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Emily FirstView profile

Assistant Professor

• Artificial Intelligence
• Theorem Proving
• Machine Learning
• +3 more

Emily First is an incoming Assistant Professor in Computer Science at Rutgers University New Brunswick starting Fall 2025. She previously served as a postdoctoral researcher at UC San Diego under Sorin Lerner and earned her PhD in Computer Science at UMass Amherst under Yuriy Brun in the Laboratory for Software Engineering Research (LASER). Her research focuses on leveraging AI for theorem proving, working at the intersection of machine learning, software engineering, and programming languages. She specializes in creating tools for automated proof generation in proof assistants like Coq, Isabelle/HOL, and Lean. Her work explores how AI can enhance human reasoning across domains, with applications in software verification and formal logic systems. Recent publications show trends in neuro-symbolic AI, software verification, and LLM integration for formal methods. Her team's research has been recognized with ACM SIGSOFT Distinguished Paper Awards at ICSE, ACL, and ESEC/FSE conferences, along with workshop presentations at AI and Theorem Proving conferences. ACM SIGSOFT Distinguished Paper Award (ICSE 2025) ACM SIGSOFT Distinguished Paper Award (ACL Main 2024) ACM SIGSOFT Distinguished Paper Award (ESEC/FSE 2023) ACM SIGSOFT Distinguished Paper Award (ICSE 2022) Contact: emfirst@ucsd.edu (new email coming soon).

• Computation Theory and Mathematics
• Computer Software
• Blockchain Technology
• +3 more

Ben Livshits is a Reader in Computing at Imperial College London's Department of Computing, part of the Faculty of Engineering. His research focuses on computation theory, computer software, and privacy-preserving technologies in blockchain systems. He holds a Ph.D. from Stanford University. Education: Ph.D. in Computer Science, Stanford University, 2007 Research Interests: Livshits specializes in cryptographic protocols for decentralized systems, ZK-Rollups optimization, MEV analysis, and privacy-enhancing technologies. His work bridges theoretical computer science with practical applications in blockchain scalability and security. Recent Research Trends: His 2024-2025 work emphasizes ZK-proof systems (e.g., zk-bench), MEV mitigation strategies, and formal foundations for rollups. He also explores community-driven verification (CrowdProve) and governance mechanisms for DeFi. Affiliations: He is affiliated with the Centre for Cryptocurrency Research and Engineering, contributing to interdisciplinary blockchain research. Grants & Labs: While specific grants are not listed, his work suggests involvement in Imperial's distributed systems and blockchain initiatives.

Venkat N. KroviView profile

Professor

• Robotics
• Autonomous Vehicles
• Human-Robot Interaction
• +5 more

Prof. Venkat N. Krovi serves as the Michelin Endowed Chair Professor of Vehicle Automation in the Departments of Automotive Engineering and Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences (CECAS). He directs the Automation, Robotics and Mechatronics Laboratory (ARMLab) at the International Center for Automotive Research (CU-ICAR), focusing on smart embedded systems for autonomy in challenging environments. He earned his Ph.D. in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania in 1998. His research leverages distributed autonomy and human-robot synergy to extend human capabilities, with applications spanning plant automation, consumer electronics, automobile, defense, and healthcare. The work emphasizes lifecycle treatment (design through verification) of robotic systems under uncertainty. Recent publications (2024-2025) demonstrate strong trends in digital twin frameworks for autonomous vehicle validation, sim2real transfer via reinforcement learning, and integration of large language models for editable simulations. Key themes include scalable cloud-based architectures, Koopman operator theory for robustness, and containerization for reproducible robotics development. His accolades include: National Science Foundation (NSF) CAREER Award Petro-Canada Young Innovator Award Multiple best paper awards at conferences and journals ASME Dedicated Service Award (2024) Prof. Krovi has advised doctoral students including Dr. Srivatsan Srinivasan (2024). His research receives substantial funding from NSF, DARPA, ARO, and industrial partners like Michelin. He leads the NSF I/UCRC RoSeHuB center and the AutoDRIVE ecosystem for autonomous driving education. As ARMLab director, he oversees projects including OpenCAV, the Robotics for AV Systems Bootcamp, and containerized terramechanics simulations. The lab specializes in mechatronic design, verification/validation frameworks, and human-autonomy coexistence studies for next-generation mobility solutions.

• Algorithms
• Theoretical Computer Science
• Programming Languages
• +6 more

Ben Rank is a Researcher at the Max Planck Institute for Software Systems (MPI-SWS), focusing on foundational aspects of computer science with a strong emphasis on theoretical rigor and practical applications. His work bridges multiple domains including algorithms, formal verification, and cyber-physical systems. Research interests include: Algorithmic methods in programming languages and verification Cyber-physical systems design and analysis Security and privacy in distributed systems Social and information systems integration Recent publications explore cutting-edge topics such as adaptive reinforcement learning in shifting environments and ethical considerations in AI systems. No scientific awards or grants are explicitly mentioned in the provided materials.

• Cybersecurity
• IoT Security
• Network Anomaly Detection
• +5 more

Gabriela F. Ciocarlie is a Researcher at SRI International, focusing on advancing cybersecurity, IoT security, and formal verification techniques. Her work bridges theoretical computer science with practical applications in critical infrastructure protection and manufacturing systems. She has contributed to over 48 publications across conferences like CCS, NDSS, and IEEE venues. Her research interests span adversarial machine learning, secure manufacturing automation, and resilient biomanufacturing systems. Notable projects include developing frameworks for verifying manufacturing design integrity and creating end-to-end security solutions for cyber-physical systems. She has also pioneered work on deployable adversarial attacks against neural networks and automated attack investigation tools like autoMPI. Key collaborations include partnerships with institutions like Columbia University (former affiliation) and industry leaders. Her work often addresses real-world challenges such as pandemic-resilient biomanufacturing and securing critical infrastructure through cyber-physical integration.

Richard ScalettarView profile

Professor

• Condensed Matter Physics
• Quantum Phase Transitions
• Superconductivity
• +5 more

Distinguished Professor of Physics at the University of California Davis College of Letters and Science since 1989. Primary affiliation with the Department of Physics, with significant cross-disciplinary collaborations in Applied Mathematics and Computer Science through NSF and DOE grants. Research focuses on quantum many-body phenomena in condensed matter systems and ultracold atomic gases. Expertise spans magnetism, superconductivity, metal-insulator transitions, and quantum phase transitions. Pioneers advanced Quantum Monte Carlo simulation techniques, particularly determinant quantum Monte Carlo for Hubbard and electron-phonon models. Current work investigates spatial inhomogeneities in quantum phases and strong interparticle interactions. Recent publications reveal growing integration of machine learning with quantum simulation. Research trends indicate deepening exploration of SU(N) symmetric systems, flat-band quasicrystals, photonic quantum simulators, and neural quantum states. Increasing emphasis on interdisciplinary approaches combining condensed matter theory, quantum information science, and computational mathematics. Key methodological focus remains on overcoming fermionic sign problems and developing scalable numerical algorithms. Principal investigator for major grants from the National Science Foundation (NSF), Department of Energy (DOE), Office of Naval Research (ONR), and Defense Advanced Research Projects Agency (DARPA). Significant funding through NSF Information Technology Research and DOE Scientific Discovery through Advanced Computing Programs for quantum simulation algorithm development.

Kuldeep S. MeelView profile

Associate Professor

• Automated Reasoning
• Formal Methods
• Artificial Intelligence
• +3 more

Kuldeep S. Meel is the Stephen Fleming Early-Career Associate Professor at Georgia Institute of Technology's School of Computer Science and an Associate Professor at the University of Toronto (currently on leave). His research focuses on the intersection of Formal Methods and Artificial Intelligence, emphasizing scalable automated reasoning techniques. He holds prestigious awards including the 2022 ACP Early Career Researcher Award and the 2019 NRF Fellowship for AI. His work has been recognized with multiple best paper awards at conferences like ICLP, CAV, and IJCAI. Meel's research spans automated reasoning, formal methods, and their applications in AI. He has developed influential tools like ApproxMC and UniGen, advancing model counting and uniform sampling. His academic journey includes roles at NUS and collaborations with institutions globally. Teaching excellence is highlighted by NUS Annual Teaching Awards (2022, 2023). Awards include Distinguished Paper Awards at CAV-23 and CAV-24, and 1st place in Model Counting Competitions. His lab has produced notable advisees securing tenure-track positions worldwide. Current projects explore distribution testing, probabilistic reasoning, and AI verification.

Kla TantithamthavornView profile

Senior Lecturer

• Software Engineering
• Empirical Software Engineering
• Software Quality Assurance
• +7 more

Dr. Kla Tantithamthavorn is a Senior Lecturer and Director of Engagement & Impact at Monash University's Faculty of Information Technology. He holds a 2020 ARC DECRA Fellowship and specializes in software engineering, explainable AI, and digital health. His research focuses on defect prediction models and their integration into CI/CD pipelines, with notable contributions like the ScottKnott ESD test R package (14,000+ downloads). He leads projects such as RAISE (Responsible AI Software Engineering) and collaborates with organizations like CSIRO and Atlassian. Education: PhD and M.Eng in Software Engineering from Nara Institute of Science and Technology (Japan). Research areas include empirical software engineering, machine learning for quality assurance, and AI-driven cybersecurity. He serves on editorial boards for IEEE Transactions on Software Engineering (TSE) and Empirical Software Engineering (EMSE). Key Projects: Automated Testing of LLMs (CSIRO), RAISE, LLM4SE (Atlassian) Media Contributions: Featured in articles on emergency care analytics and JITBot defect prediction. His work addresses critical domains like e-Health, with deployed systems reducing patient wait times in Australian hospitals. He actively supervises Honours/Master/PhD students and advocates for 'IT for Social Good' initiatives.

• Software Engineering
• Machine Learning Operations (MLOps)
• Cyber-Physical Systems
• +4 more

Prof. Dr. Klaus Schmid is a Professor in the Department of Software Systems Engineering at the University of Hildesheim, part of the Faculty of Mathematics, Natural Sciences, Economics, and Computer Science. His research focuses on Machine Learning Operations (MLOps), software product lines, adaptive systems, and variability modeling. He leads projects such as EXPLAIN and ReGaP, emphasizing explainable AI and industrial MLOps integration. His work addresses challenges in Cyber-Physical Production Systems (CPPS), including data management, model calibration, and domain knowledge integration. Key research interests include MLOps architecture design, variability modeling transformations (e.g., UVL to IVML), and incremental verification techniques for software product lines. He has published extensively in venues like IEEE ETFA, IEEE Software, and SPLC conferences. Notable achievements include a Best Paper Award for work on control patterns in self-adaptive systems. Collaborations with industry partners highlight his focus on bridging academic research and practical industrial applications. Prof. Schmid’s contributions extend to tool development, such as EASy-Producer for variability-aware software ecosystems, and frameworks for environment modeling in adaptive systems. His research addresses both foundational challenges (e.g., syntax-preserving slicing) and applied topics like MLOps platform comparisons and industrial case studies in Industry 4.0.

Eike SchneidersView profile

Lecturer

• Human-Computer and Robot Interaction
• Human-AI Interaction focusing on generative AI and its impact on society
• Multidisciplinary research in collaboration with artists, medical professionals, and lawyers

Dr. Eike Schneiders is a Lecturer at the School of Electronics and Computer Science, University of Southampton. He holds a PhD in Computer Science from Aalborg University and is a recipient of the UKRI Trustworthy Autonomous Systems Hub Early Career Researcher Award. His research focuses on autonomous and AI-infused systems, investigating how to ensure their trustworthiness and responsible interaction with humans and other agents through interdisciplinary collaborations across art, law, and medicine. His recent work explores the societal impact of Generative AI, particularly examining its influence on legal services accessibility for non-experts through the REGALS project (Principal Investigator) and its effects on creative communities via the ART-I project (Co-Investigator). Dr. Schneiders emphasizes public engagement to enhance AI literacy and employs both qualitative and quantitative methods to study human-computer interaction and ethical implications. Dr. Schneiders teaches courses including COMP6251 (Web and Cloud Application Development), ELEC6259 (Research Methods and Project Preparation), and ELEC6200 (Group Design Project). His pedagogical approach integrates foundational technical skills with critical research methodologies. He has led several interdisciplinary projects including ARTificial Intelligence: Show Me a Story about AI (ART-I) and Responsible Employment of Generative AI for Legal Services (REGALS), and contributes to the Agents, Interaction and Complexity research group. His Cat Royale project received the 2024 Webby Award for Best Integrated Experience. Honors include the Best Paper Award at ACM CHI 2024, multiple Honourable Mentions at CHI and TAS Symposiums, and membership in the Foundation for Science and Technology's Future Leaders Programme.

Sao Mai NguyenView profile

Lecturer

• Robotics
• Artificial Intelligence
• Cognitive Science
• +7 more

Sao Mai Nguyen is an Enseignante-Chercheuse (Lecturer-Researcher) at ENSTA Paris, affiliated with the Unité d'Informatique et d'Ingénierie des Systèmes (U2IS). Her research bridges robotics, artificial intelligence, and cognitive science, focusing on cognitive developmental robotics, intrinsic motivation in learning, and human-robot interaction. She explores how robots can adapt to social and physical environments, particularly in physical rehabilitation and smart home applications. Research Interests: Nguyen’s work integrates machine learning, robotic embodiment, child psychology, and neuroscience. Key areas include human-robot interaction, assistive robotics for chronic low back pain rehabilitation, activity recognition in smart homes using IoT sensors, and intrinsic motivation-driven learning frameworks. Recent Contributions: Her 2024 HDR thesis on reinforcement and imitation learning for sequential tasks underscores her expertise in strategic learning systems. Recent publications address bio-inspired robotics models, hierarchical reinforcement learning, and benchmark environments like 'Open the Chests' for activity recognition. Collaborations include projects like the R-COOL randomized trial for robot-coached physical exercises. Labs & Teams: She contributes to the U2IS lab, advancing interdisciplinary research in AI and robotics. Her work spans experimental platforms for sensorimotor learning and healthcare robotics applications.

Eric WustrowView profile

Associate Professor

• Network Security
• Internet Censorship
• Cybersecurity
• +8 more

Eric Wustrow is an Associate Professor in the Department of Electrical, Computer & Energy Engineering at the University of Colorado Boulder , affiliated with the College of Engineering and Applied Science . His research focuses on network security, internet censorship, and cryptographic protocols. Contact: ewust@colorado.edu , Phone: 734-330-8702, Office: ECCR 1B13. Academic Rank: Associate Professor Department: Electrical, Computer & Energy Engineering University: University of Colorado Boulder Eric Wustrow's research explores methods to measure and circumvent internet censorship, analyze network vulnerabilities, and enhance privacy in digital communications. His work includes studying the Great Firewall of China, DNS and TLS security, and developing anticensorship tools like ShadowTLS and WATER. He also investigates cryptographic weaknesses in network devices and secure hardware implementations. Recent publications examine the effectiveness of anticensorship tools, the impact of IPv6 on information controls, and vulnerabilities in encrypted protocols. His studies include speculative execution attacks, DNS censorship measurement, and techniques to exploit transient errors in TLS for key compromise. These works highlight evolving network threats and countermeasures. Eric's research has been supported by grants such as the CAREER: Combating Censorship from within the Network (2022) and SaTC: CORE: Medium: Collaborative: Studying the Impact of IPv6 on Information Controls and Censorship Circumvention (2020). These projects focus on developing network-level solutions to censorship and security challenges.

Hans-Peter PiephoView profile

Professor

• Statistical Methods in Plant Sciences
• Genomic Prediction
• Spatial Modeling for Field Trials
• +5 more

Prof. Dr. Hans-Peter Piepho is a faculty member at the University of Hohenheim , serving as Director of Biostatistics (340c) within the Institute of Crop Science . His research focuses on advanced statistical methods for plant sciences, including genomic prediction, spatial modeling of field trials, and network meta-analysis. Key research areas: Genotype-environment interaction, mixed model applications, and experimental design in agriculture. Collaborations span plant breeding, cultivar testing, and environmental stress analysis. His group emphasizes mixed model procedures as the backbone of methodological innovation. Recent publications highlight climate change adaptation, nitrogen optimization, and agroforestry yield dynamics. Contact: piepho@uni-hohenheim.de | Fruwirthstr. 23, Institute Building, 211, Stuttgart, Germany

Wenwen WangView profile

Associate Professor

• Computer Systems
• Compiler Design
• Computer Architecture
• +4 more

Wenwen Wang is an Associate Professor in the School of Computing at the University of Georgia's Franklin College of Arts & Sciences. His research focuses on computer systems, compiler design, and embedded systems security. He holds a Ph.D. in Computer Science from the University of Chinese Academy of Sciences (2014). Education: Ph.D., Computer Science, University of Chinese Academy of Sciences, 2014 His research emphasizes dynamic binary translation, compiler optimization, and secure embedded systems. Notable contributions include frameworks like JavART (JIT compiler optimization) and BSan (memory error detection). He received the 2021 M. G. Michael Award for Sciences from the Franklin College. Wang has secured two NSF grants totaling $1.2 million, including CSR: Small grants for FALCON (2023–2027) and Modernizing Dynamic Binary Translation Systems (2023–2027). He advises three graduate students: Ruili Fang, Yage Hu, and Boyang Yi. His work addresses challenges in cross-architecture virtualization, GPU-based graph computing, and hardware-triggered security mechanisms. Recent projects include Liberator (GPU graph processing) and InvisiGuard (embedded device integrity).

• Security of Deep Learning Systems
• Side-Channel Attacks
• Privacy-Preserving Machine Learning
• +3 more

Dr. Yuanyuan Yuan is a Researcher at the Department of Computer Science, ETH Zurich, Switzerland, based at CNB H 104.1, Universitätstrasse 6, 8092 Zurich. Her work bridges computer security and machine learning with a focus on practical vulnerabilities in deployed AI systems. Her research centers on exposing and mitigating security flaws in deep learning deployments, particularly targeting trusted execution environments (TEEs) and on-device inference systems. Key contributions include pioneering side-channel attacks against TEE-shielded neural networks (CipherSteal, HyperTheft), bit-flip attack surfaces in DNN executables (BitShield), and novel testing methodologies for neural network robustness. She investigates cache/timing side channels, ciphertext analysis, privacy leakage in partitioned ML, and concept-based explainability. Analysis of her 2023-2025 publications reveals a cohesive focus on offensive security research for AI infrastructures, with consistent contributions to top venues in security and machine learning. Her work demonstrates expertise in low-level system interactions (memory, cryptography) applied to ML security, spanning attack vectors, defensive mechanisms, and validation frameworks. The research trajectory shows increasing sophistication in exploiting hardware-software interfaces while developing practical hardening techniques for real-world deployments.