Find Academic Supervisors

Lisa FredinView profile

Associate Professor

• Theoretical and Computational Chemistry
• Electronic Structure
• Disorder in Materials
• +4 more

Lisa Fredin is an Associate Professor in the Department of Chemistry at Lehigh University , with research spanning theoretical and computational chemistry , electronic structure , disorder in materials , photoredox catalysis , and nanochemistry . Her work bridges experiment and theory , focusing on density functional theory (DFT) and quantum chemistry to model catalytic materials , charge transport , and excited-state dynamics in systems ranging from transition-metal complexes to oxide nanoparticles . Education: Ph.D. in Chemistry (Northwestern University, 2012), B.S. in Chemistry, Biochemistry, Applied Mathematics (UT Austin, 2007) Previous Appointments: Postdoctoral Researcher at Lund University (2012–2014), Research Associate at NIST (2015–2018) Her research explores: Disorder in Inorganic and Organic Materials: Modeling defects , doping , and dynamic molecular vibrations to understand their impact on electronic properties and device performance . Photophysics of Light-Harvesting Complexes: Studying Fe(II) , Ru(II) , and Pd(II) complexes to optimize charge separation , excited-state lifetimes , and photocatalytic efficiency . Surface and Nanoscale Reactivity: Predicting reactivity of oxide nanoparticles and metal surfaces for CO disproportionation , water oxidation , and photoredox reactions . Her recent publications highlight TD-DFT applications, Boltzmann transport in organic materials, and defect engineering in TiO2 and SrTiO3 . She has received the Sloan Research Fellowship (2024) and ACS-PHYS Postdoctoral Award (2016) . Prof. Fredin mentors 6 graduate students and 15 undergraduates , teaches Physical Chemistry and Quantum Chemistry , and leads the Fredin Group , which develops computational tools for materials discovery.

Clark N. TaylorView profile

Associate Professor

• Computer Engineering
• Navigation Systems
• Sensor Fusion
• +12 more

Dr. Clark N. Taylor is an Associate Professor of Computer Engineering and Director of the ANT Center at the Air Force Institute of Technology (AFIT), located at Wright-Patterson Air Force Base, Ohio. He is actively engaged in research and education within the Graduate School of Engineering and Management, focusing on advanced navigation and sensor fusion technologies for autonomous systems. Ph.D., Electrical and Computer Engineering (Computer Engineering), University of California, San Diego, 2004 M.S., Electrical and Computer Engineering, Brigham Young University, 1999 B.S., Electrical and Computer Engineering, Brigham Young University, 1995 Dr. Taylor's research spans computer engineering, navigation systems, and autonomous robotics, with a strong emphasis on sensor fusion, state estimation, and robust uncertainty modeling. His work integrates vision, inertial, magnetic, and pressure sensors for navigation in GPS-denied environments, particularly for unmanned aerial vehicles (UAVs). He is a leading expert in factor graph-based estimation, visual-inertial odometry, cooperative localization, and magnetic navigation. His publications demonstrate a consistent trend toward robust, uncertainty-aware estimation frameworks. Over the past decade, his research has evolved from early work on visual stabilization and pose estimation to advanced topics such as conservative covariance estimation, invariant filtering, and machine learning for spacecraft pose estimation. His recent articles focus on factor graphs, multi-agent fusion, and deep learning, indicating a trajectory toward intelligent, resilient navigation systems for defense and aerospace applications. Scientific awards include a Best Presentation in Session award at the ION GNSS+ conference in 2021. His research is supported by the U.S. Air Force and related defense agencies, with applications in surveillance, autonomous refueling, and on-orbit inspection. Dr. Taylor has advised numerous MS and PhD students, particularly in the areas of UAV navigation, sensor fusion, and cooperative localization. His lab, the ANT Center, focuses on advanced navigation and tracking, bringing together students and researchers to develop cutting-edge solutions for real-world operational challenges. The team conducts both simulation and experimental work, often integrating novel sensor modalities and estimation algorithms for improved system performance.

Gudrun HillerView profile

Professor

• Particle Physics
• Flavor Physics
• Beyond the Standard Model (BSM)
• +12 more

Prof. Dr. Gudrun Hiller is a faculty member in the Department of Physics at the Technical University of Dortmund, where she conducts research in theoretical particle physics. She is affiliated with the HEP theory group and actively involved in the IMAPP international master program in particle physics. Her research spans flavor physics, rare decays of beauty and charm quarks, physics beyond the Standard Model, asymptotic safety, and SMEFT. University: Technical University of Dortmund School: Faculty of Physics Department: Department of Physics Office: Room P1-04-307 Email: gudrun.hiller@tu-dortmund.de Phone: +49 (0)231 755-3580 Her research focuses on flavor physics, emphasizing rare semileptonic and radiative decays of beauty and charm quarks, precision tests with top quark observables, and the development of novel particle physics models with controlled high-energy behavior (asymptotic safety). She leads the BSM seminar, covering topics such as flavor, Higgs, dark matter, quantum gravity, and asymptotic safety. The recent publications and workshop activities indicate a strong focus on rare charm decays, CP violation, flavor anomalies, leptoquarks, dark matter models, and the interplay between flavor physics and collider phenomenology. Her group actively participates in international collaborations and workshops, including those at CERN, DESY, and Aspen Center for Physics. DFG Research Unit: Theoretical Flavor Physics Dortmund/Siegen BMBF Theorieverbund (2012-2015) Particle Theory Group at Dortmund: R&D on EOS Member of the HEP theory group at Dortmund Organizer of workshops on asymptotic safety and flavor physics Prof. Hiller supervises students and postdoctoral researchers, with recent seminar topics indicating active advising in areas like flavored dark matter, leptoquarks, proton decay, and flavor anomalies. She has taught courses including Introduction to Elementary Particle Theory, Theoretical Physics II, and General Relativity, demonstrating a strong commitment to education and mentorship.

• Game Theory
• Epistemic Game Theory
• Quantum Game Theory
• +7 more

Adam M. Brandenburger is the J.P. Valles Professor at the Leonard N. Stern School of Business, New York University, with additional appointments as Distinguished Professor at the Tandon School of Engineering, Faculty Director of the NYU Shanghai Program on Creativity + Innovation, and Global Network Professor. He previously served as a professor at Harvard Business School from 1987 to 2002. His academic foundation includes a B.A., M.Phil., and Ph.D. from the University of Cambridge. His research focuses on game theory , epistemic game theory , quantum game theory , and business strategy . He has made foundational contributions to the understanding of rationality, belief hierarchies, and the intersection of quantum mechanics with decision theory. His work spans economics, philosophy, computer science, and cognitive neuroscience, reflecting a deeply interdisciplinary approach. The recent publications show a strong trend toward integrating quantum foundations , information theory , and behavioral economics . Themes include epistemic reasoning in non-classical systems, the role of symmetry in strategic interactions, and the neural basis of decision-making. His work increasingly explores the limits of classical probability and cognition, using tools from quantum information and sheaf theory. While no formal scientific awards are listed, his publications in Nature Communications , Econometrica , and Philosophical Transactions of the Royal Society indicate high scholarly impact. His collaborations with leading researchers across disciplines suggest a central role in advancing interdisciplinary science. He advises and collaborates extensively, though specific students are not listed. His leadership in the NYU Shanghai Program on Creativity + Innovation highlights his commitment to global education and innovation. He has not received any grants explicitly mentioned, but his sustained publication output suggests active funding support. He is associated with research initiatives bridging game theory, quantum information, and cognitive science. His recent unpublished work on quantum-assisted observatories and large language models points to forward-looking research at the intersection of AI, quantum technology, and strategic reasoning.

• Wireless Networks
• Time-Sensitive Networking (TSN)
• WiFi and UWB Integration
• +7 more

Jetmir Haxhibeqiri is a Postdoctoral Researcher at Ghent University 's Faculty of Engineering and Architecture , Department of Information Technology. His work focuses on Time-Sensitive Networking (TSN) over wireless systems, WiFi optimization, and Industrial IoT solutions. Key projects: IMEC Postdoctoral Fellowship Collaborations: Jeroen Hoebeke (UGent), Ingrid Moerman (UGent), Xianjun Jiao (UGent) Research Interests : Wireless network coordination, SDN integration for heterogeneous networks, low-latency communication, and machine learning applications in network optimization. Specialized in WiFi-LPWAN coexistence , In-Band Network Telemetry , and Cross-Technology Synchronization . Recent Publications : 2025 work on Wi-Fi-UWB synchronization, 2024 studies on coordinated spatial reuse in WiFi 7, and 2022 research on hardware-efficient PTP clock synchronization. Contributions span from theoretical models to practical implementations in industrial environments. Technical Expertise : Network densification strategies, interference management, and performance evaluation of large-scale wireless deployments. Developed simulation frameworks for LoRaWAN and WiFi TSN, with a focus on ns-3 validation. Education : PhD in Industrial Wireless Communication (2019, Ghent University).

Minchen LiView profile

Assistant Professor

• Computer Graphics
• Machine Learning
• Robotics
• +4 more

Minchen Li Assistant Professor at Carnegie Mellon University's School of Computer Science (Computer Science Department). Formerly an Assistant Adjunct Professor at UCLA's Mathematics Department. Holds a Ph.D. from the University of Pennsylvania's SIG Center for Computer Graphics, followed by a postdoctoral position there. Research focuses on physics-based simulation, integrating numerical analysis, high-performance computing, and machine learning. Notable contributions include the IPC method for frictional contact simulation and large-scale material point methods. Education Ph.D., Computer and Information Science, University of Pennsylvania (2020) M.Sc., Computer Science, University of British Columbia (2018) B.Eng., Computer Science and Technology, Zhejiang University (2015) Research Interests Advances in physical simulation for visual computing, robotics, and manufacturing. Specializes in robust and efficient methods for solid/fluid dynamics, contact modeling, and GPU acceleration. Combines numerical analysis with machine learning to address challenges in simulation accuracy and versatility. Awards 2021 ACM SIGGRAPH Outstanding Doctoral Dissertation Award 2024 SCA Early Career Researcher Award Advising & Labs Leads the Simulation Intelligence Group (SIG) at CMU Graphics Lab. Advises PhD students Guying Lin, Juntian Zheng, Michael Liu, and Zhaofeng Luo. Collaborates with industry and academic partners on projects like VR-based modeling systems and scalable simulation frameworks.

Kaiyi JiView profile

Assistant Professor

• Large-scale optimization
• Machine learning
• Multi-task learning
• +6 more

Kaiyi Ji is an Assistant Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY. He earned his PhD in Electrical and Computer Engineering from Ohio State University in 2021 and completed a postdoctoral fellowship at the University of Michigan. His research focuses on large-scale optimization, machine learning, and foundation models. PhD: Electrical and Computer Engineering, Ohio State University (2021) Postdoc: University of Michigan (2022) BSc: University of Science and Technology of China (2016) Research interests include bilevel optimization, multi-task learning, continual learning, and AI4Science, with applications in robotics and crystal property prediction. Recent work explores efficient algorithms for LLM training and optimization. His publications span top venues like ICLR, ICML, NeurIPS, and IEEE Transactions on Information Theory. He received the CSE Junior Faculty Research Award (2023) and NSF CAREER Award (2025). He advises PhD students and actively participates in departmental service as Associate Chair for Graduate Student Admissions and organizer of academic workshops.

Sameer DeshpandeView profile

Assistant Professor

• Bayesian hierarchical modeling
• Bayesian regression trees
• Causal inference
• +3 more

Sameer Deshpande is an Assistant Professor in the Department of Statistics at the University of Wisconsin–Madison. His research bridges Bayesian methodology development with applications in public health and sports analytics. Prior to joining UW–Madison, he completed a postdoctoral fellowship with Professor Tamara Broderick at MIT and earned his Ph.D. in Statistics from the Wharton School under Professors Ed George and Veronika Rockova. His educational background includes undergraduate studies in mathematics at MIT and a year at Jesus College, Cambridge through the Cambridge-MIT Exchange program. His research focuses on advancing Bayesian hierarchical modeling, treed regression, and causal inference techniques, with particular emphasis on flexible tree-based methods like BART variants for complex data structures. Deshpande's recent publications reveal a strong trend toward developing scalable Bayesian methods for high-dimensional data while maintaining rigorous uncertainty quantification. His work frequently applies these techniques to sports analytics (particularly baseball and football) and public health studies examining long-term effects of adolescent sports participation. The consistent focus on methodological innovation paired with substantive applications demonstrates his dual commitment to statistical theory and real-world impact. He actively mentors graduate students at UW–Madison, requiring STAT 775 as preparation for research collaboration. His Deshpande Lab focuses on Bayesian computation and causal inference, though specific grant details are not publicly listed. Notable projects include the NFL Big Data Bowl submission analyzing quarterback decision-making using Expected Hypothetical Completion Probability. Outside academia, Deshpande maintains interests in cooking, cocktail making, and photography, while remaining a devoted fan of Dallas sports teams – often seen wearing a Texas belt buckle.

• Optical Wireless Communication
• Underwater Visible Light Communication
• Vehicular Visible Light Communication
• +3 more

Dr. Mohammed Elamassie is an Assistant Professor at Özyeğin University's Graduate School of Science and Engineering, Department of Electrical and Electronics Engineering. He co-directs the Centre of Excellence in Optical Wireless Communication Technologies (OKATEM) and holds senior memberships in IEEE and Optica. PhD in Electrical and Electronics Engineering (Özyeğin University, 2020) MSc in Electrical and Electronics Engineering (Islamic University of Gaza, 2011) BSc in Electrical and Electronics Engineering (Islamic University of Gaza, 2006) Dr. Elamassie's research focuses on optical wireless communication systems, with specific expertise in underwater visible light communication (UVLC), vehicular visible light communication (V2V), airborne free space optical (FSO) networks, and turbulence mitigation techniques. His work addresses atmospheric channel modeling, diversity techniques, and MIMO communication challenges across multiple mediums. Analysis of his 15 most recent publications reveals critical trends in UVLC turbulence modeling, FSO UAV optimization, RIS-aided systems, and vehicular communication reliability. These works demonstrate his leadership in developing practical solutions for channel degradation and mobility-induced challenges. Best Paper Award, IEEE Black Sea Conference (2019) IEEE Turkey PhD Thesis Award (2020) Senior Member, IEEE Senior Member, Optica Optica Traveling Lecturer/Speaker Dr. Elamassie serves as Review Editor for Frontiers in Communications and Networks, covering 'Non-Conventional Communications' and 'Wireless Communications' sections. He contributes to OKATEM's research on optical wireless technologies, focusing on practical implementations across underwater, vehicular, and airborne domains.

Jason M. RobertsView profile

Professor

• American political institutions
• U.S. Congress
• congressional elections
• +1 more

Jason M. Roberts is a Professor of Political Science at the University of North Carolina at Chapel Hill, specializing in American political institutions with an emphasis on the U.S. Congress. He maintains an office in Hamilton Hall and communicates via jroberts@unc.edu. His academic credentials include: B.S. in Political Science from the University of North Alabama (1998) M.A. in Political Science from Purdue University (2000) Ph.D. from Washington University in St. Louis (2005) Professor Roberts' research centers on congressional procedures, electoral systems, and institutional dynamics within the U.S. Congress. Current projects investigate interpersonal relationships among legislators and election administration practices in North Carolina, building on prior work like his 2020 book The Politics of Ballot Design . His scholarship consistently bridges historical analysis and contemporary political challenges. Analysis of his 15 most recent publications (2022-2006) reveals enduring focus on congressional institutionalization, party strategy in lawmaking, and electoral competition. Recurring themes include the impact of procedural rules on legislative outcomes, historical evolution of congressional practices, and methodological rigor in studying roll call voting—demonstrating both longitudinal depth and methodological sophistication. No scientific awards or honors were documented in the source materials. His teaching portfolio spans undergraduate courses including American Democracy in Changing Times and The U.S. Congress, alongside graduate seminars in American Politics and Congressional studies. While student advising details were unspecified, his extensive publication record and active research agenda suggest ongoing mentorship of emerging scholars.

• Volunteered Geographic Information (VGI)
• OpenStreetMap
• Spatial Data Analysis
• +3 more

Peter Mooney is a Lecturer in the Department of Computer Science, Faculty of Science & Engineering at Maynooth University. His research focuses on Volunteered Geographic Information (VGI), OpenStreetMap, spatial data analysis, and geospatial data integration in applications such as environmental monitoring and pervasive health systems. Institution: Maynooth University School: Faculty of Science & Engineering Department: Computer Science Role: Lecturer Mooney's research explores the use of crowdsourced geospatial data, particularly through OpenStreetMap, analyzing data quality, community roles, and integration into location-based services. His work bridges technical analysis with policy considerations in geospatial data management. Recent publications highlight his contributions to understanding spatial data dynamics, including attribute changes in OpenStreetMap, characteristics of edited objects, and applications of VGI in environmental systems. He also investigates the intersection of haptics and GIS for novel interaction methods. Contact: peter.mooney@mu.ie

Anne SheehanView profile

Professor

• Earthquake Seismology
• Tsunami Studies
• Active Tectonics
• +5 more

Anne Sheehan is Professor of Geological Sciences at the University of Colorado Boulder and a Fellow of the Cooperative Institute for Research in Environmental Sciences (CIRES). Her research integrates seismology, geodesy and electromagnetics to investigate earthquake sources, subduction-zone dynamics, tsunami generation and human-induced seismicity. Education Ph.D., Massachusetts Institute of Technology, 1991 B.S., University of Kansas, 1984 Research Interests Sheehan’s group pursues field and computational studies of earthquake seismology , tsunami processes , active tectonics and geophysical imaging . Major themes include: Deep structure and tectonics of mountain belts (Rockies, Himalaya, Southern Alps) Subduction-zone seismicity, slow-slip events and tsunami hazards Induced seismicity linked to wastewater injection in Colorado Novel seafloor instrumentation (ocean-bottom seismometers, pressure gauges) and satellite-based tsunami detection from ships Advanced computational techniques such as data assimilation, interferometry and machine-learning phase detection Scientific Awards Erasmus Haworth Distinguished Alumni Award, University of Kansas (2009) EarthScope Distinguished Lecturer (2013) College Scholar Award, University of Colorado (2014) New Zealand Geophysics Prize (2016, 2019) President, Seismology Section, American Geophysical Union (2020) Leadership, Grants & Teams Sheehan is Principal Investigator on major NSF experiments such as the Alaska Amphibious Community Seismic Experiment (AACSE) and the Hikurangi Ocean Bottom Investigation of Tremor and Slow Slip (HOBITSS) . She collaborates closely with the hydrogeology group of Prof. Shemin Ge and geodesist Prof. Kristy Tiampo, and mentors graduate students and post-docs in the Sheehan Geophysics Group . The group maintains real-time seismic data access for Colorado at earthquake.colorado.edu and hosts weekly seminars and reading groups.

Amanda MaycockView profile

Professor

• Climate Dynamics
• Atmospheric Science
• Climate Modelling
• +6 more

Amanda Maycock is Professor in Climate Dynamics at the University of Leeds' School of Earth and Environment, where she directs the Institute for Climate and Atmospheric Science (ICAS). She holds an MPhys from Manchester (2006), MSc (2008) and PhD (2012) in Atmosphere, Oceans and Climate from Reading. Her research specializes in climate dynamics, atmospheric circulation, stratosphere-troposphere interactions, and radiative transfer, with ongoing projects including ExtAnt, TWISTA, and the Leverhulme Prize-funded climate dynamics initiative. Research interests span: Climate variability and change mechanisms Atmospheric teleconnections and jet stream dynamics Stratospheric impacts on surface climate Chemistry-climate feedbacks including ozone layer interactions Radiative transfer modelling and climate projections Her recent publications (2020-2025) demonstrate strong focus on: Arctic-midlatitude climate linkages and jet stream behavior ENSO teleconnections and hydrological extremes Polar vortex influences on European storminess Climate mitigation impacts on stratospheric temperature Renewable energy-climate interactions Scientific honors include: Philip Leverhulme Prize (2018) Arne Richter Award for Early Career Researchers (2019) STAC Outstanding Early Career Award (2022) water@leeds Water Woman Award (2023) NERC Independent Research Fellowship (2015-2020) She leads 12 PhD students including projects on atmospheric rivers, ENSO dynamics, and jet stream extremes, supported by NERC, EPSRC, and Met Office collaborations. Current grants include £5M+ from Horizon Europe, Wellcome Trust, and UKRI for projects examining Antarctic extremes (ExtAnt), stratospheric aerosols (TWISTA), and sustainable aviation (JetZero). Directs ICAS research group with 4 postdoctoral researchers and coordinates the NERC PANORAMA DTP Atmosphere Theme. Contributes to IPCC assessments and co-chairs WCRP/SPARC initiatives on atmospheric temperature changes.

Leif KobbeltView profile

Professor

• Computer Graphics
• Geometry Processing
• Interactive Visualization
• +3 more

Leif Kobbelt serves as a University Professor at RWTH Aachen University, leading the Computer Graphics Group within the Department of Computer Science (Informatik 8). His research focuses on advancing geometry processing, interactive visualization, and computer graphics through innovative algorithmic solutions and interdisciplinary collaborations. Professor Kobbelt's research program centers on geometry acquisition and processing, with significant contributions to mesh generation, surface reconstruction, and neural rendering techniques. His work bridges theoretical geometry with practical applications in computer vision, photo-realistic image synthesis, and multimedia data transmission, often involving collaborations with industry partners and international research teams funded by DFG and EU sources. Recent publications (2023-2025) reveal a strategic integration of deep learning with traditional geometry processing, particularly in Gaussian splatting for real-time rendering, NeRF-based 4D content generation, and robust mesh Boolean operations. His group maintains leadership in quad mesh optimization and surface mapping while expanding into immersive visualization techniques for complex data analysis. The group has earned recognition through prestigious awards: Günter Enderle Best Paper Award at Eurographics 2023 Best Paper Award (1st place) at Symposium on Geometry Processing 2022 Honorable Mention for Best Paper at ACM Symposium on Virtual Reality Software and Technology Funding from Deutsche Forschungsgemeinschaft and European Union programs supports the group's research infrastructure and international collaborations. The team actively supervises graduate theses while developing open-source software tools that translate theoretical advances into practical industry applications, particularly in digital fabrication and immersive visualization systems. The Computer Graphics Group operates as a central hub for visual computing research at RWTH Aachen, maintaining strong ties with both academic institutions and technology companies. Their recent work on virtual reality educational tools and high-fidelity 3D reconstruction systems demonstrates commitment to knowledge transfer and real-world impact beyond traditional publication venues.

Jian KangView profile

Professor

• Biostatistics
• Bayesian Methods
• Gaussian Processes
• +11 more

Jian Kang is a Professor and Associate Chair for Research at the University of Michigan School of Public Health , specializing in Biostatistics . His work focuses on developing advanced statistical methods for large-scale biomedical data , with applications to precision medicine , neuroimaging , and genomics . Education: PhD in Biostatistics, University of Michigan (2011) MS in Mathematics (Statistics), Tsinghua University (2007) BS in Statistics, Beijing Normal University (2005) Research Interests include Bayesian nonparametric methods , deep learning for medical imaging , ultra-high-dimensional variable selection , and graphical models for network inference . His 2025-2023 publications demonstrate expertise in Bayesian hierarchical modeling , spatial statistics , and machine learning for healthcare . Scientific Awards : Michigan SPH Excellence in Research Award (2025) ICSA President's Citation Award (2024) Statistics in Biopharmaceutical Research Best Paper (2023) Best Paper in Biometrics by IBS Member (2022) Fellow, American Statistical Association (2021) Grants include NSF-IIS (2021-2025) for BCI statistical learning , NIGMS (2020-2022) for metabolomics biomarker selection , NIDA (2020-2025) for imaging data analysis , and NIMH (2014-2025) for multidimensional neuroimaging methods . Labs and Teams develop Bayesian computational tools for neuroimaging and spatial transcriptomics , collaborating with institutions like Emory University and University of North Carolina.