جست‌وجوی استاد و پژوهشگر

D. James Greinerمشاهده پروفایل

استاد

• Access to Justice
• Legal Empiricism
• Randomized Control Trials
• +۴ مورد دیگر

D. James Greiner is the Honorable S. William Green Professor of Public Law and Faculty Director of the Access to Justice Lab at Harvard Law School. His research focuses on randomized control trials in legal contexts, particularly in civil and criminal justice systems. Greiner holds a Ph.D. in statistics from Harvard University and previously practiced law, focusing on employment discrimination and voting rights. He teaches courses on civil procedure, expert witnesses, and access to justice. Greiner's work emphasizes empirical methods to evaluate legal interventions, including studies on unbundled legal assistance, the impact of representation in court, and algorithm-assisted decision-making in pretrial risk assessments. His Access to Justice Lab designs and implements experiments to assess interventions aimed at improving justice outcomes for underserved populations. Key projects include evaluating online dispute resolution systems, analyzing criminal justice record-clearing policies, and studying the effects of counsel at first court appearances. His research has been published in leading legal and statistical journals, including the Harvard Law Review and the Yale Law Journal.

Yun Fuمشاهده پروفایل

استاد

• Urban Design
• Architecture
• Sustainability
• +۳ مورد دیگر

Yun Fu is a Professor at the Harvard Graduate School of Design (GSD), specializing in urban design and architecture. He co-founded SEMESTER, a design and research studio, and has held roles such as coordinator of the Design Discovery program and faculty affiliate at the Joint Center for Housing Studies. His work bridges theory and practice, focusing on risk, resilience, and sustainability in urban contexts. Education: Ph.D. from Harvard University (2020), Master of Architecture I (Advanced Placement) from Harvard GSD (with AIA Henry Adams Medal), and Bachelor of Architectural Studies from UNSW Sydney (with multiple awards). Research interests include seismic design, modern architecture in Asia Pacific, and the philosophical underpinnings of design intelligence. Recent projects explore suburban densification in New Zealand and interdisciplinary approaches to urban challenges. His books, such as Thinking and Building On Shaky Ground , have won major awards like the DAM Architecture Book Prize and Graham Foundation Grant. Teaching includes core studios like Elements of Urban Design and advanced seminars on urban theory. He has led interdisciplinary studios addressing real-world issues in the Asia Pacific, with student work featured in the NZ Listener and presented to government agencies. Awards include the Rome Prize Fellowship, Sinclair Kennedy Travelling Fellowship, and multiple design accolades from Dezeen and international furniture fairs. His studio, SEMESTER, has won global recognition for projects like the Temporary Storage Garden (2024 Dezeen Award).

Doaa Abu-Elyounesمشاهده پروفایل

استاد مدعو

• AI Ethics
• Algorithmic Governance
• Public Policy
• +۳ مورد دیگر

Doaa Abu-Elyounes is an Adjunct Professor at Ecole Normale Superieure Paris Saclay and holds the title of Affiliate at the Berkman Klein Center for Internet and Society, Harvard University. She currently serves as a Program Specialist for the Ethics of Artificial Intelligence at UNESCO, where she leads global implementation of the UNESCO Recommendation on AI Ethics. Previously, she worked as a law clerk at the Supreme Court of Israel and as an advisor to the Director General of the Israeli Ministry of Justice. She earned her S.J.D. (Doctor of Juridical Science) from Harvard Law School and has consulted the OECD on AI regulation. Her research emphasizes algorithmic governance in public sectors, focusing on translating ethical principles like fairness, transparency, and accountability into actionable policy frameworks. She collaborates with interdisciplinary stakeholders, including legal experts, computer scientists, and policymakers, to address challenges in AI regulation. Her work bridges law, technology, and public policy, advocating for robust regulatory standards and ethical oversight of AI systems. While no formal awards are listed, her contributions to international AI ethics initiatives reflect her impactful scholarly and professional engagement. In advising roles, she has mentored legal and policy professionals through her advisory work and academic affiliations. Her affiliation with the Berkman Klein Center and UNESCO highlights collaborative research environments focused on societal implications of technology.

Vijayaraghavan Aravindanمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Machine Learning
• Quantum Information
• +۳ مورد دیگر

Dr. Vijayaraghavan Aravindan is an Associate Professor in the Department of Computer Science at Northwestern University, with a courtesy appointment in Industrial Engineering & Management Sciences. He is a core member of the Theory CS Group and serves as the Site Director for the NSF-funded Institute for Data, Economics, Algorithms, and Learning (IDEAL). His research focuses on theoretical computer science, particularly algorithmic foundations of machine learning, quantum information, and beyond worst-case analysis. Education: Ph.D. and M.A. in Computer Science, Princeton University B.Tech. in Computer Science and Engineering, Indian Institute of Technology Madras Research Interests: His work bridges theoretical computer science and machine learning, emphasizing efficient algorithms for high-dimensional data, quantum entanglement certification, and adversarial robustness. He explores paradigms such as smoothed analysis and stability-based approaches to provide practical algorithmic guarantees. Awards & Grants: NSF CAREER Award Google Research Scholar NSF AITF Grants (CCF-1637585, CCF-2154100) Advising & Teaching: He advises PhD students on topics like quantum computing, robust machine learning, and optimization. Courses taught include graduate algorithms, theoretical foundations of data science, and quantum computation. His lab collaborates on projects at IDEAL and with institutions like Carnegie Mellon University and TTI Chicago. Professional Leadership: Served as FOCS 2024 General Chair and organizes the McCormick Theory Workshops. Active on program committees for ICML, COLT, and NeurIPS.

Lauren Kleinمشاهده پروفایل

استاد پژوهشی

• Digital Humanities
• Data Science
• Gender Studies
• +۳ مورد دیگر

Lauren Klein is the Winship Distinguished Research Professor and Associate Professor in the Departments of Quantitative Theory & Methods and English at Emory University. She directs the Emory Digital Humanities Lab and leads the Mellon-funded Atlanta Interdisciplinary AI Network. Previously, she taught at Georgia Tech's School of Literature, Media, and Communication. Her research integrates computational methods with critical approaches to explore gender, race, and justice, producing influential works like Data Feminism (2020) and An Archive of Taste: Race and Eating in the Early United States (2020). She co-edits the hybrid print-digital series Debates in the Digital Humanities . Education: PhD in English and American Studies from CUNY Graduate Center; AB in Literature (English and French) from Harvard University. Awards include ACLS, NEH, and Mellon Foundation grants. Her work bridges data science and humanities, emphasizing ethical frameworks for AI and social justice. Director of Emory Digital Humanities Lab PI of Atlanta Interdisciplinary AI Network Editor, Debates in the Digital Humanities

• Computational Intelligence
• Cloud Computing
• Big Data Applications
• +۶ مورد دیگر

Jun Shen is a Professor at the School of Computing and Information Technology, University of Wollongong. He specializes in computational intelligence, cloud computing, and big data applications, with a focus on AI-driven solutions for real-world challenges in transport systems, healthcare, education, and environmental management. He has secured over 40 research grants totaling AU$4.5 million and supervised 26 completed PhD projects. His work spans interdisciplinary areas including bioinformatics, smart manufacturing, and digital health. Research interests include bio-inspired algorithmic optimization, AI in arts/media, and edge computing for IoT systems. He has pioneered research centers in applied computing since 2014 and holds editorial roles in top journals like IEEE Transactions. As an IEEE Distinguished Lecturer, he actively promotes AI ethics and interdisciplinary collaboration. Recent publications emphasize adversarial machine learning defenses, UAV systems, and multimodal data fusion. His supervision includes projects in intelligent transport systems, cloud computing, and e-learning. Grants include projects on resilient energy systems and UAV geolocation verification. Leadership roles include leading over 20 researchers and chairing conferences. He advocates for digital transformation in public services and has conducted fieldwork at MIT, UCI, and Georgia Tech.

Diptarka Chakrabortyمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Algorithms on large data sets
• Sublinear algorithms
• +۵ مورد دیگر

Diptarka Chakraborty is an Assistant Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He obtained his PhD from the Indian Institute of Technology Kanpur in 2016 and held postdoctoral positions at Charles University, Prague, and the Weizmann Institute of Science, Israel. His educational background includes: PhD in Computer Science and Engineering, Indian Institute of Technology Kanpur, 2016 Dr. Chakraborty's research lies at the intersection of Theoretical Computer Science and Algorithms , specializing in sublinear algorithms , approximation algorithms , graph algorithms , and data structures for massive datasets. His work addresses computational hardness in edit distance, rank aggregation, and fault-tolerant graph structures, with recent emphasis on algorithmic fairness and streaming applications. Analysis of his 2020-2025 publications reveals dominant trends in fairness-constrained algorithms (e.g., fair rank aggregation) and scalable techniques for massive data (e.g., clustering permutations in streaming models), alongside foundational contributions to equivalence testing, model counting, and reachability under failures. His scientific contributions have been recognized with: Faculty Teaching Excellence Award 2024 Google South & Southeast Asia Research Award 2022 Best Paper Award at FOCS 2018 IBM Research India Outstanding Ph.D. Student Award 2015-16 Dr. Chakraborty actively mentors two PhD students and multiple research fellows while leading three major research projects: massive data handling under edit metrics, fair rank aggregation, and fault-tolerant graph structures. His Google Research Award supports investigations into computational challenges of fairness. He maintains an active research group with international collaborations and regular seminar participation across institutions including IITs, TIFR, and UC campuses.

• Causal Machine Learning
• Topological Data Analysis
• Riemannian Optimization
• +۵ مورد دیگر

Qian Li is a Lecturer in Computing at the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) at Curtin University, Australia. She holds a Ph.D. from the Chinese Academy of Sciences and M.Sc. degrees from Shandong University and the University of Luxembourg. Her research focuses on causal machine learning, topological data analysis, and optimal transport, with applications in computer vision, data science, and recommendation systems. She has published over 50 articles in top-tier venues like IEEE Transactions and ACM conferences. Education Ph.D., Chinese Academy of Science (CAS) MSc (Research), Shandong University MSc (Research), University of Luxembourg Research Interests Dr. Li explores causal reasoning for machine learning, leveraging mathematical tools like Riemannian geometry and optimal transport to address challenges in robustness and interpretability. Her work spans causal inference, counterfactual fairness, and explainable AI, with applications in healthcare, energy, and commerce. Recent projects include causal-based recommendation systems and topological data analysis techniques. Key Achievements Secured a $120k grant from China's National Natural Science Foundation (2020-2024). Lead researcher on AI-driven solar energy storage projects with UNSW and Providence Asset Group. Recipient of prestigious scholarships including Chinese National Graduate Scholarship (2016, top 1%). Grants & Students Current Ph.D. students include Xiangmeng Wang and Tri Dung Duong. She has supervised graduates like Yangyang Shu (Adelaide University Research Associate) and Jun Yin (UTS). Labs & Teams Leads research in causal AI and topological data analysis, collaborating with institutions like UTS and the University of Melbourne.

Aleksandra Slavkovicمشاهده پروفایل

استاد

• Statistical Data Privacy
• Differential Privacy
• Algebraic Statistics
• +۵ مورد دیگر

Aleksandra Slavković is a Professor of Statistics and Associate Dean for Graduate Education at Pennsylvania State University's Eberly College of Science. She holds a PhD in Statistics from Carnegie Mellon University (2004) and has held academic roles since 2004, including appointments at the Institute for Computational and Data Sciences and Penn State College of Medicine. Her research focuses on statistical data privacy, differential privacy, algebraic statistics, and applications in social and health sciences. She has authored over 50 peer-reviewed publications and serves on editorial boards of top journals like Journal of Privacy and Confidentiality and Annals of Applied Statistics . Slavković has received major honors including Fellowships from the Institute of Mathematical Statistics (2021) and American Statistical Association (2018). She leads initiatives to enhance graduate education, including the Science Achievement Graduate Fellows Program, and actively promotes diversity in STEM through her leadership roles. Her recent work emphasizes privacy-preserving techniques for genomic, healthcare, and network data, with contributions to synthetic data generation and secure multiparty computation protocols. Her academic service includes chairing ASA committees and advising at the National Academy of Sciences. She maintains collaborative ties with institutions like Cornell University and UC Berkeley through visiting scholar programs, and her research bridges statistics, computer science, and applied mathematics.

• Data Management
• Peer Review
• Research Evaluation
• +۳ مورد دیگر

Professor Kalpana Shankar is a Full Professor at the School of Information and Communication Studies at University College Dublin (UCD). She holds roles such as former Head of School (2015-2018) and Co-Director of the UCD Centre for Digital Policy. Her research focuses on data management, peer review processes, research evaluation, and digital policy, with notable projects funded by Science Foundation Ireland, the Irish Research Council, and the U.S. National Science Foundation. Key contributions include studies on the sustainability of social science data archives and peer review at SFI. Education: PhD in Library and Information Science from UCLA, postdoctoral fellowships at UCLA (Center for Embedded Networked Sensing) and NIH (Science Policy). Professional Roles: Editor-in-Chief of Online Information Review , member of the Royal Irish Academy’s Equity Diversity and Inclusion committee, and representative on the National Open Research Forum (NORF). Teaching: Coordinator for UCD’s MSc/Diploma in Digital Policy and PhD supervision in critical data studies, open data, and research evaluation. Research Interests: Sociotechnical imaginaries of data, peer review fairness, digital policy frameworks, and the impact of pandemic responses on academia. Awards include the 2020 UCD College of Social Sciences and Law Teaching Excellence Award.

Latifur Khanمشاهده پروفایل

استاد

• Data Mining
• Cybersecurity
• Machine Learning
• +۲ مورد دیگر

Dr. Latifur Khan is a Professor in the Department of Computer Science at the University of Texas at Dallas' Erik Jonsson School of Engineering and Computer Science. He directs the Database and Data Mining Laboratory and conducts research in data mining, cybersecurity, and semantic web technologies. Research domains include: Large language models for threat detection Fairness in machine learning Vulnerability analysis in software systems Graph-based information retrieval Recent publications focus on AI security applications in transportation systems, political conflict analysis using NLP, and federated learning for IoT security. His work consistently bridges theoretical algorithms with practical cybersecurity implementations. Research grants include funding from NSF, NASA, Raytheon, Nokia, and SUN Microsystems. Teaching includes courses in Plant Breeding (PBG 450/550) and Breeding Clonal Crops (PBG 551).

Houssam Abbasمشاهده پروفایل

استادیار

• Computational Ethics
• Artificial Intelligence
• Cyber-Physical Systems
• +۳ مورد دیگر

Houssam Abbas is an Assistant Professor in the School of Electrical Engineering and Computer Science at Oregon State University. He holds a Ph.D. in Electrical Engineering from Arizona State University and has professional experience in SoC verification at Intel and postdoctoral research at the University of Pennsylvania. His work focuses on computational ethics for AI agents, design/verification of cyber-physical systems, and autonomous systems like self-driving cars and drones. Education: Ph.D., Electrical Engineering, Arizona State University (2015) M.Sc., Electrical Engineering, Arizona State University (2006) B.Eng., Computer and Communications Engineering, American University of Beirut (2004) Abbas' research integrates deontic logic for ethical obligations in AI, distributed verification techniques for autonomous systems, and fair control algorithms for aerial missions. He co-leads the F1/10 autonomous racing initiative and teaches hands-on courses on self-driving cars. Awards: 2022 NSF CAREER Award 2022 Grainger Foundation Frontiers of Engineering Symposium Participant Grants & Projects: NSF CCRI Grant for F1/10 Racecar platforms (with Penn and Clemson) FAA ASSURE project on UAV cybersecurity Lab/Teams: His work involves the Autonomous Systems Lab , focusing on ethical AI, robotics, and formal verification tools like the F1/10 platform.

• Knowledge Representation
• Semantic Web
• Ontology Engineering
• +۷ مورد دیگر

Deborah McGuinness is a Professor of Computer Science, Cognitive Science, and Industrial and Systems Engineering at Rensselaer Polytechnic Institute (RPI), holding the Tetherless World Senior Constellation Chair. She leads research in semantic web technologies, ontology engineering, explainable AI, and applications in health and environmental informatics. Her work emphasizes semantic technologies to enhance human-machine collaboration through knowledge representation and reasoning. Education: B.S./B.A. (Computer Science & Mathematics, Duke University, 1980), M.S. (Computer Science, UC Berkeley, 1981), Ph.D. (Knowledge Representation, Rutgers University, 1997). Research interests include: ontology creation/evolution, commonsense AI, machine learning fairness, clinical decision support systems, knowledge graphs for scientific data, and policy modeling. Recent work focuses on AI explainability, semantic data dictionaries for public health surveys, and leveraging knowledge graphs for personalized health recommendations. Her publications span semantic web standards, AI commonsense benchmarks, clinical informatics applications, and policy frameworks. Notable projects include the Explanation Ontology for user-centered AI and the CHEAR Data Repository for environmental health research. McGuinness has pioneered semantic technologies for data integration across diverse domains like nanomaterials science and stroke care policy analysis.

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۴ مورد دیگر

Prof Noel O'Connor is a Full Professor at Dublin City University's School of Electronic Engineering, specializing in cutting-edge research at the intersection of artificial intelligence (AI), medical imaging, robotics, and smart city technologies. His work spans applications such as cardiac MRI reconstruction, robotic manipulation using reinforcement learning, and the development of the Smart DCU Digital Twin for autism-friendly university environments. Research interests include AI-driven medical diagnostics, multimodal data fusion, and adaptive systems. His contributions to cardiac MRI reconstruction and transformer-based medical imaging analysis reflect a strong focus on healthcare innovation. He also explores ethical AI practices to reduce social bias in foundation models. Recent work emphasizes smart infrastructure projects, such as optimizing parking recommendations for electric vehicles and enhancing accessibility through digital twin frameworks. His research often integrates real-time sensor data and multi-agent systems to address complex urban challenges. No scientific awards are listed. Collaborations include the ASU-DCU International Research Program on Sensors and Machine Learning. Advising details and grant information are not explicitly provided.

Md Arifuzzamanمشاهده پروفایل

استادیار

• High-Performance Networking
• Quantum Networking
• Distributed Systems
• +۳ مورد دیگر

Dr. Md Arifuzzaman is an Assistant Professor in the Department of Computer Science at Missouri University of Science and Technology (Missouri S&T). He specializes in High-Performance Systems, Quantum Networking, and Distributed Systems, focusing on optimizing large-scale system performance and scalability. His work addresses challenges in next-generation networks and storage systems, with publications in top venues like IEEE TPDS and ACM Supercomputing. Education: Ph.D. in Computer Science and Engineering, University of Nevada, Reno (2023) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2016) Research Interests: Dr. Arifuzzaman's work spans cutting-edge topics including quantum entanglement routing, reinforcement learning for network optimization, and high-speed file transfer protocols. His research emphasizes practical solutions for emerging technologies like terabit networks and quantum communication systems. Publications: Recent work focuses on quantum network protocols, machine learning-driven network probing, and storage reliability. His articles highlight advancements in both theoretical frameworks and real-world system implementations. Awards: No specific awards mentioned in the provided information. Advising/Grants: Details regarding student advising and grant activities are not explicitly stated in the text.