جست‌وجوی استاد و پژوهشگر

Animesh Gargمشاهده پروفایل

استادیار

• Robotics
• Reinforcement Learning
• Artificial Intelligence
• +۳ مورد دیگر

Animesh Garg is an Assistant Professor at the School of Interactive Computing at Georgia Tech, where he leads the People, AI, and Robotics (PAIR) research group . He holds a Senior Researcher position at Nvidia Research and has courtesy appointments at the University of Toronto and Vector Institute. Previously, he served as Chief Scientific Officer at Apptronik (2024-2025) and Senior Staff Research Scientist at Nvidia Research (2018-2024). Education : Ph.D. in Operations Research from UC Berkeley (2011-2016), MS in Computer Science and Industrial Engineering from Georgia Tech and University of Delhi. Research Focus : Building Generalizable Autonomy through Reinforcement Learning , Control Theory , and 3D Vision , with applications in Surgical Robotics , Self-Driving Labs , and Manufacturing . Key Article Themes : His recent work emphasizes Foundation Models for robotics, Differentiable Simulation , Language-Guided Autonomy , and Structured Inductive Biases in sequential decision-making. Scientific Awards : Stephen Fleming Early Career Professorship at Georgia Tech. Teaching : Courses on AI, Deep Reinforcement Learning, and Algorithmic Intelligence in Robotics at Georgia Tech. Labs & Collaborations : Affiliated with Institute for Robotics and Intelligent Machines (IRIM) and ML@GT at Georgia Tech; collaborates intensively with Nvidia Robotics.

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

Andreas Geiger is a Professor and Head of the Department of Computer Science at the University of Tübingen, Germany. He leads the Autonomous Vision Group (AVG) within CyberValley and is a core faculty member of the Tübingen AI Center. His roles also include PI in the ML in Science Excellence Cluster and the CRC Robust Vision, as well as ELLIS Fellow and coordinator of the ELLIS PhD program. He specializes in machine learning models for computer vision, robotics, and autonomous systems, with applications in self-driving cars, VR/AR, and scientific document analysis. Educational background: While not explicitly detailed, his positions imply a Ph.D. in Computer Science or related field. His work spans interdisciplinary collaborations with institutions like ETH Zürich, Microsoft, and the University of Bonn. Research focuses on 3D scene understanding, Gaussian splatting, generative models, and reliable autonomous systems. Notable contributions include the KITTI dataset and foundational work in neural radiance fields. Awards include the Sage 10-Year Impact Award (2024), ERC Starting Grant (2019), and IEEE PAMI Young Researcher Award (2018). Key projects include the Scholar Inbox paper recommender platform, ReSim (reliable world simulation), and advancements in 3D scene generation (e.g., UrbanCAD, PrITTI). His lab maintains a strong focus on open-source tools and datasets, such as the CARLA Route Generator. Grants and funding include support from Vector Stiftung (MINT innovation program) and EU initiatives like the ML in Science Cluster. His team collaborates internationally, with recent work presented at CVPR, SIGGRAPH, and NeurIPS.

Steve Mussmannمشاهده پروفایل

استادیار

• Data-centric machine learning
• Active learning
• Experimental design
• +۲ مورد دیگر

Steve Mussmann serves as an Assistant Professor in the School of Computer Science at the Georgia Institute of Technology, where he joined in Fall 2024. His research centers on data-centric machine learning, with emphasis on active labeling, data selection, and adaptive experimental design methodologies. He maintains active collaborations through Georgia Tech's Foundations of AI (FoAI) and ML@GT research groups. Mussmann earned his PhD in Computer Science from Stanford University in 2021 under Percy Liang's supervision, following a BS in Math, Statistics, and Computer Science from Purdue University in 2015. His professional trajectory includes a machine learning researcher role at Coactive AI and an IFDS postdoctoral fellowship at the University of Washington's Paul Allen School of Computer Science and Engineering. His research program investigates theoretical and practical aspects of data efficiency in machine learning systems, particularly focusing on active learning frameworks, statistical properties of data algorithms under concept drift, and task specification via prompts or demonstrations. Current projects address challenges in label-efficient training of large language models and multimodal dataset development. Analysis of his 15 most recent publications reveals a consistent focus on advancing data-centric methodologies, with increasing emphasis on large-scale applications like multimodal datasets and language model fine-tuning. His work bridges theoretical guarantees in experimental design with practical frameworks like LabelBench for benchmarking label efficiency. Mussmann has received recognition through the IFDS postdoctoral fellowship. His contributions to the field include foundational work on active learning theory and data selection algorithms. IFDS postdoctoral fellow He currently advises five graduate students including PhD candidates Kangping Hu (CS) and Hangyu Zhou (ML), alongside MS students Kabir Kang and Kalp Vyas, and undergraduate Saloni Bedi. Former advisee Wei-Liang (Edison) Liao completed BS research under his supervision. His teaching portfolio includes graduate courses CS 7545 (Machine Learning Theory) and CS 8803-DML (Data-centric Machine Learning). Mussmann operates within Georgia Tech's Foundations of AI initiative and ML@GT collective, which provide infrastructure for large-scale data-centric research. His lab develops open-source tools like LabelBench for reproducible evaluation of data selection techniques, with ongoing projects exploring video data exploration systems and adaptive finetuning frameworks for foundation models.

• Computer Networks
• Network Security
• Machine Learning in Networking
• +۳ مورد دیگر

Mariano Scazzariello is a Lecturer at KTH Royal Institute of Technology, Sweden, affiliated with the School of Electrical Engineering and Computer Science and the Department of Network and Systems Engineering. He teaches the course 'Network Systems with Edge or Cloud Datacenters (IK2227)'. His research focuses on advanced networking topics including machine learning in networks, high-speed packet processing, network emulation, and software-defined networking innovations. His work spans contributions to network emulation tools like Kathará and Megalos, stateful packet processing at terabit scales, and leveraging large language models (LLMs) for network configuration and vulnerability detection. Recent research emphasizes low-latency protocols (e.g., SRv6/DetNet integration) and GPU-centric networking on commodity hardware. Mariano’s publications (2020–2025) highlight expertise in network function virtualization, ASIC-based switching, and optimizing network configurations through AI-driven approaches. He has pioneered frameworks for evaluating routing protocols and virtualizing large network scenarios at scale.

Otmar Hilligesمشاهده پروفایل

استاد

• Computer Vision
• Human-Computer Interaction
• Robotics
• +۴ مورد دیگر

Prof. Dr. Otmar Hilliges is a Full Professor at the Department of Computer Science at ETH Zurich. He leads the AIT lab and serves as the head of the Institute of Intelligent Interactive Systems. His research focuses on spatio-temporal understanding of human movement and interaction, leveraging algorithms and representations from videos, images, and sensor data for applications in Augmented Reality (AR), Virtual Reality (VR), and Human-Robot Interaction. Education: Diplom (MSc) in Computer Science, Technical University of Munich (TUM), Germany PhD in Computer Science, Ludwig Maximilian University of Munich (LMU), Germany (2009) Research Interests: Hilliges' work spans computer vision, robotics, and human-computer interaction. He develops methods for 3D human pose estimation, generative models for realistic avatar creation, and physically plausible simulation of human-object interactions. His research emphasizes practical applications in AR/VR and assistive robotics, aiming to bridge the gap between perception and action. Grants & Contributions: ERC Consolidator Grant (2022-2027): 'AI-Perceive: Robust Human-Centric Computer Vision for Advanced AI-Agents' Google Research Agreement (2020-2025): 'Generative Modelling of Humans' Microsoft Research Grants: Focus on human-centric robotics and interactive technologies Labs & Teams: Leads the AIT Lab at ETH Zurich, which pioneers research in intelligent interactive systems, emphasizing human-centric AI and robotics. The lab collaborates on projects ranging from drone cinematography to haptic feedback systems.

Patrick Sladeمشاهده پروفایل

استادیار

• Assistive Robotics
• Biomechanics
• Human-Robot Interaction
• +۶ مورد دیگر

Patrick Slade is an Assistant Professor of Bioengineering at Harvard University's School of Engineering and Applied Sciences (SEAS). His lab, the Slade Lab, focuses on developing assistive devices to enhance mobility through the integration of biomechanics, robotics, and human-centered artificial intelligence. Key research areas include exoskeletons, prosthetics, wearable sensors for health tracking, and navigation aids for visually impaired individuals. Research Interests: The lab emphasizes translating research into practical solutions, such as personalized exoskeletons and robotic systems to improve mobility. Recent work includes optimizing human-robot interaction algorithms and publishing in high-impact journals like Nature . Collaborations with labs like the Biodesign Lab and BIONICs Lab highlight cross-disciplinary efforts. Publications: Over 15 articles since 2017 span topics like exoskeleton design, energy expenditure modeling, and Bayesian reinforcement learning. Notable contributions include a 2022 Nature paper on personalized exoskeleton assistance and a 2021 study on navigation aids for impaired vision. Awards & Grants: Students in his group have received prestigious NSF GRFP fellowships and conference awards, reflecting the lab's emphasis on innovation. The lab actively engages in grant-funded projects to advance assistive technology. Lab & Team: The Slade Lab opened at Harvard in 2023 and includes PhD students and postdocs working on devices like robotic exoskeletons and health-tracking systems. Future work focuses on scalable solutions for mobility challenges through interdisciplinary approaches.

Ambuj K. Singhمشاهده پروفایل

استاد

• Machine Learning
• Social Networks
• Network Science
• +۷ مورد دیگر

Ambuj K. Singh is a Distinguished Professor of Computer Science at the University of California, Santa Barbara (UCSB), with a part-time appointment in the Biomolecular Science and Engineering Program. He holds a PhD from the University of Texas at Austin (1989), an MS from Iowa State University (1984), and a BTech from the Indian Institute of Technology, Kharagpur (1982). His campus affiliations include the Center for Bio-Image Informatics, Information Network Academic Research Center, and IGERT on Network Science. PhD, University of Texas at Austin, 1989 M.S., Iowa State University, 1984 B.Tech., Indian Institute of Technology, Kharagpur, 1982 Research interests span network science, machine learning, and bioinformatics, with a focus on graph-based methodologies. His work addresses: Data-centric modeling of dynamic networks Representation learning and explainability in graph neural networks Network analysis in social systems and biological networks Applications in drug discovery and brain sciences Geometry-preserving distance metrics for data integrity Recent publications highlight advancements in counterfactual explanations, GNN benchmarking, molecular graph pretraining, and self-attention for event detection. Scientific contributions include: Founding Acelot, Inc., an in silico drug discovery company Editorial roles at IEEE Transactions on Knowledge & Data Engineering and BMC Journal of Clinical Bioinformatics NSF-IGERT (2013-2018), ARL-funded Information Networks Academic Research Center (2009-2014), and US Army MURI grants Advising has involved mentoring over 50 graduate/postdoctoral students, including 30+ PhD candidates. He leads a multidisciplinary research group at UCSB and collaborates with off-campus entities like Acelot, Inc.

Sainyam Galhotraمشاهده پروفایل

استادیار

• Artificial Intelligence
• Causal Inference
• Database Systems
• +۴ مورد دیگر

Sainyam Galhotra is an Assistant Professor in the Department of Computer Science at Cornell University. His research focuses on developing data science tools for trustworthy analytics, integrating causal inference, data management, and machine learning to enhance robustness, explainability, and fairness in algorithmic systems. PhD: University of Massachusetts Amherst (2020), advised by Barna Saha B.Tech: Indian Institute of Technology Delhi (2014), advised by Amitabha Bagchi Postdoctoral Research: University of Chicago (Computing Innovation Fellow) His work spans artificial intelligence, causal inference, and responsible data science, emphasizing ethical algorithm design and reliable data integration. Recent publications highlight advancements in fair clustering, causal feature selection, and entity resolution frameworks. His research trends from 2023–2024 include contributions to spatio-temporal data correlation, community detection in geometric graphs, and distribution-aware dataset search. Key themes are fairness in machine learning, causal modeling, and scalable data management solutions. Computing Innovation Fellowship (2021) DAAD AInet Fellow (2021) ACM SIGMOD Entity Resolution Programming Contest Finalist (2021) Krithi Ramamritham Computer Science Scholarship (2019) BEST Paper Award in SIGSOFT FSE 2017 He actively seeks PhD or Master’s students interested in data science and trustworthy AI. Contact via email: sg@cs.cornell.edu .

Nadia Polikarpovaمشاهده پروفایل

دانشیار

• Program Synthesis
• Program Verification
• Type Systems
• +۵ مورد دیگر

Nadia Polikarpova is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego . She earned her PhD from ETH Zurich in 2014 under Bertrand Meyer , followed by postdoctoral research at MIT CSAIL with Armando Solar-Lezama . Her academic contributions have been recognized with prestigious awards including the 2020 Sloan Fellowship , 2020 Intel Rising Stars Award , and 2020 NSF CAREER Award . Polikarpova's research focuses on program synthesis , program verification , and type systems . She leads the Programming Systems group at UCSD and contributes to the IFIP Working Group 2.8 on Functional Programming since 2022. Her work spans foundational research and practical tools, including projects like Synquid , SuSLik , and Laurel that combine formal methods with machine learning for code generation. Her recent publications in venues like OOPSLA , NeurIPS , and ICFP reveal trends in AI-assisted programming , live programming environments , and formal verification . She has advised numerous PhD and Master’s students including Shraddha Barke , Zheng Guo , and Tristan Knoth , many of whom have moved to prominent academic and industry positions. Notable artifacts from her lab include tools like ColDeco for spreadsheet inspection and Superfusion for eliminating intermediate data structures. 2020 : Sloan Fellow 2020 : Intel Rising Stars Award 2020 : NSF CAREER Award 2021 : Distinguished Paper at POPL 2023 : Distinguished Artifact at PLDI 2023 : Distinguished Paper at OOPSLA Polikarpova actively contributes to academic service, serving on program committees for PLDI , POPL , and OOPSLA , and co-chairing the OOPSLA Review Committee in 2023. She has delivered keynotes at APLAS'20 and PLDI'24 , emphasizing the integration of large language models with formal methods.

Chris Harrisonمشاهده پروفایل

دانشیار

• Ubiquitous Computing
• Human-Centered AI
• Physical Interfaces
• +۵ مورد دیگر

Chris Harrison is an Associate Professor at Carnegie Mellon University's School of Computer Science, directing the Future Interfaces Group . His research focuses on novel human-computer interaction technologies, including haptics, AR/VR/XR, and ubiquitous computing. Research Interests: Ubiquitous Computing, Human-Centered AI, Physical Interfaces (Sensing, Haptics, Fabrication), AR/VR/XR, Social Computing Advisees: Daehwa Kim, Nathan DeVrio, Vimal Mollyn, Vivian Shen Scientific Recognition: Forbes 30 Under 30 (Science), MIT Technology Review 35 Innovators Under 35, Smithsonian Innovator (2013), Google/Microsoft/Qualcomm Fellows Contact: chris.harrison@cs.cmu.edu His recent publications explore advanced haptic systems (Reel Feel, Fluid Reality), body-centric sensing (SkinTrack), and environment-embedded interfaces (Wall++). Current work integrates UWB/IMU fusion for pose estimation and synthetic jet haptics.

Emily Wallمشاهده پروفایل

استادیار

• Data Visualization
• Visual Analytics
• Cognitive Psychology
• +۳ مورد دیگر

Dr. Emily Wall is an Assistant Professor in the Department of Computer Science at Emory University, leading the CAV Lab (Cognition and Visualization). She earned her Ph.D. in Computer Science from Georgia Tech (2020) and completed a postdoctoral fellowship at Northwestern University before joining Emory. Ph.D., Computer Science, Georgia Tech (2020) Postdoctoral Researcher, Northwestern University Current: Assistant Professor, Emory University Her research focuses on enhancing human decision-making through data visualization and visual analytics by addressing cognitive limitations and biases. Key areas include metacognition promotion, computational strategies for bias mitigation, and human-AI collaboration in data analysis. She combines cognitive psychology principles with technical implementation using tools like D3.js and Tableau. Recent publications highlight her work on LLM applications in visual analytics, cognitive bias interventions, and trust calibration in AI systems. Current students like Mengyu Chen, Shiyao Li, and Zhongzheng Xu are presenting at top conferences such as CHI and IEEE VIS. She also explores educational applications through interactive visualization pedagogy and innovative assessment methods. Dr. Wall actively engages in academic service through graduate mentorship, conference judging, and diversity initiatives. Her lab emphasizes both technical proficiency and social justice implications in visualization design, reflected in course projects that merge artistic expression with data-driven storytelling.

Dhruv Jainمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Audio AI
• Accessibility
• +۵ مورد دیگر

Dhruv Jain is an Assistant Professor in the Computer Science and Engineering Department at the University of Michigan , with affiliations in the School of Information and Michigan Medicine . He leads the Soundability Lab , focusing on transforming hearing into a programmable interface through human-centered AI systems. PhD from University of Washington MS from MIT Media Lab Formerly worked at Microsoft Research, Google, and Apple Research Interests intersect Human-Computer Interaction (HCI) , Audio AI , Accessibility , and Hearing Health . His work develops systems like SoundWatch (smartwatch-based sound awareness) and HomeSound (IoT sound visualization) to expand auditory access for DHH individuals and other domains. Recent Publications (2023–2025) span top venues like CHI , ASSETS , and ICMI , with trends including generative AI for real-time captioning , adaptive soundscapes , and clinical communication tools . SIGCHI Outstanding Dissertation Award (2023) William Chan Memorial Dissertation Award (2023) Best Poster Award at ASSETS 2024 Google Academic Research Award (2024) NIH Grant ($450k, 2023) William Demant Foundation Grant ($740k, 2025) Teaching includes Accessible Computing (undergraduate) and Advanced Accessibility (graduate), alongside global DIY workshops in five countries. The Soundability Lab collaborates with neuroscientists, clinicians, and Deaf scholars to create deployable systems like clinical communication tools now at Michigan Medicine and features integrated into Apple devices.

Shahrzad Fouladvandمشاهده پروفایل

دانشیار

• International Criminal Law
• Transnational Criminal Law
• Human Trafficking
• +۷ مورد دیگر

Dr. Shahrzad Fouladvand is an Associate Professor in International Criminal Law at the University of Sussex's School of Law, Politics and Sociology. Her research focuses on transnational criminal law, human trafficking, and corruption, particularly their interplay in contexts like Albania. She previously worked at the International Criminal Court (ICC) and the International Labour Organisation (ILO), contributing to human rights conventions. A member of RUSI's Strategic Hub for Organised Crime (SHOC), she advocates holistic approaches to combating human trafficking beyond prosecution-centric methods. Dr. Fouladvand holds a PhD from the University of Sussex and is a Fellow of the UK Higher Education Academy. Education: PhD in Law, University of Sussex (2008–2012) Common Professional Examination (CPE)/Graduate Diploma in Law (GDL), University of Huddersfield (2012–2014) Certifications in Machine Learning for Social Scientists (2023) and Social Network Analysis (2020) Research Interests: Dr. Fouladvand explores the nexus between human trafficking and corruption, emphasizing evidentiary challenges and institutional complicity. Her work integrates network analysis and responsibilisation frameworks, critiquing prosecution-centric approaches. Recent projects include a monograph on 'Human Trafficking, Corruption and Corporate Complicity' for Routledge, examining public-sector complicity in trafficking. Key Contributions: Authored over 30 publications, including journal articles and book chapters on ICC jurisprudence, anti-corruption law, and trafficking dynamics. Advised UK parliamentary committees on human trafficking and migration policies. Edited volumes on transnational organized crime and economic criminology. Awards and Fellowships: Fellow of the UK Higher Education Academy (2016) Visiting Scholarship at Max-Planck Institute (2021–2022) Elected Special Rapporteur on Contemporary Slavery for BACL Labs/Teams: Active member of RUSI's SHOC, focusing on policy-relevant research to combat organized crime.

Benjamin Langeمشاهده پروفایل

استاد پژوهشی

• Ethics of AI
• Technology Ethics
• Normative Ethics
• +۳ مورد دیگر

Benjamin Lange is a Research Professor and Junior Research Group Leader in the Ethics of AI and Machine Learning at Ludwig Maximilians University of Munich (LMU) and the Munich Center for Machine Learning (MCML). He is also an Associate Research Fellow at the Oxford Uehiro Institute for Practical Ethics and a member of LMU's Zentrum für Ethik und Philosophie in der Praxis (ZEPP). His research bridges foundational ethical theory with practical challenges in technology, business, and organizational ethics. Lange holds a BSc from the London School of Economics, and a B.Phil and D.Phil from the University of Oxford. He previously held visiting positions at Google’s Responsible Innovation team and the University of Hamburg. His research focuses on ethical frameworks for AI, digital ethics, and corporate responsibility. Key themes include the ethics of human-AI collaboration, moral partiality in relationships, and organizational ethics. He has advised the European Commission on AI governance and developed assurance audit methodologies for algorithmic systems, including work for NYC’s AI Bias Law. His consulting practice focuses on helping organizations build ethical cultures through capabilities like moral imagination and compliance frameworks. Lange’s work has been featured in media such as CBS News, Business Insider, and ZDF heute journal. He is completing a book on the ethics of interpersonal relationships and partiality, aiming to connect philosophical insights with practical applications. His recent publications explore moral imagination in engineering teams, bias mitigation in AI systems, and the ethical implications of digital duplication technologies. He advises Fortune 500 companies, startups, and public institutions across sectors including finance, technology, and education. His approach emphasizes aligning ethical practices with organizational growth and social impact through tailored ethical frameworks and stakeholder engagement strategies.

• Security Engineering
• Science of Security
• Human-Computer Interaction
• +۴ مورد دیگر

Janne Lindqvist is an Associate Professor at the Department of Computer Science, Aalto University. His research bridges security engineering, human-computer interaction (HCI), and privacy, with a focus on making security systems usable and user-centric. University: Aalto University Department: Department of Computer Science Rank: Associate Professor Email: janne.lindqvist@aalto.fi Lindqvist’s work spans security engineering , privacy systems , and user research , emphasizing practical authentication methods, password management, and human behavior in security contexts. He explores how users interact with systems like TPM APIs, gesture passwords, and mobile authentication mechanisms. Recent publications highlight trends in authentication systems , ubiquitous computing security , and mobile user behavior . Key themes include biometric authentication, gesture-based security, and balancing usability with cryptographic robustness. CHI'25 Honorable Mention Award CHI'24 Best Paper Award His research integrates empirical studies with technical implementations, such as analyzing password forgetting patterns and developing acoustic sensing for vehicle detection (e.g., Auto++, BO-Ear). Collaborative efforts span machine learning, psychology, and embedded systems.