جست‌وجوی استاد و پژوهشگر

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.

Christoph Freiمشاهده پروفایل

استاد

• Quantitative Finance
• Risk Management
• Mathematical Economics
• +۴ مورد دیگر

Christoph Frei is a Professor and Chair of the Department of Mathematical and Statistical Sciences at the University of Alberta. He holds a PhD in mathematical finance from ETH Zurich and previously worked as a researcher at École Polytechnique in Paris. His research focuses on quantitative finance, risk management, and mathematical economics, with applications to algorithmic trading, credit risk, and digital currencies. Education: PhD in Mathematical Finance, ETH Zurich Postdoctoral Researcher, École Polytechnique (Paris) Bachelor/Master in Mathematics, ETH Zurich His work bridges academia and industry through collaborations with institutions like ATB Financial, Canadian Western Bank, and the Federal Reserve System. Key research interests include over-the-counter markets, financial regulation, and machine learning applications in risk prediction. Frei has received notable recognition, including the Best Paper in Asset Pricing Award (2019). Current industry partnerships include AI-driven customer risk prediction projects with ATB Financial and credit risk analysis with Canadian Western Bank. He actively contributes to professional organizations like PRMIA Edmonton and advises on financial technology innovation. Research grants come from NSERC, SSHRC, and Mitacs. His consulting roles have spanned risk modeling at UBS and Credit Suisse, emphasizing practical applications of theoretical frameworks.

• Artificial Intelligence
• Battery Technology
• Digitalization
• +۷ مورد دیگر

Prof. Dr.-Ing. Weihan Li is a Junior Professor at RWTH Aachen University, specializing in Artificial Intelligence and Digitalization for Batteries. He is affiliated with the Institute for Power Electronics and Electrical Drives (ISEA) and the Center for Ageing, Reliability, and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL). His research bridges informatics, electrochemistry, and power electronics to advance battery technology through AI. B.Sc. in Automotive Engineering (Tongji University, 2014) M.Sc. in Automotive Engineering and Transport (RWTH Aachen, 2017) Ph.D. in Electrical Engineering and Information Technology (RWTH Aachen, 2021, summa cum laude) Prof. Li’s research focuses on AI-driven battery modeling, diagnostics, and optimization. Key areas include digital twin technology, electrochemical parameterization, and lifetime prediction using field data. He explores multi-scale kinetic processes, thermal management, and mechanical-electrochemical coupling effects in battery systems. The articles listed reflect his leadership in AI-powered battery analytics, spanning degradation prediction, fast charging, failure mode analysis, and grid-scale storage. His work emphasizes both theoretical innovation (e.g., diffusion models, physics-informed neural networks) and practical applications (e.g., second-life battery screening, automotive integration). Clarivate Highly Cited Researcher 2024 BMBF BattFutur Research Group (€2M+) German Thesis Award (Körber Foundation) Reichart Prize vgbe Innovation Prize Battery Young Research Award Umbrella Award RWTH Innovation Award Prof. Li leads an interdisciplinary research group with over €6 million in grants from BMBF, BMWK, BMDV, European Commission, and industry partners. His teams focus on battery informatics, AI-driven diagnostics, and digitalization of testing processes at CARL and ISEA.

Yelda Turkanمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Lidar Applications
• +۷ مورد دیگر

Dr. Yelda Turkan is an Associate Professor in the School of Civil and Construction Engineering at Oregon State University, where she leads research in automation, computer vision, and machine learning for sustainable infrastructure. She holds a PhD from the University of Waterloo and dual BS degrees in Civil Engineering and Geomatics Engineering from Istanbul Technical University. Her work focuses on leveraging lidar, digital twins, and BIM to improve construction operations and decision-making in the built environment. She has secured over $4M in grants from NSF, FHWA, and other agencies, and currently leads the NSF Convergence Accelerator-funded 'Deep Reality' project for AI-driven infrastructure management. Education: Ph.D., Civil Engineering, University of Waterloo, 2012 M.S., Engineering Informatics & Remote Sensing, Istanbul Technical University, 2006 B.S., Civil Engineering (double major in Geomatics Engineering), Istanbul Technical University, 2005/2003 Professional Roles: Vice President, International Association for Automation and Robotics in Construction (IAARC) Chair, ASCE Computing Division Education Committee Associate Editor, ASCE OPEN Journal Her research emphasizes automation in construction quality control, infrastructure inspection via drones and lidar, and immersive education tools using VR/AR. Recent projects include automated curb ramp compliance analysis, wildfire impact modeling, and digital twin development for timber structures. She has published over 80 peer-reviewed articles and actively promotes computing integration in civil engineering education and professional practice.

Shima Abdullateefمشاهده پروفایل

پژوهشگر ارشد

• Biomedical Engineering
• Medical Informatics
• Computational Physiology
• +۳ مورد دیگر

Shima Abdullateef is a Postdoctoral Research Fellow at the Centre for Medical Informatics within the Usher Institute, College of Medicine and Veterinary Medicine at the University of Edinburgh. Her work bridges biomedical engineering and clinical medicine through computational modeling and data science applications. Education: PhD in Biomedical Engineering, Brunel University London (2016-2020) MSc in Biomedical Engineering, University of Surrey (2014-2015) BSc in Biomedical Engineering (Bioelectrics), Science and Research IA University (awarded 2013) Research Focus: Dr. Abdullateef specializes in two interconnected domains: computational hemodynamics modeling arterial wave propagation and reflection phenomena, and machine learning-driven seizure detection using minimal-density EEG montages. Her arterial research investigates how vascular geometry impacts blood pressure dynamics, while her neuroscience work develops practical clinical tools for critical care seizure monitoring that reduce electrode requirements by 50-75% compared to standard EEG setups. Publication Trends: Her 15 most recent publications (2018-2025) reveal a strategic shift from pure cardiovascular modeling toward integrated neurological applications, with 60% focusing on seizure detection algorithms. The work consistently applies one-dimensional computational models and phase-synchrony analysis to solve clinical monitoring challenges, particularly in resource-constrained pediatric intensive care settings. Active Projects: A Window in the Brain: Developing a novel seizure detection tool for pediatric critical care (since 2020), funded through University of Edinburgh research channels Collaborative Environment: She operates within the Centre for Medical Informatics' interdisciplinary ecosystem, collaborating with clinicians from Edinburgh BioQuarter and data scientists to translate engineering solutions into clinical practice, with particular emphasis on making neurocritical care monitoring more accessible through reduced-sensor EEG technology.

Vedran Dunjkoمشاهده پروفایل

استاد

• Quantum Computing
• Quantum Machine Learning
• Quantum Artificial Intelligence
• +۷ مورد دیگر

Professor Vedran Dunjko is a faculty member at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, with affiliations to the Leiden Institute of Physics (LION). He leads the Applied Quantum Algorithms group and co-founded the Quantum@LIACS initiative, focusing on the intersection of quantum computing, machine learning, and artificial intelligence. His research interests include quantum machine learning, quantum-enhanced reinforcement learning, quantum heuristics, and the application of AI to quantum computing challenges. Dunjko's work bridges theoretical foundations with experimental implementations on near-term quantum devices, exploring both quantum advantages in learning and the use of classical AI for quantum system design. The recent publications show a strong trend toward proving quantum advantages in learning tasks, optimization, and topological data analysis, with publications in Nature , Nature Communications , and NeurIPS . Key themes include quantum policy gradients, quantum TDA, and reinforcement learning for quantum circuit optimization. ERC Consolidator Grant (2024) PNAS Cozzarelli Prize (2018) Editor’s Suggestion in Physical Review Letters (2014, 2018) Featured in Physics (American Physical Society) (2014, 2018) Dunjko advises several PhD candidates and postdocs, including Rahul Bandyopadhyay, Sofiene Jerbi, and Lea Trenkwalder. He has received competitive grants, most notably the ERC Consolidator Grant in 2024. His group fosters international collaborations with institutions across Europe and industry partners. The Applied Quantum Algorithms group and the Quantum@LIACS team combine theoretical investigations with practical implementations on quantum hardware, focusing on scalable quantum algorithms and AI-driven quantum discovery.

Deepa Kundurمشاهده پروفایل

استاد

• Cybersecurity
• Signal Processing
• Complex Dynamical Networks
• +۴ مورد دیگر

Deepa Kundur is the Professor & Chair of The Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto. She earned her BASc, MASc, and PhD in Electrical and Computer Engineering from the same institution in 1993, 1995, and 1999, respectively. Current roles: IEEE Spectrum Advisory Board Conference leadership: General Chair of 2018 GlobalSIP Symposium, TPC Co-Chair for IEEE SmartGridComm 2018, among others Her research focuses on cybersecurity , signal processing , and complex dynamical networks , particularly in smart grid applications. She has authored over 200 publications and pioneered techniques for detecting false data injection attacks, enhancing grid resilience, and integrating machine learning into power systems. Her recent work spans quantum learning for grid security , LLM-based mental health prediction , and resilient control systems . She has received 14 best paper recognitions, including IEEE SmartGridComm (2015) and IEEE INFOCOM Workshop (2008). Fellowships: IEEE Fellow (2015), Canadian Academy of Engineering Fellow (2016), Massey College Senior Fellow (2019) Teaching awards: Tenneco Meritorious Teaching Award (2005), Gordon Slemon Teaching of Design Award (2002) Early career honors: NSERC Scholarships (PGS A/B), Canada Scholarship She leads the Kundur Research Group , developing models for cyber-physical systems in smart grids and autonomous vehicle networks. Her team explores reinforcement learning for grid defense , transmissibility-based fault detection , and privacy-preserving smart grid analytics .

Chao Liuمشاهده پروفایل

پژوهشگر

• Surgical Robotics
• Teleoperation
• Haptics
• +۲ مورد دیگر

Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.

• Social Media Analysis
• Cybersecurity
• Online Disinformation
• +۳ مورد دیگر

Jeremy Blackburn is an Associate Professor in the Department of Computer Science at Binghamton University. He co-founded the International Data-driven Research for Advanced Modeling and Analysis Lab ( iDRAMA Lab ) and leads its Binghamton satellite. His research focuses on large-scale measurement and analysis of social media, particularly the behavior of malicious actors and disinformation campaigns.

• Artificial Intelligence
• Algorithmic Systems
• Consumer Trust
• +۵ مورد دیگر

Param Vir Singh is the Carnegie Bosch Professor of Business Technologies and Marketing and Associate Dean for Research at Carnegie Mellon University’s Tepper School of Business. His research examines how AI and algorithmic systems reshape markets, influence consumer trust, and redefine platform strategy, pricing, and fairness. He leads the Collaborative AI Initiative at CMU, focusing on adaptive learning environments for business education. Affiliations : Carnegie Mellon University, Tepper School of Business Editorial Roles : Senior Editor at Information Systems Research , Associate Editor at Management Science Research Themes : AI ethics, algorithmic fairness, platform economics, consumer behavior, and generative AI applications. Key Research Contributions : His work spans algorithmic pricing, bias mitigation, sharing economy dynamics, and AI-driven inequality analysis. Articles often intersect computer science, economics, and marketing. Scientific Recognition : INFORMS Information Systems Society Distinguished Fellow Award Don Lehmann Award (Winner) John DC Little Award Don Morrison Long-Term Impact Award (Finalist) AIS Senior Scholar's Best Paper Award (Winner) Academic Leadership : Served as Director of the PNC Center for Financial Services Innovation, securing $5.5M for research programs. Mentored PhD students now at Harvard, NYU, Michigan, and other top institutions.

Lorraine van Blerkمشاهده پروفایل

استاد

• Human Geography
• Street Children and Youth
• Global Social Justice
• +۵ مورد دیگر

Lorraine van Blerk is a Professor of Human Geography at the University of Dundee's School of Humanities, Social Sciences and Law. She serves as Chair of Human Geography and leads the Research Centre for Transformative Change: Educational & Life Transitions (TCELT) in Education and Society. Her work focuses on street children/youth, refugees, and global inequalities. Academy of Social Sciences Fellow (2021) Stephen Fry Awards - Public Engagement Project of the Year (2017) Market Research Society President’s Medal (2015) Research interests include: Street youth resilience mapping Intergenerational partnerships Transitions to adulthood in crises Policy development for marginalized groups Coastal socio-ecological systems Cultural geographies of homelessness Recent publications address global street youth resilience frameworks (2REST project) and cinema's role for Harare street youth. Key collaborations include universities in Pretoria, Cape Town, Makerere, and Ryerson University. Scientific contributions recognized through: 6 major research prizes 16 funded projects 149+ research outputs International visiting appointments (Toronto Metropolitan University, National University of Singapore)

• Data Mining & Machine Learning
• Network Science & Complex Systems
• Artificial Intelligence & Society
• +۲ مورد دیگر

Tina Eliassi-Rad is Professor and the Inaugural Joseph E. Aoun Chair at Khoury College of Computer Sciences, Northeastern University in Boston. She serves as Core Faculty at the Network Science Institute and holds External Faculty positions at both the Santa Fe Institute and Vermont Complex Systems Institute. Additionally, she maintains Affiliated Faculty status across six Northeastern University institutes including the NULab for Digital Humanities and Computational Social Science, Global Resilience Institute, Cybersecurity and Privacy Institute, Institute for Experiential AI, and Internet Democracy Initiative. Her research spans: Data Mining & Machine Learning Network Science & Complex Systems Artificial Intelligence & Society She leads two major research initiatives: Trustworthy Network Science , which addresses explainability, transparency, stability, and robustness in network science ML algorithms; and Just Machine Learning , which examines broader complex systems where ML operates to understand and mitigate risks. Her work bridges theoretical foundations with societal applications. Dr. Eliassi-Rad's publication record demonstrates consistent focus on applying network science to critical societal challenges. Her recent research examines pandemic mobility patterns and cybersecurity threats using network-based approaches that combine epidemiological modeling with network analysis techniques. She actively mentors doctoral students through her RADLAB research group, currently advising PhD candidates Wan He (Network Science) and David Liu (Computer Science), along with PhD students Zohair Shafi and Samantha Dies (Computer Science). Her research has secured funding from prestigious organizations including the National Science Foundation, Department of Defense, Defense Advanced Research Projects Agency, Army Research Lab, and others. As leader of RADLAB, she directs research at the intersection of data science, network analysis, and societal impact, with particular emphasis on ensuring that technical advances in AI and network science serve societal needs responsibly and equitably.

Ronita Bardhanمشاهده پروفایل

دانشیار

• Heat-Health and Built Environment
• Decarbonisation of Built Environment
• Urban Microscale
• +۱۲ مورد دیگر

Dr. Ronita Bardhan is Associate Professor of Sustainable Built Environment at the University of Cambridge , where she leads the Cambridge Sustainable Design Group and serves as Director of Research & Deputy Head of the Department of Architecture . She holds courtesy roles at Cambridge Public Health and the Department of Computer Science and Technology , and is a Fellow at Selwyn College . Education: BArch, MCP, MA, PhD (Urban Engineering) Her research intersects built environment , climate change , and public health , focusing on data-driven strategies for precision prevention of health and energy burdens. She pioneers algorithmic approaches to identify decarbonisation potential in buildings and urban areas, with a particular emphasis on gender-inclusive climate adaptation in the Global South. The 15 most recent articles span topics such as urban cooling with tree canopies , heatwave impacts on social systems , and machine learning applications for building energy efficiency . Her work addresses climate justice , hybrid workspace design , and thermal comfort in low-income housing . Awards: Top 50 Finalist Women in Engineering, UK 2024 Exceptional Woman of Excellence, Women Economic Forum Felicitated by Ministry of Health, Maharashtra, India She advises World Bank and ADB on affordable housing and climate resilience, and chairs the Research Ethics Committee at the Department of Architecture. Her lab, the Sustainable Design Group , develops solutions for health and low-carbon potential in built environments.

Iris Bohnetمشاهده پروفایل

استاد

• Behavioral Economics
• Decision-Making
• Gender Equality
• +۶ مورد دیگر

Iris Bohnet is the Albert Pratt Professor of Business and Government at Harvard Kennedy School and co-director of the Women and Public Policy Program. She serves as faculty director of the social sciences at Harvard Radcliffe Institute and has held significant leadership roles including academic dean of Harvard Kennedy School for six years and faculty chair of the World Economic Forum's executive program for Young Global Leaders for over a decade. Albert Pratt Professor of Business and Government, Harvard Kennedy School Co-Director, Women and Public Policy Program Faculty Director of Social Sciences, Harvard Radcliffe Institute Special Advisor to UN Secretary-General on Gender Equality Acceleration Plan Member, Gender Equality Advisory Council of the G7 Faculty Co-Chair, World Economic Forum's Young Global Leaders program Bohnet earned her Ph.D. in Economics from the University of Zurich in 1997 and joined Harvard Kennedy School as an assistant professor in 1998, becoming a full professor in 2006. Her research focuses on behavioral economics, combining insights from economics and psychology to improve decision-making in organizations and society, with particular emphasis on gender equality and cross-cultural perspectives. She pioneered experimental research in the Middle East and developed tools to measure behavior and beliefs across different global contexts. Her publications reveal a consistent focus on embedding fairness into organizational structures rather than relying on individual-level diversity training. Recent work emphasizes data-driven design for workplace equity, behavioral interventions to disrupt bias in hiring and performance evaluations, and creating organizational norms that support gender equality. Her research spans multiple disciplines including economics, psychology, management, and public policy, with practical applications for governments and corporations worldwide. Shortlisted for FT and McKinsey Business Book of the Year Award (2016) Financial Times Best Business Book of 2016 Named Most Influential Academics in Government by apolitical Named Most Influential People in Gender Policy by apolitical Recipient of multiple honorary degrees Bohnet advises governments and companies globally, serving on numerous boards including the Graduate Institute of International Studies in Geneva and advisory boards for institutions like the Vienna University of Economics. She directs the Harvard Decision Science Laboratory and has developed evidence-based frameworks that have influenced policy and organizational practices worldwide, particularly through her books 'What Works: Gender Equality by Design' and 'Make Work Fair'.

Janick Edingerمشاهده پروفایل

استاد

• Distributed Operating Systems
• Edge Computing
• Computation Offloading
• +۷ مورد دیگر

Prof. Dr. Janick Edinger is a Professor of Distributed Operating Systems at the Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, University of Hamburg, Germany. He leads a research group focused on distributed, context-aware, and adaptive computing systems, with a strong emphasis on edge computing, computation offloading, and assistive technologies. Education: PhD in Computer Science, University of Mannheim Studies at National Taiwan University Studies at University of Alberta, Canada Research stays at University of British Columbia, Hong Kong Polytechnic University, and Georgia State University, USA His research explores how edge computing and computation offloading can enable efficient, privacy-preserving processing of sensor and video data close to their sources, particularly in dynamic environments. He investigates the integration of autonomous and heterogeneous systems—such as drone fleets and mobile devices—into scalable middleware platforms for real-time monitoring and decision-making in logistics and industrial operations. His work also emphasizes societal impact, contributing to accessible routing, adaptive interfaces, and crowd-sourced mapping. The recent publications reflect a strong trend in edge computing, federated learning, privacy-preserving analytics, and assistive technologies. Topics include WebAssembly-based offloading, emotion prediction via eye tracking, real-time traffic detection, and predictive maintenance in Industry 4.0, showcasing a blend of foundational systems research and applied human-centered computing. Scientific Awards: PerCom 2021 Mark Weiser Best Paper Award Best Paper Award at IEEE PerCom 2021 for 'Voltaire: Precise Energy-Aware Code Offloading Decisions with Machine Learning' Prof. Edinger actively advises students and leads research projects involving grants and collaborations. His team includes PhD candidates and researchers working on middleware, edge systems, and context-aware applications. He has served on conference program committees, such as shadow PC member for EuroSys 2021, and publishes in top venues including IPDPS, PerCom, CHIIR, and COMPSAC. Labs and Teams: He leads the Distributed Operating Systems research group at the University of Hamburg, where he mentors students and collaborates on projects involving edge computing, IoT, and adaptive systems.