Find Academic Supervisors

Eunjung LeeView profile

Assistant Professor

• Educational Technology
• Gifted Education
• Teacher Education
• +3 more

Eunjung Lee serves as Assistant Professor of Business Analytics in the College of Business at Lewis University, bringing extensive industry experience from Samsung and LG alongside prior academic roles at Indiana State University. Her expertise bridges business analytics and educational technology with a pronounced focus on equity-driven research. Her educational foundation includes: Ph.D. in Information Technology Management, University of Wisconsin-Milwaukee M.B.A., Korea University B.S., Kwangwoon University Dr. Lee's research trajectory evolved from business analytics toward educational equity, with recent work emphasizing computational thinking in mathematics education, teacher preparation models, and machine learning applications for gifted identification. She investigates how digital tools transform pedagogy while addressing systemic barriers for underrepresented students. Analysis of her 2022-2025 publications reveals dominant themes in equitable gifted identification through cross-cultural validation of the HOPE rating scale, computational thinking integration in teacher training, and longitudinal studies of enrichment program impacts. Her methodology consistently combines quantitative analysis with equity-centered frameworks. No scientific awards were documented in available sources. While specific advising details remain unreported, her courses in Business Intelligence and Forecasting demonstrate applied analytics instruction. Research grants weren't specified in source materials. Collaborative structures like the Lowell Stahl Center for Entrepreneurship provide institutional context, though dedicated labs or research teams weren't explicitly referenced.

Johanna ZiegelView profile

Professor

• Statistical Forecasting
• Probability Theory
• Financial Risk Management
• +3 more

Johanna Ziegel is a Professor of Statistics at ETH Zurich, Switzerland, since 2024, and a Visiting Scientist at the Heidelberg Institute for Theoretical Studies (HITS). Previously, she held positions at the University of Bern, where she was promoted to Full Professor in 2023. Her research focuses on decision-theoretically sound methods for forecast evaluation, probabilistic forecasting, risk measures in finance, and applications in meteorology, medicine, and climate science. She is actively involved in editorial roles for journals like Bernoulli , JASA: Theory & Methods , and SIAM Journal on Financial Mathematics . Education: PhD in Stereological Analysis of Spatial Structures from ETH Zurich (2010), supervised by Paul Embrechts and Eva B. Vedel Jensen. Postdoctoral research at the University of Melbourne and Heidelberg University. Research Interests: Forecast evaluation, elicitable functionals, risk measures, isotonic regression, statistical calibration, and applications in finance, climate science, and biostatistics. Her work bridges theoretical statistics with practical challenges in uncertainty quantification and decision-making under uncertainty. Advising & Collaborations: Supervised 7 PhD students and mentored several postdocs. Collaborates with the Computational Statistics group at HITS and the Oeschger Centre for Climate Change Research. Her group explores distributional regression under order constraints and novel methods for forecast comparison. Recognition: Credit Suisse Award for Best Teaching (2022), H.I.T. Program for Academic Leadership (2021–2022). Active in professional service, including the Bernoulli Society Council and editorial boards.

Thierry WarinView profile

Professor

• Data Science for Global Transformations
• Platform Economy
• Industrial Organization
• +7 more

Thierry Warin is a Full Professor of Data Science for International Business at HEC Montréal, directing the Department of International Business. He holds the Professorship in Data Science for International Business and is a Principal Investigator at CIRANO, leading the World Economy theme. His roles include affiliations with Harvard Business School’s Microeconomics of Competitiveness program and the International Trade and Finance Association presidency (2020-2022). Education: PhD from ESSEC Business School (France, 2000). Professional development includes the Harvard Business Analytics Program (2018-2020) and GIS training at Harvard. Research Interests: Data science applications in global economic transformations, including network theory, natural language processing, and computational methods. Focus areas: algorithmic collusion, platform economies, and metadata-driven analyses. He develops open-source tools like the statcanR package and advocates for reproducible research. Articles Trends: Recent work explores AI regulation, algorithmic competition, climate transition plans, and central bank speech analysis. Methodologies span structural topic modeling, social media analytics, and entropy-based frameworks. Awards: Honored with the Highly Commended Paper Award (2017-2018) and Emerald Literati Award (2018) for reverse innovation research. Recognized for contributions to computational social science and regulatory frameworks. Advising & Grants: Supervised 16 master’s projects since 2019, focusing on data science applications in global business challenges. Active in interdisciplinary initiatives like the St. Lawrence–Great Lakes corridor data hub. Labs & Philanthropy: Founded quantum simulations and leads Ed’Haîti , an NGO addressing education in Haiti. Collaborates on Science des données au féminin en Afrique , empowering 200 African women with data science skills.

Pascale BironView profile

Professor

• Hydro-geomorphology
• River dynamics
• Stream restoration
• +5 more

Dr. Pascale Biron is a Professor in the Department of Geography, Planning and Environment at Concordia University, Montreal. Her research focuses on river dynamics, stream restoration, flood modeling, and climate change impacts. She has been at Concordia since 1998 and holds professional affiliations with key organizations like the Canadian Geomorphology Research Group and the American Geophysical Union. Education: PhD from Université de Montréal and Leeds University (fluvial geomorphology) Research Interests Her work integrates fluvial geomorphology, computational fluid dynamics, and societal aspects of river management. Key themes include: River restoration for fish habitat Floodplain modeling using LiDAR Climate adaptation strategies Socio-hydrological dimensions of restoration Hydrodynamic processes at river confluences Recent Research Trends Recent publications emphasize global salmonid habitat analysis, machine learning for fluvial hazard detection, and the socio-environmental impacts of urban flooding. Her work bridges technical hydrology with policy-relevant solutions for sustainable water management. Grants & Advising Supervises over 15 graduate students (PhD/MSc) in topics ranging from flood modeling to bioengineering. Active in collaborative projects like the 'Freedom Space for Rivers' initiative and large-scale floodplain mapping. Labs & Teams Member of GRIL (Limnology Research Group) and RIISQ (Quebec Flood Network), contributing to interdisciplinary water security research.

Teresa WangView profile

Senior Lecturer

• Entity/User Modeling
• Relational/Structural Machine Learning
• Spatial/Temporal Machine Learning
• +5 more

Dr. Teresa Wang is a Senior Lecturer in Data Science at Monash University's Faculty of Information Technology, specializing in entity/user modeling, relational/structural machine learning, and graph/network analysis. She holds a Ph.D. from the University of Queensland and degrees from Nanjing University. Currently, she directs the Master of Data Science Program and teaches courses like FIT5201 Machine Learning. Her research focuses on social, e-commerce, and health data modeling, with notable projects including the Knowledge Enriched Approach for Effective Personalization (2025–2027) and collaborations on AI in Mental Health and Site Safety. Dr. Wang has co-authored over 59 publications, emphasizing areas like ontology matching and multimodal data analysis. She actively supervises PhD students and contributes to initiatives like the CSIRO Next Generation Graduates Program for clean energy and sustainability. Education: Ph.D. in Computer Science (2017), University of Queensland Master of Computer Science (2013), Nanjing University Bachelor of Software Engineering (2010), Nanjing University Research Interests: Entity modeling, spatio-temporal data analysis, graph mining, recommender systems, and health/medical records mining. She explores applications in social media, e-commerce, and healthcare sectors. Projects: "Knowledge Enriched Approach for Effective Personalization" (2025–2027) "AI for Clean Energy and Sustainability" (2023–2027) "CSIRO Next Generation Graduates Program: AI in Mental Health" (2023–2027) "Large-scale multimodal knowledge management" (2022–2025) Grants & Collaborations: Engaged with CSIRO, Crank Group, and Pola Practice Pty Ltd. Her work aligns with UN SDGs in education and sustainable energy systems. Labs/Teams: Part of the Monash Energy Institute and Monash Data Futures Institute, contributing to interdisciplinary AI and energy research.

Junier OlivaView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Nonparametric Statistics
• +8 more

Junier Oliva is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill and Lead Faculty of the Master of Applied Data Science program. His research focuses on machine learning, artificial intelligence, and nonparametric statistics, particularly in high-dimensional density estimation, sequential modeling, and learning from complex/structured data. He holds a B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University, with prior industry experience at Yahoo! and Uber ATG. Research Interests: Machine learning, artificial intelligence, nonparametric statistics, deep learning, statistical data mining, signal processing, kernel methods, and scalability. His work bridges machine and human learning via collective approaches, emphasizing simple yet flexible models for massive datasets. Awards/Grants: $592K AIM-AHEAD/NIH Grant for Human+AI Collaboration $594K NSF Grant for Scientific Discovery $500K NSF Grant for 'Machine Detectives' Project ACM BCB Best Paper Award (2022) for transparent single-cell classification work Labs/Teams: Director of the LUPA Lab, which develops machine learning techniques for holistic data understanding across domains like healthcare, earth science, and computer vision.

Martino BanchioView profile

Assistant Professor

• Microeconomic Theory
• Market Design
• Mechanism Design
• +2 more

Martino Banchio is an Assistant Professor at the Department of Economics of Bocconi University and an Affiliate of the Innocenzo Gasparini Institute for Economic Research (IGIER). He concurrently serves as a part-time Research Scientist at Google Research. His work bridges microeconomic theory, market/ mechanism design, and computer science. His PhD thesis earned an honorable mention in the 2023 ACM SIGecom Doctoral Dissertation Award. Research interests focus on market design, industrial organization, and computer science applications in economics. Notable contributions include studies on credible auctions, AI-driven collusion, and dynamic pricing mechanisms. He frequently publishes in top venues like the ACM Conference on Economics and Computation (EC) and the ACM Web Conference (WWW). His awards include the Research Paper Competition at MIT Sloan Sports Analytics Conference 2020 and the Exemplary AI Track Paper Award. His research explores cutting-edge topics such as AI ethics in market design and algorithmic game theory, reflecting a strong interdisciplinary approach. No student advising records are explicitly listed. His work bridges academic theory and industry applications, as seen through collaborations with Google Research and affiliations with leading economic institutes.

• Structural Control and Health Monitoring
• Continuum Mechanics and Mechanics of Materials
• Structural Mechanics
• +3 more

Hans Irschik is a Full Professor and Head of the Institute of Technical Mechanics at Johannes Kepler University of Linz, Austria. He has held key academic roles, including Vice-Rector for Research (1995-2001) and Chair of the Senate (2009-2013) at JKU, and serves as Editor-in-Chief of Acta Mechanica . His work bridges structural mechanics, mechatronics, and control systems. Education: Studied Civil Engineering at Vienna University of Technology, earned Doctor of Technical Sciences (1981) and Habilitation in Mechanics (1986). Research interests include structural control, continuum mechanics, and smart materials. His contributions span theoretical developments and applied control strategies for aging structures. Recent articles focus on Lagrangian formulations, piezoelectric sensors, and beam theories, reflecting his expertise in mechanics and structural health monitoring. Scientific Awards & Memberships: Full Member, Austrian Academy of Sciences (since 2011) Honorary Doctorate, St. Petersburg Polytechnical University (2009) President, European Association of Structural Control and Health Monitoring (2008-2012) Fellow, Japanese Society for the Promotion of Science (2006) City of Vienna Award (1988) Kardinal Innitzer-Award (1988) Grants & Projects: Led Austrian Science Fund (FWF) research since 1993. Key researcher in the K2-Austrian Center of Competence in Mechatronics (since 2001). Participated in EUROMECH committees and European Science Foundation programs.

Gauthier GidelView profile

Associate Professor

• Deep Learning
• Adversarial Machine Learning
• Generative Modeling
• +5 more

Gauthier Gidel is an Associate Professor at the Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Science at Université de Montréal, where he also holds the prestigious Canada CIFAR AI Chair position. He is a core faculty member of Mila, Quebec's AI research institute, and maintains active research collaborations with leading institutions. His academic journey includes a PhD in Computer Science under the supervision of Simon Lacoste-Julien, with internships at Sierra, ElementAI, and DeepMind during his doctoral studies. Dr. Gidel's research spans multiple critical areas in machine learning, with particular emphasis on generative modeling , adversarial machine learning , and variational inequalities for machine learning. His work explores the intersection of optimization theory and practical AI systems, focusing on challenges like LLM safety alignment, multi-agent cooperation, and robustness against adversarial attacks. He is particularly known for his contributions to understanding the theoretical foundations of generative adversarial networks through variational inequality frameworks. His recent publications reveal a strong trend toward addressing critical challenges in large language model safety and alignment, with numerous 2024-2025 papers focusing on adversarial robustness, safety evaluation methodologies, and alignment techniques for LLMs. Simultaneously, his foundational work continues in optimization theory, particularly in variational inequalities and performative prediction, demonstrating his dual focus on practical AI safety concerns and theoretical machine learning foundations. Canada CIFAR AI Chair Core member of Mila Organizer of popular NeurIPS workshops on smooth games Co-founder of the ICLR blog post track Dr. Gidel actively supervises an extensive research group with approximately 10 current graduate students and numerous alumni who have secured positions at leading institutions including Inria Lyon, Oxford, and industry research labs. His research is supported by multiple substantial grants from CRSNG, MITACS, and IVADO, including the prestigious CRSNG Discovery Grant program and MITACS Acceleration Québec projects focused on fraud detection in music streaming and conditional generation. His laboratory maintains strong connections with both academic and industry partners, fostering a collaborative environment focused on advancing AI safety and theoretical understanding.

Timothy FraserView profile

Researcher

• Climate Change Adaptation
• Disaster Resilience
• Energy Policy
• +5 more

Dr. Timothy Fraser is a Computational Social Scientist serving as Ezra Systems Research Associate in Cornell University's Systems Engineering Program and Coordinator for the Center for Transportation, Environment, & Community Health (CTECH). Holding a PhD in Political Science from Northeastern University (2022), he focuses on climate change adaptation, disaster resilience, and energy policy using big data analytics, GIS, and AI. His work bridges computational methods with societal challenges, emphasizing community engagement and policy impact. Education: PhD in Political Science, Northeastern University (2022) MA in Political Science, Northeastern University BA in International and Global Studies, Middlebury College Research Interests: Fraser’s research integrates computational social science with environmental policy, exploring how social networks and governance structures influence cities' climate adaptation strategies. Key areas include renewable energy adoption, disaster evacuation dynamics, and pandemic response mechanisms. He employs mixed methods such as network analysis, statistical modeling, and fieldwork. Grants & Awards: Fulbright Fellowship (2016), Kyushu University Japan Foundation Doctoral Fellowship (2020) USDOT Multimillion Grant Administrator (2022-2023) Teaching & Mentorship: Fraser teaches statistical methods and research design at Cornell, advising over 30+ students in projects published in leading journals. He coordinates capstone teams and mentors researchers in data science and policy analysis. Labs & Projects: Leads the Climate Action in Transportation dashboard initiative (Gao Labs), developing tools for emissions visualization. Collaborates on UNDP social capital mapping projects in Mexico and Paraguay, applying spatial analysis techniques for vulnerable communities.

Kshitij SabnisView profile

Lecturer

• Experimental Aerodynamics
• High-Speed Aerodynamics
• Vehicle Aerodynamics
• +7 more

Kshitij Sabnis is a Lecturer in Aerospace Engineering at the School of Engineering and Materials Science, Queen Mary University of London. He serves as Admissions Lead and Outreach & Recruitment Lead for Aerospace Engineering, and Deputy Director of Industrial Engagement (Graduate Attributes). He is affiliated with the Centre for Intelligent Transport and conducts experimental research in high-speed aerodynamics. Education: PhD in Experimental Aerodynamics, University of Cambridge Master’s in Physics Dr Sabnis's research focuses on experimental aerodynamics across various speed regimes, particularly shock/boundary-layer interactions, vortex dynamics, and supersonic flows. His work involves wind tunnel experiments on simplified models to understand complex fluid mechanics in applications ranging from racecar wings to supersonic aircraft intakes. He employs advanced diagnostics and develops novel experimental setups to enhance physical insight into flow phenomena. His recent publications (2019–2025) reflect a strong emphasis on high-speed flow behavior, including shock-induced separation, vortex interactions, and nacelle aerodynamics. Key themes include flow control, wind tunnel design, and validation of turbulence models. His work bridges fundamental fluid dynamics with practical aerospace engineering challenges. Scientific Awards: FHEA (Fellow of the Higher Education Academy) Dr Sabnis actively supervises PhD students and leads externally funded research projects. He has secured grants from EPSRC and the Royal Society, supporting work on schlieren imaging enhancement and small-scale wind turbines for rural energy. He teaches advanced aerodynamics modules and contributes to curriculum and industrial engagement. He leads a research group focused on experimental high-speed aerodynamics and is involved in developing new diagnostic techniques and test rigs. His team investigates vortex interactions and aerodynamic performance under extreme flow conditions.

Jana ShenView profile

Professor

• Molecular Simulation
• Data Science
• Kinases
• +12 more

Jana Shen is a Professor in the Department of Pharmaceutical Sciences at the University of Maryland School of Pharmacy, where she leads an interdisciplinary research group at the intersection of chemistry, biology, physics, and computer science. Her lab develops and applies advanced simulation and data science tools to understand biomolecular mechanisms and accelerate drug discovery. Education: Postdoc, The Scripps Research Institute (2003–2007) PhD, University of Minnesota at Twin Cities (1999–2003) MS, University of Calgary, Canada (1996–1999) Diplom-Chemie, Bergische Universität Wuppertal, Germany (1991–1995) Her research focuses on molecular simulation , data science , and computational biophysics , with applications in kinases , GPCRs , transmembrane transporters , and pH-responsive materials . She has pioneered the development of continuous constant pH molecular dynamics (CpHMD) methods and their applications in drug design and biomolecular mechanisms. The recent publications highlight a strong trend in computational drug discovery , particularly in covalent inhibitors , opioid receptor mechanisms , antiviral design , and the integration of machine learning with molecular dynamics . These works span high-impact journals such as eLife , JACS , Nature Communications , and ACS journals. Scientific Awards: National Science Foundation CAREER Award American Chemical Society HP Outstanding Junior Faculty Award Junior Faculty Research Award (University of Oklahoma, 2008, 2009) Phi Kappa Phi, University of Minnesota Louise T. Dosdall Graduate Fellowship Nova Graduate Fellowship Dr. Shen has mentored numerous PhD students and postdoctoral fellows, many of whom have gone on to successful careers in academia and industry. Her research is supported by major agencies including the National Institutes of Health , National Science Foundation , and FDA . She leads the Shen Lab, which actively develops open-source tools such as DeepCys , CpHMD , and PKAD-3 , and maintains databases for covalent ligandability and pKa predictions.

Kate FarrahiView profile

Associate Professor

• Machine Learning
• Digital Health
• Human Sensing
• +3 more

Dr. Kate Farrahi is an Associate Professor in the ECS department at the University of Southampton, where she leads research in the Vision, Learning and Control (VLC) Group. Previously, she was a Research Assistant at the Idiap Research Institute and earned her PhD in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL). Her work focuses on the intersection of machine learning and digital health, particularly in developing human sensing methods using vision and wearable technologies. She currently supervises four PhD students in Computer Science and actively accepts new PhD applications. Her research interests span machine learning applications in healthcare, including wearable device analytics, epidemiological modeling via AI, and drug discovery through generative methods. She has been recognized with a Best Paper Award (2022) and contributes to interdisciplinary research groups such as the Institute for Life Sciences and Centre for Machine Intelligence. Her work bridges computational methods with real-world health challenges, emphasizing practical deployment of AI solutions in clinical and public health contexts. Research Groups: Vision, Learning and Control; Institute for Life Sciences; Centre for Health Technologies; Centre for Machine Intelligence Key Collaborations: Cross-disciplinary projects combining computer science with biomedical engineering and public health

Trevor J JonesView profile

Assistant Professor

• Soft Matter Mechanics
• Nonlinear Dynamics
• Mechanical Instabilities
• +12 more

Trevor J Jones is an Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering, where he leads the Mechanically Intelligent Engineered Structures (MInEnS) Lab. His research integrates soft matter mechanics, nonlinear dynamics, and indigenous knowledge to develop novel technologies in soft robotics, meta-materials, and manufacturing. Education: Ph.D., Chemical Engineering, Princeton University (2023) B.S., Chemical Engineering, Vanderbilt University (2017) His research focuses on harnessing mechanical instabilities, fluid-solid interactions, and granular matter to create intelligent, adaptive materials. Inspired by natural phenomena and Ojibwe beadwork traditions (reflected in the MInEnS Lab's name from the Ojibwemowin word manidoominens ), his work spans soft robotics, deployable structures, and beadwoven metamaterials. He employs an interdisciplinary approach combining crafting, experimentation, and theoretical modeling. His recent publications (2022–2024) demonstrate a strong trend in leveraging buckling, plasticity, and fluid dynamics to achieve emergent intelligence and multifunctionality in soft engineered systems, particularly through innovative fabrication techniques like bubble casting and beadwork-inspired design. Scientific Awards: AISES Lighting the Pathway Fellow Trailblazer in Engineering Rising Star in Soft and Biological Matter Jones actively mentors graduate and undergraduate researchers, including PhD students Eddie Beck and Angela Lee, and undergraduates Eleni Georgountzos and Adela Qiu. He is currently recruiting PhD students and postdocs for projects in bead-woven materials and soft matter mechanics. The MInEnS Lab fosters a highly interdisciplinary environment that values curiosity, craftsmanship, and the integration of diverse cultural perspectives in scientific inquiry.

• Seismology
• Geophysics
• Antarctic Ice Sheet Dynamics
• +3 more

Dr. Thanh-Son Pham is an ARC DECRA Research Fellow in the Geophysics Department at The Australian National University’s Research School of Earth Sciences. His research focuses on using seismic waves to study Earth’s interior structures, from polar ice sheets to the inner core. He has pioneered methods like teleseismic P-wave coda autocorrelation and coda correlation wavefield analysis, leading to breakthroughs such as detecting J-waves in the inner core and identifying an innermost inner core layer. His work has been featured in Science , Nature Communications , and international media. He holds a PhD from ANU (2019) and has supervised research projects on Antarctic seismology and earthquake source physics. Awards include the 2024 Zatman lectureship from SEDI. Current projects include probing Antarctic ice sheets via correlation seismology and advancing machine learning tools for deep Earth studies. Education: PhD in Geophysics (ANU, 2019), Graduate Diploma in Earth System Physics (ICTP, 2015), BSc in Applied Mathematics (Hanoi University, 2013) Research interests span seismic source inversion, Antarctic ice dynamics, and inner core anisotropy. His 2024 articles address Hunga Tonga eruption mechanics and PKIKP wave analysis using deep learning. Media highlights include BBC, NYT, and ANU press releases.