جست‌وجوی استاد و پژوهشگر

• Economic Development
• Regional Economics
• Applied Econometrics
• +۵ مورد دیگر

Marco Letta is a Tenure-Track Assistant Professor at the Department of Social and Economic Sciences , Sapienza University of Rome. His research focuses on economic development , regional economics , policy evaluation , and applied econometrics , with a strong emphasis on climate change impacts, food security, and machine learning applications in economic policy. University: Sapienza University of Rome Department: Department of Social and Economic Sciences Email: marco.letta@uniroma1.it His recent work explores the climate migration nexus , household resilience , and policy targeting , often leveraging machine learning and empirical econometric methods . Publications span topics such as local inequalities during the COVID-19 crisis , temperature shocks in rural Tanzania , and machine learning applications in state aid regulation . Notable trends in his research include: Integration of machine learning with traditional econometric techniques Focus on climate resilience and migration patterns Analysis of policy impacts in developing economies Investigation of local mortality estimates during global crises Development of cross-country empirical frameworks Current projects include assessing agrifood system vulnerabilities and refining counterfactual policy evaluation methodologies.

• Computer Science
• Deep Learning
• Network Security
• +۳ مورد دیگر

George Kesidis is a Professor in Computer Science and Engineering and Electrical Engineering at Penn State University. His research spans deep learning security, virtual reality optimization, and cloud computing. College of Engineering (Penn State University) Research Focus: Backdoor Attacks, DNN Robustness, Edge Caching Active in NSF and U.S. Navy-funded projects (2022-2026) His work addresses backdoor data poisoning , test-time evasion attacks , and DNN overfitting mitigation . He develops techniques like activation clipping, perturbation analysis, and statistical defense models. Recent projects include edge caching systems for VR and security-driven AI frameworks. Key article trends reveal expertise in adversarial deep learning, immersive media delivery, and cloud resource optimization. Current grants focus on multi-user VR, GPU scheduling, and serverless-cloud hybrid architectures. He collaborates extensively with researchers like David J. Miller and Xinyu Li, particularly on cloud-based adversarial defense mechanisms and VR streaming benchmarks.

Ning Zhangمشاهده پروفایل

دانشیار

• System Security
• Cybersecurity
• Computer Architecture
• +۷ مورد دیگر

Ning Zhang is an Associate Professor in the Department of Computer Science & Engineering at Washington University in St. Louis, affiliated with the Center for Trustworthy AI in Cyberphysical Systems (CPS). He joined the university in Fall 2018, following an 11-year tenure at Raytheon as a Principal Cyber Engineer and Technical Lead, where he focused on securing critical networked and cyberphysical infrastructures. Education: PhD in Computer Science, Virginia Polytechnic Institute and State University (2016) BS and MS in Computer Science, University of Massachusetts Amherst (2007) Research Interests: Professor Zhang’s work bridges security, computer architecture, and programming languages. Key areas include secure software/hardware systems, side-channel analysis, malware forensics, and automated vulnerability discovery. His recent focus includes safeguarding generative AI agents and enhancing cybersecurity in agentic systems. Awards: USENIX Distinguished Paper Award (2024) for research on generative AI security circumvention Grants & Collaborations: Co-recipient of a $1.5M DoD grant (2025) to advance generative AI robustness Labs & Teams: Leads research initiatives in system security and trustworthy AI within the McKelvey School of Engineering.

• Human-Centered Artificial Intelligence
• Natural Language Processing
• Computer Vision
• +۵ مورد دیگر

Carlos Toxtli is an Assistant Professor at Clemson University where he leads the Human-AI Empowerment Lab. His research applies Human-Centered AI to create fair workplace tools, focusing on NLP, computer vision, and crowdsourcing ethics. He holds a PhD in Computer Science from Northeastern University and previously worked at Google, Microsoft Research, and Snap Inc. Education: Ph.D. Computer Science, Northeastern University M.S. Innovation & Technological Entrepreneurship, Monterrey Institute of Technology MBA, IEDE Business School B.S. Computer Science, University of the Valley of Mexico B.S. Computer Engineering, National Autonomous University of Mexico Research Focus: Dr. Toxtli develops AI systems promoting workplace fairness through ethical frameworks. His work spans: 1) Human-AI collaboration for task management, 2) Bias mitigation in gig economies, 3) Multimodal interfaces for accessibility, and 4) Culturally adaptive systems. His lab explores how AI can augment human capabilities while ensuring transparency. Publication Trends: Recent works demonstrate strong focus on LLM reliability (7 papers), human-AI teaming (4 studies), and ethical AI frameworks (3 publications). Over 60% of 2024-2025 output addresses real-world deployment challenges. Awards: UNESCO IRCAI Global Top 100 project Google Apps Developer Challenge 1st Place Facebook Developers World Hack 1st Prize Intel Innovation Latin App 1st Prize NSF STIR Labs Research Grant Leadership: Founded the Human-AI Empowerment Lab securing $1.2M in NSF/industry grants. Supervises 8 PhD students in human-centered computing projects. Previously co-founded 5 tech startups including ComproPago (acquired by Coca-Cola FEMSA).

Andrea Groverمشاهده پروفایل

دانشیار

• Citizen Science
• Information Systems
• Open Collaboration Systems
• +۳ مورد دیگر

Dr. Andrea Grover is an Associate Professor in the Department of Information Systems and Quantitative Analysis at the University of Nebraska Omaha’s College of Information Science & Technology. Her research focuses on citizen science, technology design, and collaboration systems in data-intensive environments. She holds a Ph.D. in Information Science & Technology from Syracuse University, an MS in Information from the University of Michigan, and a BA in Mathematics from Alma College. Her research interests include organizational impacts of technology, open collaboration frameworks, and ethics in information systems. She teaches courses on management and IT ethics, employing ungrading techniques to enhance student engagement. Notable contributions include work on citizen science data quality, cyber-security tools leveraging crowdsourcing, and barriers in STEM education equity. Dr. Grover has been recognized with the 2022 Rising Star Distinguished Ecologist award and serves on editorial boards (e.g., Frontiers in Ecology) and conference committees (e.g., ACM CSCW DEI Co-chair). She has secured grants such as the NSF-funded “Streamlining Embedded Assessment to Understand Citizen Scientists' Skill Gains” (2017–2019) and contributed to media discussions on citizen science via National Public Radio. Her recent publications explore hybrid human-AI tools, agile citizen science methodologies, and systemic barriers in education. She actively participates in interdisciplinary initiatives, bridging technology, ecology, and social sciences to advance participatory research practices.

• Statistical Machine Learning
• Bayesian Inference
• Variational Methods
• +۵ مورد دیگر

Michael C. Hughes ("Mike") is an Assistant Professor in the Department of Computer Science at Tufts University's School of Engineering, where he develops statistical machine learning methods for healthcare applications. His work focuses on building predictive models that extract actionable insights from complex clinical data, including electronic health records and medical imaging. PhD, Computer Science, Brown University (2016) MS, Computer Science, Brown University (2012) BS, Computer Science, Franklin W. Olin College of Engineering (2010) Research interests center on: Bayesian hierarchical models for documents, sequences, and medical images Optimization algorithms for approximate inference Model fairness and interpretability in clinical contexts Semi-supervised learning for medical diagnostics Recent publications demonstrate these capabilities through applications in cardiovascular disease diagnosis, opioid overdose forecasting, and ICU risk prediction. His lab emphasizes reproducibility through open datasets like TMED-2 and open-source tools like BNPy. Grants include NIH R01 funding for heart valve disease detection, NSF CAREER support for model interpretability, and NSF GCR funding for educational uncertainty research. Scientific awards include: NIH R01 Award (PI) for heart valve disease detection (2025) NSF CAREER Award (2024) NSF GCR Grant (2024) Best Poster Award at Time Series Workshop (ICML 2021) Top 10% Reviewer Awards at AISTATS (2023, 2022) Teaching activities include courses on Bayesian Deep Learning, Introduction to Machine Learning, and Statistical Pattern Recognition. He previously served as postdoctoral fellow at Harvard SEAS.

Forest Agostinelliمشاهده پروفایل

استادیار

• Deep Learning
• Reinforcement Learning
• Search Algorithms
• +۷ مورد دیگر

Forest Agostinelli is an Assistant Professor in the Department of Computer Science and Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina, where he is also affiliated with the AI Institute. His research focuses on designing AI algorithms for pathfinding problems, integrating deep learning, reinforcement learning, heuristic search, and formal logic. He holds a Ph.D. in Computer Science from the University of California, Irvine, an M.S. from the University of Michigan, and a B.S. in Electrical and Computer Engineering from The Ohio State University. Research Overview : Agostinelli’s work emphasizes solving pathfinding problems in domains like robotics, theorem proving, and molecular optimization. His group develops explainable AI methods to enable collaboration between humans and machines. Key projects include DeepCubeA (solving the Rubik’s Cube via deep reinforcement learning) and neural activation function research. Funding & Awards : He has secured grants from NSF, NASA EPSCoR, and South Carolina’s ASPIRE and MADE programs. Notable awards include the NSF Graduate Research Fellowship and the Graduate Education for Minority Students Fellowship. Teaching : He teaches courses in Artificial Intelligence (CSCE 580) and Deep Reinforcement Learning and Search (CSCE 790), mentoring over 15 students at undergraduate and graduate levels. Labs & Collaborations : Active in AI-driven education and interdisciplinary projects, his lab contributes to tools like ALLURE for children’s learning and Bioinformatics platforms like CircadiOmics.

Sadaf Salehkalaibarمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Generative models
• +۵ مورد دیگر

Sadaf Salehkalaibar is an Assistant Professor in the Department of Computer Science at the University of Manitoba, Winnipeg, Canada. She holds an office in the EITC building (E2-416) and has previously held academic positions at the University of Tehran, University of Toronto as a research associate, and visiting roles at McMaster University, Telecom Paristech, and National University of Singapore. Her research focuses on explainable artificial intelligence, generative models, and information theory with an emphasis on rate-distortion-perception tradeoffs in video and image processing. Her educational background includes teaching courses such as Signals and Systems, Digital Signal Processing, and Network Security at the University of Tehran. She currently teaches COMP4190 (Artificial Intelligence) at the University of Manitoba. Research interests revolve around developing efficient algorithms for AI systems, with key contributions in learned video compression, federated learning, and privacy-preserving techniques. Notable work includes the M22 algorithm for communication-efficient federated learning and the NSERC Discovery Grant-funded project on data-driven learning efficiency. Recent publications highlight advancements in perception loss functions, Gaussian vector source analysis, and secure distributed hypothesis testing. She actively serves on editorial boards (e.g., IEEE Transactions on Communications) and conferences (ISIT, ITW). Awards include the prestigious NSERC Discovery Grant (2025). Supervision highlights 13 MSc students at the University of Tehran, focusing on topics like privacy-preserving systems and distributed learning. Labs/teams: Leads research group at University of Manitoba focusing on AI and information theory applications in multimedia systems.

Noam Lubellمشاهده پروفایل

استاد

• International Law
• International Humanitarian Law
• Human Rights Law
• +۹ مورد دیگر

Professor Noam Lubell is a leading academic in International Law of Armed Conflict at the University of Essex, where he also serves as Director of the Essex Armed Conflict and Crisis Hub (since 2017). He previously held the Swiss Chair of International Humanitarian Law at The Geneva Academy (2013–2019) and was Head of the Law School at Essex (2014–2017). His roles include Rapporteur of the International Law Association's Committee on the Use of Force (2010–2018) and Senior Research Fellow at Johns Hopkins University Applied Physics Laboratory (US), focusing on legal aspects of new military technologies. Lubell has collaborated with the ICRC on IHL investigations and works extensively with governments, UN bodies, and NGOs on issues like occupation law, self-defense, and autonomous weapons systems. Education: Ph.D. in Law LL.M. B.A. in Philosophy Research Interests: International Humanitarian Law (IHL) Human Rights Law in Armed Conflict Legal Frameworks for Use of Force Cyber Warfare and Emerging Technologies Occupation Law and Transnational Conflicts Accountability Mechanisms for Violations of IHL His work analyzes modern warfare challenges, including the application of proportionality, fragmented conflicts, and the impact of AI and cyber capabilities on legal boundaries. He advocates for ethical and lawful military practices while engaging with interdisciplinary topics like neuroscience in warfare. Publications highlight trends in cyber warfare, autonomous systems, and IHL's evolving role in contemporary conflicts. His research bridges traditional legal principles with modern technological and strategic realities, emphasizing accountability and human rights protections. Scientific Awards: None explicitly listed, though his work has received widespread academic and professional recognition. Advising & Grants: Principal Investigator on a project addressing cumulative civilian harm and proportionality (2024–present) Co-led ICRC partnership producing IHL investigation guidelines (2013–2019) Consultancies with military, governments, and media organizations Member of expert groups on occupation law, autonomous weapons, and battlefield scope No formal advisees (PhD/Master’s students) are listed in the provided texts. Labs & Teams: Essex Armed Conflict and Crisis Hub Johns Hopkins University Applied Physics Laboratory (Senior Research Fellow) International Law Amicus Curiae Clinic (Co-Director at Concord Research Centre)

Ioannis Emirisمشاهده پروفایل

استاد

• Computational Geometry
• Algebraic Algorithms
• Robotics
• +۴ مورد دیگر

Ioannis Z. Emiris is a Professor in the Department of Informatics & Telecoms at the National & Kapodistrian University of Athens and concurrently serves as President and General Director of the ATHENA Research Center in Greece. He holds a BSc in Computer Science from Princeton University (1989) and a PhD in Computer Science from UC Berkeley (1994). His research spans computational geometry, algebraic algorithms, robotics, structural bioinformatics, and optimization. He is a leading expert in sparse elimination theory, geometric modeling, and algorithmic algebra. Affiliations: ATHENA Research Center, National & Kapodistrian University of Athens, INRIA Sophia Antipolis (France via joint AROMATH team). Education: BSc (Princeton), PhD (UC Berkeley). Research Interests Emiris's work focuses on geometric algorithms, algebraic systems, and their applications. His contributions include advancements in sparse elimination theory, computational geometry for high-dimensional data, and robotics. He has developed algorithms for polynomial system solving, Voronoi diagrams, and geometric predicates for ellipses. Articles Overview His recent work bridges theoretical advances with practical applications, such as deep learning for protein structure prediction (HydraProt) and geometric algorithms for high-dimensional data analysis. He explores intersections between algebraic geometry and computational methods, with applications ranging from robotics to bioinformatics. Scientific Awards Best Paper Award at ISSAC 2003 and 2010 MSCA Network GRAPES (2019-2023) Advising & Grants Emiris has supervised numerous students and researchers, contributing to interdisciplinary projects. He has secured grants for initiatives like the GRAPES network and has led teams in algorithm design and geometric software development. His work on MARS (Maple/Matlab/C Resultant-Based Solver) exemplifies his focus on practical algorithm implementation. Labs & Teams He directs the Lab of Geometric & Algebraic Algorithms and collaborates with the AROMATH team at INRIA. His research group develops open-source tools for computational geometry and algebraic computations.

Patrick Skebaمشاهده پروفایل

عضو هیئت علمی

• Internet Privacy
• Artificial Intelligence
• Data Ethics
• +۳ مورد دیگر

Patrick Skeba is a Teaching Assistant Professor at the University of Pittsburgh's Department of Computer Science within the School of Computing and Information. He holds a PhD in Computer Science from Lehigh University (2022) and bachelor's degrees in Cognitive Science and Computer Science from Johns Hopkins University (2017). His research focuses on internet privacy, AI ethics, and the responsible use of data. He teaches courses in machine learning and programming. Research Interests: Skeba's work bridges technology and societal impact, emphasizing privacy risks in data systems, algorithmic fairness, and user-centric privacy frameworks. His recent studies explore informational friction in data collection, community-based privacy strategies, and lay-expert disparities in understanding privacy-enhancing technologies (PETs). Publications: His articles analyze privacy dynamics in digital spaces, from pandemic-era discourse on r/privacy to methodological approaches for categorizing technology non-use. His earlier work includes breakthroughs in sleep disorder diagnostics, particularly periodic leg movement (PLM) analysis and telemedicine applications for neurological conditions. Awards: No scientific awards listed. Grants and advising details are currently unspecified. Labs/Teams: No specific lab affiliations mentioned in provided materials. His teaching and research emphasize collaboration across computational and social domains.

Julia Powlesمشاهده پروفایل

دانشیار

• Privacy
• Artificial Intelligence
• Internet Governance
• +۷ مورد دیگر

Julia Powles is an Associate Professor at the UWA Law School and Director of the UWA Tech & Policy Lab. Her research focuses on privacy, intellectual property, internet governance, and the legal and political dimensions of data, automation, and AI. She has led investigations into high-profile cases like the NHS/Google DeepMind data breach and Sidewalk Labs’ smart city project. Currently, she advises on national robotics strategy, responsible AI, and privacy policies in Australia. With a background spanning academia, policy, and law, she holds roles in global committees and contributes to media outlets like the New Yorker and Financial Times. Her work bridges legal frameworks and technological innovation, emphasizing ethical governance and societal impact. Education: BSc (Hons) ANU, LLB (Hons) UWA, BCL Oxford, PhD Cantab. Expertise includes AI ethics, data privacy, and regulatory policy. Recent projects address drone delivery systems, corporate accountability of Big Tech, and governance in health monitoring. Awards include the 40 Under 40 Award for WA (2022) and Poynter Fellowship (2018). Grants include the Children’s Online Safety Program (2024–2027) and Minderoo Foundation initiatives. She chairs the PRIS Universities Network and co-chairs international tech policy summits. Roles: Director, UWA Tech & Policy Lab; Expert on National AI Centre, WA Privacy Committee. Research: Over 100 outputs, focusing on AI governance, corporate liability, and tech ethics.

• Digital Hardware Design
• Reliability Engineering
• Reconfigurable Devices
• +۳ مورد دیگر

Eleonora Vacca is a PhD student and Research Fellow in the Department of Automatic Control and Computer Science (DAUIN) at the Polytechnic University of Turin. She holds a B.S. in Electronic Engineering from the University of Palermo (2018) and an M.S. in Electronic Engineering-Embedded Systems from Politecnico di Torino (2021). Her research focuses on digital hardware design, reliability engineering, reconfigurable devices, and AI applications in aerospace and safety-critical systems. She is a member of the Aerospace and Safety Computing Lab and the CAD - Electronic CAD & Reliability Group (DAUIN). Her work addresses challenges such as radiation effects mitigation in space missions, fault-tolerant AI accelerators, and real-time anomaly detection in satellite telemetry. She has contributed to projects like the RAMSES CubeSat-1 Development (2025-2026), funded by commercial contracts. In 2024, she won the Best Student Paper Award at the NEWCAS Conference for her research on radiation effects in space missions. Vacca collaborates on teaching, including assisting in the course 'Electronic Calculators' for Computer Engineering students. Her recent publications explore AI resilience in RISC-V ecosystems, radiation environment analysis for space missions, and gesture recognition systems for smart cities. She actively contributes to conferences such as the ACM International Conference on Computing Frontiers and the IEEE International Smart Cities Conference.

Russell Tessierمشاهده پروفایل

استاد

• Reconfigurable Computing
• FPGA Architecture
• CAD Algorithms for FPGAs
• +۴ مورد دیگر

Russell Tessier is a Professor and Department Head of Electrical and Computer Engineering at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences. His research focuses on reconfigurable computing, FPGA architectures, and hardware security, with notable contributions in CAD algorithms for FPGAs, embedded systems, and multi-tenant FPGA vulnerability analysis. Education: B.S.C.S.E., Rensselaer Polytechnic Institute (1989) M.S. and Ph.D., Massachusetts Institute of Technology (1992 and 1999) Research Interests: Dr. Tessier's work spans FPGA security (e.g., side-channel attacks, power distribution vulnerabilities), reconfigurable cloud computing, and hardware acceleration for applications like SAR imaging and machine learning. His lab, the Reconfigurable Computing Group, develops open-source FPGA cores (e.g., FlexGrip GPGPU, DE4 NetFPGA) and explores cutting-edge security countermeasures. Awards and Honors: Chancellor's Leadership Fellow (2015-2016) NSF Information Technology Research Grant Lilly Teaching Fellow (2002-2003) Multiple College of Engineering Excellence Awards Grants and Projects: Active funding includes NSF SaTC grants on reconfigurable cloud security and NASA support for snowpack measurement systems. His research also addresses FPGA-based solutions for cybersecurity, such as intrusion detection and power-side channel mitigation. Labs and Teams: Leads the UMass Reconfigurable Computing Group, which collaborates on open-source FPGA tools, security frameworks, and embedded system designs. The group maintains platforms like the DE4 NetFPGA and FlexGrip architecture.

Brendan Dolan-Gavittمشاهده پروفایل

دانشیار

• Cybersecurity
• Program Analysis
• Virtualization Security
• +۵ مورد دیگر

Brendan Dolan-Gavitt is an Associate Professor in the Computer Science and Engineering Department at NYU Tandon School of Engineering and part of the NYU Center for Cybersecurity (CCS). He holds a Ph.D. in Computer Science from Georgia Tech (2014) and a BA in Math and Computer Science from Wesleyan University (2006). His research spans cybersecurity, program analysis, virtualization security, memory forensics, and embedded/cyber-physical systems, focusing on automating the understanding of large software systems to develop novel defenses. Research interests include developing techniques for static and dynamic analyses of real-world software to reveal hidden design assumptions. His work has been presented at top security conferences like USENIX Security, ACM CCS, and IEEE Security & Privacy. He led the development of the open-source PANDA platform for dynamic analysis. His publications primarily focus on AI-driven security solutions, vulnerability discovery, and automated testing tools. Recent work explores LLMs in offensive security, fuzzing enhancements, and secure code generation, emphasizing practical applications in cybersecurity. Scientific Awards: NSF CAREER Award for improving software vulnerability testing and education He leads the OSIRIS Lab, a student-run cybersecurity group, and collaborates on interdisciplinary projects addressing emerging security challenges through grants and industry partnerships.