RaskuttiView profile

Associate Professor

• Statistical Machine Learning
• Optimization Theory
• Graphical and Network Modeling
• +5 more

Garvesh Raskutti is an Associate Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. He holds joint affiliations with the Departments of Computer Science, Electrical and Computer Engineering, and the Wisconsin Institute of Discovery Optimization Group. His research focuses on statistical machine learning, optimization, graphical/network modeling, and information theory, with applications to systems biology and neuroscience. Education: MEng from the University of Melbourne (2008), PhD from UC Berkeley (2012, advised by Martin Wainwright and Bin Yu), and postdoctoral work at SAMSI. Teaching awards include Honored Instructor (2014, 2017) and Madison Teaching and Learning Excellence Fellow (2015). He advises multiple PhD and undergraduate students, including Yuan Li, Hyebin Song, and Lili Zheng. His grants include NGA HM0476-17-1-2003 (Co-PI), ARO W911NF-17-1-0357 (Co-PI), and NSF-DMS 1407028 (Sole PI). Research interests span large-scale statistical inference, computational-statistical trade-offs, and applications in systems biology and neuroscience. His work bridges optimization (e.g., gradient descent, convex regularization), high-dimensional regression, and network analysis. Recent trends in publications emphasize methods for non-convex optimization, sparse models, and network structure learning. Awards include teaching recognition and grants in statistical methodology. Advising spans theoretical and applied projects, with former students like Gunwoong Park (now at University of Korea). Collaborations include interdisciplinary teams in machine learning and signal processing.

• Global Navigation Satellite Systems (GNSS)
• Autonomous Systems
• Sensor Fusion
• +4 more

Patrick Henkel is a Professor at the Technical University of Munich (TUM) affiliated with the TUM School of Engineering and Design and the Chair of Communication and Navigation. He holds a professorship in Satellite Geodesy under Prof. Hugentobler. His research focuses on advanced positioning technologies, including Global Navigation Satellite Systems (GNSS), autonomous systems, and sensor fusion. He develops algorithms for precise positioning in challenging environments such as urban areas, alpine regions, and indoor spaces. His work also extends to environmental applications, such as snow hydrology and climate monitoring using GNSS signals. Henkel’s contributions include innovations in real-time kinematic (RTK) positioning, UAV navigation, and multi-sensor integration for robotics and autonomous vehicles. His research is supported by collaborations with industry and academic partners, addressing both theoretical and applied challenges in geodesy and navigation. Henkel leads projects on GNSS signal processing, satellite-based environmental monitoring, and autonomous driving technologies. He has contributed to the Galileo HAS service and developed methodologies for snow water equivalent estimation using multi-frequency GNSS signals. His expertise spans hardware-software co-design for navigation systems and algorithm optimization for high-precision positioning in dynamic environments. He actively publishes in top-tier journals and conferences, with a focus on advancing the reliability and accuracy of navigation systems across various domains. His advising and grants include funding for projects on sensor fusion, UAV-based measurements, and satellite receiver development. He collaborates with teams at TUM’s Navigation Lab and the Professur für Satellitengeodäsie, contributing to both academic and industrial applications. His work on low-bandwidth RTK dissemination and laser-tracker verified UAV positioning highlights his commitment to bridging theoretical advancements with real-world implementation.

Sonali DasView profile

Lecturer

• Device Electronics
• RF and Microwave MEMS
• Solar Cells
• +3 more

Sonali Das is a Lecturer in the Department of Electrical and Computer Engineering at the University of Central Florida. She earned her Ph.D. in Electrical Engineering from the Indian Institute of Engineering Science and Technology, India, in 2017. Research Interests: Her work focuses on device electronics, RF and microwave MEMS, solar cells, phototransistors, optoelectronic synaptic and neuromorphic devices, and emerging micro-nano fabrication technologies. She investigates light-trapping mechanisms in solar cells, develops biomimetic photovoltaic structures, and explores 2D material-based memristors for neuromorphic computing. Professional Activities: Das has served as a technical reviewer for the UCF Seed Funding Program (2019), instructor for Camp Connect I and II lab tours (2017–2019), and mentor for Camp Connect III programs at UCF (2019). She is an IEEE member. Scientific Awards: GAP Award, UCF Research Foundation and UCF Office of Technology Transfer (2019) Fulbright Bhaskara Advanced Solar Energy (BASE) Fellowship (2015) Teaching: She teaches courses including Semiconductor Devices (EEE 3350), Electronics I and II (EEE 3307/4309), Linear Circuits II (EEL 3123), Electromagnetic Fields (EEL 3470), Signal Analysis & Analog Communication (EEL 3552), Linear Control Systems (EEL 3657), and Digital Systems (EEE 3342).

Giovanni De MicheliView profile

Professor

• Computer Architecture
• Electrical Engineering
• Integrated Systems
• +5 more

Giovanni De Micheli is a Professor of Electrical Engineering and Computer Science at EPF Lausanne, Switzerland. He also serves as Director of the Integrated Systems Centre and the Institute of Electrical Engineering at EPFL, and chairs the Scientific Committee of CSEM in Neuchatel. Previously, he held academic roles at Stanford University for 18 years, including Full Professor, Associate Professor, and Assistant Professor in the Department of Electrical Engineering. His research spans synthesis of digital circuits, hardware/software co-design, low-power design, and Networks on Chip (NoC) technology. 2003: IEEE Emanuel Piore Award 2000: Golden Jubilee Medal of the IEEE CAS Society 2000: ACM Fellow 1994: IEEE Fellow 1990: IEEE/CS Distinguished Service Award 1988: NSF Presidential Young Investigator Award His seminal contributions include pioneering C-based synthesis and Boolean matching algorithms for digital circuits, foundational work in dynamic power management using stochastic control, and the development of Network-on-Chip (NoC) technology. His publications, such as "Networks on Chips: A New SoC Paradigm" and "Dynamic Power Management for Portable Systems" , have shaped modern SoC design practices. With over 400 technical articles, 9 books, and an H-index of 56, his work remains highly influential.