جست‌وجوی استاد و پژوهشگر

Rebecca E. Taylorمشاهده پروفایل

دانشیار

• DNA nanotechnology
• molecular and cellular mechanobiology
• bio-inspired micro- and nanosystems
• +۷ مورد دیگر

Rebecca E. Taylor is an Associate Professor in the College of Engineering at Carnegie Mellon University, holding joint appointments in Mechanical Engineering, Biomedical Engineering, and Electrical and Computer Engineering. She leads the Microsystems and Mechanobiology Lab, where her interdisciplinary research spans molecular, cellular, microscale, and macroscale systems. Her work bridges engineering principles with biological understanding to create novel nanotechnologies. Dr. Taylor's educational background includes: B.S.E. in Mechanical Engineering from Princeton University (2001) M.S. in Mechanical Engineering from Stanford University (2010) Dual Ph.D. degrees in Mechanical Engineering and Bioengineering from Stanford University (2013) Her research focuses on three primary domains: DNA nanotechnology for molecular and cellular mechanobiology Bio-inspired micro- and nanosystems Advanced manufacturing across multiple scales Dr. Taylor's work leverages structural DNA nanotechnology to create tools for measuring stress and strain in soft materials, investigating molecular and cellular mechanobiology, and developing novel approaches for drug delivery and gene therapy. Her lab has made significant contributions in creating self-assembling nanofilaments from gamma-modified peptide nucleic acid (gPNA), which form stable structures in harsh environments. She also investigates the use of DNA-based nanostructures as flexible connectors for microscale swimmer robots and as bridging materials for enhancing self-assembly processes. Her recent publications reveal a strong focus on microswimmers, DNA origami, cell encapsulation, and computational design methods for nanostructures. The research demonstrates increasing sophistication in combining top-down engineering processes with bottom-up self-assembly approaches. Dr. Taylor has received numerous prestigious awards: NSF CAREER Award (2020) AFOSR Young Investigator Program (YIP) Award (2017) Donald L. and Rhonda Struminger Faculty Fellow (2016) Dr. Taylor actively mentors a large group of students across multiple levels. Her lab currently includes multiple Ph.D. students, postdoctoral researchers, M.S. students, and undergraduates. She has successfully guided numerous students to completion of their degrees, with many alumni now working at leading institutions and companies. Her research is supported by multiple grants from agencies including NSF, NIH, and AFOSR, reflecting the interdisciplinary and high-impact nature of her work. The Microsystems and Mechanobiology Lab collaborates extensively with researchers in Chemistry, Biomedical Engineering, Physics, Developmental Biology, and Cardiovascular Medicine, highlighting the cross-disciplinary nature of Dr. Taylor's research program. Her work on workforce training tools like voice assistants for advanced manufacturing demonstrates her commitment to translating research into practical applications.

Robert D. Tiltonمشاهده پروفایل

استاد

• Colloid Science
• Nanoparticle-Cell Interactions
• Environmental Nanotechnology
• +۴ مورد دیگر

Robert D. Tilton is the Chevron Professor of Chemical Engineering and holds a joint appointment in Biomedical Engineering at Carnegie Mellon University's College of Engineering. He directs the Center for Complex Fluids Engineering. His research focuses on colloidal phenomena in biomedical and environmental systems, including surfactant-based drug delivery and nanoparticle interactions. Education: B.Ch.E., University of Delaware, 1986 M.S. & Ph.D., Chemical Engineering, Stanford University (1987 & 1991) Research Interests: Nanoparticle-cell interactions Pulmonary drug delivery systems Environmental impacts of nanotechnology Surfactant dynamics and Marangoni flows Awards: Fellow of the American Chemical Society Fellow of the American Institute for Medical and Biological Engineering NSF Career Award Victor K. LaMer Award (1993) Grants & Leadership: NIH Nanotechnology Study Section member ACS Division of Colloid and Surface Chemistry leadership roles Labs & Teams: Active research group includes postdocs and graduate students investigating complex fluid interfaces, aerosol carriers, and nanoparticle applications in agriculture and medicine.

Sossena Woodمشاهده پروفایل

استادیار

• Biomedical Engineering
• Neuroimaging
• Sickle Cell Disease
• +۴ مورد دیگر

Dr. Sossena Wood is an Assistant Professor in Biomedical Engineering at Carnegie Mellon University, with courtesy appointments in Electrical and Computer Engineering and the Neuroscience Institute. She holds a B.S. in Electrical Engineering (2011) and a Ph.D. in Bioengineering (2018) from the University of Pittsburgh, followed by a Presidential Postdoctoral Fellowship at CMU under Dr. Jana Kainerstorfer. Her research focuses on multi-modal, noninvasive imaging techniques to study cerebrovascular diseases like sickle cell disease (SCD), emphasizing neural mechanisms and healthcare equity. Key research areas include hemodynamic imaging (MRI, NIRS, EEG), pain and cognitive deficits in SCD, and inclusive neurotechnology to address biases in medical devices. She leads the Wood Neuro Research Group, collaborating with the CMU-Pitt BRIDGE Center and institutions globally. Awards include the American Society of Hematology Abstract Achievement Award (2020) and recognition as a Women of Color Technology Rising Star (2021). Her lab develops cutting-edge tools like anthropomorphic phantoms and MRI coils, and addresses algorithmic bias in devices like pulse oximeters. Educational contributions include teaching "Fundamentals of MRI and Neuroimaging Analysis" and outreach programs to engage underrepresented youth in STEM. Current collaborations focus on SCD mechanisms, pain biomarkers, and imaging equity. Lab equipment includes 3T MRI systems, EEG setups, thermal stimulators, and advanced NIRS devices. She advises multiple graduate and undergraduate students, with recent alumni including Yuxuan Gu (M.S.) and Alexis Duong (B.S./M.S.).

Suresh Kumaar Jayaramanمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Human-Robot Interaction
• Machine Learning
• +۴ مورد دیگر

Suresh Kumaar Jayaraman is a Postdoctoral Fellow at the Robotics Institute , Carnegie Mellon University . His research focuses on Human-Robot Interaction , Explainable AI , and Trust Modeling in autonomous systems, particularly in automated driving applications. Robotics Institute, Carnegie Mellon University Research Fellow His work emphasizes behavior-aware control systems , pedestrian-AV interaction modeling , and machine teaching frameworks. Key themes include trust calibration , context-adaptive decision-making , and replicability standards in HRI studies. Recent publications highlight real-time trust estimation for automated vehicles, multimodal pedestrian behavior modeling , and user-centric explainable AI systems. He employs inverse reinforcement learning and hybrid automaton models to enhance human-AV interaction safety. He is affiliated with the TBD Lab at Carnegie Mellon University, focusing on robotics and autonomous systems development.

Pragathi Praveenaمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Human-Robot Interaction (HRI)
• Computer Vision
• +۳ مورد دیگر

Pragathi Praveena is a Research Fellow at the Robotics Institute , Carnegie Mellon University. Her work focuses on advancing human-robot interaction, teleoperation systems, and robotics applications in collaborative environments. She leads the Reliable Autonomous Systems Lab , exploring topics such as Petri Net-based robotic system design, generative AI in robotics, and socially engaging robot interfaces. Her research spans autonomous systems , computer vision , and medical robotics applications . Notable contributions include Periscope (a robotic camera system for remote collaboration) and Rex (user-centered repair frameworks for robots). She investigates challenges in telemanipulation control systems, motion generation algorithms, and optimizing collaborative workflows between humans and robots. Key technical trends in her work include formal methods for system design, integration of AI/ML in robotics, and human-centered approaches to interface development. Her publications address both foundational theory (e.g., Petri Nets) and applied solutions (e.g., cinematic automation). Current lab projects emphasize robustness in robotic groupware and cross-modal interaction systems. As part of the Robotics Institute, her work intersects with computer science, engineering, and design disciplines. Her lab collaborates on projects involving drone motion optimization, real-time respiration monitoring via video analysis, and enhancing remote collaboration through robotic systems.

Elisaweta Masserovaمشاهده پروفایل

پژوهشگر ارشد

• Cryptography
• Distributed Systems
• Blockchain Technology
• +۳ مورد دیگر

Elisaweta Masserova is a Research Fellow in the Computer Science Department at Carnegie Mellon University , affiliated with the College of Engineering . She works in the Gates and Hillman Centers , focusing on advanced cryptographic protocols and decentralized systems. Research Interests : Elisaweta specializes in Cryptography , Blockchain Technology , and Secure Multi-Party Computation . Her work addresses challenges in Distributed Systems , including Atomic Swaps , Zero-Knowledge Proofs , and Privacy-Preserving Algorithms . Recent Publications highlight her contributions to scalable cryptographic frameworks, blockchain-based secret management, and collusion-resistant protocols. While no awards or student advisement details are provided, her research intersects Theoretical Computer Science , Security , and Applied Cryptography .

Martin Prammerمشاهده پروفایل

پژوهشگر ارشد

• Database Systems
• Computer Architecture
• Data Analytics
• +۳ مورد دیگر

Martin Prammer is a Post Doctoral Fellow at the Computer Science Department of Carnegie Mellon University. His research focuses on database systems, computer architecture, and data analytics acceleration through hardware-software co-design. Office: Gates and Hillman Centers, Room 9118 Email: mprammer@cmu.edu Dr. Prammer's work explores innovative approaches to database optimization, including: Algorithmic-hardware co-design for dense retrieval systems DRAM-PIM (Processing-in-Memory) integration for analytics acceleration Functional decomposition of storage formats Efficient integer encoding for skewed data processing His research has been applied to both theoretical advancements and practical implementations in SQLite optimization and mobile application testing. Current projects demonstrate strong connections between database engineering, computer architecture, and software systems research.

Ioannis Anagnostidesمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Game Theory
• Optimization Algorithms
• +۳ مورد دیگر

Ioannis Anagnostides is a Doctoral Research Assistant in the Computer Science Department at Carnegie Mellon University, advised by Tuomas Sandholm. His research focuses on the intersection of Artificial Intelligence, Game Theory, and Optimization Algorithms. He explores topics such as no-regret learning dynamics, equilibrium computation in games, and algorithmic efficiency in multi-agent systems. His work bridges theoretical foundations and practical applications, addressing challenges in computational complexity, algorithm design, and equilibrium analysis. Anagnostides' research has contributed to advancing understanding of game dynamics, welfare maximization, and the interplay between social welfare and computational tractability. His publications highlight contributions to variational inequalities, min-max optimization, and learning in extensive-form and zero-sum games. These studies often emphasize algorithmic convergence, computational efficiency, and the development of polynomial-time solutions for complex problems.

• Computational Complexity
• Cryptography
• Theoretical Computer Science
• +۳ مورد دیگر

Manuel Blum is the Bruce Nelson Professor of Computer Science at Carnegie Mellon University's School of Computer Science, Department of Computer Science. He is renowned for foundational contributions to computational complexity, cryptography, and theoretical computer science. His work includes pioneering research on CAPTCHAs, human computation, and self-testing programs. Blum has collaborated extensively with figures like Luis von Ahn, contributing to projects like Peekaboom and Verbosity. His research also extends to philosophical inquiries into consciousness, blending computer science with neuroscience and philosophy. Blum's teaching includes courses on theoretical cryptography (15-503) and undergraduate complexity theory (15-455). He has advised students such as Brendan Juba and Matt Humphrey, whose work explores computational complexity and related fields. His research spans grants on machine learning, game theory, and computational complexity, including an NSF proposal on understanding concept understanding. Blum's contributions to security, randomness, and algorithm design have had lasting impacts on computer science.

Tuomas Sandholmمشاهده پروفایل

استاد

• Artificial Intelligence
• Game Theory
• Algorithms and Complexity
• +۱۲ مورد دیگر

Tuomas Sandholm is the Angel Jordan University Professor of Computer Science at Carnegie Mellon University, where he also holds appointments in the Machine Learning Department, the Ph.D. Program in Algorithms, Combinatorics, and Optimization, and the CMU/University of Pittsburgh Joint Ph.D. Program in Computational Biology. He serves as Co-Director of CMU AI and Director of the Electronic Marketplaces Laboratory. Dr. Sandholm's research spans artificial intelligence, economics, and operations research, with particular focus on algorithms and complexity, game theory, machine learning, and electronic commerce. His work involves developing incentive-compatible market mechanisms and efficient algorithms for executing those mechanisms, as well as designing software agents that act optimally in electronic marketplaces. His research has led to significant commercial applications, including systems fielded by CombineNet, Inc. that handled over $60 billion in spend. Sandholm's recent publications reveal a strong emphasis on solving large-scale games, equilibrium computation, and applying these techniques to real-world problems. His work shows a progression from theoretical foundations to practical implementations, with applications ranging from poker AI (Libratus and Pluribus) to kidney exchange systems. His research group has pioneered techniques in game abstraction, regret minimization, and equilibrium computation that have pushed the boundaries of what's possible in imperfect-information games. Vannevar Bush Faculty Fellowship (2023) AAAI Award for AI for the Benefit of Humanity (2023) IJCAI John McCarthy Award (2021) Robert S. Engelmore Award (2021) Minsky Medal (2019) Science Breakthrough of the Year Runner-Up (2019) As an advisor, Sandholm has mentored numerous PhD students who have gone on to prestigious positions at institutions like MIT, Stanford, and CMU. His lab has secured substantial research funding for projects including solving large-scale games and heart transplantation policy optimization. The Electronic Marketplaces Laboratory he directs has been instrumental in developing algorithms that run the national kidney exchange for UNOS, resulting in approximately 10,000 life-saving transplants. Sandholm is also the founder and CEO of multiple companies including Strategy Robot, Inc., Strategic Machine, Inc., and Optimized Markets, Inc., which apply his research to defense, intelligence, business strategy, and advertising markets.

Srinivasan Seshanمشاهده پروفایل

استاد

• Distributed Systems
• Mobile Computing
• Networking
• +۱ مورد دیگر

Srinivasan Seshan is Joseph Traub Professor of Computer Science and Department Head of the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on network protocols, distributed network applications, and next-generation network architectures. His primary research explores next-generation network architectures like the eXpressive Internet Architecture (XIA) for robust internet evolution and video content delivery optimization. A secondary focus examines systems challenges in mobile computing, including power-efficient data collection, scalable infrastructure design, and privacy management for smartphone-based environmental observations. As a prominent researcher in distributed systems, Seshan has made significant contributions to transport protocols, sensor networking, and firewall design. His publications demonstrate consistent focus on network architecture innovation and mobile systems optimization.

Riad Wahbyمشاهده پروفایل

استادیار

• Cryptography
• Zero-Knowledge Proofs
• Secure Computation
• +۱ مورد دیگر

Riad Wahby is an Assistant Professor in the Electrical and Computer Engineering department at Carnegie Mellon University's College of Engineering, with affiliations in the Computer Science Department. His research focuses on theoretical computer science with applications in cryptography and security systems. Primary research areas include: Zero-knowledge proof systems and SNARKs Secure multiparty computation Cryptographic protocol design Formal verification methods His publications demonstrate consistent innovation in cryptographic compilers, constraint systems for SNARKs, and practical applications of zero-knowledge proofs. Recent work highlights include advancements in finite-field verification, decentralized auction systems, and side-channel attack analysis. Current advising includes graduate students Quang Dao and Kunming Jiang. Teaching includes courses in computer systems and cryptography (15-330).

David Woodruffمشاهده پروفایل

استاد

• Algorithms and Complexity
• Machine Learning
• Theoretical Computer Science
• +۵ مورد دیگر

David Woodruff is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on algorithms, complexity theory, machine learning, and theoretical computer science. He is particularly known for contributions to communication complexity, data stream algorithms, graph algorithms, and numerical linear algebra. His work bridges foundational theory with practical applications in privacy, security, and efficient computation. Research Interests: Algorithms and Complexity Machine Learning Data Stream Algorithms Communication Complexity Sketching and Randomized Numerical Linear Algebra Recent publications emphasize adversarial robustness in sketching, space-efficient streaming algorithms for graph problems, and eigenvalue approximation techniques. His work on privacy-preserving GWAS and transformer optimizations showcases interdisciplinary impact. He advises three PhD students and teaches courses in algorithms (15451, 15651, 15851). Advising and Grants: Active mentor to Honghao Lin, Hoai-An Nguyen, and Madhusudhan Pittu. His research has been supported by grants focusing on streaming algorithms, communication complexity, and machine learning foundations.

Zhihao Jiaمشاهده پروفایل

استادیار

• Artificial Intelligence
• Systems
• Quantum Computing
• +۳ مورد دیگر

Zhihao Jia is an Assistant Professor in the Computer Science Department at Carnegie Mellon University (CMU). He is affiliated with the CMU Catalyst Group and the Parallel Data Lab, focusing on advancing systems for machine learning, quantum computing, and large-scale data analytics. Previously, he was a research scientist at Facebook and earned his PhD from Stanford University (2020), advised by Alex Aiken and Matei Zaharia. His bachelor's degree is from Tsinghua University's Special Pilot CS Class under Andrew Yao. His research emphasizes accelerating deep learning computations on modern hardware and optimizing quantum circuits for intermediate-scale quantum devices. Notable contributions include speculative reasoning techniques, efficient LLM serving systems, and quantum circuit simulators. He teaches advanced courses such as 15418 and 15618 at CMU. Zhihao advises PhD students including Zhuoming Chen, Zikun Li, and Xinhao Cheng. His work bridges systems research with emerging applications, aiming to enhance computational efficiency and scalability. He collaborates on projects like Specexec, Helix, and Atlas, addressing challenges in distributed computing and quantum simulation.

Eric Xingمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Bioinformatics
• +۳ مورد دیگر

Eric Xing is a Professor in the Machine Learning Department at Carnegie Mellon University, with an affiliation to the Computer Science Department. His research focuses on advancing artificial intelligence, particularly in machine learning, bioinformatics, and interdisciplinary applications. He advises three students: Aviv Bick, Lingjing Kong, and Yonghao Zhuang. His recent work spans cutting-edge topics such as graph neural networks, generative AI, biosecurity safeguards, and scalable language model evaluation. Notable contributions include foundational models for RNA prediction, efficient video diffusion systems, and ethical frameworks for AI deployment. He actively bridges AI with fields like genomics, healthcare, and urban planning through collaborative projects. Xing’s research often emphasizes practical applications, such as edge computing-optimized inference systems and open mathematical corpora. His work has implications for both technical advancements and societal challenges, reflecting a commitment to impactful, interdisciplinary AI research.