جست‌وجوی استاد و پژوهشگر

Christian Poellabauerمشاهده پروفایل

استاد

• Mobile Sensing
• Data Analytics
• Healthcare Technology
• +۷ مورد دیگر

Christian Poellabauer is a Professor at Florida International University (FIU) in the Knight Foundation School of Computing and Information Sciences, serving as Interim Associate Dean for Research and Graduate Studies in the College of Engineering & Computing. He holds a Ph.D. from Georgia Institute of Technology (2004) and a Diplom-Ingenieur from TU Vienna (1998). His research focuses on mobile sensing, data analytics, and healthcare technologies, leading the MOSAIC Lab which develops solutions for healthcare, IoT, and smart cities. He previously led the Mobile Computing Lab at the University of Notre Dame and held leadership roles in data science institutes. Research Interests: His work spans digital biomarkers for neurodegenerative diseases, speech analysis for mental health, wearable device authentication, and wireless sensor networks. The MOSAIC Lab addresses challenges like real-time sensor data analysis on constrained devices and translating insights into clinical applications. Teaching: He has taught courses on Operating Systems, Mobile Computing, and Smart Health at both FIU and Notre Dame. Recent courses include COP4610 (Operating Systems Principles) and COP5614 (Graduate Operating Systems) at FIU. Service: He serves as Associate Editor for IEEE Transactions on Network Science and Engineering, and has organized conferences like ICNC 2023 and IEEE MASS 2021. His academic service includes roles on editorial boards and technical program committees for major conferences in distributed computing and networking. Advising & Labs: Advises current Ph.D. students in areas like multi-modal sensing for affective computing and mental health crowdsensing. Past students have pursued roles in academia and industry (e.g., Rose-Hulman Institute of Technology, Facebook, Microsoft). The MOSAIC Lab collaborates on projects like digital clinical outcome assessments and motor impairment detection.

Alexander Pretschnerمشاهده پروفایل

استاد

• Software Engineering
• Testing
• Information Security
• +۵ مورد دیگر

Prof. Alexander Pretschner is a Professor of Software & Systems Engineering at the Technical University of Munich (TUM) and Founding Director of the Bavarian Research Institute for Digital Transformation (bidt). He also serves as Scientific Director of fortiss, a Bavarian research institute for software-intensive systems. His research focuses on software engineering, testing, information security, and ethical software development. Pretschner holds a PhD from TUM and has held academic positions at Karlsruhe Institute of Technology (KIT) and TU Kaiserslautern. He is a co-editor of several prestigious journals, including IEEE Transactions on Reliability and the Journal of Software Testing, Verification and Reliability. Education: PhD in Computer Science, Technical University of Munich MSc in Computer Science, University of Kansas (on Fulbright Scholarship) Diplom in Computer Science, RWTH Aachen University Research Interests: His work spans testing methodologies, secure software design, and ethical considerations in agile development. Notable contributions include frameworks for metamorphic testing, distributed data usage control, and accountability mechanisms for cyber-physical systems. Awards: IBM Faculty Award (2012, 2013) Google Focused Research Award (2011, 2012) EARTO Innovation Prize (2014) 2nd Platz Supervisory Award (2020) Advising & Grants: Pretschner has supervised numerous PhD and Master’s students, contributing to over 200 publications. He leads projects like EDAP (Ethical Deliberation in Agile Processes) and collaborates with industry partners on cybersecurity and AI ethics initiatives. Labs & Teams: His work is anchored in bidt, fortiss, and TUM’s Chair of Software & Systems Engineering, focusing on societal impacts of digitalization and trustworthy AI systems.

Mike Kirbyمشاهده پروفایل

استاد

• Scientific Computing
• Physics-Informed Machine Learning
• Computational Science and Engineering
• +۴ مورد دیگر

Mike Kirby is a Professor at the Kahlert School of Computing, University of Utah. He also holds adjunct professorships in the Department of Bioengineering and the Department of Mathematics. His current roles include leadership in scientific computing and informatics initiatives, including former directorships of the Utah Informatics Initiative (2019-2023) and the Multi-Scale Multidisciplinary Modeling of Electronic Materials (MSME) Collaborative Research Alliance (2016-2022). He has extensive experience in strategic research initiatives, including serving as Assistant Vice President for Research (2024-2025). Education: Dr. Kirby earned a PhD in Applied Mathematics (2002) and MS in Computer Science (2001) from Brown University, and a BS in Applied Mathematics and Computer Science from Florida State University (1997). Research Interests: Focus on large-scale scientific computing, physics-informed machine learning, computational science and engineering, high-order numerical methods, and visualization. His work bridges applied mathematics and computer science to address real-world engineering challenges. Publications: Over 150 peer-reviewed articles, including high-impact contributions in journals like Journal of Computational Physics and SIAM Journal on Scientific Computing . Recent work emphasizes machine learning for differential equations, topology optimization under uncertainty, and multi-fidelity modeling. Awards: Recognized for leadership in computational science and informatics, including contributions to University of Utah’s Clery Compliance Program. Advising & Grants: Supervised over 50 graduate students and postdocs. Secured funding from NSF, DOE, and industry partnerships, totaling millions in research grants. Active in interdisciplinary collaborations across engineering, materials science, and medicine. Labs/Teams: Scientific Computing and Imaging (SCI) Institute, Utah Informatics Initiative, and the Center for Multiscale Modeling of Electronic Materials (MSME).

• Requirements Engineering
• Goal Modeling
• Conceptual Modeling
• +۷ مورد دیگر

Sotirios Liaskos is an Associate Professor in the School of Information Technology at the Faculty of Science, York University. He is a leading researcher in requirements engineering and conceptual modeling, with a focus on goal models, empirical evaluation, and model-driven engineering. Research Interests: Requirements Engineering Goal and Conceptual Modeling Empirical Software Engineering Model-Driven Design and Automation Uncertainty and Decision-Theoretic Reasoning in Models Applications in Blockchain and Reinforcement Learning His recent work emphasizes the empirical validation of modeling constructs, the integration of decision theory into goal models, and the automated generation of secure workflows and AI training environments. He has led and co-authored numerous experimental studies on model comprehensibility and semantic quality. Publication Trends: His publications consistently appear in top venues like ER, RE, and iStar. The last five years show a strong trend toward empirical evaluation of modeling languages, decision-theoretic goal models, and applications of goal modeling in emerging domains such as blockchain and reinforcement learning simulation design. Scientific Contributions: Developed frameworks for empirical evaluation of modeling language ontologies (Peira framework). Advanced decision-theoretic approaches to goal modeling under uncertainty. Pioneered model-driven methods for generating blockchain simulators and reinforcement learning environments. Conducted foundational empirical studies on the comprehensibility of contribution links and visualization alternatives in goal models. Advising and Collaboration: He has advised several researchers including Ibrahim Jaouhar, Wisal Tambosi, Mehrnaz Zhian, and Saba Zarbaf. He maintains a highly collaborative research profile with frequent co-authorship with John Mylopoulos, Shakil M. Khan, and other international researchers. He has served as a co-editor for multiple iStar workshop proceedings, indicating leadership in the goal-oriented requirements engineering community. Labs and Teams: While no specific lab name is mentioned, his work is closely associated with research groups focused on requirements engineering and conceptual modeling, likely within York University’s software engineering research cluster. His collaborations span institutions in Canada, Europe, and beyond.

Anil Sethمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Computational Neuroscience
• Consciousness Science
• +۱۲ مورد دیگر

Professor Anil Seth is a leading cognitive and computational neuroscientist at the University of Sussex, where he holds a professorship in the School of Engineering and Informatics. He is Director of the Sussex Centre for Consciousness Science and Co-Director of the CIFAR Program on Brain, Mind, and Consciousness and the Leverhulme Doctoral Scholarship Programme. His research bridges neuroscience, psychology, philosophy, and AI to investigate the biological basis of consciousness and selfhood. His research interests include: Predictive processing approaches to perception Virtual and augmented reality in self-experience studies Mathematical modeling of perception and emergence Machine learning applications in subjective perception modeling Interoception and selfhood Neural mechanisms of conscious experience The recent publications reflect a strong focus on consciousness, neural dynamics, predictive models, and interdisciplinary approaches. Trends include the use of computational modeling, neurophenomenology, causal analysis, and the ethical implications of emerging neurotechnologies. His work increasingly integrates large language models and data-driven methods for analyzing subjective experience. His scientific awards include: Segerfalk Award Perspectives Award Highly Cited Researcher (Web of Science, 2019–2022) Royal Society Michael Faraday Prize (2023) Seth has secured substantial research funding from the European Research Council (ERC), EPSRC, Wellcome Trust, CIFAR, and the Sackler Foundation. He has supervised numerous research projects and doctoral students through interdisciplinary programs. He leads the Dreamachine project and has been an Engagement Fellow with the Wellcome Trust. He serves on editorial boards including Philosophical Transactions of the Royal Society B and is Editor-in-Chief of Neuroscience of Consciousness . He leads a multidisciplinary research group at the Sackler Centre, bringing together psychologists, mathematicians, neuroscientists, computer scientists, and philosophers. His team conducts innovative research using virtual reality, neuroimaging, and computational modeling to explore the nature of consciousness and self.

Bruno Felisberto Martins Ribeiroمشاهده پروفایل

دانشیار

• Networking and Operating Systems
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Bruno Felisberto Martins Ribeiro is an Associate Professor of Computer Science at Purdue University, joining the department in Fall 2015. His research focuses on endowing machine learning algorithms with robust invariant representations for relational and temporal data, emphasizing causal and associational tasks. Key research areas include Networking and Operating Systems, Artificial Intelligence, Machine Learning, and Natural Language Processing. He holds a Ph.D. in Computer Science from the University of Massachusetts Amherst (2010). Education: Ph.D., Computer Science, University of Massachusetts Amherst, 2010 Research Interests: Explores invariances in mathematics and machine learning to improve model robustness. Key topics include graph and tensor invariances, causal relationships, adversarial robustness, and applications in recommendation systems, robotics, and drug discovery. His lab’s work has advanced counterfactual task frameworks and causal reasoning in machine learning. Recent Contributions: Recent publications address zero-shot generalization in graph neural networks, causal discovery methods, and defenses against adversarial attacks. His work spans conferences like ICML, NeurIPS, and SIGCOMM. Awards: Best Paper Award at ACM CODASPY 2021 Best Paper Award at SIGMETRICS 2016 Best Paper Award at IEEE NetSciCom 2014 Advising & Students: Supervises current PhD students Beatrice Bevilacqua, Jincheng Zhou, and Yucheng Zhang, along with MSc student Ipsit Mantri. Notable former students include S Chandra Mouli (Meta), Yangze Zhou (Spotify), and Jianfei Gao (Vector Institute). Labs & Teams: Leads research in invariant representations and causal ML, collaborating with institutions like Stanford during his sabbatical. His work bridges theory and practice, impacting areas like network analysis and AI-driven healthcare.

Carla P. Gomesمشاهده پروفایل

استاد

• Artificial Intelligence
• Constraint Reasoning
• Optimization
• +۳ مورد دیگر

Carla P. Gomes is a Professor of Computer Science at Cornell University with joint appointments in the Department of Computer Science and the Dyson School of Applied Economics and Management. She holds a PhD in computer science from the University of Edinburgh and an M.Sc. in applied mathematics from the University of Lisbon. Her research focuses on artificial intelligence, constraint reasoning, optimization, and computational sustainability. As Director of the Institute for Computational Sustainability (ICS) and co-director of the Cornell University AI for Science Institute, she leads efforts to integrate AI with sustainability challenges. Her research themes include the integration of constraint reasoning, machine learning, and operations research to solve large-scale problems. She pioneered the field of Computational Sustainability, addressing environmental, economic, and societal challenges through AI. Gomes directed two NSF Expeditions in Computing awards and established CompSustNet, a large-scale sustainability research network. Key awards include the 2021 ACM–AAAI Allen Newell Award, AAAI Feigenbaum Prize, and fellowships from AAAI, ACM, and AAAS. Her work spans over 200 publications, with contributions to AI, sustainability, and materials discovery. She advises numerous PhD students and oversees postdocs in AI, sustainability, and interdisciplinary projects. Gomes' lab focuses on AI for scientific discovery, including autonomous materials synthesis and crystal-structure phase mapping. She collaborates with institutions like JCAP and the Materials Project, advancing AI-driven solutions for energy and environmental challenges. Current projects include Schmidt AI in Science postdoc initiatives and AI-driven materials discovery platforms like DRNets and SARA.

Sanjay Jainمشاهده پروفایل

استاد

• Inductive Inference
• Recursion Theory
• Complexity Theory
• +۳ مورد دیگر

Sanjay Jain is a Provost's Chair Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). His research focuses on theoretical computer science with particular emphasis on inductive inference, recursion theory, complexity theory, and computational learning theory. Education: B.Tech. in Computer Science from Indian Institute of Technology Kharagpur, India (1986) M.S. in Computer Science from University of Rochester, USA (1988) Ph.D. in Computer Science from University of Rochester, USA (1990) Professor Jain's research spans multiple areas of theoretical computer science. His primary contributions are in computational learning theory, where he has made significant advances in understanding the intrinsic complexity of language identification and the limits of inductive inference. His work on recursion theory explores fundamental questions about computability and complexity, while his research in complexity theory addresses structural aspects of computational problems. A notable achievement was his work on "Deciding Parity Games in Quasipolynomial Time," which won the prestigious STOC 2017 best paper award and later the EATCS-IPEC Nerode Prize. Professor Jain's publication record shows a consistent focus on theoretical foundations of computer science, particularly in learning theory and computational complexity. His recent work has expanded into automatic structures, semiautomatic models, and connections between computational learning and algebraic structures. There is a clear progression from foundational work on language identification to more complex models involving automatic functions, transducers, and connections to mathematical logic. Scientific Awards: STOC 2017 Best Paper Award for "Deciding Parity Games in Quasipolynomial Time" EATCS-IPEC Nerode Prize (2021) Professor Jain has served on the editorial board of Information and Computation and has been actively involved in the academic community through program committee memberships for major conferences including COLT, ALT, LATA, TAMC, and PRICAI. He has held leadership roles as program co-chair for ALT 2000 and ALT 2013, and conference chair for ALT 2005. His work has been supported by various research grants, though specific details are not provided in the available materials. Professor Jain leads research in theoretical computer science at NUS, where he has built a strong research group focused on computational learning theory and related areas. His work often involves collaborations with researchers from around the world, particularly with Frank Stephan, with whom he has co-authored numerous papers. His research group has made significant contributions to understanding the fundamental limits and possibilities of computational learning models.

Dawn Elizabeth Cavanaghمشاهده پروفایل

پژوهشگر

• Intellectual Disability Healthcare
• Mental Health Policy
• Healthcare Access for Marginalized Groups
• +۳ مورد دیگر

Dr Dawn Elizabeth Cavanagh is a Researcher at Manchester Metropolitan University, affiliated with the Faculty of Health and Education. Her work focuses on healthcare equity for individuals with intellectual disabilities and autism, particularly addressing systemic issues like restrictive practices and long-term segregation. She holds a PhD from the University of South Wales, where her research explored annual health checks and self-management for people with learning disabilities. Dr Cavanagh’s research has included NIHR-funded studies on psychotropic medication decision-making and the impact of the pandemic on people with learning disabilities. She is currently evaluating the HOPE(S) Programme to End Long-Term Segregation, focusing on mothers of children detained in secure hospitals. Her advocacy extends to campaigning against institutional segregation through initiatives like Stolen Lives Wales and her role as a trustee of the Paul Ridd Foundation. Her research interests intersect personal advocacy, as she is autistic and the parent of an adult son with multiple disabilities whose experiences informed her work. She co-chaired the 2023 RRN conference and has been recognized with awards for her contributions to restraint reduction and disability advocacy. Education: PhD in Intellectual Disability Healthcare (University of South Wales) Dr Cavanagh’s articles emphasize ethical healthcare practices, policy reform, and co-produced solutions for marginalized groups. Her work bridges academic research with grassroots advocacy, aiming to dismantle systemic barriers in healthcare access and institutional care. Awards: Restraint Reduction Network’s 2024 Outstanding Contribution Award, Cardiff Parents’ Federation 2024 Trustee Award She collaborates with organizations like the Rightful Lives Team and leads Stolen Lives Wales, advocating for systemic change in healthcare provision for vulnerable populations.

Marina Blantonمشاهده پروفایل

دانشیار

• Applied Cryptography
• Information Security
• Privacy-Preserving Computation
• +۳ مورد دیگر

Marina Blanton is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, and Faculty Director of Women in Science and Engineering within the School of Engineering and Applied Sciences. She holds a PhD in Computer Science from Purdue University (2007), along with multiple advanced degrees in Computer Science and Electrical Engineering from prestigious institutions in the US and Russia. Her research focuses on applied cryptography, information security, and privacy-preserving computation and outsourcing. She has pioneered work on secure multi-party computation protocols, privacy-preserving biometric authentication, and secure data analytics across distributed systems. Her contributions include foundational frameworks like PICCO, a compiler for private distributed computation, and advancements in protocols for genomic data analysis and floating-point secure computation. Blanton has been recognized with numerous awards, including IEEE and ACM Senior Membership (2016/2015), the ACM CCS Test of Time Award (2015), and the AFOSR Young Investigator Award (2013). Her research has been supported by grants such as NSF SaTC awards and AFOSR funding. Her work emphasizes practical implementations of secure computation, with applications in healthcare, biometrics, and distributed data systems. She has advised numerous students and contributed to educational initiatives promoting women in STEM through her leadership roles.

Steven H. H. Dingمشاهده پروفایل

استادیار

• Cybersecurity
• Machine Learning
• Data Mining
• +۷ مورد دیگر

Dr. Steven H. H. Ding is an Assistant Professor at McGill University's School of Information Studies, specializing in cybersecurity, machine learning, and data mining. His research focuses on AI-driven solutions for malware detection, software vulnerability analysis, and reverse engineering. He holds a PhD from McGill University and has been supported by BlackBerry Cylance and DRDC. His work bridges theoretical advancements with practical applications in military systems and avionics cybersecurity. Dr. Ding earned his PhD in 2019 with notable awards including the FRQNT Doctoral Research Scholarship and McGill's Dean’s Graduate Award. His educational background includes degrees from McGill, Concordia University, and the University of Shanghai for Science and Technology. His research interests span cybersecurity domains such as zero-day malware identification, code obfuscation countermeasures, authorship verification for digital forensics, and AI applications in avionics anomaly detection. He actively contributes to open-source tools like the Kam1n0 MapReduce-based assembly clone search system. Recent work emphasizes adversarial machine learning for evasive malware generation, transformer-based anomaly detection in avionics, and automated SBOM generation for firmware analysis. His publications reflect a focus on real-world cybersecurity challenges in both civilian and defense sectors. Dr. Ding leads the L1NNA Lab and collaborates with industry partners on cutting-edge projects. His contributions include novel techniques for phishing detection leveraging large language models and innovative approaches to reverse engineering software composition in JavaScript applications.

Tamás Budaváriمشاهده پروفایل

دانشیار

• Computational Statistics
• Bayesian Inference
• Astronomy
• +۶ مورد دیگر

Tamás Budavári is an Associate Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with joint appointments in Physics and Astronomy and a secondary appointment in Computer Science. He is affiliated with the Whiting School of Engineering and the Institute for Data-Intensive Engineering and Science (IDIES). His research focuses on computational and statistical methods for big data in astronomy and interdisciplinary applications such as urban blight analysis. Education: PhD in Astrophysics (2001), Eötvös Loránd University, Budapest Master’s in Theoretical Physics (1997), Eötvös Loránd University Research Interests: Budavári develops algorithms for handling large astronomical datasets, including Bayesian inference, streaming algorithms, and GPU-accelerated processing. His work includes SkyQuery (an online astronomy data tool), photometric redshift estimation, and cross-matching catalogs. He also applies computational methods to urban planning, such as optimizing strategies to address vacant housing in Baltimore City. Publications & Tools: Budavári’s recent work spans topics like deep learning for astronomical image restoration, combinatorial optimization for urban policy, and probabilistic catalog matching. His tools, such as CUDAHM and NWAY, enable scalable analysis of multi-epoch survey data and N-way catalog cross-identification. Awards & Grants: Recipient of the Gordon and Betty Moore Fellowship and SAMSI Research Fellowship Funded by NSF, STScI, NIH, and others Leadership & Outreach: He serves on the Steering Committee of the 21st Centuries Cities Initiative and is a founding editor of the Journal of Astronomy and Computing. His interdisciplinary work bridges astrophysics, data science, and urban systems.

Juan Garayمشاهده پروفایل

استاد

• Cryptography
• Information Security
• Cryptographic Protocols
• +۷ مورد دیگر

Juan Garay is a Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on cryptography, information security, and distributed systems, with notable contributions to cryptographic protocols, blockchain technologies, and consensus mechanisms. He holds a leadership role in advancing theoretical and applied aspects of secure computation and network security. Research Interests: Cryptography and Information Security Secure Multiparty Computation Cryptocurrencies and Blockchain Protocols Consensus Algorithms Distributed Computing Game Theory in Cryptography Publications highlight his work on the Bitcoin Backbone Protocol, secure multiparty computation, and post-quantum cryptographic systems. He actively contributes to conferences and workshops in cryptography and distributed systems. He advises graduate students in computer science and engineering, though specific advisee names are not listed. His work is supported by grants from the National Science Foundation (NSF) and other institutions, focusing on secure protocols and distributed systems. Office: Peterson Building (PETR 429) Contact: garay@cse.tamu.edu

Tengyao Wangمشاهده پروفایل

استاد

• High-dimensional Statistics
• Change-point Analysis
• Sparse Signal Detection
• +۷ مورد دیگر

Tengyao Wang is a Professor in the Department of Statistics at the London School of Economics and Political Science (LSE), serving as the MSc Statistics (Financial Statistics) Programme Director. Prior to LSE, he held positions as a Lecturer at University College London and a Research Fellow at the Cantab Capital Institute for the Mathematics of Information, University of Cambridge. His research focuses on high-dimensional statistics, computational efficiency, and statistical limitations imposed by computational constraints. Education: PhD in Statistics under Prof Richard Samworth at the University of Cambridge, with earlier studies including a Part III Essay in Empirical Process Theory. Research interests include sparse signal detection, change-point analysis, dimension reduction, robust statistics, and applications in medical statistics, financial data analysis, and material discovery. Key contributions include methodologies for handling missing data, high-dimensional change-point detection algorithms, and statistical learning techniques. Publications span theoretical advancements and applied innovations, with recent work emphasizing deep learning with missing data, residual permutation tests, and semi-supervised learning via random projections. His work has been recognized with awards such as the Royal Statistical Society Research Prize (2019) and the Guy Medal in Bronze (2023). He is an Associate Editor of the Journal of the Royal Statistical Society, Series B (JRSS B), and actively contributes to open-source tools like the 'ocd' and 'MissInspect' R packages for changepoint detection and missing data analysis.

Shivani Agarwalمشاهده پروفایل

دانشیار

• Machine Learning
• Data Science
• Statistical Learning Theory
• +۳ مورد دیگر

Shivani Agarwal is an Associate Professor of Computer and Information Science and (by courtesy) Statistics and Data Science at the University of Pennsylvania. Her research focuses on computational, mathematical, and statistical foundations of machine learning, including algorithm design, theory, and applications in life sciences. She holds leadership roles in initiatives like the NSF-funded Penn Institute for Foundations of Data Science (PIFODS) and the Penn Research in Machine Learning (PRiML) forum. Previously, she was a Radcliffe Fellow at Harvard, and held academic positions at MIT, Indian Institute of Science, and the University of Illinois at Urbana-Champaign. Education: PhD in Computer Science from the University of Illinois, Urbana-Champaign. Prior roles include Assistant Professor (Ramanujan Fellow) at IISc, postdoctoral lecturer at MIT, and Radcliffe Fellow at Harvard. Research interests span machine learning theory, ranking systems, bandit algorithms, noisy label learning, and interdisciplinary applications in economics, operations research, and psychology. She has organized numerous conferences and workshops, including COLT 2020 and NIPS workshops on ranking and learning. Key professional activities include leadership in Indo-US research collaborations and editorial roles for the Journal of Machine Learning Research and Harvard Data Science Review.