جست‌وجوی استاد و پژوهشگر

Diyi Yangمشاهده پروفایل

استادیار

• Computational Social Science
• Natural Language Processing
• Socially Aware NLP
• +۴ مورد دیگر

Dr. Diyi Yang is an Assistant Professor in the Computer Science Department at Stanford University. She leads the Social and Language Technologies (SALT) Lab, affiliated with the Stanford NLP Group, Stanford HCI Group, Stanford AI Lab (SAIL), and Stanford Human-Centered Artificial Intelligence (HAI). Her research focuses on socially aware natural language processing, large language models (LLMs), and human-AI interaction, aiming to improve human-human and human-computer communication through socially grounded AI systems. Education: Ph.D. in Language Technologies Institute, Carnegie Mellon University (2013–2019) B.S. in ACM Honored Class, Shanghai Jiao Tong University (2009–2013) Research Interests: Dr. Yang’s work bridges computational social science and NLP. She explores how AI can understand social contexts in language use and develop systems that respect cultural norms, ethical standards, and human values. Her lab’s projects include AI companions for skill training (e.g., Rehearsal Dialects), norm-aware LLMs (NormBank), and frameworks for human-AI collaboration (Co-Gym). Recent efforts address bias in AI, societal impacts of LLMs, and ethical evaluation of human-AI systems. Awards & Honors: 2024: Sloan Research Fellowship, ONR Young Investigator Award 2023: Adamic-Glance Young Distinguished Award (ICWSM), Kavli Fellow (NAS) 2022: NSF CAREER Award, Microsoft Research Faculty Fellow 2020: IEEE AI’s 10 to Watch Advising & Grants: Dr. Yang advises over 10 PhD students and postdocs, co-leading projects on LLM evaluation (SWE-bench/SWE-smith), human-AI ethics, and culturally aware NLP. Her research is supported by NSF, Amazon, DARPA, Google, and Stanford’s HAI initiative. Labs & Teams: The SALT Lab collaborates across disciplines, with projects spanning computer science, linguistics, and social sciences. Current initiatives include developing AI tools for mental health support (AI Partner & Mentor) and auditing societal impacts of LLMs.

Roland Büchiمشاهده پروفایل

استاد

• Control Systems Engineering
• Robotics
• Artificial Intelligence
• +۴ مورد دیگر

Prof. Dr. Roland Büchi is a Professor at the School of Engineering, Zurich University of Applied Sciences (ZHAW), specializing in control systems engineering and applied AI. His affiliation includes leadership roles in research projects such as the ongoing 'Digital Bridge to Computer Science' initiative. With a career spanning decades, he maintains active research output and industrial collaborations. Research Interests: Büchi focuses on control systems optimization , particularly PID controller tuning using AI methods, system identification, and applications in robotics and drone technology. His work bridges theoretical control theory with practical implementations in mechatronics. Recent efforts explore machine learning for hysteresis modeling and swarm optimization for control systems. Publication Trends: His 15 most recent works (2018-2024) emphasize AI-driven control optimization , drone technology advancements, and engineering education. Dominant themes include PID parameter tuning for time-delayed systems, drone telemetry, and adaptive learning algorithms. A notable shift toward AI/ML applications in control engineering emerged post-2020. Labs and Teams: Büchi collaborates with researchers like Lukas Gruber and has historical ties to ETH Zurich’s robotics projects. His work involves experimental validation at ZHAW’s engineering facilities, with patents in turbocharger magnetic bearing systems.

Amitabha Bagchiمشاهده پروفایل

استاد

• Data Algorithmics
• Probability and Networks
• Theoretical Computer Science
• +۷ مورد دیگر

Amitabha Bagchi is a Professor in the Department of Computer Science and Engineering at IIT Delhi. His research spans data algorithmics, probability, networks, and theoretical computer science, with applications in distributed systems, social networks, and AI-driven platforms. He has published extensively in leading venues such as SIGMOD, VLDB, ICDE, AAAI, and KDD, often collaborating with students and researchers on problems involving graph algorithms, fairness, and large-scale data analysis. Research Interests: His primary research interests include Data Algorithmics, Probability and Networks, Theoretical Computer Science, Distributed Algorithms, Graph Algorithms, and Machine Learning Theory. He investigates algorithmic foundations for real-world problems such as food delivery optimization, social network analysis, and efficient data structures for streaming and large graphs. Publication Trends: Recent publications focus on fairness in gig economy platforms, efficient solvers for graph Laplacians, generalization in neural networks, and temporal graph querying. His work combines theoretical rigor with practical impact, often involving GPU acceleration, distributed computing, and data-aware algorithm design. Scientific Service: Editor, Algorithms (2020–present) Editor, Journal of Discrete Algorithms , Elsevier (2006–2018) Guest Editor, special issue on Algorithms for Shortest Paths in Dynamic and Evolving Networks , Algorithms (2021) Volume Editor for proceedings of ESA, ATMOS, COCOON, and others Conference Leadership: He has served on numerous program committees and as chair for conferences including ESA (Engineering Track, 2016), ATMOS (2020), and ICALP (2019). His involvement spans algorithmic engineering, transportation optimization, and theoretical computer science forums. Teaching: He currently teaches COL863: Special Topics in Theoretical Computer Science on concentration inequalities and their applications. He has previously taught advanced courses in algorithms and data structures.

• Robotics
• Robot Learning
• Machine Learning
• +۷ مورد دیگر

Lerrel Pinto is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University (NYU), where he leads the General-purpose Robotics and AI Lab (GRAIL) as part of the CILVR research group. His work bridges the gap between theoretical machine learning and practical robotics applications, with a focus on enabling robots to generalize and adapt in real-world environments. Dr. Pinto received his undergraduate degree from IIT Guwahati, followed by a PhD from the Robotics Institute at Carnegie Mellon University (CMU). He then completed a postdoctoral fellowship at the University of California, Berkeley before joining NYU as faculty. His research program centers on robot learning and decision making, with several key thrusts that demonstrate his innovative approach to robotics. Pinto's work emphasizes large-scale learning techniques that leverage both extensive data and sophisticated model architectures. A significant portion of his research focuses on representation learning for sensory data, particularly developing methods that enable robots to make sense of visual, tactile, and auditory inputs. His lab has made notable contributions to reinforcement learning algorithms that allow robots to adapt to new scenarios with minimal retraining. Pinto also champions open-source robotics , developing affordable robot platforms that democratize access to robotics research. Analysis of Pinto's recent publications reveals a strong trend toward multimodal perception in robotics, integrating visual, tactile, and auditory information to create more robust robot systems. His work increasingly focuses on zero-shot and few-shot learning capabilities, enabling robots to handle novel situations without extensive retraining. There's also a clear progression toward general-purpose robotics , moving away from task-specific solutions toward more flexible systems that can handle diverse real-world challenges. Dr. Pinto's scientific contributions have been recognized with several prestigious awards: Sloan Research Fellowship (2025) NSF CAREER Award (2024) RAL Early Career Award (2024) Best Student Paper Award at ICRA (2016) Outstanding Paper Award at MFM-EAI workshop at ICML (2024) Best Paper Award at NGSM workshop at ICML (2024) Best Student Paper Award at RSS (2023) As an advisor, Pinto has mentored numerous students who have gone on to impactful careers in both academia and industry. His former PhD student Denis Yarats co-founded Perplexity.AI, while Mahi Shafiullah became a postdoc at UC Berkeley and Meta AI. Many of his Masters students have pursued PhDs at top institutions like CMU, MIT, and Stanford, or joined leading robotics companies including 1X, Fauna Robotics, and NVIDIA. Pinto's lab has secured significant research funding, including the NSF CAREER award and likely other grants supporting his robotics research program. The General-purpose Robotics and AI Lab (GRAIL) that Pinto leads brings together a diverse team of researchers working on cutting-edge robotics challenges. The lab maintains strong collaborations with industry partners and other academic institutions, facilitating technology transfer and real-world impact. GRAIL's research spans multiple robotics platforms and focuses on developing algorithms that enable robots to learn from diverse experiences and generalize across environments.

Bo Daiمشاهده پروفایل

استادیار

• Reinforcement Learning
• Machine Learning
• Data-Driven Decision Making
• +۳ مورد دیگر

Bo Dai is an Assistant Professor at the School of Computational Science and Engineering, Georgia Institute of Technology, and a Staff Research Scientist at Google DeepMind. His research focuses on Agent AI, Generative Models, and Representation Learning, aiming to create decision-making agents through world modeling. He holds a Ph.D. from Georgia Tech (2013–2018) and previously worked at Google Brain. Dai has authored numerous influential papers in top conferences like NeurIPS, ICML, and ICLR, and received the AISTATS Best Paper Award (2016). Education: Ph.D., School of Computational Science and Engineering, Georgia Tech (2013–2018) Research Interests: Reinforcement Learning and Representation Learning for decision-making agents Generative Models and their integration with Agent AI Provable and scalable algorithms for real-world applications His work bridges theory and practice, emphasizing spectral representations and provable guarantees in complex systems. Key Article Trends: His recent work emphasizes scalable spectral methods for multi-agent systems, diffusion policies, and representation-based techniques in reinforcement learning. He also explores LLM alignment and sim-to-real transfer learning. Awards: AISTATS Best Paper Award (2016) NeurIPS Workshop Best Paper (2017) Ross Fellowship (2011–2012) Advising & Grants: Supervises 6 current Ph.D. and M.S. students. Active in organizing workshops on reinforcement learning and serves as an Area Chair for top conferences. Labs & Software: Leads development of Representation-based Reinforcement Learning and Repr-Control toolboxes for nonlinear control and stochastic systems.

Christopher G. Atkesonمشاهده پروفایل

استاد

• Humanoid robotics
• Human aware environments
• Machine learning
• +۱۳ مورد دیگر

Christopher G. Atkeson is a Professor at the Robotics Institute at Carnegie Mellon University (CMU), where he has been since 2000 after previously holding positions at MIT and Georgia Institute of Technology. His research focuses on fulfilling the science fiction vision of machines achieving human levels of competence in perception, cognition, and action, with particular emphasis on understanding how to get machines to generate and perceive human behavior. Atkeson's work spans two complementary approaches: humanoid robotics and human aware environments. His research interests include nonparametric learning, memory-based learning, reinforcement learning, learning from demonstration, and modeling human behavior. He is particularly known for his work on robot learning of challenging dynamic tasks such as juggling, trajectory-based optimization, and soft robotics (including his contributions to the Baymax character in Disney's Big Hero 6). His recent publications demonstrate a strong focus on tactile sensing (FingerVision), human-in-the-loop optimization for exoskeletons, deep learning for locomotion control, and trajectory-based optimization methods. His work consistently bridges theoretical foundations in machine learning with practical implementations on physical robots. Among his scientific recognitions are an NSF Presidential Young Investigator Award, a Sloan Research Fellowship, and a Teaching Award from the MIT Graduate Student Council. Atkeson has advised numerous students who have gone on to successful careers in academia and industry, including notable researchers like Andrew Moore and Stefan Schaal. His teaching includes courses on dynamic optimization, humanoids, kinematics, dynamics, and control.

• Plant Biotechnology
• Molecular Biology
• Synthetic Biology
• +۵ مورد دیگر

Jun Hyung Lee is a Visiting Assistant Professor in the Department of Environmental Biology at SUNY College of Environmental Science and Forestry (ESF). His research focuses on advancing forest tree improvement and conservation through molecular and synthetic biology approaches, with a particular emphasis on enhancing plant resilience to environmental stresses via beneficial microbial interactions. He teaches courses in plant biotechnology and tissue culture methods. Education includes a Ph.D. in Forest Genetics from Purdue University (USA), and M.S. and B.S. degrees in Plant Science from Seoul National University (South Korea). His work integrates cutting-edge genetic engineering techniques with ecological studies to address challenges in plant stress tolerance, symbiosis, and epigenetic regulation. Recent projects include identifying novel symbiosis pathways for thermotolerance and analyzing flooding tolerance in hybrid poplars. Publications highlight contributions to plant-microbe interaction research, synthetic biology applications, and genome editing epigenetic impacts. Collaborations span institutions like Oak Ridge National Laboratory and the University of Georgia, reflecting his transdisciplinary approach to plant science. Lee’s teaching emphasizes practical skills in biotechnology, bridging laboratory innovation with field applications.

Avi Turetskyمشاهده پروفایل

استاد مدعو

• Private Equity
• Portfolio Management
• Quantitative Research
• +۳ مورد دیگر

Avi Turetsky is an Adjunct Professor at the Weatherhead School of Management (Case Western Reserve University) and a Research Fellow in private equity. Currently a Partner and Co-Head of the Quantitative Research Group (QRG) at Ares Management, he oversees original research, quantitative software development, and tools production teams. Previously served as Chief Operating Officer for The Riverside Company's Europe Fund and holds advisory roles at EDHEC and INSEAD . Research Focus: Turetsky’s work bridges private equity practice and quantitative finance, examining distributional patterns in investment outcomes, competency frameworks for financial professionals, and mathematical models for performance evaluation. His recent publications explore robust statistical methods in portfolio construction, direct alpha calculations for skill assessment, and sector-based value creation strategies in private equity-owned companies. Key Article Trends: His research spans 2016–2023, emphasizing quantitative methodologies in private equity, including skew analysis , value creation metrics , and competency clustering . Topics range from alliance changes in intercollegiate athletics to mathematical modeling of investment professional performance. Leadership & Teams: At Ares Management, Turetsky co-leads the QRG, managing cross-functional teams in Original Research , Quantitative Software Development , and Tools Production . He collaborates with investment teams to integrate quantitative strategies into decision-making processes.

• Educational Technology
• Informatics Education
• Programming Pedagogy
• +۳ مورد دیگر

Snježana Babić serves as Associate Professor and Head of the Graduate Study Program Informatics - Teaching Major at the Faculty of Informatics in Pula , University of Pula. With a PhD obtained in 2016 and university affiliation since 2017, she teaches across undergraduate and graduate programs focusing on ICT fundamentals, web design, digital competencies, and informatics education methodology. Her educational background includes graduation in 1993 followed by doctoral studies culminating in 2016. Though specific institutions aren't detailed in the text, her career trajectory shows progression from foundational ICT teaching to specialized informatics education leadership. Babić's research centers on educational technology adoption with particular emphasis on programming pedagogy for young learners through the MEMA method. Her work investigates digital competencies across multiple educational contexts, from primary schools to higher education. Recent publications demonstrate rapid adaptation to emerging technologies, with 2025 studies examining ChatGPT integration in Croatian educational settings and cross-cultural applications of programming methods. Analysis of her 15 most recent publications reveals consistent focus on technology acceptance models applied to Croatian educators and students. The research trajectory shows evolution from foundational e-learning systems (2016-2020) to cutting-edge AI and robotics applications (2022-2025), maintaining strong methodological focus on teacher motivation , implementation barriers , and culturally-specific adoption factors . Her teaching portfolio demonstrates deep integration with research interests: Undergraduate: ICT Fundamentals, Web Design and Application Graduate: Digital Competencies in Education, Informatics Education Methodology, Practicum for IT Educators Cross-disciplinary: ICT in Nursing, Healthcare Informatics Babić actively contributes to institutional leadership as program head while maintaining robust research productivity. Her work bridges theoretical technology adoption frameworks with practical implementation challenges in Croatian educational contexts, particularly examining how emerging technologies like humanoid robots and AI assistants can be effectively integrated into teaching practices.

• Decision Analysis
• Behavioral Decision-Making
• Neuroeconomics
• +۵ مورد دیگر

Franz Heukamp is the Director General of IESE Business School at the University of Navarra and Professor in the Department of Decision Analysis. He holds the Antonio Valero Chair of Business Administration since 2016 and previously served as Vice Dean of the MBA program and Secretary General of IESE. His academic background includes a Ph.D. in Engineering from the Massachusetts Institute of Technology, an Ingénieur Civil des Ponts et Chaussées from Ecole Nationale des Ponts et Chaussées in Paris, and a Diplom-Ingenieur from Technische Universität München. Professor Heukamp's research focuses on behavioral decision-making with a special interest in neuroeconomics, examining how people make decisions under uncertainty, particularly regarding time-probability trade-offs. His work bridges business decision theory with neuroscience, as evidenced by his publications in journals like Management Science , European Neuropsychopharmacology , and British Journal of Pharmacology . In recent years, he has expanded his research interests to include artificial intelligence applications in management and sustainability. His recent scholarly output shows a progression from fundamental decision theory research to practical applications of AI in business leadership and management education. He has published extensively on risk preferences, intuition in decision making, and the neurological basis of economic choices, with increasing focus on how digital transformation and AI are reshaping management practices. As Director General, Heukamp has championed several strategic initiatives including internationalization efforts across North America, Latin America, and Central Europe, development of new degree programs like the Master in Management and Munich Executive MBA, and integration of digital learning technologies while maintaining the school's commitment to in-person educational excellence. Professor Heukamp is an active member of both the Institute for Operations Research and the Management Sciences and the Neuroeconomics Society, reflecting his interdisciplinary approach to business education and research. His leadership prioritizes supporting faculty research, advancing lifelong learning solutions for executives, and maintaining IESE's mission of service to society through academic innovation.

Karthik R. Narasimhanمشاهده پروفایل

استاد

• Language Agents
• Reinforcement Learning
• Natural Language Processing
• +۳ مورد دیگر

Karthik R. Narasimhan is a Professor at Princeton University's School of Engineering and Applied Science in the Department of Computer Science. Previously, he earned his PhD from MIT under Regina Barzilay and served as a visiting research scientist at OpenAI during 2017-18. His research focuses on the intersection of language and decision-making, building autonomous agents that learn from both experience and human knowledge. His research spans multiple high-impact areas including language agents (Text-DQN, CALM, ReAct, Tree of Thoughts), reinforcement learning (h-DQN, Multi-Objective RL), and AI safety (Toxicity in ChatGPT, DataMUX). He has developed critical datasets and benchmarks such as WebShop, InterCode, SWE-bench, and SILG that have become standard evaluation tools in the field. Current work emphasizes agent capabilities, software engineering automation, and multimodal interaction. His publication trends show strong focus on practical agent deployment (SWE-agent, Tree of Thoughts), safety evaluation (Probing AI Safety), and efficiency improvements (DataMUX). Recent work increasingly addresses real-world challenges in software engineering, security, and human-AI collaboration through rigorous benchmarking. Co-author of foundational GPT (2018) paper Key developer of Text-DQN (2015), CALM (2020), ReAct (2022), Tree of Thoughts (2023) Creator of influential benchmarks: WebShop (2022), SWE-bench (2023), InterCode (2023) He actively advises students through Princeton's computer science program, with research supported by multiple grants focused on autonomous agent development and language-based decision systems. His GitHub repositories (nlp-datasets, text-world-player) demonstrate strong community engagement in open-source research tools. Current projects include advancing language agent capabilities through Reflexion (2023) and Tree of Thoughts (2023) frameworks while addressing critical safety and efficiency challenges.

Verena Zimmermannمشاهده پروفایل

استادیار

• Usable Security
• Privacy
• Human-Computer Interaction
• +۳ مورد دیگر

Verena Zimmermann serves as Assistant Professor (Tenure Track) for Security, Privacy and Society at ETH Zurich within the Department of Humanities, Social and Political Sciences. She leads the Security, Privacy and Society research group, focusing on human-centered approaches to safety, IT security, and privacy challenges in emerging technologies. Her academic foundation includes a PhD in Usable Security from TU Darmstadt, where her dissertation From the Quest to Replace Passwords towards Supporting Secure and Usable Password Creation earned dual awards from the German Association for Data Protection and Data Security (GDD) and the Ernst-Ludwigs-Hochschulgesellschaft. Zimmermann's research pioneers the shift from viewing humans as security vulnerabilities to recognizing them as integral solutions. Her work spans password security, phishing interventions, smart home privacy, and cybersecurity education, consistently applying human factors principles to design systems that align with natural user behavior. Current projects explore emotional dimensions of security engagement, quantum governance frameworks, and LLM-related privacy concerns through interdisciplinary lenses combining computer science, psychology, and safety science. Recent publications reveal strong thematic trends toward understanding user motivations in security behavior, developing personalized training through augmented reality and adaptive systems, and translating safety science principles to cybersecurity incident management. Her work increasingly addresses societal implications of emerging technologies like quantum computing and generative AI. Her distinguished recognitions include: GDD Dissertation Award for Usable Security research Ernst-Ludwigs-Hochschulgesellschaft Dissertation Award IANUS Award (2nd place) for Moving from a 'Human-as-Problem' to a 'Human-as-Solution' Cybersecurity Mindset Zimmermann actively mentors through ETH's Human-Centered Security and Privacy Lab while collaborating internationally with institutions including ATHENE (German National Center for Applied Cybersecurity), Abertay University's Cybersecurity Division, and the Safety Science Innovation Lab. Her research receives support from Swiss and European funding bodies focused on privacy-preserving technologies and organizational security behavior. At ETH Zurich, she directs the Security, Privacy and Society group which employs design thinking, LEGO Serious Play, and mixed-methods research to tackle real-world security challenges. The team partners with industry stakeholders to develop practical solutions for phishing resistance, smart home privacy, and cybersecurity education while maintaining strong ties to the Research Training Group 2050 Privacy and Trust for Mobile Users.

• Urban Planning
• Land-Use Regulation
• Urban Development
• +۷ مورد دیگر

Raphaël Fischler is a Full Professor and former Dean of the Faculty of Urban Planning and Landscape Architecture at the University of Montreal. He holds dual expertise in urban planning theory, history, and practice, with a focus on North American urban development, land-use regulation, and governance. His academic roles include teaching at McGill University and serving as Dean from 2018–2023, during which he led strategic initiatives like the Quartier de l’Innovation and addressed urban challenges such as housing affordability and metropolitan governance. **Education:** PhD in Urban Planning, UC Berkeley (1993) Master of City Planning, MIT (1987) Master of Science in Architecture, MIT (1987) Bachelor of Engineering, Eindhoven University of Technology (1984) **Research Interests:** Fischler’s work explores urban planning’s historical foundations, particularly zoning’s evolution, and integrates reflective practice into pedagogy. He advises on municipal projects and contributes to policy debates on density, public participation, and sustainable urbanism. His research bridges academia and practice, emphasizing ethical planning and community engagement. **Articles Trends:** His recent publications analyze zoning’s historical role, social justice in urban redevelopment, and governance challenges in Montréal. He critiques systemic issues like housing affordability and advocates for equitable urban policies. **Awards & Recognition:** Fellow of the Canadian Institute of Planners (2020) Émérite Member, Order of Quebec Urban Planners (2014) McGill Principal’s Excellence in Teaching Award (2013) **Grants & Teams:** He collaborates with institutions like the Centre de recherche interdisciplinaire en études montréalaises (CRIEM) and advises public agencies on metropolitan development. His work spans academic leadership, professional practice, and public advocacy, addressing urban challenges through interdisciplinary approaches. **Labs/Teams:** Affiliated with the CRIEM , focusing on Montréal’s urban history and contemporary planning issues. He mentors students in urban policy and design through studio courses and thesis supervision.

Pamela Hindsمشاهده پروفایل

استاد

• Organizational Behavior
• Human-Computer Interaction
• Cross-Cultural Collaboration
• +۳ مورد دیگر

Pamela J. Hinds is the Rodney H. Adams Professor and Fortinet Founders Chair in Stanford University's School of Engineering, holding a joint appointment in the Department of Management Science and Engineering. She co-directs the Center on Work, Technology, and Organization and serves on the Director's Council for the Hasso Plattner Institute of Design. Her research focuses on technology's impact on teams, collaboration, and innovation in global and cross-cultural contexts. She earned her Ph.D. in Organizational Science and Management from Carnegie Mellon University (1997). Research Interests: Dr. Hinds explores cross-boundary work teams, particularly those spanning national borders, examining cultural dynamics, language barriers, and site visits' role in knowledge sharing. She investigates human-robot interaction in workplaces and emerging technologies' effects on work practices, including AI, 3D printing, and open innovation systems. Her work bridges organizational behavior, human-computer interaction, and global work dynamics. Awards & Recognition: Lasting Impact Award (2018) Senior Editor of Organization Science Co-editor of Distributed Work (MIT Press) Advising & Leadership: As department chair, Hinds shapes educational and research agendas. Her grants include NSF-funded studies on digital technologies in agriculture and crowd-based innovation. She advises initiatives at the intersection of technology, work design, and global collaboration. Labs & Teams: Co-leads the Center on Work, Technology, and Organization, fostering interdisciplinary research on modern work environments. Engages in Stanford's Design School (d.school) to integrate human-centered design principles into workplace innovation.

Stefanie Muellerمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Computational Fabrication
• 3D Printing
• +۷ مورد دیگر

Stefanie Mueller is the TIBCO Career Development Associate Professor at MIT's Electrical Engineering and Computer Science Department, with joint affiliation in Mechanical Engineering. She leads the HCI Engineering Group at the Computer Science and Artificial Intelligence Laboratory (CSAIL), focusing on advancing fabrication techniques through hardware/software innovations that enable novel object interactions. Develops computational fabrication methods combining photochromic dyes, lenticular lenses, birefringent materials, and optical illusions Co-chaired ACM CHI 2023 and ACM UIST 2020 program committees Recipients of 9 MIT EECS Best Undergraduate Researcher Awards among mentees Her research spans four key directions: Appearance-changing Objects: Photo-Chromeleon (ACM UIST 2019), Lenticular Objects (ACM UIST 2021), and Polagons (ACM CHI 2023) demonstrate reprogrammable surfaces through advanced materials and optical engineering. Tracking Systems: InfraredTags (ACM CHI 2022) and G-ID (ACM CHI 2020) enable passive object tracking via infrared markers and slicing artifacts. Embedded Sensing: MechSense (ACM CHI 2023) and Sprayable User Interfaces (ACM CHI 2020) integrate sensing capabilities into complex geometries. Curved Surface Prototyping: FlexBoard (ACM CHI 2023) and CurveBoard (ACM CHI 2020) develop specialized tools for non-planar electronics. Her recent publications focus on functionality segmentation (UIST 2023), fluorescent markers (UIST 2023), and machine-knitted haptics (UIST 2023). These works combine machine learning, material science, and interactive design principles to push fabrication boundaries. Scientific recognition includes: 2022 MIT Technology Review Innovators Under 35 2020 Microsoft Research Faculty Fellowship 2020 Alfred P. Sloan Research Fellowship 2019 ACM UIST Best Paper Award 2019 NSF CAREER Award 2018 MIT EECS Outstanding Educator Award 2017 Forbes 30 Under 30 in Science Mentoring 9 PhD students and over 20 master's students, her lab has produced 20+ publications at top HCI conferences. She redesigned MIT's 6.810 Engineering Interactive Technologies course during the pandemic, maintaining hands-on learning through home electronics kits and Slack-based collaboration.