William Eisenstadt is a Professor in the Department of Electrical and Computer Engineering at the University of Florida. His research encompasses electronics, IoT applications for agriculture and public health, RF/microwave circuit design, and semiconductor testing. He holds a Ph.D., M.S., and B.S. in Electrical Engineering from Stanford University. Dr. Eisenstadt's research focuses on integrating IoT technologies with agricultural pest control, developing advanced testing methodologies for semiconductor devices, and designing RF/microwave systems. His work bridges hardware innovation with real-world applications in health, safety, and environmental monitoring. Recent publications demonstrate strong emphasis on semiconductor testing (BGA sockets, LDO regulators), 3D IC signal integrity, smart IoT devices for mosquito control, and biomedical ICs for drug delivery. Trends indicate cross-disciplinary convergence of microelectronics with biomedical and agricultural applications.
Professor Georgios Makris is a faculty member in the Department of Electrical and Computer Engineering at the Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas. He leads the Trusted and RELiable Architectures (TRELA) Research Laboratory, the Safety, Security and Healthcare Thrust of the Texas Analog Center of Excellence (TxACE), and the UT Dallas site of the NSF CHEST I/UCRC. Previously, he was a faculty member at Yale University for over a decade. Education : Dipl. Eng. (1995) from University of Patras, M.S. (1998) and Ph.D. (2001) from UC San Diego. Research Interests His research spans hardware security, machine learning applications in IC design, analog/RF circuit testing, counterfeit IC detection, and statistical analysis. Current work includes statistical side-channel fingerprinting, hardware Trojans in wireless cryptographic ICs, on-die learning architectures, and security in synthetic biology. Recent article trends focus on hardware security (8/15), machine learning applications (7/15), and analog/RF test/reliability (6/15), with subfields including side-channel analysis, statistical modeling, and emerging technologies. Scientific Awards Elevated to IEEE Fellow (2025) Sheffield Distinguished Teaching Award (2006) Best Paper Awards: DATE’13, VTS’15, DCAS’22 Best Hardware Demonstration Awards: HOST’16, HOST’18 Erik Jonsson School Faculty Research Award (2020) His research has been supported by NSF, ARO, AFRL, DARPA, and industry partners. He has advised Ph.D. students whose work received the David Daniel Thesis Award (2021).
Ronald D. Blanton serves as the Joseph F. and Nancy Keithley Professor and Associate Department Head for Research in the Department of Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. He directs the Advanced Chip Test Laboratory (ACTL) and co-founded the Security Assurance of Fabricated Electronics Center. His educational background includes: Ph.D. in Electrical Engineering and Computer Science (1995) - University of Michigan, Ann Arbor M.S. in Electrical and Computer Engineering (1989) - University of Arizona B.S. in Engineering (1987) - Calvin College (now Calvin University) Blanton's research focuses on integrated circuit testing, hardware security, and machine learning applications for electronic design automation. His work addresses critical challenges in semiconductor manufacturing where approximately 10% of newly manufactured chips fail initial testing, with particular emphasis on safety-critical applications like autonomous vehicles where failure detection is essential. His research spans semiconductor test methodologies, hardware security mechanisms, and machine learning techniques for analyzing IC test data. His single verifiable publication demonstrates expertise at the intersection of neural network optimization and hardware implementation. The 2017 award-winning paper explored efficient neural network architectures that bridge conventional deep learning models with highly optimized binarized networks suitable for hardware deployment. Blanton's scientific recognition includes: NSBE Golden Torch Award for Lifetime Achievement in Academia (2022) Calvin University Distinguished Alumni Award (2024) NSF CAREER Award (1997) IBM Faculty Partnership Awards (2005, 2006) IEEE Fellow designation As a dedicated mentor, Blanton has significantly increased diversity in CMU's engineering programs, growing PhD enrollment of students of color from 'a handful' to over 150. He personally supports students through financial assistance, research opportunities, and career guidance, as exemplified by his mentorship of Danielle Duvalsaint who transitioned from PhD studies to a position at Nvidia. During the pandemic, he organized online social events to maintain student community and provided direct support to local underserved schools. Blanton leads the Advanced Chip Test Laboratory which collaborates with industry partners including Google, Broadcom, Qualcomm, and GlobalFoundries to develop data-mining techniques that improve integrated system security, operation, design, manufacturing, and testing.
Alex McAvoy is an Assistant Professor in the School of Data Science and Society at the University of North Carolina at Chapel Hill. He holds a postdoctoral background in the Center for Mathematical Biology at the University of Pennsylvania. His research focuses on evolutionary dynamics, game theory, multi-agent learning, and network science, particularly examining how behaviors spread in populations and the emergence of prosocial behaviors in structured networks. McAvoy's work bridges applied mathematics with biological systems, exploring pathologies like inequality amplification from prosocial actions and the role of selfishness in population dynamics. His academic trajectory includes affiliations with Harvard University, University of British Columbia, and Shanghai Jiao Tong University through collaborations. He has published extensively since 2015, with key contributions on asymmetric evolutionary games, dynamic networks, and fixation probabilities in evolutionary processes. McAvoy’s publications often emphasize interdisciplinary applications of mathematical models to biological and social systems. Recent research highlights include studies on imitation dynamics in incomplete information networks (2023), strategy evolution on dynamic networks (2023), and evolutionary instability of selfish learning in repeated games (2022). His work consistently addresses theoretical frameworks with empirical relevance, such as multilayer population structures and public goods dilemmas in heterogeneous societies (2020). McAvoy collaborates with leading researchers in mathematical biology and network science, including Martin Nowak (Harvard), Christoph Hauert (UBC), and Qi Su (Shanghai Jiao Tong). His research has been cited over 1,500 times, reflecting significant impact in evolutionary game theory and complex systems analysis.
Vasilios Tenentes is an Associate Professor at the Department of Computer Engineering and Informatics, University of Ioannina, Greece. He is affiliated with the VLSI Systems and Computer Architecture Laboratory (VCAS), where he conducts research in reliable and controllable digital systems. His academic journey includes postdoctoral research at the University of Southampton, UK (2014–2018), and a research engineer role at ARM Ltd, Cambridge (2017–2018). His educational background includes a B.Sc. in Computer Science from the University of Piraeus (2003), and M.Sc. and Ph.D. degrees in Computer Science and Engineering from the University of Ioannina (2007, 2013). His doctoral research was funded by the 'Heraclitus 2' program and focused on embedded control architectures for testability in digital systems. His research interests include embedded systems, hardware/software co-design, design for testability, fault modeling, and reliability in digital systems. He has contributed to projects such as PRiME (Power-efficient, Reliable, Many-core Embedded systems) and the Artemis Program on secure supply chain traceability. His work emphasizes reliability assessment for IoT applications and wear-out effects modeling in integrated circuits. The available publication data is limited, but his work spans international journals and conferences on digital system reliability and testability. Though specific article titles and years are not fully listed, his research consistently targets energy-efficient and dependable embedded architectures. Member, IEEE (since 2007) Member, HiPEAC (since 2023) He advises students through the university's student advisor program and is involved in supervising doctoral candidates within the VCAS lab. While no specific grants are named, his participation in EU-level research programs indicates active grant involvement. His work bridges industry collaboration (e.g., ARM, Intel, Microsoft Research via PRiME) and academic innovation. He is a core member of the VLSI Systems and Computer Architecture Laboratory (VCAS), collaborating with faculty including Prof. George Tsiatouchas and Prof. Chrysovalantis Kavousianos, and contributing to a strong research environment in digital system design and reliability.
Giusy Iaria is a Research Fellow at the Department of Control and Computer Science (DAUIN) of Politecnico di Torino, Italy. She serves as an External Lecturer and Teaching Assistant for Automotive Engineering courses within the Computer Sciences program since the 2022/2023 academic year. Department of Control and Computer Science (DAUIN) CAD - Electronic CAD & Reliability Group Automotive Engineering Teaching Her research focuses on automotive system-on-chip reliability and fault diagnosis , particularly through innovative applications of logic built-in self-test (BIST) systems. She develops test cost optimization models and SEU injection setups for improving automotive electronics reliability. Recent publications analyze faulty gate layout correlations and pattern selection algorithms for reducing test costs. Her work addresses critical challenges in automotive functional safety and industrial electronics domains.
Sylvain Schmitz is a Professor of Computer Science at Université Paris Cité, affiliated with the Institut de Recherche en Informatique Fondamentale (IRIF). He leads the automata and applications team and is a member of the modelling and verification team. His research focuses on logic, well quasi-orders, verification, formal languages, and database theory. He is actively involved in the academic community, serving on steering and program committees for major conferences such as STACS, LICS, and CSL. PhD in Computer Science, defended on September 24, 2007, at I3S Laboratory, Sophia Antipolis. Habilitation titled Algorithmic Complexity of Well-Quasi-Orders , defended on November 27, 2017, at ENS Paris-Saclay. His research interests span logic, well quasi-orders, verification, formal languages, database theory, theoretical computer science, complexity theory, program verification, automata theory, and model checking. His recent work explores complexity bounds in vector addition systems, reachability problems, and logical foundations of verification. His publications exhibit a strong focus on theoretical foundations of computation, particularly in the analysis of infinite-state systems, complexity hierarchies beyond elementary, and logical characterizations of computational problems. Key themes include well-structured transition systems, non-elementary complexity, and applications of ordinal analysis to verification. IUF junior (2018–2023) Sylvain Schmitz has supervised several PhD students, including Hector Buffière, Aliaume Lopez, Anthony Lick, and Simon Halfon, often in joint supervision with researchers such as Jean Goubault-Larrecq, David Baelde, and Philippe Schnoebelen. His research has been funded by multiple ANR projects including BraVAS, PRODAQ, ReacHard, and AVeriSS. He is a key member of the IRIF laboratory, contributing to research in automata, verification, and logic. He has organized major events such as ICALP 2022 and RP 2020, and is deeply involved in the European theoretical computer science community through committee memberships and conference organization.
Shawn Blanton is a Professor in the Electrical and Computer Engineering Department at Carnegie Mellon University's College of Engineering. He serves as the Associate Head of Research and founded the Advanced Chip Test Laboratory (ACTL) , focusing on data-mining techniques for secure and efficient integrated systems. Ph.D., Electrical and Computer Engineering, University of Michigan, Ann Arbor (1995) MS/BS in Electrical and Computer Engineering/Engineering, University of Arizona and Calvin College (1989) Blanton's research spans hardware security, semiconductor test methodologies, and machine learning applications in electronic design automation. He founded TestWorks, a CMU spinout analyzing IC test data, and co-founded the Security Assurance of Fabricated Electronics Center (SAFECENTER) under CyLab. His recent work includes the BRIDGE framework integrating graph models and large language models for EDA, published at the 2025 IEEE International Conference on LLM-Aided Design. His publications and patents reflect 30+ years of innovation in chip testing and security. NSF CAREER Award (1997) IBM Faculty Partnership Awards (2005, 2006) Lifetime Achievement in Academia Award (2022) Best Paper Award at GLSVLSI (2024) Blanton has delivered >100 invited talks across academia and industry, including Stanford, IBM, and Qualcomm, and held editorial/leadership roles in IEEE/ACM conferences and journals.
Pia Raffler serves as Assistant Professor of Government at Harvard University, conducting research at the intersection of comparative politics and political economy with focus on accountability mechanisms in Sub-Saharan Africa and Germany. Her work combines experimental fieldwork with qualitative analysis to examine bureaucratic oversight and voter behavior in weakly institutionalized settings. Her academic background includes a Ph.D. in Political Science from Yale University and a postdoctoral fellowship at Princeton University's Niehaus Center and Center for Study of Democratic Politics. Raffler's research spans bureaucracy, democracy, gender representation, electoral systems, political economy, public policy, and voter behavior. She designs large-scale field experiments in collaboration with government agencies and civil society organizations to test accountability interventions, particularly in Uganda where she previously established Innovations for Poverty Action's office. Her methodological approach integrates qualitative insights with quantitative testing to uncover causal mechanisms. Her publication record reveals consistent focus on experimental analysis of accountability in developing democracies, with recent expansion into digital media effects and German politics. Key themes include information asymmetries in electoral accountability, bureaucratic power dynamics, and institutional design impacts on service delivery. Her scientific recognition includes: Best Fieldwork Award (2016, American Political Science Association) Best Dissertation Award in Experimental Research (APSA) Best Experimental Paper Award (APSA) Raffler maintains active research partnerships with government agencies and political parties to implement testable reforms. She holds affiliations with Harvard's Evidence for Policy Design, Institute for Quantitative Social Science, Weatherhead Center for International Affairs, Center for African Studies, and Center for International Development. As a member of EGAP and editorial board member for the British Journal of Political Science, she contributes to shaping experimental governance research globally.
Dhruva Raman is an Assistant Professor in Computer Science & AI (Informatics) at the University of Sussex , affiliated with the School of Engineering and Informatics . His research focuses on computational modeling of neural circuits, particularly on how biological systems balance functional goals through mathematical and computational frameworks. The key research theme involves understanding tradeoffs in associative learning, such as the insect mushroom body's adaptation to environmental shifts. He also explores cerebellar architecture, synaptic plasticity, and design principles that generate experimentally testable hypotheses. His work emphasizes comparative neurobiology, linking insights across species to mammalian brain structures like the cerebellum. His recent publications span computational neuroscience, neurobiology, and systems engineering, addressing topics like prototype learning, parameter identifiability, and robustness in nonlinear systems. He has collaborated on tools such as MinimallyDisruptiveCurves.jl for computational analysis.
Angelica-Beatrice Bacivarov is a Professor at the Politehnica University of Bucharest , Faculty of Electronics, Telecommunications and Information Technology, with over 35 years of academic experience. She has conducted research and taught courses on fault-tolerant systems , hardware/software reliability , and statistical process control , including pioneering work on submicron/nanoelectronics. She has held visiting professorships at institutions in France, the UK, Italy, Portugal, and the Netherlands. Doctor of Engineering (1980), Politehnica University of Bucharest Engineer in Applied Electronics (1971), Politehnica University Baccalaureate (1966), George Bacovia High School Her research focuses on reliability modeling for complex systems, software/hardware dependability , and automated testing . She has developed innovative methods for fault tolerance in micro/nano technologies and redundancy synchronization. Her work spans optoelectronics , telecommunications , and connectionist systems for quality prediction. Her recent publications (2008–2001) emphasize fault-tolerant architectures , boundary-scan testing , and reliability in optical systems . She has authored 10 books, 78 articles, and 64 conference papers. Distinction of the Ministry of National Education (1987) Editorial Board, Asigurarea Calitatii – Quality Assurance President, CT 328 Technical Standardization Committee Scientific advisor in doctoral thesis defenses She has supervised 65 PhD theses and over 150 projects since 1973. Her lab work includes collaborations with institutions like Institut National Polytechnique Grenoble and ISTIA Angers , focusing on complex integrated systems and quality assurance .
Lubomír Bulej serves as an Associate Professor at the Department of Distributed and Dependable Systems within the Faculty of Mathematics and Physics at Charles University, Prague. His office is located in room S 205 in the historic Lesser Town district, with contact details including email bulej@d3s.mff.cuni.cz and phone +420 951 554 189. He maintains an active research profile through platforms like Google Scholar, DBLP, and GitHub. His research centers on dynamic program analysis and software performance evaluation , with specific focus on analysis composition, program instrumentation, profiling accuracy, and observability in managed platforms. He develops methods for automatic performance evaluation during development, performance change detection, and testable documentation of performance assumptions. Additional interests span object-oriented programming, programming languages, operating systems, and computer architectures, reflected in his teaching of courses like Computer Architecture and Operating Systems. Analysis of his 2021-2025 publications reveals a consistent trajectory in performance-aware software development , with increasing emphasis on compiler technologies (particularly GraalVM), self-adaptive systems for cloud/edge environments, and cost-optimized performance testing methodologies. His work bridges theoretical program analysis with practical industrial applications, especially in avionics and distributed systems. Dr. Bulej actively contributes to major research projects including GraalVM compiler evaluation, FitOptiVis (avionics systems), ASHLEY (aircraft demonstrators), Ferdinand (model-driven design evaluation), Q-ImPrESS (service-oriented systems), and CoCoME (component modeling). He maintains critical software infrastructure such as the Renaissance Benchmark Suite, DiSL instrumentation framework, and Java Performance Measurement Framework.
Peter Manohar is a postdoctoral researcher in the Computer Science and Discrete Math group at the Institute for Advanced Study , focusing on Theoretical Computer Science with emphasis on algorithms, coding theory, and cryptography. His work explores spectral algorithms for semirandom and smoothed instances of NP-hard constraint satisfaction problems, linking these methods to coding theory, extremal combinatorics, and cryptography. Education: PhD in Computer Science from Carnegie Mellon University, advised by Venkatesan Guruswami and Pravesh K. Kothari B.S. in EECS from UC Berkeley, advised by Alessandro Chiesa and Ren Ng Research Trends: His recent publications highlight advancements in spectral refutation techniques, locally decodable/correctable codes, and connections between complexity theory and coding. Articles span venues like FOCS, STOC, APPROX, and arXiv, reflecting his interdisciplinary approach. Awards: He has received prestigious NSF and Cylab Presidential Fellowships, along with ARCS scholarships during his PhD. His work on quantum proofs (TCC 2019) and constraint satisfaction problems has been recognized in invited journal special issues. Teaching & Collaboration: Peter has taught courses at Carnegie Mellon, including Quantum Computing and Computer Graphics. He interned at TTIC in Summer 2023 and co-organized CMU's Theory Club, demonstrating active engagement in academic communities.
Yael Niv is a Professor and Director of Graduate Studies at the Princeton Neuroscience Institute, Princeton University, specializing in reinforcement learning, decision-making, and computational cognitive neuropsychiatry. Her work bridges neural mechanisms with clinical applications for psychiatric disorders including depression, OCD, schizophrenia, and addiction. Her academic foundation includes: Ph.D., The Hebrew University of Jerusalem Dr. Niv employs computational models to dissect how attention and memory interact with reinforcement learning, emphasizing normative explanations for brain algorithms. Her lab prioritizes model-based experimentation to define testable hypotheses about behavior. Recent expansion into computational cognitive neuropsychiatry leverages these tools for diagnosing and treating mental illness through the Rutgers-Princeton Center for Computational Cognitive Neuropsychiatry. Analysis of her recent publications reveals dominant trends in computational psychiatry: reinforcement learning models applied to depression/anxiety symptomatology, fear extinction mechanisms, and emotion-decision interactions. Key subfields include latent-cause inference, reward sensitivity in social contexts, and schema-based representation learning. She received the Graduate Mentoring Award for exceptional guidance of graduate students. Her research is supported by major collaborative funding, notably a $16 million grant for the Rutgers-Princeton Center advancing mental illness research. Dr. Niv advises Branson Byers, Jamie Chiu, Sevan Harootonian, and Dan-Mircea Mirea. She leads the Niv Lab in developing computational frameworks for clinical translation, with ongoing projects focused on personalizing cognitive behavioral therapy and modeling psychiatric symptom networks.
Andrés Carvajal is a Professor at the University of California, Davis specializing in economic theory with emphases in general equilibrium, financial economics, and mathematical economics. He serves as Editor-in-Chief of the Journal of Mathematical Economics starting January 2020 and previously held editorial roles at the Canadian Journal of Economics and Journal of Mathematical Economics. His academic credentials include: Ph.D. in Economics, Brown University (2003) M.A. in Economics, Brown University (2000) M.A. in Economics, Universidad de los Andes, Colombia (1996) B.A. in Economics, Pontificia Universidad Javeriana, Colombia (1995) Professor Carvajal's research examines how market imperfections impact economic systems and societal welfare. His work spans empirical tests of rational behavior models, informational foundations of equilibrium theory, environmental economics, and financial market design. He connects theoretical frameworks with real-world economic phenomena through rigorous mathematical analysis. His recent publications reveal an evolving research trajectory toward interdisciplinary applications while maintaining core theoretical focus. Key trends include sophisticated modeling of financial market behavior, innovative revealed preference methodologies, and exploration of heavy-tailed distributions in economic contexts, alongside unexpected forays into medical and agricultural genetics through collaborations. His distinguished awards include: Graduate Professor of the Year, University of Western Ontario (2014) ESRC Seminar Grant for Games and Economic Behavior Group (2010-2012) ESRC Research Grant for Non-competitive Behaviour (2009-2012) Cowles Foundation Postdoctoral Fellowship (2005) Abramson Award for Exceptional Dissertation (2002) Professor Carvajal has secured substantial research funding including ESRC grants and contributed significantly to academic governance as Co-Chair of the 2018 Econometric Society Summer Meeting. His teaching portfolio covers microeconomic theory, financial economics, and mathematical methods for economics.
