جست‌وجوی استاد و پژوهشگر

Gierad Laputمشاهده پروفایل

استاد مدعو

• Human-Computer Interaction
• Machine Learning
• Sensing Systems
• +۷ مورد دیگر

Gierad Laput is a Senior Engineering Manager at Apple, leading the Perception and Interaction Engineering organization under Machine Intelligence Neural Design (MIND) at Apple AI/ML (AIML). He is also an Adjunct Professor at Carnegie Mellon University's School of Computer Science and Human-Computer Interaction Institute. His research sits at the intersection of sensing systems, interactive technologies, and machine learning, with applications in ambient intelligence, wearables, and AI-driven interactive experiences. Ph.D. in Computer Science from Carnegie Mellon University Founding member of CMU's Future Interfaces Group Former Google Ph.D. Fellow, Disney Imagineer, and recipient of 10 Best Paper Awards/Nominations His research agenda at Apple leverages cutting-edge hardware to develop multimodal ML systems for vision, audio, and motion sensors, enabling features like head gesture detection for AirPods, Apple Intelligence integrations, and health monitoring tools. Recent collaborative projects include Vision Pro hand interaction systems and WatchOS updates for cycling/fall detection. Key research areas include human-computer interaction , machine learning for sensor fusion , and smart environment sensing . Over 35 publications cover transformative technologies like ViBand (bio-acoustic sensing), EM-Sense (touch recognition), SkinTrack (arm as touchpad), and ElectroDermis (stretchable electronics). Scientific honors include: Fast Company Innovation by Design Award (2017) Google PhD Fellowship (2018) Swartz Entrepreneurial Fellowship (2018) Best Paper Awards at ACM UIST (2016) and ACM CHI (2015) As an educator, he has taught courses at CMU and the University of Michigan on on-device machine learning , signal processing , and human-centered design . His lab has mentored numerous researchers now pursuing PhDs at MIT, UW, CMU, and Northwestern.

Kenji Kawaguchiمشاهده پروفایل

استادیار

• Deep Learning
• Machine Learning
• Theoretical Foundations of Deep Learning
• +۱۲ مورد دیگر

Kenji Kawaguchi is the Presidential Young Professor in the Department of Computer Science at the National University of Singapore (NUS), where he leads the Deep Learning Lab and is a faculty affiliate at the NUS Institute of Data Science. His research bridges theoretical and applied machine learning, focusing on deep learning, large language models, and physics-informed neural networks. His educational background includes a Ph.D. and S.M. in Computer Science and Electrical Engineering from the Massachusetts Institute of Technology (MIT), advised by Leslie Pack Kaelbling, and a postdoctoral fellowship at Harvard University’s Center of Mathematical Sciences and Applications. Dr. Kawaguchi’s research interests center on the theoretical foundations of deep learning, optimization, generalization, and applications in areas such as molecular modeling, AI safety, and efficient training of large models. He has made significant contributions to understanding in-context learning, diffusion models, and neural operators for partial differential equations. His recent publications (2023–2025) reflect a strong trend toward improving the efficiency, robustness, and interpretability of large-scale models, particularly in language and scientific domains. Key themes include LLM alignment and safety, diffusion model optimization, and physics-informed learning for high-dimensional problems. Presidential Young Professor He has served as Area Chair and PC Member for top-tier conferences including NeurIPS, ICML, ICLR, AAAI, and UAI, and as reviewer for journals such as JMLR and Annals of Statistics. He has delivered invited talks at Harvard, MIT, Stanford, CMU, Brown, and Google Research, reflecting his international recognition. He actively mentors students and welcomes PhD candidates and postdocs to join his research group.

• 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.

Daniel Epsteinمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Ubiquitous Computing
• Social Computing
• +۶ مورد دیگر

Daniel Epstein is an Associate Professor in the Department of Informatics at the University of California, Irvine (UCI), where he leads the Personal Informatics Everyday (PIE) Lab. He holds a courtesy appointment in the Department of Computer Science and is affiliated with the Connected Learning Lab, Institute for Future Health, and Accessibility Research Collective. Epstein earned his Ph.D. in Computer Science & Engineering from the University of Washington and a B.S. in Computer Science from the University of Virginia. His research focuses on how personal tracking technology can better serve diverse users' needs, emphasizing ethical design, health equity, and long-term user engagement. Notable areas include pregnancy tracking, wearable technology for children and ADHD management, and AI-driven health chatbots. Epstein has received grants from NSF, Snap Inc., and UCI programs, totaling over $650k in funding. Epstein's work bridges HCI and health informatics, with over 60 peer-reviewed publications. He advises six Ph.D. students and has mentored numerous undergraduates. Awards include the ACM Senior Member distinction (2025), CHI Best Paper (2023), and NSF CAREER Award (2023). Professional service includes program committees for CHI, CSCW, and UbiComp, plus leadership roles in workshops like the Workgroup on Interactive Systems in Healthcare (WISH). Beyond academia, Epstein enjoys hiking, collecting turtle figures, and advocating for accessible technology.

Olga Russakovskyمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Human-Computer Interaction
• +۷ مورد دیگر

Olga Russakovsky is an Associate Professor of Computer Science at Princeton University and Associate Director of the Princeton Laboratory for Artificial Intelligence. Her research focuses on computer vision , machine learning , human-computer interaction , and fairness, accountability, and transparency in AI systems. Princeton University faculty member since 2025 Affiliated with Princeton's Center for Statistics and Machine Learning and Center for Information Technology Policy Scientific Recognition: Presidential Early Career Award for Scientists and Engineers (2025) PAMI Young Researcher Award (2022) AnitaB.org Emerging Leader Abie Award (2020) CRA-WP Anita Borg Early Career Award (2020) MIT Technology Review 35-under-35 Innovator (2017) PAMI Everingham Prize (2016) As a co-founder and current Board Chair of AI4ALL , she drives initiatives to expand diversity in AI. Her recent publications demonstrate expertise in vision-language models , deepfake detection , and ethical AI systems .

Paul Gölzمشاهده پروفایل

استادیار

• Social Choice Theory
• Algorithmic Fairness
• AI Ethics
• +۳ مورد دیگر

Paul Gölz is an Assistant Professor at Cornell University's School of Operations Research and Information Engineering (ORIE), with affiliations in Computer Science. His research focuses on computational social choice, algorithmic fairness, and AI ethics, addressing topics like democratic innovation, fair resource allocation, and AI systems for diverse users. Education: Undergraduate studies at Saarland University (Germany), PhD in Computer Science from Carnegie Mellon University, followed by postdoctoral research at Harvard University, UC Berkeley, and the Simons Laufer Mathematical Sciences Institute. His work has been recognized with awards including the JPMorgan Chase AI Research Fellowship (2021) and honorable mentions for prestigious dissertation awards (Dantzig and ACM SIGecom). Research Highlights : Developed Panelot, a tool for selecting citizens' assemblies using state-of-the-art algorithms. His work on fair refugee resettlement and apportionment methods has been featured in Operations Research and Nature . Recent projects include AI alignment distortion analysis and generative social choice mechanisms. Teaching : Teaches Optimized Democracy (Spring 2025) and Mathematical Programming (Fall 2024). Supervises PhD students in ORIE, focusing on interdisciplinary applications of optimization and social choice. Awards & Grants : Received a $40K Structural Democracy Fellowship (Crankstart) and an OpenAI grant for Democratic Inputs to AI. Active in policy briefs (e.g., mini-public selection strategies). Labs/Tools : Co-developed Panelot.org , a nonprofit platform for fair citizen assembly selection. Active in open-source tool development for social choice applications.

• Machine Learning
• Deep Learning
• AI Safety
• +۱۲ مورد دیگر

Tim G. J. Rudner is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, a Faculty Member at the Vector Institute, and a Title A Fellow at Trinity College, University of Cambridge. He was previously an Assistant Professor and Faculty Fellow at New York University. University: University of Toronto School: Faculty of Arts and Science Department: Department of Statistical Sciences Affiliation: Vector Institute, Trinity College (Cambridge) He holds a PhD in Computer Science and an MSc in Statistics from the University of Oxford, where he was advised by Yee Whye Teh and Yarin Gal, and a BS in Applied Mathematics and Economics from Yale University. PhD: Computer Science, University of Oxford MSc: Statistics, University of Oxford BS: Applied Mathematics and Economics, Yale University His research focuses on building robust, transparent, and trustworthy machine learning systems, particularly for high-stakes applications. He develops probabilistic models that improve generalization under distribution shifts, provide reliable uncertainty estimates, and enable fair and interpretable predictions. His work spans generative models, large language models, healthcare, and biomedical discovery. The recent publications highlight a strong trend toward function-space modeling, Bayesian regularization, and AI safety. Tim's work emphasizes principled uncertainty quantification, robustness to subpopulation and semantic shifts, and the development of frameworks for AI governance and specification. His research bridges theoretical advances with real-world applications, especially in safety-critical domains like medicine and defense. Tim has received numerous accolades including being named a Rhodes Scholar, Qualcomm Innovation Fellow, and 2024 Rising Star in Generative AI. He was awarded a $700,000 Foundational Research Grant and a $30,000 Apple Seed Grant for improving LLM trustworthiness. Rhodes Scholar Qualcomm Innovation Fellow AISTATS Notable Paper Award (2024) Outstanding Paper Award, ICLR GenAI4DM Workshop (2024) Apple Seed Grant ($30,000) Foundational Research Grant ($700,000) NeurIPS Spotlight Talk 2024 Rising Star in Generative AI He actively mentors students, particularly first-generation and low-income scholars, and has contributed to major policy frameworks including the OECD AI Classification Framework and a series of CSET issue briefs on AI safety. His work demonstrates a strong commitment to responsible AI development, combining technical rigor with societal impact. Tim leads research efforts at the intersection of machine learning theory and practical deployment, with ongoing projects in generative modeling, reliable LLMs, and AI governance. His lab produces high-impact work regularly published at top-tier conferences such as NeurIPS, ICML, and AISTATS.

• Computer Vision
• Computer Graphics
• Machine Learning
• +۴ مورد دیگر

Prof. Matthias Nießner is a Professor at the Technical University of Munich , where he leads the Visual Computing Lab . Prior to this, he held a Visiting Assistant Professor position at Stanford University . His work bridges computer vision , graphics , and machine learning , focusing on 3D reconstruction , semantic scene understanding , and AI-driven video synthesis . Prof. Nießner has published over 150 works in top venues like SIGGRAPH , CVPR , and ECCV , with several receiving best paper awards (SIGCHI’14, HPG’15, SPG’18, SIGGRAPH’16 Emerging Tech). His research has garnered international media attention, including features in the New York Times , Wall Street Journal , and MIT Technological Review , as well as TV demonstrations (e.g., Jimmy Kimmel Live for Face2Face technology). Awards : TUM-IAS Rudolph Moessbauer Fellowship (2017–ongoing) Google Faculty Award (2017) Nvidia Professor Partnership Award (2018) ERC Starting Grant (2018, €1.5M) Eurographics Young Researcher Award (2019) Research Trends : 3D Gaussian Splatting for real-time rendering Neural Radiance Fields (NeRF) with mesh supervision Audio-driven facial animation via diffusion models Latent space diffusion for 3D scenes Self-supervised and zero-shot methods for 3D and image analysis As a co-founder and director of Synthesia Inc. , he drives democratization of synthetic media. His YouTube channel has over 5 million views, reflecting his impact beyond academia.

• Multibody Dynamics
• Control of Nonlinear Systems
• Model Predictive Control
• +۳ مورد دیگر

Dr. Michael Mühlebach is a Lecturer at the Department of Information Technology and Electrical Engineering, ETH Zurich, and affiliated with the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He holds a Bachelor's (2010) and Master's (2013) from ETH Zurich, recognized with awards for academic excellence in Robotics, Systems, and Control. His research focuses on multibody dynamics, nonlinear system control, and model predictive control, with applications in robotics and aerospace systems. His work spans theoretical advancements in variational integrators and practical implementations in systems like the Cubli (a reaction wheel-based 3D inverted pendulum) and flying platforms for ducted fan actuation. Publications highlight contributions to model predictive control schemes with stability guarantees, nonlinear analysis, and distributed event-based state estimation. Key awards include the Outstanding D-MAVT Bachelor Award and Willi-Studer Prize. His research integrates control theory with real-world applications, emphasizing both foundational mathematics and engineering implementations. No grants or lab affiliations are explicitly listed in the provided text.

Tim Conleyمشاهده پروفایل

استاد

• Econometrics
• Development Economics
• Empirical Industrial Organization
• +۳ مورد دیگر

Tim Conley is Professor and Chair at the Department of Economics, University of Western Ontario. He holds a Ph.D. from the University of Chicago (1996). His research focuses on applied econometrics with emphasis on spatial dependence, cross-sectional analysis, and empirical industrial organization. His primary research interests include methodological development in econometrics, particularly around dependence modeling in cross-sectional data and spatial analysis techniques. He has made significant contributions to understanding technology adoption in developing economies and detection of collusion in market mechanisms. Professor Conley's publications demonstrate consistent focus on developing robust statistical methods for economic applications, with recent work emphasizing practical applications in policy evaluation and market analysis. His methodological innovations have been implemented in statistical software packages used by researchers worldwide.

Alanson Sampleمشاهده پروفایل

دانشیار

• Human Computer Interaction
• Cyber-Physical Systems
• Wireless Technology
• +۴ مورد دیگر

Alanson Sample is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan (since 2018), leading the Interactive Sensing and Computing Lab. His research focuses on Human-Computer Interaction, Cyber-Physical Systems, and wireless technology innovations. He holds a PhD in Electrical Engineering from the University of Washington (2011), with prior postdoctoral work there developing implantable medical devices. Before academia, Sample held senior roles at Disney Research (2013-2018) as Executive Lab Director and Principal Research Scientist, leading teams in Robotics, AI, Computer Vision, and HCI. Earlier, he worked at Intel Labs (2008-2013) on energy harvesting for wearables and IoT. Key research themes include wireless power systems, embedded sensing, and privacy-aware technologies. Over 150+ publications span topics like magnetic sensing (MagDesk), acoustic gesture recognition (HandSAW), and privacy-preserving activity tracking (PrivacyMic). His work bridges academic and industry innovation, particularly in scalable interactive systems and medical technology. Notable projects include the Quasistatic Cavity Resonance wireless charging system, RFID-based sensing platforms (WISP), and wearable health monitoring devices. His lab develops hardware-software co-design frameworks for energy-efficient embedded systems. Current research emphasizes medical applications, smart infrastructure, and ubiquitous computing interfaces.

David Lindlbauerمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Mixed Reality
• Augmented Reality
• +۸ مورد دیگر

David Lindlbauer is an Assistant Professor at the Human-Computer Interaction Institute (HCII) of Carnegie Mellon University, where he leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center. His research bridges human perception, extended reality (AR/VR), and computational interaction techniques, focusing on developing systems that dynamically adapt interface elements based on environmental context, user cognition, and task requirements. He completed his PhD at TU Berlin under Prof. Marc Alexa and held a postdoctoral position at ETH Zurich's Advanced Interactive Technologies lab. His work has been published extensively at top venues including ACM CHI, UIST, and IEEE VR, with research themes spanning gaze tracking, spatial audio optimization, haptic feedback, and multimodal notification systems. Media outlets like MIT Technology Review and Fast Company Design have featured his innovations. Dr. Lindlbauer has received prestigious grants from Meta, NSF, and ETH Zurich, and serves on program committees for CHI, UIST, and ISMAR. He has been recognized with Best Paper awards at ISS 2023 and CHI 2016, and his lab develops tools like MineXR for personalized XR interfaces and RealityReplay for temporal change visualization in mixed reality environments.

Martin Jakobssonمشاهده پروفایل

استاد

• Marine Geology
• Arctic Ocean Glacial History
• Paleoceanography
• +۴ مورد دیگر

Martin Jakobsson is a Professor of Marine Geology and Geophysics at Stockholm University's Department of Geological Sciences. His career spans international research expeditions and leadership roles, including Dean of Earth Science (2021) and Head of Department (2012-2018). A key member of the Marine Geology group, he studies Arctic and Antarctic glacial history through sediment analysis and seafloor mapping. PhD from Stockholm University (2000) Academy Fellow, Royal Swedish Academy of Sciences (2004-2009) Professor II position at University Centre of Svalbard (2011-) His research focuses on marine geological archives to reconstruct glacial cycles, ice-ocean interactions, and bathymetric mapping. As GEBCO Vice-Chair (2013-2020), he led initiatives for global seabed mapping through the Seabed 2030 project. Recent publications reveal expertise in: Arctic Ocean paleocirculation Methane release from thawing permafrost Ice sheet sensitivity to oceanic warming High-resolution bathymetric analysis Scientific recognition includes: Election to Royal Swedish Academy of Sciences (2012) 1st Vice President, Royal Swedish Academy of Sciences (2016-2019) Membership in Norwegian Scientific Academy for Polar Research (2016) He leads international collaborations on polar research vessels, with over a year of cumulative ship time and multiple Co-Chief Scientist roles on Arctic expeditions.

Jörg F. Löfflerمشاهده پروفایل

استاد

• Bulk Metallic Glasses
• Metallic Biomaterials
• Magnetic Materials
• +۴ مورد دیگر

Jörg F. Löffler is a Full Professor of Metal Physics and Technology at the Department of Materials, ETH Zürich , where he has been since 2003. He previously served as Chairman of the Department (2010–2013) and holds Adjunct Professor positions at Tohoku University (Japan) and a Visiting Faculty role at Caltech. His research focuses on bulk metallic glasses , metallic biomaterials , and magnetic materials , combining synthesis, characterization, and applications in biomedical and structural contexts. Educations : Studied Physics and Materials Science at Saarland University (Germany), earned his doctorate at ETH Zurich and Paul Scherrer Institute on magnetism and neutron scattering (1997). Research Trends : Recent work explores additive manufacturing (e.g., laser powder bed fusion), biodegradable magnesium alloys for implants, and magneto-structural coupling in metallic glasses. Collaborative efforts integrate in situ analysis with computational modeling. Scientific Awards : International Magnesium Science and Technology Award (2023) MRS Fellow (2021) DGM 'Breakthrough' Prize (2016) Masing Memorial Prize (2005) ETH Zurich Medal (1998) Alexander von Humboldt Fellowship (1998-2001) Löffler advises the Department of Materials Science and Engineering at UC Davis and serves on editorial boards. His group at ETH Zürich investigates advanced metallic materials using synchrotron radiation and neutron scattering facilities.

Joseph Bentsmanمشاهده پروفایل

استاد

• Control of nonlinear systems
• Control of distributed parameter systems
• Nonlinear oscillations
• +۷ مورد دیگر

Joseph Bentsman is a Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign's Grainger College of Engineering. He also holds affiliate appointments in the Department of Aerospace Engineering (since 2015) and the Department of Electrical and Computer Engineering (since 2018). His academic journey began with an M.S. from Byelorussian Polytechnic Institute in Minsk, USSR (1979), followed by a Ph.D. in Electrical Engineering from Illinois Institute of Technology (1984). Professor Bentsman's research focuses on control of nonlinear and distributed parameter systems, nonlinear oscillations, network control, stability theory, and stochastic multiscale methods. He pioneered a new class of dynamical systems with active singularities that admit control actions during singular phases of motion, which represent a novel category of hybrid systems characterized by impulsively controlled discrete transitions. His recent work has expanded into biomedical applications, particularly thermophysical modeling of tissue during electrosurgery and control of phase change processes. His recent publications (2021-2024) reveal a strong trend toward biomedical applications of control theory, particularly in modeling heat conduction in biological tissues, electrosurgical processes, and phase change phenomena. Approximately 60% of his recent work focuses on biomedical applications, while the remainder continues his foundational work on nonlinear control systems, distributed parameter systems, and systems with active singularities. Key subfields include Stefan problems, enthalpy-based control, telegraph equation modeling, and PDE-based control of complex physical processes. NSF Presidential Young Investigator Award (1989) Life Fellow of American Society of Mechanical Engineers Life Senior Member of IEEE IEEE Control Systems Society Technical Committee Chair on Power Generation (2015-2019) International Society of Automation POWID Achievement Award (2014) 2018 AIST Computer Applications Best Paper Award Featured in 'People in Control', IEEE Control Systems Magazine (2018) Professor Bentsman has been instrumental in developing educational approaches that integrate signal processing, instrumentation, control, and machine learning, as evidenced by his two textbooks. His work on the steel continuous casting process, particularly the mold oscillation system, has led to practical industrial applications. He has also made significant contributions to power plant control systems and boiler/turbine control. His research group appears to focus on both theoretical control systems development and practical implementation in industrial and biomedical settings, with strong connections to steel manufacturing, power generation, and medical device industries.