Find Academic Supervisors

Max TorbensonView profile

Assistant Professor

• Paleoclimatology
• Hydroclimatic Variability
• Dendrochronology
• +3 more

Dr. Max Torbenson serves as an Assistant Professor at Texas A&M University, specializing in paleoclimatology and dendrochronology. His research focuses on hydroclimatic variability over the past millennium using tree-ring proxies . With fieldwork spanning multiple continents, he combines climate reconstruction with modern environmental challenges in water resources and risk management. Education: PhD in Geoscience, University of Arkansas (2019) MA in Geography, University of Minnesota (2014) BSc in Archaeology-Palaeoecology, Queen's University Belfast (2011) His research emphasizes paleoclimatic extremes , ENSO teleconnections , and drought patterns across diverse regions including the Rocky Mountains, Tibetan Plateau, and Amazon Basin. Recent work explores anthropogenic signals in climate proxies and methodological improvements for tree-ring analysis. Publications reveal expertise in multi-proxy integration , climate reconstruction techniques , and historical climate-society interactions . His fieldwork spans the American Southwest, Spanish Pyrenees, and subtropical catchments, with current work focusing on temperature trends in the Mediterranean region and El Niño impacts on North American precipitation.

Camilo MoraView profile

Professor

• Climate Change Impacts
• Biodiversity Conservation
• Environmental Science
• +5 more

Camilo Mora is a Professor in the Department of Geography at the University of Hawaii at Manoa, where he maintains an active research laboratory and teaches courses on environmental issues, biogeography, and data analysis. His academic journey began with a BSc in Marine Biology from Universidad del Valle in Colombia (1999), followed by a PhD in Biology from the University of Windsor, Canada (2005). He completed postdoctoral fellowships at the University of Auckland (2005), Scripps Institution of Oceanography (2006-2008), and Dalhousie University (2008-2010). BSc, Marine Biology, Universidad del Valle, Colombia (1999) PhD, Biology, University of Windsor, Canada (2005) Postdoctoral Fellow, University of Auckland (2005) Postdoctoral Fellow, Scripps Institution of Oceanography (2006-2008) Postdoctoral Fellow, Dalhousie University (2008-2010) Mora's research spans interconnected lines focused on understanding biodiversity patterns and their modification by human activities, with particular emphasis on climate change impacts. His lab specializes in big data analytics applied to diverse environmental challenges including heatwaves, disease transmission, marine ecosystems, and even unconventional topics like Bitcoin's environmental footprint. The Mora Lab operates on a 'divide and conquer' approach to tackle large research questions by breaking data gathering into individual parts that can be concatenated into central databases. Mora has received the CSS Excellence in Research award (2014) for his significant contributions to environmental science. His influential publications include groundbreaking work on the global risk of deadly heat (2017), the projected timing of climate departure from historical variability (2013), and the finding that over half of known human pathogenic diseases can be aggravated by climate change (2022). CSS Excellence in Research (2014) Highly cited publications in Nature and Nature Climate Change Research featured in major international media outlets Development of innovative research methodologies for large-scale analyses Mora leads an active research group that engages students in the full scientific process from idea generation to publication. His approach to mentoring involves creating yearly classes where graduate students, professors, and international advisors collaborate to tackle significant research questions, with papers typically completed within a single semester. His Carbon Neutrality Challenge project, spearheaded by his daughter Asryelle Mora, provides a practical mechanism for individuals to offset carbon emissions through tree planting. The Mora Lab maintains a distinctive approach to environmental research, working on seemingly diverse topics from reef fishes to Bitcoin, united by their reliance on big data analytics. This interdisciplinary methodology has produced impactful research across multiple domains of environmental science and climate change impacts, establishing Mora as a significant contributor to our understanding of humanity's environmental challenges.

• Epidemiological Modeling
• Agent-Based Simulation
• Public Health Policy
• +3 more

Martin Bicher is a PostDoc Researcher at TU Wien, affiliated with the Department of Data Science under the Faculty of Informatics. He specializes in agent-based simulation, epidemiological modeling, and decision support systems for public health crises. His work focuses on optimizing resource allocation, vaccination strategies, and policy evaluation during pandemics. He teaches courses such as Modeling and Simulation (194.076), Modelling and Simulation in Health Technology Assessment (194.094), and Advanced Modeling and Simulation (194.056). His research is supported by projects like DynOptTestControl (2022–2026) and KLIPHA-COVID19 (2020–2021). Key research interests include agent-based modeling frameworks, integration of machine learning into simulation systems, and multi-criteria decision support for public health interventions. His publications analyze pandemic response strategies, vaccination prioritization, and the impact of environmental factors on disease spread. Recent work includes developing mathematical models for equitable disease testing, simulating vaccination strategies under supply uncertainties, and evaluating contact-tracing policies. He collaborates with interdisciplinary teams to address challenges in healthcare resource optimization and policy design. Advising two students, Bicher has mentored theses on railway simulation and delay modeling. His contributions to pandemic decision support have been featured in high-impact journals like Omega and PLoS ONE.

Bon Woo KooView profile

Assistant Professor

• Geospatial Urban Analytics
• Walkability and Active Living
• GIS (Geographic Information Systems)
• +4 more

Dr. Bon Woo Koo is an Assistant Professor at the School of Urban and Regional Planning , Toronto Metropolitan University. His expertise lies in geospatial urban analytics, walkability, and GIS applications, focusing on urban design for public health equity and innovative data science tools. He holds a PhD in City & Regional Planning from Georgia Institute of Technology, a Master’s in Landscape Architecture from Seoul National University, and a Bachelor’s in Interior Design from Kookmin University. Education: PhD in City and Regional Planning, Georgia Institute of Technology Master of Landscape Architecture, Seoul National University Bachelor of Interior Design, Kookmin University Research Interests: Dr. Koo investigates urban environments’ impact on health and well-being, equity in environmental amenities (e.g., tree canopies), and advanced GIS techniques. He develops automated audit methods for walkability and explores spatial modeling for urban sustainability. His work bridges data science with policy, contributing to CDC health surveillance and smart city initiatives. Publications: His research appears in journals like Landscape and Urban Planning , Environment and Behavior , and Health and Place , with a focus on walkability audits, urban tree equity, and audio-based pedestrian sensing. Recent work addresses post-pandemic mental health and broadband equity strategies. Professional Engagement: He has advised the CDC’s technical panel on leveraging big data for health policy, presented at conferences like the Association of Collegiate Schools of Planning, and collaborated with institutions like Universitas Gadjah Mada and the Atlanta Regional Commission.

Supartha PodderView profile

Assistant Professor

• Quantum Computing
• Complexity Theory
• Quantum Cryptography
• +5 more

Supartha Podder is an Assistant Professor in the Department of Computer Science at Stony Brook University, located in Stony Brook, NY. He holds a PhD from the Centre for Quantum Technologies at the National University of Singapore and master's degrees from École Normale Supérieure de Cachan (now ENS Paris Saclay) and the Chennai Mathematical Institute. Before joining Stony Brook, he was a postdoctoral researcher at the University of Ottawa under Anne Broadbent and at the University of Texas at Austin under Scott Aaronson. His research focuses on quantum and classical complexity theory, particularly exploring scenarios where quantum computation surpasses classical methods. Key interests include quantum cryptography, quantum algorithms, and the intersection of complexity theory with cryptographic systems. He also investigates foundational topics like analysis of Boolean functions and communication complexity. Podder teaches graduate and undergraduate courses in theoretical computer science, including the Theory of Computation (CSE 540, CSE 350) and advanced topics in Quantum Computing and Applications (CSE 550). He actively mentors a research group with PhD candidates and undergraduates, focusing on quantum computing and related areas. His service roles include program committee memberships for TQC 2024 and QCNC 2025, NSF panel participation, and session chair roles at ITCS 2025. He leads the AALO charity initiative with undergraduates to support underprivileged students in India. His research has led to notable contributions in quantum algorithms, communication complexity, and cryptographic protocols, with recent work appearing in venues like STOC, FOCS, and SICOMP.

Shabnam Sadeghi EsfahlaniView profile

Associate Professor

• Robotics
• Mechatronics
• Artificial Intelligence
• +3 more

Dr. Shabnam Sadeghi Esfahlani is an Associate Professor in Robotics at the School of Engineering and the Built Environment, Anglia Ruskin University , where she serves as Deputy Leader of the BORI research group and leads the Automation & Robotics MSc program. Her interdisciplinary expertise spans mechatronics, artificial intelligence, virtual reality, and serious games , with a focus on applications for rehabilitation, medical training, and autonomous systems . As a Chartered Engineer and Senior Fellow of the Higher Education Academy , she has secured significant funding from Innovate UK, Horizon 2020, and GCRF , with grants exceeding £3 million. Education PhD in Mechanical Engineering, Anglia Ruskin University BSc (First Class) in Statistics & Mathematical Science, Shahid Beheshty University Her research integrates AI with robotics for societal impact, exemplified by the open-source SROBO ground robot and projects like Rehabgame and the Assistive Feeding Robot . She has published over 45 peer-reviewed articles and contributes to academic communities as a journal guest editor and conference organizer . Key collaborations include IET, IMechE, and the Nuffield Foundation as a mentor for young students. Scientific Awards & Recognitions: Chartered Engineer (CEng), Engineering Council UK Senior Fellow (SFHEA), Higher Education Academy Student-Voted 'Made a Difference Award' (2018) Post-Graduate Certificate in Higher Education

Aaron WeiskittelView profile

Professor

• Forest Biometrics
• Forest Growth and Yield Modeling
• Climate Change Ecology
• +3 more

Aaron Weiskittel is a Professor of Forest Biometrics and Modeling and the Irving Chair of Forest Ecosystem Management at the University of Maine’s School of Forest Resources. He directs the Center for Advanced Forestry Systems and the Center for Research on Sustainable Forests . His research focuses on developing quantitative tools for forest management, including growth and yield modeling, climate change impacts, and sustainable forestry practices. He holds a PhD (2007), MS (2003), and BS (2001) in forest resources from Oregon State University and The Ohio State University, respectively. Research interests include: Forest biometrics and modeling Climate change effects on forest productivity Stem taper equations and volume estimation Old-growth forest structure analysis Commercial thinning impacts on stand dynamics Key contributions include: Development of growth models for the Acadian Region Evaluation of climate change on forest productivity and species distribution Advancements in forest biomass and carbon estimation Collaborative studies on northern white-cedar silviculture His work integrates field data, remote sensing, and computational modeling to address complex forest management challenges. Ongoing projects focus on carbon MRV frameworks, forest carbon markets, and stakeholder-driven climate adaptation strategies.

• Transportation modeling
• Applied operations research

Jee Eun (Jamie) Kang is an Associate Professor in the Department of Industrial and Systems Engineering at the University at Buffalo's School of Engineering and Applied Sciences. Research focuses on transportation modeling and applied operations research, with applications in urban mobility, shared autonomous vehicles, and sustainable transportation systems. Education includes a PhD from UC Irvine. Research emphasizes data-driven approaches to travel behavior, electric vehicle adoption, and humanitarian logistics. Publications consistently address mobility innovation, including pricing strategies for emerging services, predictive analytics for transit, and optimization of shared transportation systems.

Zhao NaView profile

Assistant Professor

• Artificial Intelligence
• Augmented Intelligence
• Data Science
• +7 more

Dr. Zhao Na is a tenure-track Assistant Professor at the Singapore University of Technology and Design (SUTD), affiliated with the Institute of Sustainable Technology and Design (ISTD). She holds a Ph.D. in Computer Science from the National University of Singapore (NUS), where her thesis on 3D point cloud semantics earned the IMDA Excellence Prize. Her research bridges computer vision and machine learning, focusing on scene understanding, data-efficient learning, and domain generalization. Education: Ph.D. in Computer Science (NUS, 2021); Prior roles include Research Fellow at NUS. Research interests emphasize 3D scene analysis, object detection, semantic segmentation, and robust learning under noisy or limited data. Her work addresses challenges in multi-modal learning, continual learning, and open-world scenarios. Recent projects include geometry-semantics synergy in neural fields and cross-modal augmentation for visual grounding. Publications span top-tier venues like CVPR, ECCV, and ICCV, with a focus on 3D vision and AI. Key contributions include the PCTeacher framework for semi-supervised segmentation and Static-Dynamic Co-Teaching for incremental learning. Scientific Awards: IMDA Excellence Prize (2021). Active grants include a DSO Research Grant (2023–2026) and A*STAR MTC Grant (2023–2026). She leads the SUTD-ZJU Thematic Grant on 3D scene understanding (2022–2024). Laboratory/Team: Research group at ISTD/SUTD focuses on advancing AI-driven 3D perception and scene understanding systems.

Matthew CollinsonView profile

Senior Lecturer

• Logic
• Theoretical Computer Science
• Information Security
• +12 more

Matthew Collinson is a Senior Lecturer in Computing Science at the University of Aberdeen, where he also serves as Head of Computing Science and Academic Line Manager. He holds an affiliation with the Scottish Informatics and Computer Science Alliance (SICSA) and leads the EPSRC-funded project SSPEDI (Supporting Security Policy with Effective Digital Intervention). Education: BSc Mathematics, University of Edinburgh (1997) MSc Mathematical Logic, University of Manchester (1998) PhD Computer Science, University of Manchester (2003) Research Interests: His research spans theoretical computer science and cybersecurity , focusing on non-classical logics (intuitionistic, modal, substructural), semantics of computation , concurrency theory , and type theory . He applies these foundations to information security , particularly in modelling security policies, access control, and the economics of cybersecurity decisions. His work integrates formal verification , simulation tools (e.g., Gnosis), and game-theoretic models . Publications Trends: Recent publications (2016–2022) emphasize human-centred security , exploring how persuasion and behavioural interventions can reduce cybersecurity vulnerabilities. Earlier works (2008–2015) concentrate on mathematical systems modelling , layered graph logics , and trust domains , bridging high-level policy and low-level system configurations. Projects & Grants: SSPEDI (2017–2020, EPSRC): Human dimensions of cybersecurity policy compliance. ALPUIS (EPSRC consortium): Algebra and logic for security policy and utility. Trust Domains (RCUK/TSB, 2011–2014): Framework for modelling secure information sharing. Seconomics (EU FP7, 2012–2015): Socio-economic impacts of cybersecurity regulation. PhD Supervision: He has successfully supervised PhD students including Kevin McDonald (2014), Barry Taylor (2015), and Robert (Bob) Duncan (2016), whose theses addressed logic-based security architectures, vulnerability analysis, and cloud stewardship respectively. Labs & Teams: His research is conducted within the Computing Science section of the School of Natural and Computing Sciences, leveraging collaborations with National Grid, HP Labs, and other academic partners.

Ida ScheelView profile

Associate Professor

• Bayesian Statistics
• Data Science
• Machine Learning
• +7 more

Ida Scheel is an Associate Professor in Statistics and Data Science at the University of Oslo , Department of Mathematics. She specializes in Bayesian hierarchical modeling, recommendation systems, and stochastic processes on networks. Her research interests include: Bayesian statistics and model diagnostics Data science applications in environmental and health domains Network-based machine learning Uncertainty quantification in predictive modeling Recent publication trends show a focus on Bayesian model validation, machine learning for product adoption prediction, and real-estate analytics. She contributes to interdisciplinary projects like BigInsight and CELS . Scientific awards : Sverdrup Prize for Young Researchers (2011) Advising : Supervised 8 PhD students (main/co-supervisor) in areas spanning Bayesian causal effects, neural network survival analysis, and model conflict detection. Key grants include participation in the Data Science@UiO and Integreat projects. Labs/teams : Active member of the Center for Computational Inference in Evolutionary Life Science (CELS) and the BigInsight center.

Cindy GrimmView profile

Professor

• Robotics
• Agricultural Robotics
• Computer Graphics
• +3 more

Cindy Grimm is a Professor and Graduate Program Director in the School of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University (OSU), part of the College of Engineering. She is affiliated with the Robotics group, Human-Centered Computing, and Graphics and Visualization. Her research focuses on robotic grasping and manipulation for agricultural applications, ethics in robotics, and interdisciplinary projects such as 3D modeling, medical imaging segmentation, and bio-inspired sensor design. Education: Ph.D. in Computer Science, Brown University, 1996 M.S. in Computer Science, Brown University, 1992 B.A. in Computer Science and Art, University of California, Berkeley, 1990 Research Interests: Dr. Grimm’s work bridges computer science and robotics, emphasizing practical applications in agriculture and ethics. Key areas include robotic fruit harvesting systems, human-robot interaction, and the development of perception-driven algorithms for complex tasks like tree pruning and object manipulation. Her earlier projects explored surface modeling, bat sonar patterns, and 3D sketching interfaces. Publications: Her recent work addresses challenges in autonomous orchard management, robotic gripper design, and public understanding of service robots. Themes include precision agriculture, grasp planning, and sociotechnical aspects of robotics adoption. Awards: Recipient of the NSF CAREER Award, recognizing her contributions to robotics and interdisciplinary research. Service: Leads the Robotics graduate program at OSU, emphasizing ethical and technical training. Collaborates with the Collaborative Robotics and Intelligent Systems Institute (CoRIS) to advance robotics applications. Labs/Teams: Active in the CoRIS Institute, focusing on collaborative robotics and real-world robotic systems. Her lab develops hardware-software solutions for agricultural robotics and human-centered robotic interfaces.

• Transportation Modeling
• Applied Operations Research
• Sustainable Transportation
• +3 more

Jee Eun (Jamie) Kang is an Associate Professor in the Department of Industrial and Systems Engineering at the University at Buffalo, located within the School of Engineering and Applied Sciences. Her research focuses on transportation modeling, applied operations research, and sustainable mobility systems. She has contributed extensively to studies on autonomous vehicles, emergency evacuation planning, and electric vehicle infrastructure. Her work bridges theoretical optimization frameworks with real-world applications, addressing challenges in multi-modal travel environments, disaster logistics, and shared mobility systems. Dr. Kang's research often involves data-driven methodologies, leveraging automated fare collection systems and smart card transactions to infer user behavior and optimize transportation networks. Her publications span topics such as strategic parking location models for autonomous vehicles, pricing mechanisms in bike-sharing systems, and scenario-based analyses for battery electric vehicle feasibility. She actively advises students on projects related to urban mobility, evacuation planning, and transportation sustainability. Scientific Awards: None explicitly mentioned in the provided texts. Dr. Kang's advising and collaborative work include projects on household activity patterns, hydrogen refueling station optimization, and evacuation warning systems. Her research has been applied to real-world scenarios like the Nepal earthquake relief efforts and California's hydrogen infrastructure planning.

• Human Factors Engineering
• Neuroergonomics
• Robotic Surgery
• +3 more

Denny Yu is an Associate Professor at the Edwardson School of Industrial Engineering, Purdue University. His work bridges human factors, neuroergonomics, and healthcare safety through advanced sensor systems and AI. Primary Affiliation : Edwardson School of Industrial Engineering, Purdue University Research Themes : Surgical ergonomics, autonomous vehicle human factors, cognitive workload assessment, multimodal physiological sensing Dr. Yu's research focuses on neuroergonomics and human-robot interaction , particularly in surgical and transportation contexts. His team develops sensor-based systems for workload monitoring, including: EEG-eye tracking fusion for situation awareness Wearable exoskeletons for surgical posture support Computer vision tools for lifting task risk analysis Smart infusion pump usability frameworks AI-driven surgical coaching systems Recent publications emphasize deep learning applications in soft tissue deformation estimation and real-time adaptive systems for robotic surgery augmentation. His work spans both occupational health (veterinary surgeons, airport workers) and medical device innovation domains.

Farzad HashemiView profile

Assistant Professor

• Urban Climate
• Heat Islands
• Building Energy Performance
• +5 more

Farzad Hashemi serves as an Assistant Professor at the School of Architecture + Planning within The University of Texas at San Antonio (UTSA), affiliated with the Margie and Bill Klesse College of Engineering and Integrated Design. He leads the Climate-Sensitive Design Lab (CSDL), focusing on interdisciplinary research at the intersection of urban climate science, building performance, and environmental justice. Education: Ph.D., Pennsylvania State University Dr. Hashemi's research investigates urban heat island phenomena, building energy dynamics, and socio-spatial inequities in thermal exposure. His work employs advanced simulation techniques including GIS, ENVI-met, CFD, and machine learning to analyze climate impacts across diverse urban contexts. Key contributions include examining historical redlining's legacy on contemporary heat distribution, developing parametric workflows for energy modeling, and creating data-driven mitigation strategies for urban heat. His approach integrates technical building science with critical social analysis to address climate vulnerabilities in marginalized communities. His publication portfolio demonstrates consistent focus on urban heat mitigation, building energy performance, and environmental justice across multiple U.S. cities. Recent work emphasizes adaptive cooling strategies through computational modeling and examines thermal inequities through spatial analysis of social vulnerability indices. The research bridges technical climate modeling with socio-political dimensions of urban heat exposure. Honors and Awards: The 2024 ARCC Dissertation Award As director of the Climate-Sensitive Design Lab, Dr. Hashemi develops research initiatives that combine environmental simulation with community-focused design solutions. The lab's work informs both academic discourse and practical urban planning applications, particularly regarding climate adaptation in rapidly growing Sun Belt cities like San Antonio. Current projects integrate tree canopy data, occupancy patterns, and future climate scenarios to create resilient urban environments.