جست‌وجوی استاد و پژوهشگر

• Data Privacy
• Differential Privacy
• Cryptography
• +۵ مورد دیگر

Amrita Roy Chowdhury is an Assistant Professor in the Department of Computer Science at the University of Michigan, Ann Arbor. Her research focuses on developing systems that enable safe, decentralized data analytics while ensuring provable privacy guarantees through the synergy of differential privacy and cryptography. Key research areas: Data Privacy, Cryptography, Secure Data Analytics, and Privacy-Preserving Machine Learning. Recent work explores prompt sanitization for LLMs (NDSS 2026), robust graph analysis (ASIACCS 2025), and metric differential privacy (CCS 2024). She has received awards including Best Paper at Private ML@ICLR'24 and Best Poster at ITA'23. Current Ph.D. advisees include Mushtari Sadia, Yiyi Sun, and Samanway Sadhu. Her work spans conferences like IEEE S&P, CCS, USENIX Security, and ICML.

• Generative AI
• Information Theory
• Machine Learning
• +۴ مورد دیگر

Alexandros G. Dimakis is a Professor at the University of California, Berkeley's Department of Electrical Engineering and Computer Sciences (EECS), College of Engineering. He is also Co-Director of the National AI Institute for Foundations of Machine Learning and Co-Founder of BespokeLabs.ai. PhD (2008) and Diploma (2003) in Electrical Engineering His research focuses on Generative AI , Information Theory , and Machine Learning . Recent work includes advancements in diffusion models, compressed sensing, and causal inference. His publications (150+) emphasize inverse problems, neural network verification, and generative model optimization. Recent publications highlight trends in Diffusion Models for inverse problems, Language Model Scaling , and 3D-Aware Generative Systems . Collaborative projects span biomedical applications, large-scale dataset curation (Datacomp-LM), and parameter-efficient model fine-tuning. Scientific Awards : IEEE Fellow (2022) James Massey Award (2018) NSF CAREER Award (2011) Google Research Faculty Award Best Paper awards at UAI workshops Eli Jury Dissertation Award (UC Berkeley) He advises PhD students in generative modeling, compressed sensing, and information theory. His research group collaborates with institutions like MIT, NYU, and IBM Research. Former students hold positions at Google, Amazon, and academic institutions like Purdue University.

• Machine Learning
• Artificial Intelligence
• Computer Science
• +۵ مورد دیگر

Gordon Geoffrey J. is a Professor at Carnegie Mellon University, specializing in Machine Learning , Artificial Intelligence , and Computer Science . His research spans Reinforcement Learning , Domain Adaptation , Graph-based Planning , and Relational Learning , with significant contributions to Adversarial Learning , Deep Learning Dynamics , and Mobile Sensor Networks . He has pioneered algorithms like ARA* and Anytime Dynamic A* for efficient planning under constraints. Key research areas: Transfer Learning , Bayesian Knowledge Tracing , Multi-agent Systems , Database Optimization His work has been cited thousands of times across foundational topics in AI and robotics, demonstrating sustained impact in algorithm design and applications to complex systems.

• Machine Learning
• Probabilistic Inference
• Decision Making
• +۱ مورد دیگر

Professor Carl Edward Rasmussen is affiliated with the University of Cambridge , where he focuses on Machine Learning , Probabilistic Inference , Decision Making , and Reasoning Under Uncertainty . His work bridges theoretical advancements with practical applications in robotics, control systems, and computational biology. Academic Affiliation: University of Cambridge Academic Role: Professor His research emphasizes scalable Gaussian process methods, Bayesian system identification, and reinforcement learning. Recent projects include transfer learning for antibacterial discovery , graph neural processes for molecular functions , and efficient variational inference techniques . Key themes in his publications highlight uncertainty quantification , model generalization , and nonparametric approaches . Notable Scientific Contributions Advancements in sparse Gaussian process hyperparameter estimation Framework for Bayesian system identification in dynamic systems Hybrid models combining transformers and Gaussian processes

• Quantum Optics
• Photonics
• Quantum Information Science
• +۷ مورد دیگر

Klaus Mølmer is a Professor at the Niels Bohr Institute, University of Copenhagen, specializing in Quantum Optics and Photonics. His research spans quantum information, entanglement, and cavity QED, leveraging machine learning and Grover's algorithm for quantum state engineering. His recent work focuses on spin squeezing, Rydberg atom interactions, and mechanical resonator cooling. A leader in quantum simulation and superradiance, he collaborates on cavity-mediated emission and quantum network design. The 15 most recent articles highlight advancements in quantum state manipulation, entanglement protocols, and robust differential phase sensing. These studies bridge theoretical frameworks with experimental applications in cavity QED, Rydberg arrays, and zero-photon detection.

Brent Watersمشاهده پروفایل

استاد

• Cryptography
• Computer Security
• Theoretical Computer Science
• +۴ مورد دیگر

Brent Waters is a Professor at the Department of Computer Science, University of Texas at Austin , where he has been since 2008. He received his Ph.D. in Computer Science from Princeton University (2004) and held a postdoctoral position at Stanford University (2004-2005). His research focuses on cryptography and computer security, with groundbreaking work in Identity-Based Encryption, Functional Encryption, Attribute-Based Encryption, and code obfuscation. He is a founder of Functional Encryption and Attribute-Based Encryption. Education Ph.D., Computer Science, Princeton University (2004) Research Interests Cryptography, Security Protocols Functional Encryption, Attribute-Based Encryption Indistinguishability Obfuscation, LWE-Based Systems Zero-Knowledge Proofs, Key-Dependent Message Security Selected Publications Trends Recent work (2025) addresses adaptive security in broadcast encryption, SNARGs, and multi-authority ABE systems using LWE and bilinear maps. Key themes include collusion resistance, witness encryption, and optimizing cryptographic assumptions like CRS size reduction. Scientific Awards IEEE Fellow (2025), IACR Fellow (2024), ACM Fellow (2021) Simons Investigator (2019), Grace Murray Hopper Award (2015) Presidential Early Career Award (2011), Packard Fellowship (2011) Advising Current Ph.D. students: Shafik Nassar, George Lu Past Ph.D. students: Rachit Garg (2024), Satya Vusirikala (2021), Rishab Goyal (2019), Venkata Koppula (2018), Yannis Rouselakis (2013), Allison Bishop (2012) Contact Email: bwaters@cs.utexas.edu Phone: (512) 232-7464 | Office: GDC 6.810

Mona Singhمشاهده پروفایل

استاد

• Computational Biology
• Machine Learning
• Algorithms
• +۳ مورد دیگر

Mona Singh is a Professor of Computer Science at Princeton University, with affiliations to the Lewis-Sigler Institute for Integrative Genomics and the Department of Molecular Biology. She has been a faculty member since 1999. Ph.D., Massachusetts Institute of Technology, 1995 A.B. and S.M. degrees in Computer Science from Harvard University Her research focuses on computational molecular biology, integrating machine learning and algorithms to analyze biological networks, protein interactions, and mutational impacts. Key areas include DNA/RNA binding prediction, protein structure analysis, and network-based disease gene discovery. Her recent work highlights trends in protein language models, kinase-substrate prediction, and equitable MHC binding algorithms. These span sub-fields like structural bioinformatics, network biology, and functional genomics. Scientific Awards: Presidential Early Career Award for Scientists and Engineers (PECASE) Rheinstein Junior Faculty Award ACM Fellow (2019) ISCB Fellow (2018) She has taught an introductory computational biology course with Professor Coleen Murphy, covering sequence analysis, phylogenetics, and network reconstruction. Her group has developed tools like dPUC , nCOP , and DiffMut . Her lab collaborates with institutions including Carnegie Mellon, Duke University, and the Broad Institute, advancing applications in cancer genomics, metabolic disease, and precision medicine.

Michael J. Freedmanمشاهده پروفایل

استاد

• Distributed Systems
• Networking
• Security
• +۴ مورد دیگر

Michael J. Freedman is the Robert E. Kahn Professor of Computer Science at Princeton University and co-founder/CTO of Timescale. He received his Ph.D. from NYU’s Courant Institute and degrees from MIT. Current roles: Professor, Co-founder & CTO Affiliations: Princeton University, SNS Group, CITP Associate Education: Ph.D. (NYU), S.B./M.Eng. (MIT) His research spans distributed systems, networking, and security, with innovations like CoralCDN, DONAR, and Ethane. His work impacts decentralized content delivery, software-defined networking, and privacy-enhancing technologies. His recent publications address scalable fusion algorithms, GPU acceleration for data systems, and distributed GPU resource management. These works intersect with cloud infrastructure, network optimization, and security. Scientific honors include: Presidential Early Career Award for Scientists and Engineers (PECASE) Sloan Fellowship NSF CAREER Award Office of Naval Research Young Investigator Award Test of Time Award (Theory of Crypto Conference) ACM SIGOPS Mark Weiser Award He advises graduate students like Sam Ginzburg and Ashwini Raina, who joined Meta AI and Timescale post-PhD. His projects have secured substantial grants, including $110M Series C funding for Timescale. Key labs/teams: Princeton SNS Group Co-founder, Timescale (enterprise data platform) Co-founder, iobeam (IoT analytics, acquired by Timescale) Collaboration with FCC on Consumer Broadband Test Contributions to OpenFlow/SDN standardization

Andrew S. Rosenمشاهده پروفایل

استادیار

• Energy and Environment
• Surface Science and Catalysis
• Theory and Computation
• +۵ مورد دیگر

Andrew S. Rosen is an Assistant Professor in the Department of Chemical and Biological Engineering at Princeton University, leading the Rosen Research Group since his appointment. He became an associated faculty member of the Princeton Plasma Physics Laboratory (PPPL) in June 2025, expanding his collaborative impact in energy research. His work focuses on computationally guided materials discovery to address urgent sustainability challenges beyond traditional trial-and-error approaches. Education Ph.D. in Chemical Engineering, Northwestern University (2021) B.S. in Chemical Engineering, Tufts University (2015) Research Focus Dr. Rosen integrates quantum-chemical calculations , high-throughput computing , and machine learning to design novel materials for energy storage, catalysis, and environmental applications. His group specializes in porous framework solids and molecular materials with tailored electronic properties, emphasizing synthesis pathways to bridge computational predictions with experimental realization. This research targets unprecedented materials for clean energy technologies through AI-enhanced quantum modeling. Research Support NSF grant awarded June 25, 2025 ScienceAtScale NERSC Award received June 10, 2025 Invited speaker at ASE CECAM Workshop (June 24, 2025) Group and Collaborations The Rosen Research Group leverages open-source software and big data initiatives like the Materials Project, maintaining strong ties with experimental teams to validate computational discoveries. Their work directly addresses climate challenges through materials innovation, with recent focus on electronic structure properties for catalytic applications.

• Geometry
• Discrete Mathematics
• Linear Algebra
• +۳ مورد دیگر

Maria Rita Casali is a Full Professor at the Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia. Her research focuses on geometry, topology, and mathematical structures, particularly in relation to PL-manifolds and colored tensor models. Teaching: Courses in Geometry, Discrete Mathematics, and Linear Algebra for Engineering and Strategic Sciences degrees. Research: Investigates combinatorial invariants (regular genus, G-degree, gem-complexity) for compact 4-manifolds, linking them to quantum gravity and tensor models. Publications: Recent works include studies on trisections of 4-manifolds, classifications via colored graphs, and combinatorial properties of the G-degree. Her contributions to crystallization theory and PL-manifold representation have advanced the understanding of geometric topology and its applications in theoretical physics.

Pratyush Mishraمشاهده پروفایل

استادیار

• Cryptography
• Computer Security
• Zero-Knowledge Proofs
• +۲ مورد دیگر

Pratyush Mishra is an Assistant Professor at the University of Pennsylvania in the Department of Computer and Information Science, where he is affiliated with the Security and Privacy Laboratory. His research focuses on the intersection of cryptographic proof systems and computer security , particularly on efficient implementations of zero-knowledge proofs and secure computation protocols. Pratyush completed his PhD in Computer Science at UC Berkeley , advised by Alessandro Chiesa and Raluca Ada Popa , and holds a BSc in EECS from UC Berkeley, where he worked with David Wagner . His current research group includes PhD students Anubhav Baweja , Tushar Mopuri , Bharath Namboothiry , and Alireza Shirzad , along with Matan Shtepel , a former research assistant who moved to CMU for his PhD. His work spans advanced cryptographic techniques like zkSNARKs , accumulation schemes , and private delegation of provers , with applications in decentralized systems and secure inference. His recent publications focus on optimizing polynomial commitments , read-write streaming for SNARKs, and horizontally scalable proofs . Key awards include: ACM SIGSAC Doctoral Dissertation Award Runner-Up (2022) CSAW Applied Research Award (2016) for Hidden Voice Commands He teaches courses like CIS 5560: Cryptography and 7000-2: Theory and Practice of Succinct Zero Knowledge Proofs , covering topics such as symmetric cryptography, public-key encryption, digital signatures, zero-knowledge proofs, and secure multiparty computation. His lab contributes to the arkworks ecosystem for zero-knowledge proof libraries and co-founded the startup Aleo based on his research.

• Strategic Marketing
• Marketing Management
• Value Propositions
• +۷ مورد دیگر

Mika Yrjölä serves as a University Lecturer in Marketing at Tampere University's Faculty of Management and Business. He is an active member of the Customer-Oriented Research Group with extensive research experience in strategic marketing, value propositions, and business models. His academic work spans over a decade with 53 research outputs from 2013 to 2025. Yrjölä's research primarily focuses on strategic marketing, including value propositions, business models, capabilities, and customer orientation. He employs both quantitative and qualitative methods in his investigations, with particular interest in digital transformation, artificial intelligence in retail, and business-to-business platforms. His work contributes to understanding how organizations can create value in evolving market contexts, especially through digital channels and platform-based business models. His publication record shows consistent scholarly output with recent emphasis on AI-enhanced platforms, value creation in ecosystems, and retail transformation. The research demonstrates a clear trajectory toward understanding digital transformation in marketing contexts, with increasing focus on AI applications in services and retail environments. His work spans both theoretical contributions and practical applications for businesses navigating digital disruption. Marvin Jolson Award (2022) - for research in sales management Yrjölä has supervised master's students and doctoral candidates while actively participating in academic service as a journal reviewer and conference committee member. His activities include 41 scientific journal reviews, 19 media appearances, and 11 master's student supervisions. He has also served as an external reviewer for The French National Research Agency (ANR). As a member of the Customer-Oriented Research Group, Yrjölä collaborates with colleagues on projects examining consumer behavior, retail transformation, and digital marketing strategies. His research increasingly focuses on the intersection of AI and service delivery, particularly in platform-based business models and retail contexts.

• Machine Learning
• Dynamical Systems
• Optimization
• +۳ مورد دیگر

Michael Muehlebach leads the independent Learning and Dynamical Systems research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. His interdisciplinary work bridges machine learning, dynamical systems theory, and control engineering to develop algorithms for cyber-physical systems with theoretical guarantees and practical implementations. Dr. Muehlebach received his B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich in 2010 and 2013, specializing in robotics and control systems. He completed his Ph.D. at ETH's Institute for Dynamic Systems and Control under Prof. R. D'Andrea in 2018, followed by postdoctoral research with Prof. Michael I. Jordan at UC Berkeley. His research focuses on constrained optimization, reinforcement learning, and control theory with applications in robotics. He pioneered approaches that express constraints in terms of velocities rather than positions, enabling more efficient optimization algorithms. His work spans theoretical foundations to physical implementations, including the One-Wheel Cubli balancing robot and electromagnetic navigation systems. Recent publications reveal a strong trend toward physics-informed machine learning, particularly for robotics applications requiring real-time performance and safety guarantees. Dr. Muehlebach has received numerous prestigious awards: Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellowship (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) He actively mentors doctoral researchers including Hao Ma, Melis Ilayda Bal, and Onno Eberhard, with research supported by multiple grants. His group maintains strong collaborations with Bernhard Schölkopf's Empirical Inference group at the Max Planck Institute. The Learning and Dynamical Systems group develops innovative hardware and software platforms, including Floaty (a wind-harnessing flying robot), advanced electromagnetic navigation systems, and data-efficient learning methods for robotic table tennis. Their approach combines rigorous theoretical analysis with practical validation on physical systems, emphasizing the integration of known physical structure into machine learning algorithms to improve sample efficiency and ensure generalization.

Richard S. Suttonمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Computational Intelligence
• +۵ مورد دیگر

Richard S. Sutton is a Professor in the Department of Computing Science at the University of Alberta , a Research Scientist at Keen Technologies, and the Principal Investigator at the Reinforcement Learning and Artificial Intelligence Lab . He also serves as the Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute (Amii) and as Chief Scientific Advisor there. Additionally, he founded the Openmind Research Institute and is Chief Scientific Officer at ExperienceFlow. His research focuses on identifying general computational principles underlying intelligence and goal-directed behavior. He emphasizes the interaction between intelligent agents and their environment , exploring how goals, choices, and information sources emerge from this interaction. His work spans reinforcement learning (RL) , agent-environment dynamics, and ethical considerations in AI, such as robot rights and moral philosophy. His articles include technical works on RL, ethical frameworks, and philosophical essays like The Bitter Lesson (2019) and The Definition of Intelligence (2016). Canada CIFAR AI Chair Co-author of foundational RL textbook Developed Tile Coding Software for RL

Jason Liمشاهده پروفایل

استادیار

• Fast Graph Algorithms
• Algorithm Design
• Minimum Cut Problems
• +۶ مورد دیگر

Jason Li is an Assistant Professor in the Department of Computer Science at Carnegie Mellon University's School of Computer Science. He teaches advanced algorithms courses including 15-754 Spectral Graph Theory (Spring 2025), 15-451 Design and Analysis of Algorithms (Fall 2024), and 15-850 Advanced Algorithms (Spring 2024). His research focuses on fast graph algorithms , particularly solving longstanding open problems through modern algorithmic techniques. Key research themes include preconditioning and locality , which serve as reductions from worst-case to well-behaved and local instances respectively. His work has produced breakthroughs in deterministic global minimum cut algorithms, all-pairs minimum cut (Gomory-Hu trees), and near-optimal parallel shortest path algorithms. Analysis of his recent publications reveals a consistent trend toward almost-linear time algorithms for fundamental graph problems, with significant contributions to dynamic graph algorithms, minimum cut variants, and parallel computation. His work frequently appears in top venues including STOC, FOCS, and SODA, often with multiple best paper recognitions. EATCS Distinguished Dissertation Award (2021) Best Paper Award at SODA 2024 Invited to HALG 2024 Invited to TALG and JACM for SODA 2024 paper Machtey Best Student Paper at FOCS 2019 Professor Li actively advises graduate students including Henry Fleischmann and George Li. His research is supported by collaborations with leading institutions and frequent invitations to present at major conferences. He maintains an open-door policy for CMU students and collaborators, though notes the high volume of research inquiries he receives weekly.