جست‌وجوی استاد و پژوهشگر

Oshani Seneviratneمشاهده پروفایل

استادیار

• Decentralized Systems
• Blockchain Technology
• Health Informatics
• +۵ مورد دیگر

Oshani Seneviratne is an Assistant Professor of Computer Science at Rensselaer Polytechnic Institute (RPI), leading the BRAINS Lab. She holds a Ph.D. and S.M. from MIT (Computer Science) and a B.Sc. (Hons) from the University of Moratuwa, Sri Lanka. Her research focuses on decentralized systems, blockchain, health informatics, and federated learning. She previously directed RPI's Health Data Research division. Education Ph.D. in Computer Science, MIT S.M. in Computer Science, MIT B.Sc. (Hons) in Computer Science and Engineering, University of Moratuwa Research Interests Her work bridges decentralized technologies with clinical and financial applications. Key areas include blockchain-based frameworks for federated learning, explainable AI in healthcare, and semantic web technologies for data interoperability. She emphasizes ethical AI, data provenance, and scalable systems for real-world challenges. Recent Work Trends Recent publications highlight advancements in smart contract auditing, LLM applications in financial systems, and blockchain-enhanced data provenance. Her work often combines technical innovation with societal impact, such as improving health data sharing and combating misinformation. Awards & Grants No awards explicitly listed, but her research has been supported by grants related to blockchain, health informatics, and decentralized systems. Advising & Labs Leads the BRAINS Lab, focusing on resilient, intelligent networked systems. Collaborates on projects like the Punya platform for clinical apps and BlockIoT for health data integration. Future Work Expanding into AI explainability for clinical decision support, decentralized mental health monitoring, and blockchain interoperability solutions.

• Network Optimization
• Infrastructure Resilience
• Supply Chain Risk
• +۳ مورد دیگر

Nafiseh Ghorbani-Renani is a Teaching Professor in the Department of Systems and Enterprises at Stevens Institute of Technology. Her research develops mathematical frameworks for managing risk in interconnected systems such as critical infrastructure, supply chains, and energy networks. Education includes: PhD in Industrial and Systems Engineering (University of Oklahoma) MS in Industrial Engineering (University Technology Malaysia) BS in Industrial Engineering (Yazd University) Research integrates optimization techniques with machine learning for resilience planning in interdependent networks. Current projects examine microgrid energy trading, supply chain disruption management, and infrastructure restoration scheduling. Recent publications focus on tri-level optimization models for critical infrastructure protection, energy equity in community microgrids, and machine learning applications for network resilience. Articles demonstrate consistent advancement in modeling complex interdependencies across infrastructure systems. As Faculty Advisor for the Software Division, she guides student projects bridging theoretical models with practical implementation. Course instruction spans software engineering fundamentals, agile methodologies, and systems development lifecycle management.

Bin Huمشاهده پروفایل

استادیار

• Resilient Cyber-Physical Systems (CPS)
• AI-Human Collaboration Safety & Security
• Big Data & Machine Learning-Based Control
• +۵ مورد دیگر

Dr. Bin Hu is an Assistant Professor in the Department of Engineering Technology at the University of Houston (2022–present) and previously held the same position at Old Dominion University (2019–2022). He earned a Ph.D. in Electrical Engineering from the University of Notre Dame (2016), an M.S. in Control Science from Zhejiang University (2010), and a B.S. in Electrical Engineering from Hefei University of Technology (2007). His research focuses on resilient Cyber-Physical Systems (CPS), AI-human collaboration safety, machine learning-driven control, and vehicular networks. Key projects include NASA-funded work on adaptive human-autonomy responsibility allocation (2023–2026) and NSF-supported AI-human collaboration in autonomous vehicles (2020–2023). He has secured over $1.2 million in grants, including a $700k NASA grant (pending) and an ONR Rapid Solutions grant ($42k). Dr. Hu’s work bridges control theory, machine learning, and human factors. Recent contributions include safer leader-follower robotic systems, fault-tolerant UAV control, and studies on driver trust in AI-driven vehicles. His research has been recognized through awards like the 2024–2025 APeX Excellence Speaker Series. Teaching experience includes courses on C++ programming and electrical engineering fundamentals at both the University of Houston and Old Dominion University. He advises multiple undergraduate and graduate students in projects ranging from LiDAR systems to cyber-physical security. Key Labs/Teams : NAIL Lab (Networked Autonomous Intelligence & Learning), part of the University of Houston’s Cybersecurity and Robotics initiatives. Grants : NASA TTT Grant ($700k), NSF CHS Grant ($500k), ONR RSLP Project ($42k).

• Feminist Pedagogy
• Critical Pedagogy
• Open Education
• +۳ مورد دیگر

Dr. Suzan Koseoglu is a Lecturer in Higher Education Teaching and Learning at the University of Greenwich , affiliated with the Information and Library Services Directorate . Previously, she held similar roles at Goldsmiths, University of London, and the University of Minnesota, where she earned her PhD in Learning Technologies, Curriculum and Instruction. Her teaching focuses on postgraduate courses in higher education pedagogy for academic staff and students, emphasizing feminist and critical pedagogical approaches. Her research explores feminist pedagogy, critical pedagogy, and open education. Notable works include Critical digital pedagogy in Higher Education (Athabasca University Press), 30 Years of Gender Inequality (JIME), and Access as Pedagogy (AJDE). A recent manuscript on rhizomatic pedagogy is under preparation for an edited book on rhizomatic learning. Suzan’s academic journey and upbringing in Turkey inform her belief that education should cultivate holistic development and social awareness, challenging systemic inequities. She critiques formal education’s role in perpetuating hierarchy and advocates for pedagogical approaches that foster critical thinking and civic engagement.

Ralph Detersمشاهده پروفایل

استاد

• Multi-Agent Systems
• Scalability & Dependability of Distributed Systems
• Mobile & Ubiquitous Computing
• +۴ مورد دیگر

Ralph Deters is a Professor in the Department of Computer Science at the University of Saskatchewan , where he co-leads the MADMUC (Multi-Agent Distributed Mobile and Ubiquitous Computing) Lab . He has taught courses including CMPT 298, CMPT 350, CMPT 436, CMPT 842, and CMPT 898, and served as co-coordinator for CMPT 400/CMPT 405. Education Ph.D. in Computer Science, Federal Armed Forces University Munich (1998) MS (Diploma) in Computer Science, Technical University Berlin (1992) BS in Computer Science, Technical University Berlin (1988) Research Interests focus on multi-agent systems , scalable distributed systems , mobile/ubiquitous computing , and privacy-preserving technologies . Current lab projects include: Middleware infrastructures for decentralized systems (cloud/fog/mobile) Social software for sustainable user collaboration Decentralized trust-based data storage Persuasive technologies for health, physical activity, and learning Ethical AI and dark patterns in UI design Blockchain applications in IoT and access control Publications highlight trends in digital twins , blockchain integration , synthetic data for privacy , and machine learning for social impact , with recent work spanning health analytics, agricultural AI, and smart city applications. Lab Impact : The MADMUC Lab, established in 2000 with a $895,687 CFI grant, has graduated 120 researchers (17 PhD, 103 MSc), including two Vanier Scholars. The lab emphasizes awards, scholarships, and interdisciplinary collaboration with 152 total alumni.

Paulina Jo Peschمشاهده پروفایل

استادیار

• Internet and IT law
• Data protection law
• Electronic payments
• +۳ مورد دیگر

Prof. Dr. Paulina Jo Pesch is an Assistant Professor in the Department of Law and Technology at Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Germany. Her research focuses on Internet/IT law, data protection law, electronic payments, and decentralized virtual currencies. She has held postdoctoral positions at the University of Münster (part-time) and University of Innsbruck, alongside legal training as a Trainee Lawyer in Berlin’s judicial system, including roles at the Berlin Public Prosecutor’s Office, Berlin Commissioner for Data Protection, and law firms. Education includes a Ph.D. (Dr. iur.) in Law from the University of Münster (2016), a specialization in Information/Telecommunication Law, and legal examinations (First and Second State Examinations). Her work bridges legal and technical domains, addressing challenges posed by emerging technologies like blockchain, AI, and generative models under GDPR and cybersecurity frameworks. Publications emphasize regulatory frameworks for cryptocurrencies, AI ethics, digital forensics, and consent management in ad-tech. She co-authored the BITCRIME project’s recommendations for cryptocurrency regulation and contributed to studies on financial sector cybersecurity for Germany’s BaFin. Her research frequently intersects cybercrime prevention, intellectual property, and cross-border legal harmonization. Prof. Pesch’s career combines academic rigor with practical legal experience in tech sectors. She advises on IT security law, participates in interdisciplinary projects, and teaches at FAU. Current work explores generative AI’s legal implications, blockchain governance, and data protection in decentralized systems.

Jiri Chodمشاهده پروفایل

استاد

• Operations Management
• Industrial Organization
• Corporate Finance
• +۵ مورد دیگر

Jiří Chod , the Ferrante Family Professor and Chairperson at Boston College , is a leading scholar in operations management, industrial organization, and corporate finance. His work explores blockchain technology applications in finance, corporate governance, and supply chain management. Education: MS from University of Economics, Prague; PhD from University of Rochester Teaching: Undergraduate and graduate courses in operations management, economics, and data analytics His research focuses on blockchain technology , smart contracts , and supply chain transparency , with recent publications analyzing cryptocurrency competition, platform tokenization, and ICO dynamics. Articles appear in Journal of Financial Economics , Management Science , and Operations Research . Scientific appointments include: Associate Editor, Management Science (2019–Present) Senior Editor, Production and Operations Management (2010–2020) Associate Editor, Manufacturing & Service Operations Management (2012–2015)

Hanqing Jinمشاهده پروفایل

دانشیار

• Mathematical Finance
• Stochastic Analysis
• Portfolio Optimization
• +۳ مورد دیگر

Dr. Hanqing Jin is an Associate Professor in the Mathematical Institute at the University of Oxford, specializing in Mathematical Finance and Applied Stochastic Analysis. His research focuses on investor decision-making under non-utility behavior, dynamic portfolio optimization, and stochastic control problems in financial markets. Prior to Oxford, he held an Assistant Professorship at the National University of Singapore. He has contributed to foundational work on mean-variance portfolio selection, behavioral finance, and the application of stochastic processes to financial modeling. His academic background includes extensive work on continuous-time portfolio optimization, risk management frameworks, and the intersection of behavioral economics with quantitative finance. Notable contributions include studies on optimal lockdown policies during pandemics, blockchain consensus algorithms analysis, and the development of novel biomarker detection techniques using Raman spectroscopy. Dr. Jin's research integrates advanced mathematical techniques with real-world financial challenges, bridging theoretical models with practical applications in areas such as robo-advising systems and cryptocurrency frameworks. His work on transaction cost analysis and dynamic mean-variance strategies has been widely cited in both academic and industry contexts.

Siamak Shahandashtiمشاهده پروفایل

مدرس ارشد

• Cyber Security
• Privacy-Enhancing Technologies
• Applied Cryptography
• +۴ مورد دیگر

Dr. Siamak Shahandashti is a Senior Lecturer in the Department of Computer Science at the University of York, UK. He leads the Cyber Security & Privacy Research Group and is actively involved in academic governance roles including Chair of the Physical Sciences Ethics Committee and Programme Lead for the MSc Cyber Security programme. His research focuses on applied cryptography, privacy-preserving technologies, electronic voting systems, and blockchain applications. He holds a PhD in Computer Science and has contributed to over 40 peer-reviewed publications. Education: PhD in Computer Science, MSc in Telecommunications Engineering, BSc in Electrical Engineering. Professional qualifications include a PGCert in Academic Practice. Research interests span cyber security fundamentals, usable security mechanisms, and the intersection of cryptography with real-world systems. Notable projects include verifiable electronic voting systems and DoS-resilient blockchain protocols. He has led funded projects on e-voting scalability and contributed to standards reviews for ISO/IEC protocols. Award-winning educator in cyber security curriculum development, with active roles in admissions and academic ethics compliance. His work bridges theoretical cryptography with practical implementations, emphasizing user-centric security design principles. Labs/Teams: Cyber Security & Privacy Research Group (lead), Departmental Ethics Team (lead). Collaborations include institutions in Australia, France, and across Europe.

Javad Fattahiمشاهده پروفایل

استادیار

• Energy Equity and Inclusion
• Distributed Energy Resources (DER)
• Software-Defined Nanogrids
• +۶ مورد دیگر

Dr. Javad Fattahi is an Assistant Professor at the School of Electrical Engineering and Computer Science, University of Ottawa. He holds roles as an IEEE Senior Member, Professional Engineer (P.Eng.), and member of the Standards Council of Canada. He leads the IEEE 2030.5 smart-grid interoperability verification initiative as its VP and chief editor. Dr. Fattahi earned his BSc in Electrical Engineering from Iran University of Science and Technology (IUT), followed by graduate studies at Sharif University of Technology (SUT). He completed his PhD and postdoctoral research at the University of Ottawa, specializing in energy systems and optimization. His research focuses on energy equity, distributed energy resources (DERs), software-defined nanogrids, game theory, smart mobility, and nonlinear systems. His recent work emphasizes data-driven digital twins for grid stability, V2G integration in residential complexes, and federated Byzantine agreement models for EV infrastructure. Key contributions include transactive energy systems under IEEE standards and energy disaggregation in institutional buildings. Dr. Fattahi has received accolades including IEEE Senior Membership and P.Eng. certification. His lab develops solutions for grid-edge technologies, smart mobility, and resilient energy systems. Graduate opportunities are available via his UniWeb page.

• Human-AI Value Alignment
• Ethical Machine Learning
• Algorithmic Fairness
• +۵ مورد دیگر

Neil S. Gaikwad is an Assistant Professor at the University of North Carolina, Chapel Hill, in the School of Data Science and Society and Department of Computer Science. He holds a PhD in Society-Centered AI from MIT and has affiliations with the MIT Dalai Lama Center for Ethics and UNC’s Parr Center for Ethics. His research focuses on Human-AI Alignment, ethical machine learning, and policy design for global equity, particularly in underserved populations. His work integrates ethical principles into AI systems to address societal disparities, climate change, and algorithmic fairness. Dr. Gaikwad has led projects such as the AI and Policy research program, developed systems like Daemo and EteRNA, and contributed to high-impact publications in AAAI, ACM CSCW, and PNAS. He has mentored over 30 students, many of whom have secured fellowships and advanced AI fairness research. Awards include MIT’s Karl Taylor Compton Prize and Facebook Research Fellowship. His teaching spans courses on AI ethics, policy, and societal impact at MIT, CMU, and UNC, with student evaluations praising his mentorship and course design. He has advised global institutions like the UN and EU on AI policy, and his work bridges academia, industry, and policy through initiatives like Data-driven Humanitarian Mapping. His art installations, such as Beyond Boundaries , explore environmental and ethical themes, reflecting his interdisciplinary approach to science and society.

Ludovic Renouمشاهده پروفایل

استاد

• Economic Theory
• Game Theory
• Information Design
• +۳ مورد دیگر

Ludovic Renou is a Professor of Economics at Queen Mary University of London, affiliated with the School of Economics and Finance. He serves as a CEPR Fellow and Associate Editor at Games and Economic Behavior . His research focuses on economic theory, particularly game theory, information design, and strategic interactions. He has contributed to topics including repeated games, persuasion mechanisms, and networked economic systems. Recent work explores themes like human-machine interaction in decision-making, cross-verification in communication networks, and contracting under persistent information. His 2020 paper in the American Economic Review advanced revealed preference analysis under risk and uncertainty. Awards: CEPR Fellowship Renou advises on research projects but no formal advisees are listed. His professional activities include serving on the REF2021 Economics sub-panel. Outside academia, he is an avid runner with personal bests in multiple distances, emphasizing training regimens and collaborative running ethics outlined in his 'Four Axioms for Collaboration.'

• Distributed Computing
• Distributed Algorithms
• Fault-Tolerance
• +۸ مورد دیگر

Marina Papatriantafilou is an Associate Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. Her research focuses on distributed computing, fault-tolerance, parallel algorithms, and concurrency control. She has contributed to methods for fault-tolerant distributed systems, visualization tools for distributed algorithms, and scalable overlay networks. Her academic roles include teaching advanced courses on distributed systems, computer communication, and operating systems. She advises graduate students in areas like distributed algorithms and parallel computing. Key research interests include lock-free synchronization, memory reclamation, and self-stabilizing systems. She has authored over 100 publications in top-tier conferences and journals, with recent work on data streaming frameworks, energy-sharing optimization, and vehicular network processing. Professional involvement includes roles in program committees for conferences like OPODIS, SWAT, and SSS, plus membership in research evaluation boards for Swedish and European funding agencies. She pioneered educational tools like the Lydian environment for distributed algorithm visualization.

Jarek Nabrzyskiمشاهده پروفایل

استاد

• Quantum Computing
• Blockchain Technology
• Distributed Ledger Technologies
• +۴ مورد دیگر

Jarek Nabrzyski is the founding and current director of the Center for Research Computing (CRC) at the University of Notre Dame and a concurrent Professor in the Department of Computer Science and Engineering. He co-directs the Blockchain Research Lab with Ian Taylor and leads the Quantum Computing Lab. His research focuses on distributed ledger technologies, quantum computing, and resource management in distributed, cloud, and exascale systems. He has overseen over 50 national and international research projects developing cyberinfrastructure for science and industry, emphasizing collaboration and team-building in complex projects. Key areas of research include blockchain applications in decentralized systems, quantum algorithm optimization for near-term hardware, and cybersecurity protocols for data integrity. He actively engages in interdisciplinary initiatives like the Center for Social Science Research and has pioneered frameworks for verifiable credentials and reproducible scientific data. His work bridges theoretical advancements with practical industry partnerships. Notable contributions include hybrid cross-chain protocols, quantum Poisson solvers, and frameworks for blockchain trust analytics. His recent publications highlight advancements in AI-driven blockchain stabilization, suspicious transaction detection, and scalable quantum computing solutions. Despite no explicit mentions of awards, his leadership roles and project count underscore significant contributions to computational science and infrastructure. As a mentor, he fosters student engagement in cyberinfrastructure through initiatives like the Cyberinfrastructure Center of Excellence. His labs and collaborations drive innovation in quantum computing, blockchain interoperability, and high-performance computing systems. Current efforts include transparent data tracking for aerospace supply chains and secure scientific data reproducibility.

Nicolas Loizouمشاهده پروفایل

استادیار

• Large-Scale Optimization
• Machine Learning
• Federated Learning
• +۴ مورد دیگر

Nicolas Loizou is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with a secondary appointment in Computer Science and affiliation with the Mathematical Institute for Data Science (MINDS). He leads the Optimization and Machine Learning Lab and is part of the Data Science and AI Institute. His research focuses on large-scale optimization, machine learning, federated learning, game theory, and distributed/decentralized algorithms. He holds a PhD in Operational Research from the University of Edinburgh (2019), an MSc in Computing from Imperial College London (2015), and a BSc in Mathematics from the National and Kapodistrian University of Athens (2014). Key achievements include the OR Society’s 2019 Doctoral Award (runner-up), IVADO Postdoctoral Fellowship, COAP 2020 Best Paper Award, and a Cisco Research Grant for developing open-source federated learning tools. His work bridges theoretical guarantees with practical applications, particularly in stochastic optimization methods like the Polyak step-size and extragradient frameworks. Research interests span federated learning architectures, sharpness-aware minimization, and multiplayer systems in AI. He has pioneered frameworks like Locally Adaptive Federated Learning and Multiplayer Federated Learning, addressing challenges in medical imaging and distributed model training. His recent event at JHU highlighted advancements in adaptive optimization algorithms for large-scale models. Education: PhD (Edinburgh), MSc (Imperial College), BSc (Athens University) Affiliations: MINDS, Data Science Institute Grants: Cisco Research Gift, Catalyst Award Labs: Optimization and Machine Learning Lab Recent work includes improving communication efficiency in federated systems and analyzing stochastic algorithms through control-theoretic lenses. His contributions are published in top venues, with a focus on both foundational theory and real-world impact in healthcare and distributed AI.