جست‌وجوی استاد و پژوهشگر

Raúl Pardoمشاهده پروفایل

دانشیار

• Privacy
• Computer Security
• Formal Methods
• +۵ مورد دیگر

Raúl Pardo is an Associate Professor in Computer Science at the IT University of Copenhagen (ITU). He previously held postdoctoral positions at ITU's SQUARE group and Inria's Privatics team. He earned his PhD from Chalmers University of Technology, focusing on formalizing privacy policies for social networks under Gerardo Schneider's supervision. His research integrates formal methods with privacy and security, addressing topics such as privacy risk analysis, probabilistic programming, and GDPR compliance. Key domains include genetic data, IoT, and social networks. Publications highlight contributions to privacy quantification, formal verification of consent mechanisms, and probabilistic systems analysis. He actively participates in conferences like SEFM, SOSYM, and ETAPS, and has organized tracks on verification and learning for autonomous systems. Pardo teaches courses on probabilistic programming, formal methods, and security at ITU, INSA Lyon, and Chalmers. Notable students include Rasmus Carl Rønneberg (SEFM'23) and Ivana Kellyérová (TIME'16). His work has been recognized, including a nomination for the Danish MSc thesis award. He leads the SQUARE research group at ITU, focusing on privacy-preserving software systems and probabilistic modeling.

Niko Suenderhaufمشاهده پروفایل

استاد

• Robotic Vision
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Professor Niko Suenderhauf is a leading researcher in robotic vision and AI at Queensland University of Technology (QUT). He focuses on enabling robots to understand and interact with their environment through advancements in SLAM, scene understanding, and semantic mapping. His work bridges robotics, computer vision, and machine learning, aiming to create reliable, relatable, and sustainable robotic systems that collaborate with humans to address societal challenges like climate change and aging populations. His key achievements include pioneering graph-based SLAM methods integrated into the ROS ecosystem, developing object-based semantic SLAM systems, and demonstrating the efficacy of deep learning for place recognition. Collaborations with institutions like DeepMind and Amazon reflect his interdisciplinary approach. Awards include recognition for groundbreaking SLAM research and highly cited publications. Research interests emphasize open-world navigation, privacy-preserving vision, and embodied AI, with a focus on practical applications in construction, environmental monitoring, and domestic robotics. His work on uncertainty quantification and robust perception ensures systems operate safely in unpredictable environments. Professor Suenderhauf actively engages in education, mentoring students across undergraduate and postgraduate levels, and emphasizes the societal impact of technology through collaborations with neuroscience, law, and design experts.

Amit K. Roy-Chowdhuryمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Robot Autonomy
• +۳ مورد دیگر

Amit K. Roy-Chowdhury is a Professor and UC Presidential Chair at the University of California, Riverside (UCR), where he chairs the Robotics Program and co-directs the UC Riverside AI Research (RAISE) Institute and the Center for Networked Configurable Command, Control, and Communications (NC4). He previously chaired the Department of Electrical and Computer Engineering at UCR. His research focuses on computer vision, machine learning, and robotics, with applications in autonomous systems and AI for science. Education: He holds a PhD in Electrical and Computer Engineering from the University of Maryland, College Park (2000), an MS in Systems Science and Automation from the Indian Institute of Science, Bangalore (1995), and a BEng in Electrical Engineering from Jadavpur University, Kolkata (1993). Research Interests: His work spans foundational areas of computer vision, image processing, and machine learning, with a focus on robot autonomy, statistical signal processing, and AI applications in science. His Vision and Learning Group has published over 250 peer-reviewed papers and leads DoD-funded research in AI and robotics. Recent Research Trends : His articles emphasize AI safety, domain adaptation, 3D scene understanding, and embodied agents. Themes include robustness to occlusion, ethical AI, and cross-modal learning. Awards : IEEE Fellow (2020), NAAI Fellow (2022), IAPR Fellow (2023), UCR Doctoral Mentoring Award (2019), UMD Distinguished Alumni (2020). Grants & Leadership : Leads a DoD Center of Excellence, oversees AI initiatives at RAISE, and collaborates with industry/government on AI, robotics, and medical imaging projects. His work on plant cell tracking and face recognition has gained media attention, including a PBS/National Geographic documentary. Labs & Teams : Directs the Vision and Learning Group (formerly Video Computing Group) and co-leads RAISE and NC4, fostering interdisciplinary research in AI and robotics.

• Computational Statistics
• Data Science
• Statistical Algorithms
• +۴ مورد دیگر

Dr. Joshua Bon is a Lecturer in the School of Computer and Mathematical Sciences at the University of Adelaide, South Australia. He holds degrees in statistics, mathematics, computer science, and commerce. His research focuses on designing computationally efficient and privacy-preserving algorithms for statistical inference, with applications in psychology, sports science, ecology, and public health. He also develops open-source software tools for statisticians and industry. Previously, he served as a researcher at Université Paris-Dauphine and a postdoctoral fellow at Queensland University of Technology, where he completed his PhD. Dr. Bon is eligible to supervise Masters and PhD students, though no specific advisees or grants are listed in the provided texts. His work emphasizes uncertainty quantification and federated learning methodologies.

Neeraj Suriمشاهده پروفایل

استاد

• Cybersecurity
• Cloud Computing
• Dependable Systems
• +۴ مورد دیگر

Professor Neeraj Suri is the Distinguished Professor & Chair in Cybersecurity at Lancaster University (UK and Germany) and an Adjunct Professor at the Department of Computer Science, University of Massachusetts Amherst . He co-directed Lancaster's university-wide Security Institute (2019-2024) and previously held the Chair in Dependable Systems & Software at TU Darmstadt . PhD from University of Massachusetts Amherst Positions at AlliedSignal/Honeywell Research, Boston University, Saab Endowed Chair, Microsoft Research (multiple sabbaticals), and visiting roles at University of Texas Austin, Academia Sinica, PolyU Hong Kong, and Technion Research Areas: Trustworthy cloud systems, data-centric cybersecurity, and security quantification. His work focuses on security-by-design principles, adversarial machine learning, and resilient autonomous systems. Selected Research Grants: EPSRC Programme Grant SCULI, H2020 CONCORDIA, EPSRC National Hub on Edge AI, and funding from NSF, DARPA, Microsoft, Google, and other leading institutions. Scientific Awards: NSF CAREER Award Microsoft Faculty Award IBM Faculty Award Professional Leadership: Associate EIC for IEEE Transactions on Dependable & Secure Computing, PC-Chair for DSN, ICDCS, SRDS, HASE conferences, and member of IFIP WG 10.4 on Fault Tolerance.

Cathy Yi-Hsuan Chenمشاهده پروفایل

استاد

• Blockchain analytics
• Digital currencies
• Machine learning in finance
• +۵ مورد دیگر

Professor Cathy Yi-Hsuan Chen is a Professor of Finance at the University of Glasgow's Adam Smith Business School, specializing in corporate finance and banking. She previously held positions at Humboldt-Universität zu Berlin and is a Mercator Fellow. Her research focuses on FinTech, blockchain analytics, machine learning in finance, and network modeling. She leads the EU-funded FinTech Action and has been a key contributor to privacy-preserving data ecosystems and financial risk quantification. Her research interests bridge technology and finance, with notable work on cryptocurrency sentiment analysis, systemic risk quantification, and network-driven financial systems. She has authored over 29 peer-reviewed articles in journals like Journal of Econometrics and Quantitative Finance . Her recent studies analyze Bitcoin-stock market dynamics, social network influencers, and tail risk propagation. Prof. Chen has secured significant grants, including EPSRC funding (£179k) and an EU FinTech Action grant (€520k). She supervises doctoral students exploring topics like robo-advisors and compressed time series analysis. She serves on editorial boards of Review of Quantitative Finance and Accounting and Digital Finance , and is a Fellow of the Blockchain Research Center in Berlin. Her work emphasizes practical applications of data science in finance, including risk modeling, crypto-market dynamics, and policy-relevant financial stability analysis.

• Machine Learning
• Computer Vision
• Multimodal Learning
• +۷ مورد دیگر

Yunhui Guo is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His research focuses on advanced machine learning techniques including multimodal learning, continual learning, audio-visual recognition, and domain adaptation. He explores challenges in model robustness, cross-modal interactions, and efficient training strategies for deep neural networks. Key research areas include: Developing robust multimodal models for video entailment and dynamic 3D human reconstruction Improving audio-visual segmentation and sound separation through novel adaptation frameworks Advancing continual learning methods to handle domain shifts and out-of-distribution data Creating submodular optimization strategies for active learning in 3D object detection His recent work emphasizes real-world applications like medical image analysis (skin cancer sub-typing), robotics (LiDAR segmentation), and secure AI systems (model watermarking). The research also addresses foundational AI topics such as model uncertainty quantification and adaptive predictive systems. Publications focus on cutting-edge areas like multimodal LLM adaptation, hierarchical out-of-distribution detection, and bimodal online adaptation techniques. Current projects explore the intersection of multimodal perception and lifelong learning systems.

• Sociology of Old Age
• Social Gerontology
• Sociology of Family
• +۶ مورد دیگر

Piotr Czekanowski is an Associate Professor at the University of Gdańsk, Faculty of Social Sciences, Institute of Sociology, currently employed in the Department of Sociology of Sport and Health. Previously, he served as Head of the Department of Applied Sociology and remains an active member of the Discipline Council for Sociological Sciences and the Institute of Sociology Program Council. Born in 1956 in Gdańsk, he earned his Doctor of Humanities in Sociology (2013) from Nicolaus Copernicus University in Toruń with a dissertation on population aging, a Doctor of Humanities (1993) from the University of Silesia in Katowice focusing on elderly loneliness, and a Master of Science in Economics (1982) from the University of Gdańsk. Additional training includes fundraising at Indiana University (1996) and English language certification (1986). His research centers on sociology of old age and social gerontology , examining socio-psychological, health, family, and material conditions of the elderly. He pioneered studies on family caregiving and local community organization for aging populations, with strong emphasis on Polish and Gdańsk-specific contexts. International collaborations include Oxford's EAST network and EU-funded projects like EUROFAMCARE. Recent publications (2015-2020) reveal consistent focus on methodological challenges in elderly research, socio-demographic determinants of urban aging, and policy frameworks for social isolation. Key trends include cross-national comparisons, municipal-level interventions, and the intersection of family dynamics with healthcare systems. His scientific awards include: University of Gdańsk 50th Anniversary Medal (2021) Rector's Individual First Degree Award for monograph (2013) Medical University of Białystok Team Scientific Award (2008) University of Gdańsk Team Award for 'Polish Old Age' (2003) Polish Gerontological Society Dissertation Award (1993) Czekanowski secured grants from Polish and EU bodies including the Polish Committee for Scientific Research (1999-2001) and EU Fifth Framework Programme (EUROFAMCARE). He developed the nationally approved 'Local Community Organization' specialization for social workers and maintains active supervision of academic clubs like the Interfaculty Scientific Sociological Club. His expert roles span municipal councils including the Pomeranian Council for Senior Policy (since 2017). He remains central to the Eastern-European Ageing Societies in Transition (EAST) research network at Oxford's Institute of Population Ageing since 2007, with recent workshop participation in Canada (2019) and ongoing EU policy engagement.

Xiao Zhangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Statistical Machine Learning
• Convex Optimization
• +۴ مورد دیگر

Xiao Zhang is a tenure-track faculty member at the CISPA Helmholtz Center for Information Security and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). He obtained his Ph.D. in Computer Science from the University of Virginia in 2022 under Prof. David Evans, following an M.S. in Statistics from the same institution and a B.S. in Mathematics from Tsinghua University. Research Focus Zhang's research spans foundational and applied aspects of trustworthy machine learning, with emphasis on: Security vulnerabilities in ML systems (adversarial attacks, data poisoning) Robustness guarantees for learning algorithms Optimization methods for deep learning Privacy-preserving techniques Multimodal and physical-world applications His recent work focuses on developing theoretically-grounded defenses against emerging threats to AI systems. Publication Trends Zhang's 15 most recent publications (2019-2025) demonstrate consistent focus on adversarial machine learning, with growing emphasis on: 1) Physical-world attacks/defenses, 2) Multimodal model security, and 3) Certified robustness guarantees. The work consistently appears at top-tier venues (NeurIPS, ICLR, CCS). Academic Activities He actively recruits students and collaborators for trustworthy ML research, with open positions for PhD candidates and research assistants. No awards or specific grant information is documented in the provided materials.

• Cybersecurity
• Security Logistics
• Cyber Threats
• +۵ مورد دیگر

Monika Szyłkowska is a Professor at the Military University of Technology , specializing in Security Studies with a focus on cybersecurity, digital privacy, and national security systems. Her work addresses challenges in cyber conflict, information warfare, and legal frameworks governing security operations. Affiliations: Department of Security Studies, Military University of Technology Email: monika.szylkowska@wat.edu.pl Her research interests span cybersecurity , digital privacy , national defense strategies , and legal aspects of security systems . She has published extensively on topics such as AI in criminal prevention, cyber threats to logistics systems, and hybrid warfare tactics. Dr. Szyłkowska has supervised 27 promoted theses and led 18 research projects . Her work emphasizes practical applications of security technologies and international collaboration in addressing cyber risks. She has contributed to 52 publications and maintains a strong focus on modernizing security frameworks in the digital age. Key areas of contribution: Cyber defense mechanisms Strategic risk management Ethical use of AI in security Legal compliance in cybersecurity Her research bridges theoretical frameworks with real-world implementation, shaping policy and practice in both national and international security domains.

Shruti Topleمشاهده پروفایل

پژوهشگر

• Systems Security
• Privacy
• Machine Learning
• +۳ مورد دیگر

Shruti Tople is a Principal Researcher at Microsoft Research in the Azure Research – Security and Privacy group. She holds a Ph.D. from the School of Computing at the National University of Singapore (NUS) , where she received the Dean's Graduate Research Excellence Award . Her work focuses on quantifying and mitigating information leakage in machine learning models while preserving their utility. Key Research Areas include: Systems Security Privacy in Machine Learning Differential Privacy Causal Learning Transfer Learning for Vision & Language Models Recent Publications address challenges such as membership inference attacks, federated backdoor defense, and privacy-enhanced deep learning. Her open-source projects like Analyzing PII Leakage and RobustDG provide practical tools for differential privacy and domain generalization. She collaborates with interns from institutions like NUS , University of Waterloo , and Imperial College London . Scientific Awards : Dean's Graduate Research Excellence Award (NUS)

Chris Wigginsمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Computational Biology
• +۱۲ مورد دیگر

Chris Wiggins is an Associate Professor of Applied Mathematics and Systems Biology at Columbia University, with affiliations in the Department of Statistics. He is based in the School of Engineering and Applied Science and conducts interdisciplinary research at the intersection of data science, machine learning, and biological systems. His work bridges computational modeling with applications in genomics, single-molecule biophysics, digital health, and disinformation studies. His research interests include: Data Science and its societal implications Machine learning for biological networks and gene regulation Bayesian and statistical inference in single-molecule experiments Privacy-aware data analysis and algorithmic ethics Modeling in digital health, including menstrual cycle prediction and cancer drug screening Information-theoretic approaches to network structure and dynamics The trends in his recent publications reveal a growing focus on the ethical and societal dimensions of data science, as seen in works like How Data Happened and An Agenda for Disinformation Research , while maintaining strong technical contributions in biomedical modeling, particularly in cancer and women’s health. His work combines rigorous statistical methods with real-world data challenges. Notable scientific contributions include foundational work on ARACNE for gene regulatory network inference, Bayesian modeling of single-molecule dynamics, and methodological advances in dose-response modeling. His publications span top journals in bioinformatics, computational biology, and data science. He has advised doctoral students in applied mathematics and computational biology, though specific names are not listed in the provided text. His research has been supported by interdisciplinary grants, particularly in systems biology and data science initiatives. He is actively involved in shaping the philosophy and pedagogy of data science as a field. Chris Wiggins is associated with research centers at Columbia that focus on applied statistics, probability, and data science. He contributes to seminars and educational programs that promote quantitative reasoning across disciplines, particularly in the context of modern data-driven challenges.

• Artificial Intelligence
• Neuroscience
• Applied Mathematics
• +۳ مورد دیگر

Anastasia Borovykh is an Assistant Professor in the Department of Mathematics at Imperial College London, affiliated with the Faculty of Natural Sciences and the Imperial-X initiative. Her research focuses on developing computational models to enhance artificial intelligence systems, integrating insights from neuroscience, applied probability, signal processing, and mathematical modeling. Key application areas include healthcare (e.g., Parkinson’s disease biomarkers), neuroscience (brain connectivity analysis), and privacy preservation in neural networks. Her affiliations include Academic Women in Mathematics, the Applied Mathematics and Mathematical Physics group, and the Artificial Intelligence Network. She leads a research group emphasizing interdisciplinary approaches to AI, leveraging tools from statistical mechanics and advanced signal processing techniques. Recent publications highlight her work on diffusion models, privacy in machine learning, and reinforcement learning applications in chaotic systems control. Her research often bridges theoretical foundations with practical implementations in healthcare and cybersecurity domains. No scientific awards are explicitly listed in the provided information. Her advising and grants section remains unspecified, though her active research group suggests ongoing collaborative projects. She is part of Imperial-X, a platform for cross-disciplinary innovation.

Nianqiao Juمشاهده پروفایل

استادیار

• Statistical Inference
• Markov Chain Monte Carlo
• Bayesian Inference
• +۳ مورد دیگر

Nianqiao Ju is an Assistant Professor of Statistics at Purdue University's Department of Statistics within the College of Science. They hold a B.A. in Mathematics and Physics from Wellesley College (2016) and a Ph.D. in Statistics from Harvard University (2021). Their research focuses on computational methods for statistical inference, particularly Markov Chain Monte Carlo (MCMC) techniques and Bayesian inference from privatized data. Notable work includes developing privacy-preserving statistical methods and analyzing infectious disease transmission dynamics. Research interests span computational statistics, data privacy, MCMC algorithms, and applications in epidemiology. Their articles explore topics like SOMA samplers, privacy-aware Bayesian inference, and agent-based modeling of disease spread. Ju has received the IMS Hannan Graduate Student Travel Award (2020). Current research emphasizes bridging statistical methodology with computational challenges in data privacy and large-scale epidemiological models. Office: Math 508 | Phone: 765-494-0021

Bo Zhouمشاهده پروفایل

استاد پژوهشی

• Medical Imaging
• Deep Learning
• Radiology
• +۴ مورد دیگر

Bo Zhou is a Research Professor in Radiology at Northwestern University's Feinberg School of Medicine, specializing in medical imaging and artificial intelligence. He holds a PhD from Yale University (2024) and focuses on advancing deep learning techniques for low-dose PET/SPECT imaging, including denoising, attenuation correction, and federated learning applications. His work bridges cutting-edge computational methods with clinical radiology to improve diagnostic accuracy and reduce radiation exposure. Education: PhD in Radiology, Yale University (2024) Affiliations: Member of RSNA, IEEE, American Heart Association, and SPIE Medical Imaging His research interests emphasize developing AI-driven solutions for nuclear medicine challenges, such as optimizing cardiac imaging protocols and enhancing image quality through diffusion models and federated learning. Notable contributions include noise-aware PET denoising frameworks and population-prior-aided networks for low-count imaging. Recent publications highlight advancements in PET reconstruction, federated learning for medical imaging, and motion-correction techniques. He has been recognized with the IEEE Bruce Hasegawa Young Investigator Award (2024) for his contributions to medical imaging science.