Find Academic Supervisors

Lizzie MullerView profile

Associate Professor

• Critical Heritage Studies
• Museum and Archive Studies
• Art History and Theory
• +9 more

Associate Professor Lizzie Muller serves as Director of Research in the School of Art & Design at the University of New South Wales. An accomplished curator and researcher, she specializes in audience experience and interdisciplinary collaboration, with a particular focus on the future of museums as sites of knowledge production. Her research bridges curatorial practice with theories and methods from participatory design and interaction design, developing audience-centered curatorial methodologies and innovative approaches to audience research. Her work extends to preservation and archiving, particularly experiential documentation and oral histories of media art. Co-author of Curating Lively Objects: Exhibitions Beyond Disciplines (Routledge Museum Studies Series, 2022) Elected Councillor of the Sydney Culture Network Co-founder of the bi-monthly Sydney Culture Data Salon with Keir Winesmith Muller's research output demonstrates a consistent trajectory exploring the intersection of art, science, and technology through major international exhibitions including Human Non Human (Powerhouse Museum, 2018/19) and A Working Model of the World (staged across UNSW Galleries, Parsons School of Design, and University of Dundee). Her recent work focuses on art-science collaborations, deep time history through the ARC Centre for Excellence in Australian Biodiversity and Heritage, and the development of audience-centered curatorial frameworks. Scientific Awards: 2024 ADA Deans Award for Excellence in Higher Degree Supervision Muller leads significant research initiatives as Chief Investigator on multiple ARC-funded projects including the ARC Linkage project Curating Third Space: The Value of ArtScience Collaboration and SSHRC-funded research on The Living Effect and Curating Lively Objects . She has supervised numerous PhD and MFA candidates, with a special focus on curatorial practice-based research, and currently serves as Program Director of the Master of Curating and Cultural Leadership at UNSW Art & Design.

• Biomedical Imaging
• X-ray Computed Tomography
• Phase-Contrast Imaging
• +6 more

Prof. Julia Herzen holds the Associate Professorship of Physics in Biomedical Imaging at the Department of Physics , TUM School of Natural Sciences , Technical University of Munich . Her research focuses on advancing X-ray imaging techniques using synchrotron radiation and laboratory sources, with applications in medical diagnostics and tissue analysis. Position: Associate Professor Department: Physics School: TUM School of Natural Sciences University: Technical University of Munich Contact: julia.herzen@tum.de Her core research interests include: Quantitative multi-modal X-ray imaging (spectral & phase-contrast) 3D virtual histology of human tissue Breast cancer detection improvement Lung disease imaging (emphysema, pneumonia) X-ray phase-contrast tomography Dark-field imaging material decomposition Recent publications demonstrate expertise in dark-field imaging for lung pathology , phase-contrast CT for organoid visualization , and spectral X-ray applications in multi-material differentiation . Her team explores clinical translation of X-ray techniques for non-invasive diagnostics . She supervises PhD students and teaches Biomedical Engineering courses, including: Quantitative X-Ray Imaging (3 VI) Image Processing in Physics (2 VO) Biostatistics (2 VO) Advanced Lab Courses in X-ray Micro-CT

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.

Michel LarocheView profile

Professor

• Consumer Behavior
• Culture
• Social Media
• +7 more

Michel Laroche serves as Professor of Marketing and holds the Royal Bank Distinguished Professorship at Concordia University's John Molson School of Business. He maintains active research leadership through editorship of the Canadian Journal of Administrative Sciences and extensive scholarly contributions across global marketing domains. His educational background includes a Diplôme d'ingénieur from École Centrale de Paris, M.S.E. from Johns Hopkins, and M.Ph./Ph.D. from Columbia University, complemented by an honorary Doctor of Science from the University of Guelph. This multidisciplinary foundation underpins his research approach. Consumer behavior modeling Social media/digital marketing analytics Cross-cultural retailing dynamics Advertising effectiveness in global contexts Internet of Things consumer adoption Artificial intelligence in marketing systems His publication portfolio demonstrates consistent innovation in digital consumer behavior, with recent work examining social commerce cultural dynamics, TikTok authenticity metrics, and pandemic-driven media consumption patterns. Articles consistently integrate technological advancement with deep cultural analysis. Fellow of the Royal Society of Canada Recipient of the Jacques-Rousseau Medal for multidisciplinary research Living Legend of Marketing Award (HEC-Montreal) Hans B. Thorelli Award for international marketing research JBR Outstanding Associate Editor Award (namesake) Laroche has supervised 64 M.Sc. and 27 Ph.D. theses while securing major funding from SSHRC and FQRSC. His editorial leadership spans 16 years as Managing Editor of the Journal of Business Research and current Editor-in-Chief role at CJAS , establishing significant infrastructure for marketing scholarship. Professional service includes presidential roles in ASAC and AMS, plus extensive journal editorial board participation.

Jacques GautierView profile

Assistant Professor

• Meteorological data visualization
• Epidemiological data visualization
• 2D/3D Geovisualization
• +2 more

Jacques Gautier is an Assistant Professor in Geovisualization at LASTIG, part of the French National Geographic Institute (IGN France) since September 2020. He is a member of the GEOVIS research team focusing on advanced geovisualization techniques for spatio-temporal data analysis. Prior to his current position, he served as a Postdoctoral Researcher at LASTIG working on the Urclim European project, developing geovisualization methods for climate data in urban environments. His educational background includes a PhD in Geography from Université Grenoble Alpes (2015-2018), where his dissertation focused on "GrAPHiST: An exploratory analysis approach for identifying the dynamics of spatio-temporal phenomena," and an Engineering degree in Geographical Information Science from ENSG (2009-2012). Dr. Gautier's research focuses on innovative approaches to visualize complex spatio-temporal data across multiple domains. His expertise spans meteorological data visualization, epidemiological data visualization, 2D/3D geovisualization techniques, and exploratory data analysis of spatio-temporal phenomena. He has developed specialized methods for identifying cyclic patterns in time-series data, visualizing uncertainty in ensemble forecasting systems, and creating interactive visualization environments for domain experts in urban planning, public health, and emergency response. Analysis of Dr. Gautier's publication record reveals a consistent focus on developing visualization techniques that bridge theoretical advances with practical applications. His work spans urban climate analysis, pandemic response (particularly during COVID-19), and mountain rescue operations. A distinctive aspect of his research is the integration of harmonic analysis with visual exploration to identify cyclic patterns in spatio-temporal data, as demonstrated in his GrAPHiST framework. Dr. Gautier has been actively involved in several significant research projects including ORACLES (focusing on ensemble forecasts of marine submersion), Urclim (aiming to develop integrated Urban Climate Services), and Choucas (an interdisciplinary project to assist mountain rescue operations). These projects highlight his ability to translate visualization research into practical decision-support tools for critical situations. As a member of the GEOVIS research team, Dr. Gautier contributes to advancing geovisualization methodologies through both theoretical development and practical implementation. His work on mixed temporal diagrams, helical time representations, and uncertainty visualization has provided new approaches for exploring complex spatio-temporal datasets across multiple disciplines.

• Political Science
• Comparative Politics
• Latin American Politics
• +5 more

Hélène Combes is a CNRS Research Professor affiliated with the Center for International Studies (CERI) at Sciences Po Paris. She directs research on political parties and social movements with a specific focus on Latin America, particularly Mexico. Her work bridges comparative politics, urban studies, and political sociology, examining how spatial organization, class dynamics, and gender intersect in political mobilization. Combes specializes in the relationship between political parties and social movements, with extensive fieldwork in Mexico spanning over 15 years. Her research examines political clientelism, urban protest movements, and the spatial dimensions of political mobilization. She has conducted in-depth studies on the emergence of the Morena party in Mexico and the transformation of social movements into political power, culminating in her 2024 book De la rue à la présidence. Foyers contestataires à Mexico . Her work reveals how working-class communities manage political occupations and how family dimensions shape protest movements in Latin America. Combes has served as director of the journal Critique internationale (2017-2023), is a member of the board of the French Political Science Association (AFSP) since 2017, and serves on the scientific committee of Casa de Velázquez since 2018. Her international recognition is evident through her invited professorships at Columbia University, University of Salamanca, Pablo de Olavide University, University of Mar de Plata, University of Chile, and various Mexican institutions. Currently teaching in the Master's research program in political science, university college, and School of International Affairs at Sciences Po Paris, Combes has previously taught at Beaux-Arts de Paris and University of Dauphine. Her work on Mexican politics has gained particular relevance with Claudia Sheinbaum (whom she followed for 15 years) becoming Mexico's presidential candidate. Combes holds a doctorate in engineering with specialization in climate change and ecology, bringing a transnational perspective to her political analysis.

Omobolanle OgunseijuView profile

Assistant Professor

• Workforce Development
• Construction Safety
• Artificial Intelligence
• +7 more

Omobolanle Ogunseiju is an Assistant Professor in the School of Building Construction at Georgia Institute of Technology . She holds a Ph.D. in Environmental Design and Planning from the Department of Building Construction at Virginia Tech. Education: Ph.D. in Environmental Design and Planning, Virginia Tech Current Role: Assistant Professor, Georgia Tech School of Building Construction Her research focuses on integrating wearable robotics and Artificial Intelligence (via digital twin , cyber-physical systems , and data sensing ) to improve construction workforce safety, health, and well-being . She explores ethical implications of automation in construction, particularly in human-technological dynamics. Key research trends include: Advancing smart communities through robotics and AI Exoskeleton evaluation for ergonomic risk reduction Mixed reality environments for construction education Data analytics for cognitive and physical risk assessment Professional identity development in construction engineering students Industry-academia alignment for sensing technology integration Scientific awards: Outstanding Doctoral Candidate, Myers-Lawson School of Construction Outstanding Doctoral Student, College of Architecture and Urban Studies at Virginia Tech Teaching philosophy emphasizes experiential learning , engagement techniques , and hierarchical assessments . She developed the Construction Cost Management course at Georgia Tech and will lead Construction Technology courses. Previously, she taught Smart Construction , Building Systems Technology , and Wireless Sensing in Construction Management at Virginia Tech.

Kilian Q. WeinbergerView profile

Professor

• Machine Learning
• Computer Vision
• Deep Learning
• +4 more

Kilian Q. Weinberger is a Professor of Computer Science at Cornell University's College of Engineering, focusing on Machine Learning, Deep Learning, and AI applications. He has held previous roles as Associate Professor at Washington University in St. Louis and Research Scientist at Yahoo! Research. His research spans metric learning, resource-constrained learning, Gaussian Processes, and advancements in 3D perception for autonomous systems. Education : Ph.D. in Machine Learning (University of Pennsylvania), BA in Mathematics and Computing (University of Oxford) Key Research Areas : AI in Science, Computer Vision, Autonomous Vehicles, and Neural Network Efficiency His recent work emphasizes interpretable machine learning, large language models, and multimodal applications. Awards include NSF CAREER (2012) Daniel M Lazar '29 Teaching Award (2016) Ann S. Bowers Excellence Award (2024) ACM and AAAI Fellow (2024) He teaches advanced courses like CS6784 (Cornell) and has mentored numerous PhD students across institutions. Current affiliations include the Sloan Research Fellowships Selection Committee since 2024.

Adela IsvoranuView profile

Assistant Professor

• Network Analysis
• Psychopathology
• Mental Health Modeling
• +3 more

Adela Isvoranu serves as an Assistant Professor in the Department of Psychology at the National University of Singapore (NUS), where she investigates developmental pathways from mental health to mental illness using complexity-based frameworks. Her work challenges traditional syndrome-focused approaches by emphasizing symptom interactions within psychopathological networks. Academic Background: Ph.D. (cum laude) from University of Amsterdam M.Sc. from University of Amsterdam B.Sc. (Hons.) from University of Leeds Her research program integrates network analysis with clinical psychology to model mental health conditions as dynamic systems. This approach examines fuzzy boundaries between diagnostic categories and aims to develop novel intervention strategies through collaborations with clinical psychologists, practitioners, and methodologists. Her methodological innovations focus on translating complex network theory into practical clinical applications. Recent publications reveal a strong trajectory in advancing network psychometrics methodology while applying these frameworks to psychosis and trauma research. The 2022 book establishes foundational R-based tools for behavioral scientists, the 2021 methodological paper provides crucial estimation guidelines, and the 2017 psychosis study demonstrates clinical utility in mapping trauma-symptom pathways. Scientific Awards: No awards explicitly mentioned in source material While specific student mentorship details are unavailable, her collaborative research environment with clinical teams and methodologists indicates active supervision of research projects. Her work is supported through university affiliations with infrastructure including dedicated research space (AS4-02-32) and computational resources for network analysis. Research Environment: Operates within NUS Psychology Department's clinical research ecosystem Collaborates with GROUP Investigators consortium Integrates statistical methodology with clinical practice Maintains research website (www.adelaisvoranu.com) for dissemination

Aaron PuriView profile

Assistant Professor

• Chemical Ecology
• Drug Discovery
• Microbiome
• +7 more

Aaron Puri is an Assistant Professor of Chemistry at the University of Utah, specializing in chemical ecology and natural product discovery. His research focuses on bacterial interactions in methane-oxidizing communities and the biosynthesis of secondary metabolites. Education: B.S. from University of Chicago, Ph.D. from Stanford University School of Medicine Dr. Puri's work bridges microbiology and chemistry, with projects targeting: Chemical Ecology: Decoding interspecies signaling in methane-oxidizing bacteria Natural Products: Discovering therapeutics from underexplored bacterial genomes Biosynthesis: Activating cryptic gene clusters for novel compound production Recent publications highlight advancements in quorum sensing mechanisms (2025), inverse stable isotopic labeling techniques (2024), and methanotroph community dynamics. His research also explores spatially resolved model ecosystems for studying microbial phenotypes (2023-2024). Key methods include GNPS Dashboard for mass spectrometry analysis (2021-2022). Dr. Puri leads the CAREER-funded project on quorum sensing in methanotrophs (2024) and has developed genetic tools for industrial methanotrophs (2015). His lab maintains a strong focus on environmental microbiology and biotechnological applications.

Steve TanimotoView profile

Professor

• Novice programming environments
• Collaborative problem-solving environments
• Technology for educational assessment
• +11 more

Steve Tanimoto is a Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, with an adjunct appointment in the Department of Electrical & Computer Engineering. His work focuses on human-centered computing, particularly in educational technology and collaborative problem-solving environments. He has made significant contributions to the understanding of liveness in programming environments and their application to education, including a keynote at the International Conference on Live Coding (2015) that traced historical influences leading to widespread use of liveness in modern software environments. Dr. Tanimoto's research spans several interconnected domains: Novice programming environments and educational technology Collaborative problem-solving environments and tools Technology for educational assessment, particularly using pattern-recognition methods for teaching written language on tablets Liveness in programming environments and its applications Image processing from interdisciplinary perspectives (as detailed in his MIT Press book "An Interdisciplinary Introduction to Image Processing: Pixels, Numbers, and Programs") His recent publications demonstrate a consistent focus on the intersection of computing education, human-computer interaction, and collaborative problem-solving. A notable trend is the exploration of "liveness" in programming environments and how this concept can enhance educational experiences. His work increasingly integrates AI technologies with educational applications, particularly in the areas of writing instruction and collaborative problem-solving, with significant NIH funding support (P50 HD071764 and U54 HD083091). His notable recognition includes: VL/HCC Best Showpiece Award in 2015 for "Solving Problems by Drawing Solution Paths" Dr. Tanimoto has advised several graduate students through to completion, including Robert Thompson (2019), Sandra Fan (2013), and Tyler Robison (2012). He currently advises Emilia Gan (co-advised with B. Mako Hill) and Edward Misback. His research has been supported by NIH grants for work on computerized writing and reading instruction for students with learning disabilities. His CoSolve research group has developed experimental facilities for collaborative problem-solving, exploring tools that support problem formulation, visualization of problem spaces, and team collaboration dynamics, with applications in education, design, and various problem-solving domains.

Amber DoironView profile

Assistant Professor

• Biomaterials
• Nanotechnology
• Drug Delivery
• +5 more

Amber Doiron is an Assistant Professor and Graduate Program Director in the Department of Electrical and Biomedical Engineering at the University of Vermont (UVM), part of the College of Engineering and Mathematical Sciences. Her research focuses on developing biomaterials and nanotechnology solutions for drug delivery systems, wound care, and MRI contrast agents. Applications include combating bacterial biofilms, detecting inflammation, and studying nanoparticle-cell interactions. Doiron holds an M.S. and Ph.D. in Biomedical Engineering from the University of Texas at Austin. She completed her postdoctoral training as the T. Chen Fong Fellow in Radiology and Chemical Engineering at the University of Calgary. She also served as Chief Scientific Officer at NanoPulse Biosciences, a startup developing gold nanoparticle biomedical applications. Her research interests span biomaterials design, nanomedicine, and translational applications. Key areas include drug delivery mechanisms, magnetic resonance imaging contrast agents, and strategies to disrupt biofilm infections. She actively integrates education through courses like BME 5150 (Nanobiomaterials) and BME 2050 (Materials and Transport). Recent work explores nanoparticle-based inflammation detection, synergistic antimicrobial therapies, and cerebrovascular effects of nanoparticles. Her lab investigates ROS generation in biofilm control and metabolite-depleting materials for anti-biofilm strategies, supported by grants like the NSF CAREER award. Doiron’s educational initiatives include project-based learning approaches and STEM outreach programs aimed at expanding gender diversity in engineering. Her lab’s interdisciplinary focus bridges materials science, biomedical applications, and clinical translation.

David KlindtView profile

Assistant Professor

• NeuroAI
• Computational Neuroscience
• Sensory Processing
• +3 more

David Klindt is Assistant Professor at Cold Spring Harbor Laboratory, leading research at the intersection of biological systems and artificial intelligence. His lab investigates how brains process sensory information and generalize knowledge across contexts, studying neural representations to inspire robust AI models. Research combines computational neuroscience and machine learning to develop algorithms mimicking biological learning efficiency. Current projects examine latent computing in biological neural networks through dynamical systems frameworks, sparse coding principles in neural representations, and geometric organization in visual processing. His group develops methods for mechanistic interpretability, self-supervised learning identifiability, and compute-efficient inference. Recent publications analyze toroidal representations in grid cells, retinal feature detection, and Cryo-EM structure disentanglement. Dr. Klindt's work has been recognized through publications in Nature Communications, eLife, and NeurIPS. Before joining CSHL, he was a Machine Learning Research Scientist at Meta Reality Labs and postdoctoral researcher at Stanford University and NTNU. He holds a Ph.D. in Computational Neuroscience and Machine Learning from the University of Tübingen.

Alexei KoulakovView profile

Professor

• Neuroscience
• Theoretical Neurobiology
• Applied Mathematics
• +5 more

Alexei Koulakov is a Professor at Cold Spring Harbor Laboratory (CSHL) and the Charles Robertson Professor of Neuroscience. His research focuses on applying mathematical and computational approaches to unravel the principles of brain organization, particularly in sensory systems like olfaction and vision. Koulakov's work explores how neural circuits form during development, the role of genetic and experiential factors, and the evolutionary basis of brain architecture. Education: PhD in Physics from the University of Minnesota (1998). Key Research Areas: Olfactory system development, neural network modeling, and AI inspired by biological computation. Koulakov's recent publications emphasize cross-disciplinary integration of neuroscience and AI, including NeuroAI initiatives and DeepNose models predicting olfactory percepts. His team investigates how innate abilities are encoded genomically and how experience shapes neural networks. Scientific contributions include studies on primacy coding in olfaction, stochastic learning mechanisms , and high-throughput neural mapping . Awards include the Charles Robertson Professorship , reflecting his leadership in theoretical neuroscience. Koulakov collaborates extensively, with notable work on genomic bottlenecks , odor mixture interactions , and neural integrator models . His lab at CSHL is at the forefront of NeuroAI research, leveraging brain circuit insights to advance artificial intelligence.

Robert P. AndersonView profile

Professor

• Biogeography
• Species Distribution Modeling
• Biodiversity Conservation
• +5 more

Robert P. Anderson is a Professor of Biology in the Division of Science at City College of New York (CCNY), part of the City University of New York (CUNY) system. His research laboratory is located in Marshak Science Building (Room 810), with additional affiliation as a Research Associate at the American Museum of Natural History (AMNH) Mammalogy Department. As a Highly Cited Researcher (2019-2023) and AAAS Fellow (2023), he leads an interdisciplinary biogeography research program focused on modeling species niches and distributions. Dr. Anderson's research spans biodiversity modeling, biogeography, and ecology with specialization in mammals. His lab develops ecological modeling software widely applied in conservation biology, invasive species management, zoonotic disease studies, and climate change impact assessments. Key research themes include: Characterizing spatial configuration of environmental suitability for species Developing machine learning approaches (particularly Maxent) for species distribution modeling Studying climate change effects on biodiversity Conservation applications of biogeographic models Neotropical mammal systematics and ecology His work has resulted in significant software contributions including Wallace, ENMeval, and spThin, with recent publications emphasizing methodological improvements in species distribution modeling and conservation applications. The lab maintains active projects funded by NASA and the National Science Foundation, focusing on small mammals of North and South America. Scientific recognition includes: AAAS Fellow (2023) Web of Science Highly Cited Researcher (2019-2023) Blavatnik Science Scholar (New York Academy of Sciences) Most Downloaded Paper in Ecography (2023-2024) Most Cited Paper in Ecography (2023) Dr. Anderson mentors graduate students through the CUNY Graduate Center and CCNY Master's programs, with recent advisees receiving prestigious awards including the ASM Horner Award and NASA FINESST Fellowship. His lab trains students in environmental biology through interdisciplinary research combining fieldwork, morphology, climatology, remote sensing, physiology, and genetics. Current lab members include Andrew Gaier (NASA Fellow), Mariano Soley-Guardia, and Kass (lead author on highly cited Wallace v2 paper). The Anderson Lab operates from CCNY's Marshak Science Building as part of the university's biodiversity group studying ecology, evolution, and geography of life on Earth. The lab emphasizes software co-design between end-users and developers to enhance conservation utility, with recent work focusing on neighborhood approaches for range estimation and operationalizing expert knowledge in species assessments.