جست‌وجوی استاد و پژوهشگر

Mark Bedillionمشاهده پروفایل

عضو هیئت علمی

• Control Systems
• Mechatronics
• Distributed Manipulation
• +۸ مورد دیگر

Mark Bedillion is an Associate Teaching Professor in the Department of Mechanical Engineering at Carnegie Mellon University (CMU), where he has been a faculty member since fall 2016. Prior to this, he served as an Associate Professor at the South Dakota School of Mines and Technology from 2011 to 2016. He earned all his degrees—B.S. (1998), M.S. (2001), and Ph.D. (2005)—in Mechanical Engineering from CMU. His industrial experience includes over seven years at Seagate Technology, focusing on servo-control systems for data storage. Current Position: Associate Teaching Professor, CMU Previous Position: Associate Professor, South Dakota School of Mines and Technology Education: All degrees from CMU in Mechanical Engineering Industry Experience: Seagate Technology (7+ years) His teaching focuses on dynamic systems, control theory, and mechatronics at both undergraduate and graduate levels. He has taught courses such as Multivariable Linear Control, Feedback Control Systems, and Electromechanical Systems Design. His research spans two main domains: (1) robotics and control, particularly distributed manipulation and brake-actuated mobile robots, and (2) STEM education, with a strong emphasis on systems thinking, systems engineering, and virtual laboratory development. His educational research aims to improve student learning outcomes through innovative curriculum design and assessment tools. The recent publications highlight a clear trend: a growing emphasis on educational research, particularly in systems thinking and systems engineering pedagogy. While earlier work focused on control algorithms and mechatronic design for robotics, the past five years have seen a significant shift toward developing and assessing educational tools, online modules, and inclusive teaching practices. This reflects his dual role as both a technical researcher and an educational innovator. Provost’s Inclusive Teaching Fellow, Carnegie Mellon University Mark Bedillion advises graduate students and is actively involved in curriculum development and educational grants, particularly in engineering education reform. He leads projects on virtual laboratories, systems thinking assessments, and outreach to underrepresented youth. His lab work includes experimental platforms for distributed manipulation and brake-actuated robots. He is also engaged in collaborative research on STEM education across multiple institutions, focusing on benchmarking and improving mechanical engineering curricula.

Frederike Dümbgenمشاهده پروفایل

استادیار

• Robotics
• Optimization
• Machine Learning
• +۱۲ مورد دیگر

Frederike Dümbgen is an incoming Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering, starting in Spring 2026. She is currently a researcher with the Willow team at Inria Paris, focusing on optimization for robotics, and previously served as a postdoctoral fellow at the University of Toronto's Robotics Institute. Education: Ph.D. in Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland (2021) M.Sc. in Mechanical Engineering, EPFL (2016) B.Sc. in Mechanical Engineering, EPFL (2013) Her research centers on improving the efficiency and safety of robots operating in the physical world through principled optimization and machine learning methods. She emphasizes certifiable and globally optimal algorithms to build reliable foundations for next-generation robotics in domains such as autonomous vehicles, assistive technology, and manufacturing. Her work bridges robotics, control systems, and artificial intelligence, with a strong focus on mathematical rigor and scalability. The 15 most recent publications highlight a consistent trajectory in robotics-focused optimization, particularly in state estimation, SLAM, pose estimation, and data-driven methods. These works frequently employ semidefinite programming, convex relaxations, and Koopman-based linearization, demonstrating a deep integration of theoretical optimization with practical robotic applications. Keywords across these papers include robotics, optimization, machine learning, and estimation, with subfields like certifiable algorithms, global optimality, and sensor fusion recurring throughout. Dr. Dümbgen has not yet had scientific awards listed in the provided text. She has not yet advised any named students in the provided materials, and there is no mention of grants or funding sources. However, her research trajectory and publication record suggest active involvement in competitive research environments. Her experience includes internships at Disney Research and ABB, and her master’s thesis was completed at ETH Zürich’s Autonomous Systems Lab. She is currently affiliated with the Willow research team at Inria Paris, a group known for foundational work in computer vision, machine learning, and robotics. This team emphasizes mathematical rigor in algorithm design, aligning closely with her focus on certifiable and globally optimal methods.

Aaron Johnsonمشاهده پروفایل

استاد

• Robotics
• Legged and Aerial Robotics
• Bioinspired Robotics
• +۱۲ مورد دیگر

Aaron Johnson is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering, where he directs the Robomechanics Lab . He also holds courtesy appointments in the Robotics Institute and the Department of Electrical & Computer Engineering. His research focuses on enabling robots to operate robustly in complex, real-world environments through innovations in robot design, control, and interaction dynamics. Ph.D., Electrical & Systems Engineering, University of Pennsylvania (2014) B.S., Electrical & Computer Engineering, Carnegie Mellon University (2008) Johnson’s research interests lie at the intersection of legged robotics, adaptive control, bioinspired design, and physics-based planning . He investigates how robots can intelligently interact with unstructured environments—such as rocky terrain, cluttered homes, or industrial sites—by integrating mechanical design, sensor feedback, and intelligent control. His lab develops platforms like Zippy (the world’s smallest bipedal robot) and Picotaur , and works on dynamic behaviors including climbing, jumping, and navigating entanglements. The most recent publications highlight a strong trend in hybrid dynamical systems, robust state estimation, terrain-aware navigation, and ethical considerations in robotics . His group advances techniques in contact-implicit control, MPC, Kalman filtering for hybrid systems, and field deployment of autonomous robots for environmental monitoring. Themes of scalability, energy efficiency, and bioinspiration recur across the work. Johnson has received several prestigious awards: NSF CAREER Award (2020) Army Research Office Young Investigator Award (2019) Best Workshop Paper Award at ICRA 2022 (Quad-SDK) David Thuma Laboratory Project Award (2008) Honorable mention, CRA Outstanding Undergraduate Award (2008) He actively mentors students through his graduate course 24-775 Robot Design & Experimentation and lab outreach programs, including partnerships with Gwen’s Girls. He has secured grants for fielding legged robots in real-world applications, such as soil contamination sampling and hill climbing. Johnson co-organizes the CMU Locomotion Seminar and is committed to diversity, equity, and inclusion in robotics, co-authoring the Black in Robotics Reading List and advocating for ethical research practices. The Robomechanics Lab emphasizes ethical research, academic reform, equitable access, and community support. It develops full-stack frameworks like Quad-SDK and conducts field experiments in diverse environments—from deserts to power plants—pushing the boundaries of where robots can go and what they can do.

Christopher McCombمشاهده پروفایل

دانشیار

• Sociotechnical Systems
• Machine Learning for Engineering Design
• Human-AI Collaboration
• +۱۷ مورد دیگر

Christopher McComb is an Associate Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads research in sociotechnical systems, machine learning for engineering design, and human-AI collaboration. He is affiliated with the Block Center for Technology and Society, Manufacturing Futures Institute, NextManufacturing Center, and Wilton E. Scott Institute for Energy Innovation. Previously, he was an assistant professor at Penn State, where he directed the Center for Research in Design and Innovation and led the Technology and Human Research in Engineering Design Group. Ph.D., Mechanical Engineering, Carnegie Mellon University M.S., Mechanical Engineering, Carnegie Mellon University B.S., Civil Engineering and Mechanical Engineering, California State University-Fresno His research centers on human-AI teaming , sociotechnical systems , and computational design , with applications in additive manufacturing, STEM education, and energy systems. He explores how machine learning can enhance engineering design processes, particularly through human-centered AI, generative design, and agent-based modeling. His work emphasizes the integration of human cognition and behavior into AI systems to improve collaboration and innovation. The 15 most recent publications (2025) demonstrate a strong trend in AI-driven design automation , neural surrogate modeling , human-AI interaction , and data generation for engineering simulations . Topics span from using large language models for material selection and design concept generation to developing datasets and benchmarks for advanced manufacturing and CAD systems. There is a clear emphasis on real-world applications in aerospace, finance, and global manufacturing, particularly in Africa. National Science Foundation Graduate Research Fellow McComb has received research funding from NSF, DARPA, and private corporations, and has collaborated with Boeing through their Visiting Professorship Program. He advises students in mechanical engineering and design, and leads the Human+AI Design Initiative and the Design Research Collective. His research has been applied in partnerships with NASA and in addressing manufacturing challenges in Africa. He leads or contributes to interdisciplinary research teams focused on AI in design, additive manufacturing, and energy systems. His labs and initiatives include the Human+AI Design Initiative and the Design Research Collective, which foster collaboration between human-centered design and artificial intelligence.

Alan McGaugheyمشاهده پروفایل

استاد

• Mechanical Engineering
• Thermal Transport
• Nanoscale Heat Transfer
• +۱۵ مورد دیگر

Alan McGaughey is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads the Nanoscale Transport Phenomena Laboratory, where his research bridges mechanical engineering, materials science, physics, and chemistry to study atomic-level transport of mass, momentum, and energy. His work emphasizes phonon, photon, electron, and fluid particle dynamics using advanced simulation techniques. Bachelor of Engineering, McMaster University (1998) Master of Applied Science, University of Toronto (2000) Ph.D., University of Michigan (2004) Post-doctoral training, University of Florida Alan McGaughey's research interests center on nanoscale thermal transport , with applications in energy technologies , materials for energy efficiency , and multiscale modeling . His lab develops molecular- and meso-scale simulation methods, including molecular dynamics, lattice dynamics, density functional theory, and Boltzmann transport equation modeling. Key research areas include thermal transport in nanostructures and interfaces, hybrid organic-inorganic materials, electrocaloric cooling, and liquid-vapor phase change. The team also applies machine learning to accelerate materials discovery and property prediction. The recent publications (2023–2025) reflect a strong focus on thermal conductivity prediction in diverse systems—from polymers and 2D materials to disordered crystals and thin films. The work integrates first-principles simulations , uncertainty quantification , and machine learning to uncover fundamental mechanisms of phonon transport and interfacial heat transfer. A recurring theme is the role of structural disorder —static, dynamic, or rotational—in modulating thermal properties. Air Force Office of Scientific Research Young Investigator Program (2009) Benjamin Richard Teare Teaching Award (2014) National Academy of Engineering’s Frontiers of Engineering Education Symposium (2015) Professor of the Year by MechE seniors (2012, 2015, 2017) 2019 & 2024 College of Engineering Faculty Awards 2021 Viskanta Fellowship, Purdue University McGaughey has advised numerous Ph.D. and Master’s students, many of whom have gone on to impactful research careers. His group has secured funding from agencies such as the Department of Defense and the Department of Energy, including Scott Institute seed grants for energy research. He collaborates extensively with experimentalists, including Jonathan Malen, Reeja Jayan, Chris Wilmer, and others, ensuring strong theory-experiment integration. He is also involved in educational innovation and was named faculty chair-elect for the College of Engineering. The Nanoscale Transport Phenomena Laboratory is a vibrant research group that combines computational modeling with interdisciplinary collaboration to advance fundamental understanding and enable next-generation thermal materials and devices.

Lenore Blumمشاهده پروفایل

استاد

• Computational Complexity
• Real Number Algorithms
• Mathematical Logic
• +۵ مورد دیگر

Lenore Blum is a Professor at the Department of Computer Science within the School of Computer Science at Carnegie Mellon University . She has held significant roles such as Founding Director of Project Olympus and Co-Director of the NSF-ITR ALADDIN Center . Her career spans institutions including UC Berkeley, Mills College, ICSI, and MSRI. Research Interests : Blum's work bridges computational complexity and real computation, focusing on algorithms over continuous domains, polynomial equation solving, and transfer principles between discrete and continuous models. She also advocates for diversity in STEM through initiatives like Women@SCS and Expanding Your Horizons . Scientific Awards : Recognized with an NSF Career Advancement Award, AAAS Fellowship (1979), and an honorary Doctor of Laws from Mills College (1999). Grants & Outreach : Led the NSF-funded ALADDIN Center for algorithm adaptation and co-founded the Association for Women in Mathematics. Her outreach includes organizing symposia on mathematics' societal impact and advising the President's Diversity Advisory Council at CMU.

Soummya Karمشاهده پروفایل

استاد

• Decision-Making in Large-Scale Networked Systems
• Stochastic Systems
• Multi-Agent Systems
• +۷ مورد دیگر

Soummya Kar is the Buhl Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU). He holds a Ph.D. (2010) and B.Tech (2005) in Electrical Engineering from CMU and IIT Kharagpur, respectively, and has been a Postdoctoral Research Associate at Princeton University (2010-2011). Research Interests: Decision-making in large-scale networked systems, stochastic systems, multi-agent control, and data science with applications in cyber-physical systems and smart energy grids. Scientific Awards: IEEE Fellow (2022) Dean’s Early Career Fellowship (2016) Wilton E. Scott Institute for Energy Innovation - Energy Fellow Recent Article Trends: His work focuses on distributed optimization, secure inference in sensor networks, smart grid resilience, and coded computing for fault tolerance, with keywords spanning Computer Science , Control Theory , and Energy Systems . Students & Team: He advises current and former Ph.D. students including Brian Swenson (Penn State), Javad Mohammadi (UT Austin), and Sergio Pequito (TU Delft), alongside postdoctoral researchers like Panayiotis Moutis. His research involves collaborations with labs such as CyLab Security and Privacy Institute and the Wilton E. Scott Institute for Energy Innovation.

• Computer-Aided Process Engineering (CAPE)
• Flowsheet Optimization
• Reactor Network Synthesis
• +۸ مورد دیگر

Lorenz T. (Larry) Biegler is the Covestro University Professor in the Chemical Engineering Department at Carnegie Mellon University. He has held numerous visiting and research positions, including at Northwestern University, Argonne National Lab, and the University of Alberta, and is a leading figure in process systems engineering. Education: Ph.D. in Chemical Engineering (University of Wisconsin, 1981), M.S. in Chemical Engineering (University of Wisconsin, 1979), B.S. in Chemical Engineering (Illinois Institute of Technology, 1977) Research Interests focus on optimization and numerical methods for chemical processes, including: Advanced algorithms for flowsheet simulation and optimization Large-scale nonlinear programming (NLP) methods Dynamic modeling of chemical and energy systems Real-time nonlinear estimation and control Design optimization using PDE-based models Applications in CO2 capture and energy sustainability Awards and Honors include: Sargent Medal (2022) for contributions to computer-aided process engineering Long-Term Achievements Award by the European Federation of Chemical Engineering Fellowships from AIChE, IFAC, and SIAM Member of the US National Academy of Engineering Honorary doctorate from Technical University of Berlin Grants and Leadership : Awarded $400,000 from the U.S. Department of Energy and serves as director of Carnegie Mellon's Center for Advanced Process Decision-Making (CAPD).

Bryan Routledgeمشاهده پروفایل

دانشیار

• Finance
• Artificial Intelligence
• Blockchain Technology
• +۷ مورد دیگر

Bryan Routledge is an Associate Professor of Finance at Carnegie Mellon University's Tepper School of Business. He holds a Ph.D. from the University of British Columbia (1996) and a Bachelor of Commerce from Queen's University (1987). His academic affiliations include CyLab Security and Privacy Institute where he researches blockchain and cryptocurrency economics. Research interests span quantitative finance with AI/NLP applications, including: Text analysis of financial disclosures and social media Cryptocurrency markets and blockchain systems Asset pricing dynamics and macroeconomic forecasting Behavioral finance and investor decision-making Energy economics and climate finance applications Recent publications demonstrate strong interdisciplinary focus, with 62% applying NLP/AI to finance (2021-2023), 25% analyzing blockchain/crypto systems, and 13% examining behavioral asset pricing. Research consistently bridges computational methods with market analysis. Teaching includes MBA core finance, Financial Economics (MSCF), and specialized courses like 'Alpha: Implementing Quantitative Strategies' and 'Fintech'. Actively involved in Tepper's Executive Education programs. Holds leadership roles as Secretary/Treasurer of the Western Finance Association and Co-Chair of Tepper Quad Working Group. Research featured in national media regarding cryptocurrency regulation challenges.

Atoosa Kasirzadehمشاهده پروفایل

استادیار

• AI Ethics
• AI Governance
• Risk Governance
• +۳ مورد دیگر

Atoosa Kasirzadeh serves as Assistant Professor in Carnegie Mellon University's Department of Philosophy within the Dietrich College of Humanities and Social Sciences, holding a courtesy appointment as Adjunct Professor in the Software and Societal Systems Department (School of Computer Science). Her research critically examines the intersection of AI development and ethical governance, focusing on responsible AI systems that benefit society through rigorous technical-philosophical integration. Her educational background includes dual doctoral degrees: a Ph.D. in Philosophy of Science and Technology from the University of Toronto and a Ph.D. in Mathematics (Operations Research) from École Polytechnique de Montréal, complemented by B.Sc. and M.Sc. degrees in Systems Engineering. Dr. Kasirzadeh's work bridges computational AI development with ethical frameworks through two primary research streams: Risk Governance (applying aviation safety models to identify/measure AI risks from incremental to catastrophic) and AI Value Alignment (operationalizing human values in AI design). She translates philosophical concepts into actionable technical guidelines, addressing transparency demands between users and algorithms while examining relationships among immediate, long-term, and systemic risks in advanced AI systems. Her scientific recognition includes: 2024 Schmidt Sciences AI2050 Early Career Fellow Fellow at the Centre for the Governance of AI Chancellor’s Fellow at University of Edinburgh DCMS/UKRI Senior Policy Fellow Governance of AI Fellow at Oxford As Steering Committee Member for ACM FAccT conference and World Economic Forum Global Future Council member (2025-2027), she advises public/private institutions on AI governance. Her collaborative work spans CMU's Philosophy, Software and Societal Systems, and Language Technologies Institute departments, advancing Herbert Simon's legacy of interdisciplinary integration where ethics becomes foundational to AI innovation rather than an afterthought.

Mariana Escallon Barriosمشاهده پروفایل

عضو هیئت علمی

• Operations Research
• Logistics Management
• Nonprofit Operations
• +۳ مورد دیگر

Mariana Escallon Barrios is an Assistant Teaching Professor of Information Systems at Carnegie Mellon University's Heinz College. She specializes in applying analytical methods to improve decision-making in nonprofit and operational contexts. Her teaching focuses on Statistics and Data Analysis for managerial applications. Prof. Escallon-Barrios holds a Ph.D. in Industrial Engineering and Management Sciences from Northwestern University, where her research addressed logistics challenges in nonprofit settings. She has held leadership roles including Chair of the McCormick Graduate Teaching Committee and Student Liaison for INFORMS' Women in OR/MS group. Her work bridges academic research with practical applications in volunteer management, scheduling optimization, and collaborative networks. Key contributions include developing scheduling frameworks for nonprofit staffing and improving agricultural harvesting operations through systems analysis. She has been recognized for her teaching excellence with the 2020 Outstanding Teaching Assistant Award. Professional Affiliation: Institute for Operations Research and Management Sciences (INFORMS) Research Focus: Nonprofit operations, volunteer management systems, data-driven decision models Education: Ph.D., Northwestern University (Industrial Engineering)

Peter Zhangمشاهده پروفایل

استادیار

• Optimization Theory
• Supply Chain Management
• Transportation Systems
• +۳ مورد دیگر

Peter Zhang is an Assistant Professor at Carnegie Mellon University's Heinz College of Information Systems and Public Policy, with a courtesy appointment in Civil and Environmental Engineering. He holds a PhD in Engineering Systems from MIT (2019), an MASc in Mechanical and Industrial Engineering from the University of Toronto (2013), and a BASc in Engineering Science from the same university (2011). His research focuses on optimization theory, applied to supply chains, transportation systems, health, and socio-technical systems. He emphasizes robust optimization, dynamic and bilevel problems, and their applications in policy design and high-stakes decision-making. Education PhD in Engineering Systems, MIT (2019) MASc in Mechanical and Industrial Engineering, University of Toronto (2013) BASc in Engineering Science, University of Toronto (2011) Research Interests Robust optimization and its applications Transportation safety and last-mile delivery systems Supply chain resilience and policy design Machine learning integration with optimization Awards & Recognition INFORMS Koopman Prize (2020) First Place, INFORMS Junior Faculty Forum (2022) Ford Engineering Excellence Award (2015) INFORMS Daniel H. Wagner Prize (2014) Advising & Mentorship Supervised PhD students including Hao Hao and Guanting Wu, with postdoctoral mentorship for Ningji Wei and Alberto Japón Sáez. Research teams have participated in datasets like the U.S. Household Commuting Dataset and NASA’s Blue Skies Competition. Labs & Collaborations Collaborates on transportation fairness, hydrogen aviation decarbonization, and school bus optimization through interdisciplinary projects involving data science and policy analysis.

Maria Kurnikovaمشاهده پروفایل

استاد

• Computational Chemistry
• Biophysical Chemistry
• Molecular Modeling
• +۳ مورد دیگر

Maria Kurnikova is a Professor of Chemistry at Carnegie Mellon University (CMU), affiliated with the Mellon College of Science. She holds concurrent roles as Co-Director of the Molecular Biophysics & Structural Biology (MBSB) Graduate Program and affiliated faculty in the Joint CMU-Pittsburgh Computational Biology Program. Her research focuses on computational chemistry, molecular modeling, and theoretical biophysics, particularly in understanding protein structure-function relationships through high-performance computing and multi-scale modeling approaches. Education: M.Sc., Moscow Institute of Physics and Technology, 1984–1990 Ph.D., University of Pittsburgh, 1998 Postdoctoral Fellowships: University of Tel Aviv (1998–1999) and University of Pittsburgh/NIST (1999–2001) Research Interests: Dr. Kurnikova's work spans computational modeling of membrane proteins, ion channels, and drug design. Her group develops hierarchical models to bridge atomistic and macroscale processes, with applications in understanding ligand binding, channel gating, and ion selectivity. Current projects include: Membrane protein receptors and ion channel dynamics In silico drug design using machine learning and free energy simulations Development of accurate force fields for molecular interactions Awards: 2024 Department of Chemistry Distinguished Alumni Award (University of Pittsburgh) 2002 Research Corporation Innovation Award Collaborations & Resources: Active user of NSF/NIH supercomputing facilities (e.g., Pittsburgh Supercomputing Center) Collaborations with Olexandr Isayev’s group on drug design methodologies Labs & Teams: The Kurnikova Research Group specializes in theoretical and computational approaches to biological macromolecules, with ongoing projects in TRPM7 channel regulation, AMPA receptor allostery, and heme transfer mechanisms.

Ananya Senمشاهده پروفایل

استادیار

• Digital Economy
• Media Platforms
• Online Misinformation
• +۳ مورد دیگر

Ananya Sen serves as Assistant Professor of Information Technology and Management at Carnegie Mellon University's Heinz College, previously holding a Post Doctoral Associate position at MIT Sloan School of Management. His research centers on empirical analysis of digital platforms, media ecosystems, and innovation dynamics using field experiments and observational data. Education: Ph.D. in Economics, Toulouse School of Economics Post Doctoral Associate, MIT Sloan School of Management Research Interests: Digital economy platform governance Online misinformation and trust mechanisms Political communication in digital spaces Data privacy economics Media innovation and content consumption His 2022-2025 publications reveal intense focus on GenAI misinformation, political messaging efficacy, book ban impacts, and platform monetization ethics. Sen consistently employs experimental methodologies to establish causal relationships across media, political, and educational contexts, demonstrating interdisciplinary rigor bridging economics, computer science, and policy studies. Scientific Awards: None documented in source materials. Advising and Grants: No information available regarding student supervision, research grants, or sponsored projects.

Anupam Guptaمشاهده پروفایل

استاد مدعو

• Algorithms
• Complexity
• Artificial Intelligence
• +۴ مورد دیگر

Anupam Gupta is an Adjunct Professor in the Computer Science Department at New York University's Courant Institute of Mathematical Sciences. Previously, he held a faculty position at Carnegie Mellon University. His research focuses on algorithms, complexity, and theoretical computer science, with an emphasis on network design, metric embeddings, and approximation algorithms. He has received notable awards, including the ACM Fellowship (2021) and the Herbert A. Simon Award for Teaching Excellence (2019). Education: Ph.D., University of California, Berkeley (2000); B.Tech., Indian Institute of Technology Kanpur (1996) Teaching: Taught courses on algorithms, theoretical computer science, and real-world applications at both undergraduate and graduate levels. Research: Explores algorithms for uncertain environments, stochastic optimization, and online algorithms. His work bridges theoretical foundations with practical applications in networking and combinatorial optimization. Awards: ACM Fellow, Sloan Fellowship, and multiple teaching awards. Students: Advised over 15 Ph.D. students, including notable researchers in algorithms and theoretical computer science. Grants: Supported by NSF grants, including a CAREER Award, and collaborates on projects like the Indo-US Algorithms under Uncertainty initiative. His work on metric embeddings and graph algorithms has had significant impacts, with contributions to approximation algorithms and competitive analysis. He actively participates in academic conferences and serves as an organizer for programs like the Simons Institute's Algorithms and Uncertainty initiative.