جست‌وجوی استاد و پژوهشگر

Yuhao Baمشاهده پروفایل

استادیار

• Environmental Governance
• Public Policy Process
• Non-state Governance
• +۳ مورد دیگر

Yuhao Ba is an Assistant Professor at the Lee Kuan Yew School of Public Policy, National University of Singapore. His research focuses on non-state governance, environmental policy, and public policy processes. He holds a PhD in Public Administration from North Carolina State University. Research interests include corporate-led environmental governance, interactions between governance systems and policy processes, and sustainability outcomes. He is also committed to promoting equity and inclusion in public policy through his work with APPAM’s Equity & Inclusion fellowship. Education: PhD (Public Administration), North Carolina State University Office: 469C Bukit Timah Road, Singapore 259772 Email: yba@nus.edu.sg His recent work explores themes like policy innovation diffusion, climate change mitigation, and organizational performance evaluation. Publications span journals in public policy, environmental governance, and statistical methodologies. No grants or student advisement information is listed in the provided materials.

Srini Devadasمشاهده پروفایل

استاد

• Computer Architecture
• Security and Cryptography
• Integrated Circuits and Systems
• +۳ مورد دیگر

Srini Devadas is the Edwin Sibley Webster Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT). He is affiliated with MIT’s School of Engineering and leads research in computer architecture, security and cryptography, integrated circuits, systems, and theoretical computation. His work emphasizes privacy-preserving technologies, cryptographic hardware design, and secure computing frameworks. He contributes to initiatives like the MIT Secure and Ethical Responsible Computing (SERC) initiative, addressing societal impacts of computing technologies. His research focuses on advancing cryptographic accelerators, zero-knowledge proofs, and privacy-preserving machine learning. Recent work includes methods to safeguard AI training data and lightweight oblivious transfer protocols. He explores hardware-software co-design for secure systems, such as CraterLake and F1 accelerators, and develops defenses against side-channel attacks (e.g., Citadel). Dr. Devadas has contributed to foundational projects like Path ORAM and Sanctum processor architecture. His teaching excellence is recognized through MIT’s EECS department activities, and he advises on secure enclaves, distributed systems, and anonymous communication protocols. He collaborates with industry and academia on hardware security, privacy, and scalable cryptographic solutions. His research has led to innovations in secure memory management (e.g., Remote Direct Memory Introspection) and privacy-aware algorithms (e.g., PAC Privacy framework). He actively engages in interdisciplinary efforts to balance technological advancements with ethical and societal responsibilities.

Dimitrios Adamosمشاهده پروفایل

عضو هیئت علمی

• Brain-Computer Interfaces (BCI)
• Neurotechnology
• Artificial Intelligence
• +۶ مورد دیگر

Dimitrios Adamos is an Honorary Senior Research Fellow in the Department of Computing at Imperial College London, Faculty of Engineering. He specializes in brain wave decoding for virtual environments and clinical rehabilitation, with a focus on translating neurotechnology into practical applications through AI-driven solutions. His academic work bridges theoretical advancements and real-world applications in brain-computer interfaces (BCI) and healthcare technologies. Dr. Adamos holds a Ph.D. in Neuroinformatics and co-founded Cogitat, an Imperial spinout company where he serves as CTO. At Cogitat, he develops ML/AI technologies for BCI applications, securing patents and pre-seed funding. His research emphasizes ethical challenges in GenAI-powered BCIs and causal perspectives in brainwave modeling. Key achievements include leading a team to first place in the 2021 NeurIPS EEG Transfer Learning Competition and partnering with NHS for neurotechnology in stroke rehabilitation. His research interests span biomedical engineering, artificial intelligence, cognitive sciences, and clinical applications of EEG signal processing. Dr. Adamos collaborates with institutions like the Science Museum London and media outlets such as BBC News and The New Statesman, highlighting his work on mind-controlled VR for healthcare and BCI ethics. His publications focus on EEG decoding, graph theory in brain signal analysis, and causal inference in neurotechnology.

Emmanuel Abbéمشاهده پروفایل

استاد

• Machine Learning
• Information Theory
• Probability Theory
• +۷ مورد دیگر

Emmanuel Abbé is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , holding the Chair of Mathematical Data Science jointly under the School of Basic Sciences (SB) and School of Computer and Communication Sciences (IC) . His research focuses on the mathematical foundations of data science, combining probability, statistics, and discrete mathematics to advance machine learning and information theory. Research Interests : Community detection and stochastic block models Information theory and coding Deep learning theory and generalization bounds Graph-based data analysis and spectral methods Neural network regularization and optimization Mathematical aspects of AI and ML Scientific Awards : Foundation Latsis International Prize Bell Labs Prize NSF CAREER Award Google Faculty Research Award Walter Curtis Johnson Prize von Neumann Fellowship IEEE Information Theory Society Paper Award Simons-NSF Mathematics of Deep Learning Collaborative Research Award Advising and Grants : Prof. Abbé supervises multiple PhD students including Lotfi Jandaghi Aryo, Pushkin Denys, and Senouf Ortal Yona. His research is supported by grants from the Swiss National Science Foundation and other international bodies.

Tom Gurمشاهده پروفایل

استاد

• Theoretical Computer Science
• Quantum Computing
• Classical and Quantum Complexity Theory
• +۶ مورد دیگر

Tom Gur is a Professor in the Department of Computer Science and Technology at the University of Cambridge, with research affiliations in the Algorithms and Complexity and Quantum Computing groups. His work bridges theoretical computer science and quantum computation, supported by major grants including the UKRI Future Leaders Fellowship and ERC Starting Grant . Research Focus : Tom Gur's research centers on Quantum Complexity Theory , Sublinear Algorithms , and Coding Theory , with deep connections to mathematical domains like Harmonic Analysis and Additive Combinatorics . His work explores the interplay between classical and quantum computational models, emphasizing Interactive Proofs , Zero-Knowledge Systems , and Quantum Learning Algorithms . Article Trends reveal a focus on quantum advantage, complexity theory, and cryptographic protocols. Key themes include probabilistically checkable proofs , quantum generalization bounds , and sublinear verification systems across venues like STOC, FOCS, and QIP. Scientific Awards : UKRI Future Leaders Fellowship Advising includes supervision of current PhD and Master's students like Hugo Aaronson and Jack O'Connor , while alumni such as Marcel Dall'Agnol (Princeton) and Aditya Jain (PsiQuantum) reflect his mentorship impact. Professional leadership includes organizing STOC workshops and serving on editorial boards for Quantum and SICOMP .

Anil Vullikantiمشاهده پروفایل

استاد

• Randomized Algorithms
• Combinatorial Optimization
• Distributed Computing
• +۶ مورد دیگر

Anil Vullikanti is a Professor in the Department of Computer Science and a core faculty member of the Biocomplexity Institute at the University of Virginia. He holds affiliations within the School of Engineering and Applied Science. His research bridges theoretical computer science and applied domains like epidemiology, critical infrastructure, and public health. Previously, he held roles at Virginia Tech (2005–2018) and Los Alamos National Lab (2003–2005), and completed postdoctoral work at the Max Planck Institute for Computer Science. Education: B. Tech from Indian Institute of Technology, Kanpur (1993); PhD from Indian Institute of Science, Bangalore (1999). Research Interests: Randomized Algorithms, Combinatorial Optimization, Distributed Computing, Network Science, Machine Learning, and AI applied to epidemiological modeling, infrastructure protection, and healthcare systems. His work emphasizes scalable algorithms for real-world systems like pandemic forecasting, evacuation planning, and infection control. Key Awards: College of Engineering Faculty Fellow Award (Virginia Tech, 2017) Excellence in Research Award (Biocomplexity Institute, 2017) DOE Early Career Award (2010) NSF CAREER Award (2009) Grants & Projects: Focus on large-scale computational epidemiology, disaster management systems, and AI-driven public health interventions. His lab develops frameworks for epidemic scenario modeling, agent-based simulations, and privacy-preserving analytics. Labs & Teams: Leads research groups at the Biocomplexity Institute, collaborating on interdisciplinary projects with engineers, epidemiologists, and policymakers to address societal challenges like pandemic response and infrastructure resilience.

Mikael Skoglundمشاهده پروفایل

استاد

• Information theory
• Communications
• Signal processing
• +۵ مورد دیگر

Mikael Skoglund is a Professor and Head of the Division of Information Science and Engineering at KTH Royal Institute of Technology. His academic roles include membership in the Executive Committee of Digital Futures, and leadership in projects such as Humanizing the Sustainable Smart City (HiSSx) and PERCy. He holds a Ph.D. from Chalmers University of Technology (1997) and became a Professor at KTH in 2003. Education: Ph.D. in Signal Processing, Chalmers University of Technology, 1997 Docent in Signal Processing, KTH, 2001 Professor of Communication Theory, KTH, 2003 Research Interests: Focus on Information Theory, Communications, Signal Processing, Networks, Security and Privacy, Quantum Information, and Inference & Learning. His work bridges theoretical foundations with applications in wireless systems, federated learning, and privacy mechanisms. Projects & Collaborations: Leading initiatives in integrated sensing and communication (ISAC), federated learning, and smart city technologies. Active in strategic research areas like ICT-TNG and Digital Futures. Awards: IEEE Fellow (2023). Students & Grants: Supervised over 40 Ph.D. and postdoc researchers. Notable students include Borja Rodríguez Gálvez (Ph.D. 2024) and Amirreza Zamani (Ph.D. 2023). Active in securing grants for projects like PERCy and HiSSx. Labs/Teams: Part of the Digital Futures research center and the Division of Information Science and Engineering at KTH, fostering interdisciplinary innovation in digital technologies.

Rob Salomoneمشاهده پروفایل

پژوهشگر ارشد

• Statistics
• Machine Learning
• Computational Methods
• +۳ مورد دیگر

Dr. Rob Salomone is a Senior Research Fellow in Data Science at the Queensland University of Technology's (QUT) Centre for Data Science. He earned his Ph.D. in Statistics from the University of Queensland in 2018, focusing on advanced Monte Carlo methodology. Before joining QUT, he held research fellowships at the University of Queensland and UNSW Sydney. His research bridges statistics and machine learning, with an emphasis on developing robust computational techniques for large datasets and complex models. He specializes in creating scientific models that integrate advanced machine learning, domain expertise, and tailored computational methods to address intricate mathematical challenges. Salomone’s work spans Bayesian computation, Monte Carlo methods, and applications in computational biology and environmental systems. His contributions include advancements in nested sampling, CRISPR guide RNA optimization, and graph neural networks for anomaly detection. He also explores federated learning and uncertainty quantification in high-dimensional settings. Notable collaborations include work on agent-based tumor growth models and spectral subsampling for time series analysis. Though no formal students are listed, his research impact is evident through interdisciplinary projects and methodological innovations.

Hadi M. Dolatabadiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Deep Learning
• Adversarial Robustness
• +۳ مورد دیگر

Hadi M. Dolatabadi is a Research Fellow in machine learning at the University of Melbourne node of the ARC Centre of Excellence for Automated Decision-Making & Society (ADM+S). He is affiliated with the School of Computing and Information Systems at the University of Melbourne, where he has nearly completed his Ph.D. focusing on robustness in deep learning. His research contributes to the ADM+S Centre's Machines Research Program, specifically developing algorithms for systematic treatment of bias and unfairness in AI systems. Hadi earned his Ph.D. from the School of Computing and Information Systems at the University of Melbourne, with his thesis examining current notions of robustness in neural networks and challenging them from novel perspectives. His doctoral research bridges theoretical foundations with practical applications in machine learning. Hadi's research interests center on machine learning with a strong emphasis on robustness and fairness. He specializes in adversarial robustness, coreset selection, and generative modeling techniques including normalizing flows, GANs, and diffusion models. His approach uniquely combines statistical perspectives with generative modeling frameworks to address fundamental challenges in AI. His work has significant implications for developing more reliable and equitable automated decision-making systems, particularly in contexts where bias and unfairness could have serious societal consequences. Analysis of Hadi's publication record reveals a strong focus on the intersection of theoretical machine learning and practical AI ethics. His work consistently addresses the tension between model performance and robustness, while increasingly incorporating fairness considerations. The progression of his research shows a movement from foundational robustness questions toward more applied problems related to bias detection and mitigation in real-world AI systems. His publications span top-tier AI conferences including NeurIPS, ECCV, and AISTATS, demonstrating both technical depth and relevance to contemporary AI challenges. Hadi completed a six-month research internship at Amazon Science during his Ph.D. studies, gaining valuable industry experience while maintaining strong academic research output. His technical expertise spans both theoretical aspects of machine learning and practical implementation of complex algorithms. While specific grants aren't mentioned in the available information, his position at the ARC Centre of Excellence indicates involvement in significant collaborative research funding. As part of the ADM+S Centre's Machines Research Program, Hadi contributes to a multidisciplinary team addressing challenges in automated decision-making. The Centre brings together researchers from humanities, social sciences, and technological fields to create knowledge and strategies for responsible, ethical, and inclusive automated decision-making systems. Hadi's technical expertise in machine learning provides crucial foundation for the Centre's work on bias and fairness in AI systems.

Srinivasan Balajiمشاهده پروفایل

استادیار

• Stochastic Processes
• Probability Theory
• Mathematical Finance
• +۳ مورد دیگر

Srinivasan Balaji is an Assistant Professor of Statistics at The George Washington University , specializing in stochastic processes and their applications. His research focuses on stochastic differential equations , diffusion processes , and Markov chains , with implications in mathematical finance, queueing networks, and statistical learning theory. Education Ph.D. in Statistics from the Indian Statistical Institute (1997) Research Interests Balaji investigates the mathematical foundations of diffusion processes in constrained domains, including recurrence/transience properties and boundary behavior. His work extends to risk-sensitive control , PAC-learnability , and reliability theory , bridging probabilistic models with real-world applications. Publication Trends His publications from 1997–2001 reveal a consistent focus on stochastic differential equations and Markov chains , with applications spanning mathematical finance , queueing networks , and machine learning . Notable subfields include reflecting diffusions in orthants , passage time moments , and risk-sensitive dynamic programming .

Oleg Sokolskyمشاهده پروفایل

استاد پژوهشی

• Cyber-Physical Systems
• Formal Verification
• Runtime Monitoring
• +۲ مورد دیگر

Research Professor at University of Pennsylvania leading projects on cyber-physical systems safety. Directs DARPA-funded research in assured autonomy and attack-resilient control systems. Education: PhD in Computer Science from SUNY Stony Brook (1996). Research: Develops formal methods for runtime monitoring and verification of embedded systems. Current projects include medical device interoperability and physiological closed-loop control. Created MaC runtime verification framework and contributed to AADL standardization. Teaching: Advises PhD students in real-time systems and formal verification. Teaches courses on mathematical foundations of computer security. Awards: Best Paper, IEEE/ACM CPS Week (2014) Best Student Paper, RTAS (2012) Labs & Projects: Member of PRECISE Center. Leads DARPA projects on assured autonomy and SPARCS. Collaborates with FDA on medical device safety frameworks.

• Health Informatics
• Medical Informatics
• Computer-Mediated Communication
• +۵ مورد دیگر

Pieter Jelle Toussaint is a full-time Professor at the Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Faculty of Information Technology and Electrical Engineering. His research focuses on Health Informatics and Medical Informatics , particularly in computer-mediated communication and computer-supported cooperative work (CSCW) in healthcare settings. MSc in Informatics and General Linguistics from Leiden University PhD in Medical Informatics from Leiden University His research examines how technology can improve clinical workflows and coordination in hospitals. Current projects involve process mining in healthcare, designing awareness systems to reduce synchronous communication, and analyzing information quality in hospital-community healthcare exchanges. He has led major research initiatives like the SIKT and COSTT projects. Professor Toussaint has acquired significant research funding including 23 million NOK from the Norwegian Research Council for the COSTT project and 2.4 million NOK for the SIKT project. His publications explore topics such as electronic health records (EHR), mobile sensing in substance use research, and challenges in clinical software design. He has supervised three completed doctoral students and currently mentors five PhD candidates. His leadership roles include heading the Information Systems and Software Engineering research group at NTNU and serving on the strategic board of NTNU Health.

Chris Rodgersمشاهده پروفایل

استادیار

• Neuroscience
• Neural Circuits
• Sensorimotor Control
• +۵ مورد دیگر

Chris Rodgers serves as an Assistant Professor in the Department of Neurosurgery at Emory University School of Medicine, with additional affiliation in Biology. His research explores how neural circuits enable perception and action through touch and sound, and how these processes are disrupted in brain disorders. Education: B.Eng in Engineering, McGill University (2007) Ph.D. in Neuroscience, University of California, Berkeley (2013) Research Focus: The Rodgers Lab (Perception & Action Lab) investigates sensorimotor neural circuits using mouse models. They develop behavioral paradigms where mice actively explore their environment, then apply computational analysis, machine learning, electrophysiology, and optogenetics to dissect neural computations. The lab aims to understand how distributed brain regions collaborate during behavior and how disorders alter these dynamics, with translational goals for rehabilitation. Advising and Environment: Dr. Rodgers fosters a lab culture centered on mutual respect, good-faith mentorship, and honest feedback. He supports trainees in achieving scientific excellence and individual career objectives through collaborative research on neural circuit mechanisms and their implications for brain health. Lab Facilities: The PAC Lab operates from the Whitehead Biomedical Research Building and Woodruff Memorial Research Building, utilizing advanced tools for behavioral analysis, neural recording, and manipulation to study active sensing and motor control.

Umut Simsekliمشاهده پروفایل

پژوهشگر

• Mathematical Machine Learning
• Markov Chain Monte Carlo
• Deep Learning Theory
• +۴ مورد دیگر

Umut Simsekli is a Researcher at INRIA - SIERRA team and École Normale Supérieure de Paris, Computer Science Department. He earned his PhD (2015) and MSc (2010) from Boğaziçi University, and a BSc from Sabancı University (2008). His career includes roles at Télécom Paris (2016-2020) and visiting positions at Oxford and Boğaziçi University. Research Focus: Mathematical Machine Learning, emphasizing Deep Learning theory, Heavy-Tailed Optimization, Bayesian Inference, and Stochastic Dynamics. Grants: Principal Investigator for ERC Starting Grant DYNASTY (€1.5M, 2022-2027) and co-PI for ANR/TUBITAK grant FBIMATRIX (2016-2022). Recent Article Trends: Explores PAC-Bayesian generalization, heavy-tailed SGD dynamics, and topological stability in optimization algorithms. Scientific Awards: ERC Starting Grant (2021) ICASSP Best Student Paper Award (2020) Multiple IEEE/SPS Student Travel Grants (2012-2020) Victor L. Wooten Bass/Nature Camp Scholarship (2005) Teaching & Advising: Teaches Deep Learning at École Polytechnique and previously led courses on Probabilistic Graphical Models at Télécom Paris. Supervises PhD students Dario Shariatian, Benjamin Dupuis, and others.

Adam Klivansمشاهده پروفایل

استاد

• Machine Learning
• Theoretical Computer Science
• Learning Theory
• +۵ مورد دیگر

Adam Klivans is a Professor at the University of Texas at Austin's Department of Computer Science and serves as Director of the Institute for Foundations of Machine Learning (IFML) and the Machine Learning Lab. His work bridges machine learning and theoretical computer science, focusing on learning theory, computational complexity, and Gaussian space analysis. He has made foundational contributions to robust learning, distribution shift, and neural network trainability. Research Interests: Machine learning algorithms Computational complexity Robust learning under noise Distribution shift Gaussian space analysis Protein engineering Selected Publications Analysis (2023-2025): Foundational TDS learning framework for distribution shift Polynomial-time algorithms for halfspace and ReLU regression Key results on SQ lower bounds for neural networks Advances in list-decodable regression and outlier-robust learning Novel approaches to protein stability prediction Applications to Ising models and diffusion models Scientific Honors: NSF CAREER Award (2007) Microsoft Azure Data Science Initiative Award (2017) Best Student Paper COLT (2006) Best Paper Award at COLT (2024) Spotlight Presentations at NeurIPS (2019) Long-Term Participant Simons Institute (2018) Academic Leadership: Director, Institute for Foundations of Machine Learning Director, UT Machine Learning Lab Editorial Board, Theory of Computing Journal Mentoring current students: Kostas Stavropoulos, Gautam Chandrasekaran, Kulin Shah Alumni advisees: Surbhi Goel, Pravesh Kothari, Sushrut Karmalkar