Find Academic Supervisors

Christopher A. BaldassanoView profile

Associate Professor

• Perception
• Memory
• Cognitive Neuroscience
• +2 more

Christopher A. Baldassano is an Associate Professor in the Department of Psychology at Columbia University, maintaining offices in Schermerhorn Hall (370 for office, 312 for lab). Contact is available via email c.baldassano@columbia.edu or phone +1 212 854 1902 by appointment. Education Ph.D., Stanford University, 2015 Research Focus Dr. Baldassano leads the Dynamic Perception and Memory Lab investigating how humans process and recall complex real-world experiences through event segmentation, temporal/spatial structure modeling, and neural representation formation. His work integrates cognitive neuroscience with machine learning approaches to analyze fMRI data during narrative, movie, and virtual reality experiments. Key research themes include event cognition dynamics, memory summarization mechanisms, and how prior knowledge shapes mental representations of everyday experiences. Scientific Awards No awards or fellowships were documented in the provided materials. Advising and Grants While specific student advisees and grant details weren't listed, his lab structure implies active mentorship of graduate researchers in cognitive neuroscience methodologies. Funding likely supports fMRI experimentation and computational modeling infrastructure. Laboratory Operations The Dynamic Perception and Memory Lab employs functional MRI combined with data-driven machine learning techniques to model neural representation variations across stimuli and individuals. Current projects examine event boundaries in continuous experiences using ecologically valid paradigms like movies and virtual environments, with emphasis on how temporal/spatial world structures influence cognitive processing.

Leif RistrophView profile

Assistant Professor

• Fluid Dynamics
• Fluid-Structure Interactions
• Biophysics
• +6 more

Leif Ristroph is an Assistant Professor of Mathematics at the Courant Institute of Mathematical Sciences , New York University . His research bridges experimental physics and applied mathematics , focusing on fluid-structure interactions in both biological and geophysical contexts. Key research areas include: Biophysical Flows : Aerodynamics of insect flight ( Applied Mathematics Laboratory ), hydrodynamics of fish schooling, and flow-sensing mechanisms via the fish lateral line system Geophysical Flows : Shape evolution during erosion, dissolution patterns in fluid flows, and bubble formation dynamics Recent publications (2019-2015) explore: Flow interactions in flapping swimmers and hovering systems Evolutionary optimization of wing shapes and self-sculpting processes Stability mechanisms in tandem flapping and insect flight His work has been featured in major media outlets like Nature , New York Times , and ScienceDaily . Ristroph's lab at NYU's Applied Mathematics Laboratory combines physical experiments, computational models, and theoretical analysis to study complex fluid-structure interactions.

Xiaoning QianView profile

Professor

• machine learning
• Bayesian methods
• optimal experimental design
• +5 more

Xiaoning Qian is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, where he also serves on the Faculty Advisory Committee for the Texas A&M Institute of Data Science (TAMIDS) and the Executive Committee for the Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He holds a joint appointment in the Applied Math group within the Computational Science Initiative at Brookhaven National Laboratory (BNL). Previously, he was an Associate Professor (2018-2022) and Assistant Professor (2013-2018) at Texas A&M, and an Assistant Professor in the Department of Computer Science and Engineering at the University of South Florida (2009-2013). Dr. Qian received his B.S.E. and M.S.E. degrees from Shanghai Jiaotong University, China, and his M.Ph. and Ph.D. degrees in Electrical Engineering from Yale University. Dr. Qian's research focuses on developing mathematical models and computational algorithms in signal processing, machine learning, and Bayesian methods, particularly in learning, uncertainty quantification, and experimental design. His work spans multiple disciplines, with applications in life sciences and materials science. His research group, the Biomedical Imaging, Sensing, and Genomic Signal Processing Group, actively applies probabilistic models and optimization algorithms to solve complex problems in interdisciplinary domains. His research has evolved from foundational work in bioinformatics and biomedical image processing to more recent applications in materials science and broader AI for science initiatives. Dr. Qian has received numerous scientific awards and recognitions including: National Science Foundation (NSF) CAREER Award Segers Family Dean's Excellence Professorship II in the College of Engineering TEES (Texas A&M Engineering Experiment Station) Senior Faculty Fellow Montague-Center for Teaching Excellence Scholar J. T. Oden Faculty Fellow at the University of Texas, Austin Finalist of the 2023 INFORMS QSR Best Paper Faculty Impact Fellow from the Department of Electrical & Computer Engineering As an advisor , Dr. Qian has mentored numerous graduate students through their PhD and MS programs, with many of his alumni securing positions at prestigious institutions and companies including NIH/NCBI, Microsoft, Baidu Research Lab, and Qualcomm. His research has been supported by multiple grants, including an NSF CAREER award and collaborative research funding from the Information Integration and Informatics program. He is actively recruiting postdoc and graduate student research assistants for projects in machine learning and optimization methods with applications in bioinformatics and materials science. Dr. Qian is involved with several research initiatives including the Objective-Based Uncertainty Quantification (ObjectiveUQ) project, which provides a mathematical framework for integrating prior knowledge and data while enabling effective operational and experimental design under uncertainty. He also co-organizes the Bio-Seminar series for the Biomedical Imaging, Sensing & Genomic Signal Processing group at Texas A&M.

Wenjing RaoView profile

Associate Professor

• VLSI Test
• Fault-Tolerance
• Reliability
• +3 more

Wenjing Rao is an Associate Professor at the Department of Electrical and Computer Engineering, College of Engineering, University of Illinois at Chicago (UIC). Her research focuses on VLSI test, fault-tolerance, reliability, and hardware security in emerging nanoelectronic systems. Education: Ph.D. in Computer Science from University of California, San Diego (2008) B.S. in Computer Science from Peking University (2001) Her work explores novel computation paradigms through physical unclonable functions (PUFs), defect-tolerant logic implementation, and scalable fault tolerance in many-processor arrays. Publications emphasize hardware security, reconfiguration strategies, and reliability challenges in nanoscale architectures. Notable honors include the 2017 Harold A. Simon Award for Excellence in Teaching and the 2012 NSF CAREER Award. She has contributed to key journals like IEEE Transactions on Computer-Aided Design and conferences including DATE, ASPDAC, and NANOARCH.

Estela SuarezView profile

Professor

• high performance computing (HPC)
• heterogeneous HPC system architectures
• modular supercomputing architecture (MSA)
• +4 more

Prof. Dr. Estela Suarez is a Professor of High Performance Computing at the Institute for Computer Science, University of Bonn (W2 in the Jülich Model) and Joint Lead of the Division "Novel System Architecture Design" at the Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH. She also leads the Research Group "Next Generation Architectures and Prototypes" at JSC and serves as Spokesperson of Helmholtz Information Program 1, Topic 2. Currently on sabbatical during the 2024/2025 and 2025 academic years, she remains active in research leadership roles. 2010: PhD in Physics from University of Geneva, Switzerland 2004: Master in Physics, Specialization in Astrophysics, University Complutense of Madrid, Spain Professor Suarez specializes in high performance computing with particular expertise in heterogeneous HPC system architectures and modular supercomputing architecture (MSA). Her research spans hardware prototyping and evaluation, system software development, operational data analysis, and co-design methodologies. She has pioneered approaches to address hardware heterogeneity through system-wide orchestration of diverse computing resources, enabling more efficient scientific computing across multiple domains. Her work bridges theoretical computer architecture with practical implementation challenges in exascale computing environments, focusing on real-world applications that require specialized hardware configurations. Professor Suarez's publication record shows a clear evolution from foundational work on the DEEP project (2016) through the development of modular supercomputing concepts (2019-2021) to current applications across diverse scientific domains (2022-2024). Her recent publications demonstrate how modular architectures can be effectively applied to climate modeling, neuroscience simulations, quantum chemistry calculations, and other computationally intensive fields. This trend highlights her focus on practical implementation challenges and the growing importance of adaptable computing architectures in modern scientific research. 2023/2024 Lehrpreis der Universität Bonn: UniBonn teaching award Professor Suarez has secured significant research funding through major projects including NUMERIQS (Projects A05, B02, and Z02), European Processor Initiative (EPI), DEEP-SEA (Software for Exascale Architectures), IFCES2 (optimization of simulation algorithms for exascale supercomputers), and AIDAS (virtual laboratory between Forschungszentrum Jülich and CEA on AI and data analytics). While currently not accepting new students due to sabbatical, she has previously mentored graduate students in high performance computing techniques and has delivered numerous invited lectures at international conferences. Professor Suarez leads the "Next Generation Architectures and Prototypes" research group at JSC and serves as Joint Lead of the "Novel System Architecture Design" division. She chairs the Research and Innovation Advisory Group (RIAG) from EuroHPC Joint Undertaking since 2024. Her work involves close collaboration with international research teams on advancing supercomputing architectures, including contributions to the University of Bonn's new HPC system "Marvin" which ranks on both the TOP500 and GREEN500 lists.

• Statistical Machine Learning
• Algorithmic Statistics
• Robust Statistics
• +11 more

Sivaraman Balakrishnan is a Professor at Carnegie Mellon University with joint appointments in the Department of Statistics and Data Science and the Machine Learning Department. His research bridges statistical machine learning, algorithmic statistics, and robust inference. Education: Ph.D. in Computer Science from Carnegie Mellon University (Language Technologies Institute, advised by Jaime Carbonell); postdoctoral work at UC Berkeley (Department of Statistics, advised by Martin Wainwright and Bin Yu). Research Interests: Spanning robust statistics, domain adaptation, minimax hypothesis testing, assumption-light inference, causal inference, statistical optimal transport, non-parametric statistics, ranking, crowdsourcing, optimization, and topological data analysis. Key Research Trends: Recent work focuses on domain adaptation under label/misingness shifts, robust gradient estimation, smooth optimal transport maps, and conditional independence testing. He explores minimax optimal methods, univariate mean estimation, and high-dimensional regression with missing data. Scientific Awards: IMS Lawrence D. Brown Student Award (2021, 2020) NVIDIA Pioneer Award (2018) Franklin V. Taylor Memorial Best Paper Award (2018) Grants and Editorial Roles: NSF grants (CCF-1763734, DMS-1713003, DMS-2113684, DMS-2310632), Amazon Research Award (2021), Google Research Scholar Award (2021). Associate Editor for JASA and JRSSB ; Editorial Board member for Foundations and Trends in Statistics . Collaborative Groups: Co-organizes the Statistics and Machine Learning Reading Group and participates in the Causal Inference Working Group at CMU.

Jeff ZhangView profile

Assistant Professor

• Deep learning
• Computer architecture
• Embedded systems
• +5 more

Jeff Zhang is an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. He joined ASU in January 2023 after completing a postdoctoral fellowship at Harvard University. His research spans deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. Education: Ph.D., New York University M.Eng., B.Eng., Hunan University Dr. Zhang's research bridges theoretical machine learning with practical hardware implementation, developing novel architectures that optimize performance, power consumption, and reliability. He has pioneered approaches for hardware acceleration of large language models, efficient sparse matrix operations, and novel memory technologies for AI workloads. His work has received multiple awards including IEEE Top Picks in Test and Reliability (2023) and IEEE Micro Best Paper Award (2022). His recent publications demonstrate a strong trend toward heterogeneous computing, with significant work in chiplet-based AI accelerators, photonic computing for AI, and 2.5D/3D integration techniques. The research spans from high-level compiler frameworks to circuit-level innovations, with a consistent theme of co-designing algorithms and hardware for optimal AI performance. His work on the SODA toolchain has been particularly influential in bridging Python to silicon. Scientific Awards: IEEE Top Picks in Test and Reliability, IEEE ITC, 2023 Best Paper Award, IEEE Micro, 2022 Best Paper Award Candidate, IEEE DATE, 2022 Best Presentation Award Nomination, ACM SIGDA DATE PhD Forum, 2020 Best Paper Award Nomination, IEEE VLSI Test Symposium, 2018 Ernst Weber Ph.D. Fellowship, New York University, 2015, 2016 Dr. Zhang actively mentors a diverse group of graduate and undergraduate students, with several alumni now working at leading technology companies including Apple, TSMC, and Ansys. His research is supported by prestigious grants from NSF, Sandia National Labs, and industry partners. He serves on technical program committees of numerous top conferences and has organized special sessions on emerging topics like Gen AI for Chip Design and LLM-Aided Design. Dr. Zhang leads a vibrant research group that collaborates extensively with industry partners and national laboratories. Current projects focus on next-generation AI hardware, including chiplet-based systems, photonic accelerators, and novel memory technologies for large language models. His group has developed several open-source tools and frameworks, including the SODA toolchain for bridging Python to silicon.

Abolfazl AsudehView profile

Associate Professor

• Algorithm Design
• Data Science
• Artificial Intelligence
• +4 more

Abolfazl Asudeh is an Associate Professor in the Department of Computer Science at the University of Illinois Chicago and director of the Innovative Data Exploration Laboratory (InDeX Lab) . He is a Senior Member of ACM and IEEE , serving as Associate Editor for IEEE Transactions on Knowledge and Data Engineering , VLDB Ambassador , and VLDB Endowment Liaison to NSF . His research focuses on Algorithm Design for Data and AI problems , emphasizing efficient, accurate, and responsible solutions through Approximation Algorithms , Randomized Methods , and Computational Geometry . Recent work explores LLM optimization ( Needle ), fair data structures ( FairHash ), and responsible AI frameworks ( Chameleon ). Scientific awards include Communications of the ACM Research Highlight Google Research Scholar Award SIGMOD 2019 Research Highlight Best of VLDB 2020 SIGMOD 2017 Reproducibility Award Grants: NSF IIS-2348919 (2024-2027): Fairness-aware Data Structures NSF IIS-2107290 (2021-2024): Collaborative Fairness Research The InDeX Lab develops systems like Needle (image retrieval) and RSR (matrix multiplication). His work integrates fairness , reliability , and computational efficiency across data structures , LLMs , and responsible AI implementations.

Sebastian SeungView profile

Professor

• Neuroscience
• Machine Learning
• Connectomics
• +5 more

Sebastian Seung is a Professor at Princeton University , affiliated with both the Department of Computer Science and the Princeton Neuroscience Institute . His career spans Harvard University (Ph.D., 1990), Bell Laboratories, and Massachusetts Institute of Technology before joining Princeton in 2014. An External Member of the Max Planck Society and 2008 Ho-Am Prize recipient, Seung merges machine learning with neuroscience . Research Focus : Pioneering connectomics , Seung developed technologies for reconstructing neural circuits from high-resolution brain images, including FlyWire for collaborative brain mapping. His work explores brain function, development, and plasticity , drawing parallels between fly visual systems and convolutional networks . Awards & Affiliations : 2008 Ho-Am Prize in Engineering External Member, Max Planck Society Technical Contributions : Led breakthroughs in 3D connected component labeling and high-throughput EM imaging for mammalian brains, partnering with NIH’s BRAIN Initiative to scale connectomics to whole mouse brains. Seung’s team has shifted from EM analysis to interpreting connectomes , focusing on neural circuit function and biological mechanisms in flies and mice. His lab alumni network spans institutions, advancing AI and neuroscience globally.

Ava Fatah gen. SchieckView profile

Professor

• Media Architecture
• Urban Digital Interaction
• Human-Computer Interaction
• +7 more

Dr. Ava Fatah gen. Schieck is a Professor of Media Architecture and Urban Digital Interaction at The Bartlett School of Architecture, University College London (UCL) , where she has been affiliated since 2001. She teaches in the MSc Architectural Computation program, focusing on embodied interactions and urban digital experiences. Education: ATQ03 - Recognised by the HEA as a Fellow (UCL, 2008) Certificate in Learning and Teaching (UCL, 2007) Master of Science (UCL, 2001) Her research explores the intersection of architecture, digital media, and urban spaces. Key themes include: Mediated spatial experiences in museums and public environments Embodied interactions through dance, movement, and performative design Urban screens and community-centered digital installations Smart city critiques focusing on marginalized communities Pervasive computing's impact on social dynamics Her publication corpus (149+ outputs) predominantly addresses media architecture, HCI, museum technology, and sensory interactions , with recent work emphasizing olfactory interfaces, pandemic urbanism, and heritage digitization. Awards & Honors: Fellow of the Royal Society of Arts (FRSA, 2023) Projects & Grants: As Principal Investigator for the EPSRC-funded 'Screens in the Wild' , she examines networked urban screens for community engagement. She previously led the 'SCREAM' project (UrbanBuzz program) and contributed to EPSRC's 'Urban Design and Pervasive Systems'. She reviews grants for EPSRC, UKRI, SNSF, and other international bodies. She directs research through UCL's Space Research Group and VR Centre for the Built Environment , collaborating globally. As program chair for the Media Architecture Biennale (2012-2020), she fosters interdisciplinary dialogue across architecture, HCI, and interaction design.

Yinzhi CaoView profile

Associate Professor

• Security and privacy of machine learning
• Web security and privacy
• Mobile security and privacy
• +1 more

Yinzhi Cao is an Associate Professor at the Johns Hopkins University Department of Computer Science . He serves as Technical Director of the Johns Hopkins Information Security Institute and is affiliated with the Data Science and Artificial Intelligence Institute and the Institute for Assured Autonomy . Cao joined JHU in 2018 from Lehigh University, where he was an Assistant Professor. Doctor of Philosophy (PhD) in Computer Science, Northwestern University (2014) Bachelor of Engineering (BE) in Electronic Engineering, Tsinghua University (2008) Research Interests focus on security and privacy of web, mobile, and machine learning systems . Key projects include Vulnerability Analysis of Web Applications and Security, Privacy, and Fairness Analysis of ML Systems . His work addresses prototype pollution in JavaScript, node.js vulnerabilities, browser fingerprinting, federated learning privacy, and automated exploit generation. Scientific Recognition includes the NSF CAREER Award (2021) DARPA Young Faculty Award (2022) & Director's Fellowship (2024) Amazon Research Awards (2022, 2017) IEEE Security & Privacy Test of Time Award (2025) Distinguished Paper Awards at IEEE S&P 2025, CCS 2023, USENIX Security 2022 Advising & Grants highlight mentorship of 20+ PhD and Master’s students across institutions. Major grants include $1.2M collaborative CICI TCR grant (2024-2026) with Dr. John Aucott $750K DARPA YFA grant (2022-2025) $500K NSF SaTC grant (2022-2025) NSF EAGER grant (2016-2017) Labs & Teams : Affiliated with Johns Hopkins Information Security Institute , Data Science AI Institute , and Institute for Assured Autonomy . Collaborates with institutions like Columbia, UC Santa Barbara, and SRI International. His group investigates real-world vulnerabilities in over 2,500 websites and NPM packages, uncovering 80+ zero-day issues.

Xiaofeng LiuView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Medical Imaging
• +5 more

Xiaofeng Liu is an Assistant Professor at Yale University School of Medicine in the Departments of Radiology & Biomedical Imaging and Biomedical Informatics & Data Science. He is also an Associate Member at the Broad Institute of MIT and Harvard. Previously, he held faculty positions at Harvard Medical School and research roles at Massachusetts General Hospital and Beth Israel Deaconess Medical Center. PhD in Mechatronics from University of Chinese Academy of Sciences Dual Bachelor's degrees in Automation (Wang-Daheng Elite Class) and Communication from University of Science and Technology of China His research integrates trustworthy AI, medical imaging, and data science to improve diagnosis, prognosis, and treatment monitoring for neurological disorders, cancer, and cardiovascular diseases. Key focus areas include domain adaptation techniques, diffusion models, and interpretable AI systems. Led special issues in IEEE Transactions on Pattern Analysis and Medical Image Analysis Developed novel frameworks like Ordinal UDA and Memory-Consistent Adaptation Scientific accolades include the Trailblazer R21 Award (NIBIB), OpenAI Research Award, and National Artificial Intelligence Research Resource Pilot Award. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and actively contributes to MICCAI and NIH review panels. His lab at Yale (XLiu Lab) investigates neural basis of intelligence to inspire AI development, with applications in brain tumor segmentation, cardiac imaging, and cross-modal medical diagnostics.

Ohad FriedView profile

Associate Professor

• Computer Graphics
• Computer Vision
• Generative AI (GenAI)
• +5 more

Ohad Fried is an Associate Professor of Computer Science at Reichman University. He was previously a postdoctoral research scholar at Stanford University under Prof. Maneesh Agrawala and completed his PhD with Prof. Adam Finkelstein as part of the Princeton Graphics group. He holds an M.Sc. in Computer Science and a B.Sc. in Computational Biology from The Hebrew University. His research lies at the intersection of computer graphics, computer vision, and Generative AI , focusing on tools, algorithms, and paradigms for photo and video editing and synthesis . His work has been widely recognized in top conferences including CVPR, SIGGRAPH, and ECCV, with recent contributions to tiled diffusion models, expressive 4D facial motion generation, and synthetic image detection. Ohad has received numerous awards, including the Israel Science Foundation personal research grant (2021) , the Outstanding faculty researcher at Reichman University (2022) , and the Siebel Scholar award (2017) . He has advised multiple students in research projects, and his work is covered by media outlets like Wired , The Washington Post , and CNN . Teaching roles include courses at Reichman University such as "GenAI for Games & Entertainment" and "Synthetic Media Detection", and at Stanford University "Computational Video Manipulation". Key Research Themes: Neural Rendering Diffusion Models 3D Facial Animation Image/Video Editing Media Forensics Scientific Awards: ISF Personal Grant (2021) Siebel Scholar (2017) Google PhD Fellowship (2014-2016) Gordon Y.S. Wu Fellowship (2012-2013) Excellence Scholarships

• Journalism and Communication
• Media Phenomenology
• Philosophy of Technology
• +2 more

Wang Yingji is a tenured professor and doctoral supervisor at the School of Art and Media, Tongji University. He holds a PhD in Literary Studies and has served as director of the China Culture and Art Communication Research Center, member of the China Artists Association, and editorial board member of "Media Criticism" journal. BA: Chinese Language and Literature, Renmin University of China (1995) MA: Journalism, Beijing Normal University (2004) PhD: Literary Studies, Beijing Normal University (2007) His research focuses on the intersections between Journalism and Communication, Media Phenomenology, Philosophy of Technology, and Traditional Chinese Culture. His work explores embodied cognition in media interaction, technological mediation of human perception, and historical epistemology of media systems. The articles demonstrate his expertise in media phenomenology (Hubert Dreyfus analysis), digital reading behavior studies, VR technology ideologies, and historical reconstruction of Chinese communication theories. His publications span from 2016-2022 with consistent CSSCI indexing. National Social Science Fund General Project: "Chinese Modern Public Opinion Thought History" (2021) Major Project Subtopic: "Chinese Media Archaeology in Civilizational Diversity" (2020) Ministry of Education Project: "Husserl's Media and Communication Thought" (2020) Completed NSFC Project: "Rumor Propagation and Governance in Public Emergencies" (2011) As educator, he teaches courses like "Chinese Classical Media Art", "Media Research Methods", and "Communication Theory". His administrative roles include leadership positions in the Chinese Society of Journalism History and multiple journal editorial boards.

• Optimization for Machine Learning
• Collaborative Learning
• Federated Learning
• +6 more

Sebastian U. Stich is a tenured Professor at CISPA Helmholtz Center for Information Security and a member of the European Lab for Learning and Intelligent Systems (ELLIS). His research focuses on optimization for machine learning, collaborative learning (distributed, federated, and decentralized methods), efficient optimization techniques, adaptive stochastic methods, and privacy/security in machine learning. Recent appointments include ERC Consolidator Grant 2024 for the CollectiveMinds project. Key contributions in federated/decentralized learning, uncertainty estimation, and communication-efficient optimization. Active in workshop organization, including the Optimization for Machine Learning workshop at NeurIPS 2024. Teaching modern optimization methods at Saarland University (2023-2025). His work has been recognized with awards such as the Google Research Scholar Award (2023) and Meta Privacy-Enhancing Technologies Award (2022) . His research group includes postdocs Dr. Anton Rodomanov and Dr. Rotem Mulayoff, and PhD students Xiaowen Jiang and Yuan Gao.