Find Academic Supervisors

Ingenuin GasserView profile

Professor

• Modelling with Partial Differential Equations
• Kinetic and Fluid Dynamic Equations
• Renewable Energy Systems
• +3 more

Prof. Ingenuin Gasser is a Professor of Modelling and PDEs at the University of Hamburg's Department of Mathematics. He holds roles including UHH Coordinator for the Joint Master's Programme 'MathMods' and former Vice Dean for Internationalization and Young Researcher Support (2012-2022). His research focuses on mathematical modelling with PDEs, renewable energy systems, traffic flow dynamics, and semiconductor equations. He leads interdisciplinary projects on urban systems and energy technologies. Education & Career: Born 1965 in Brixen, Italy Diplom in Technical Physics (1989, TU Wien) Diplom in Technical Mathematics (1992, TU Wien) PhD in Mathematics (1996, TU Berlin) Habilitation in Mathematics (2000, UHH) Junior Professor (2002–2003, UHH) Full Professor (W3) since 2010 at UHH Research Interests: Modelling with PDEs, kinetic/fluid dynamics, renewable energy systems (solar updraft towers, osmosis power), traffic flow models, semiconductor equations, and interdisciplinary urban system modelling. Publications: Over 67 publications, recent works focus on PRO power stations, solar energy systems, and urban modelling. Key themes include energy efficiency optimization, nanofluid applications, and mathematical models for sustainable infrastructure. Grants & Projects: Participated in EU-funded networks (e.g., TMR), DFG projects on quantum transport, and international collaborations in applied mathematics and renewable energy.

Michele BirdView profile

Lecturer

• Statistics Education
• Applied Statistics
• Statistical Tutoring

Michele Bird serves as an Academic Professional Lecturer in the Department of Mathematics and Statistics at the University of Wyoming, teaching foundational courses like Stat 2000: Statistics and the World and Stat 2050: Introduction to Statistics. She also coordinates the statistics program for the Center for Assistance with Statistics and Mathematics (CASM), directing tutoring services and seminars. Education : M.S. in Statistics from the University of Nevada Las Vegas. Her professional focus centers on statistics education methodology and student support systems, with emphasis on developing effective tutoring frameworks for introductory statistics learners. As CASM Statistics coordinator, she designs training for tutors and optimizes resource allocation to enhance course comprehension. Bird's work directly impacts student success in quantitative disciplines through structured academic assistance, leveraging her expertise in statistical pedagogy to bridge learning gaps in core curriculum requirements.

Maria Christina MarianiView profile

Professor

• Applied Mathematics
• Nonlinear partial differential equations
• Stochastic differential equations
• +5 more

Maria Christina Mariani is a Professor and Department Chair in the Department of Mathematical Sciences at the University of Texas at El Paso (UTEP). Her interdisciplinary research bridges mathematics with applications in public health, geophysics, physics, and finance, with a focus on developing novel mathematical models for complex data analysis. Dr. Mariani earned her Ph.D. in Mathematics from the University of Buenos Aires in 1992, where she received an Outstanding dissertation award. She also holds an M.S. in Physics (1996) and an M.S. in Mathematics (1987), both from the University of Buenos Aires with highest honors. Her research interests span Applied Mathematics, Nonlinear partial differential equations, Stochastic differential equations, Machine Learning techniques, Mathematical Finance, Mathematical Physics, and Numerical Methods. She has developed mathematical models for medical data analysis (particularly breast cancer, heart disease, and prostate cancer), seismic and explosive data, and financial markets. Her work emphasizes the development of mathematical models to enhance understanding of medical data and extreme events in various phenomena. Dr. Mariani's recent research focuses on applying machine learning and stochastic models to complex data sets across multiple domains. Her work demonstrates consistent innovation in developing novel algorithms for medical diagnosis and prognosis, analyzing seismic data, and modeling financial markets using Levy processes, Ornstein-Uhlenbeck models, and wavelet techniques. She has mentored numerous students throughout her career, including PhD candidates, MS students, and post-doctoral researchers, demonstrating her commitment to academic development and knowledge transfer. Dr. Mariani has served as Department Chair and holds the Shigeko K. Chan Distinguished Professor title in Mathematical Sciences, reflecting her significant contributions to the field and institution.

Renata DividinoView profile

Assistant Professor

• Graph Knowledge Representation
• Machine Learning
• Web Science
• +5 more

Renata Dividino is an Assistant Professor in the Department of Computer Science at Brock University, Canada. She holds a BSc from the University of Campinas (Brazil), an MSc from Universität des Saarlandes (Germany), and a PhD from Universität Koblenz – Landau (Germany). Her research focuses on graph knowledge representation, machine learning, and their applications in web science, semantic web foundations, and provenance systems. She has worked at institutions like DFKI, Fraunhofer IGD, and the Big Data Analytics Lab at Dalhousie University, bridging academic and industrial sectors. Her industry experience includes roles as an AI Scientist and Director of Data Science in the maritime sector, where she developed patented technologies for AI-driven maritime operations and risk assessment systems. Key research contributions include improving AI system reliability via provenance analysis and advancing knowledge graph applications. Education: BSc in Computer Science, University of Campinas MSc in Computer Science, Universität des Saarlandes PhD in Computer Science, Universität Koblenz – Landau Research Interests: Provenance systems, semantic web foundations, knowledge graphs, graph-based AI, maritime AI applications, and federated learning. Her work emphasizes practical applications in complex networks, web-scale data, and social networks. Awards: No specific awards mentioned, but her contributions include patented technologies and peer-reviewed publications on provenance-driven AI transparency. Advising & Grants: Secured industry grants for R&D projects in maritime AI and data science. Her work on vessel risk assessment and infectious disease prediction demonstrates applied research impact.

Kimberly TamView profile

Associate Professor

• Maritime Cybersecurity
• Autonomous Systems Security
• IT/OT/IoT Security
• +3 more

Dr. Kimberly Tam is an Associate Professor in Cybersecurity at the School of Engineering, Computing and Mathematics, University of Plymouth. She specializes in maritime cybersecurity, focusing on ship IT/OT/IoT security, port infrastructure protection, autonomous vessels, offshore renewable energy systems, and smartphone security. She is affiliated with the Alan Turing Institute as the Theme Lead for Marine and Maritime. Her research addresses critical challenges in maritime autonomy, cyber-physical systems, and regulatory frameworks (e.g., IMO MASS). She has supervised 7 PhD students and 7 research staff across multiple projects, contributing to cyber resilience training and risk assessment tools like CERP and MaCRA. Her work emphasizes practical applications, such as penetration testing frameworks (BridgeInsight) and cost-benefit analysis methods for energy infrastructure cybersecurity. Teaching responsibilities include leading modules like Security Operations & Incident Management , Cyber-Physical Systems Security , and unmanned maritime systems operations. She has developed specialized courses in Hong Kong and Sri Lanka, integrating industry-relevant training. Her recent publications (2023–2025) highlight advancements in adversarial AI defense, ransomware mitigation in maritime contexts, and digital twin technologies for port optimization. She collaborates internationally, hosting visiting researchers from China, Turkey, and Norway, and supports early-career professionals through work experience programs.

Shahin KamaliView profile

Associate Professor

• Algorithms Design
• Algorithms Analysis
• Online Algorithms
• +9 more

Shahin Kamali is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. Previously, he served as an Assistant Professor at York University (2022-2024) and the University of Manitoba (2017-2022), where he continues to hold an Adjunct Professor position. He completed his Ph.D. in Computer Science at the University of Waterloo in 2014 and was a postdoctoral fellow at MIT's CSAIL lab from 2015-2017. Dr. Kamali received his B.Sc. from the University of Tehran and his M.Sc. from Concordia University, both in Computer Science. He has earned teaching certifications including the Kaufman Teaching Certificate Program from MIT and the Certificate in University Teaching from the University of Waterloo. His research focuses on algorithms' design, analysis, and limitations, with particular emphasis on online problems such as bin packing, paging, list update, and k-Server. His work extends to big-data applications of algorithms in data compression, graph partitioning, resource allocation in the cloud, and algorithmic aspects of blockchain technology. His recent publications (2023-2025) demonstrate a strong focus on learning-augmented algorithms, online computation with predictions, and novel applications in blockchain and graph theory. His research has been supported by multiple NSERC grants since 2010, with recent projects investigating models, applications, and limitations of online algorithms. He has successfully supervised numerous graduate students and serves on various committees related to algorithms conferences and academic governance. University of Waterloo Doctoral Thesis Completion Award (2014) University of Waterloo Mathematics Graduate Experience Award (2008) Dr. Kamali teaches advanced courses in algorithms and data structures and is scheduled to teach Design and Analysis of Algorithms (EECS 3101) in Fall 2025 and Advanced Data Structures (EECS 4101/5101) in Winter 2026. He is actively involved in organizing major conferences, including CCCG and WADS 2025 at York University.

Cory SimonView profile

Associate Professor

• Machine Learning
• Bayesian Optimization
• Computational Materials Science
• +3 more

Cory Simon serves as Associate Professor in the Department of Chemical, Biological, and Environmental Engineering within Oregon State University's College of Engineering. His research integrates machine learning, optimization, and chemical engineering to advance materials discovery and environmental sensing systems. His academic foundation includes a Ph.D. in Chemical Engineering from the University of California, Berkeley and a B.S. in Chemical Engineering from The University of Akron. Simon's work centers on Bayesian methodologies for scientific challenges, featuring: Bayesian optimization for adaptive materials synthesis Statistical inversion of physical systems with uncertainty quantification Computational design of nanoporous sensor arrays Stochastic algorithms for robotic environmental monitoring Recent publications demonstrate accelerating focus on multi-fidelity optimization for molecular design and atmospheric water harvesting, bridging chemical engineering with computational science through data-driven approaches. Leading The Simon Ensemble research group, Simon champions a versatile 'buffet-style' research philosophy—drawing from mathematics, statistical mechanics, and machine learning to address interdisciplinary problems across chemistry, materials science, and environmental engineering.

• Additive Manufacturing
• Computer-Integrated Manufacturing
• Intelligent Modeling and Optimization
• +3 more

Souran Manoochehri is a Professor and Chair of the Department of Mechanical Engineering at Stevens Institute of Technology, within the Charles V. Schaefer, Jr. School of Engineering and Science. He joined Stevens in 1989 as an Assistant Professor, advancing to Associate Dean for Research and Technology (2004-2009) and Director of the Design and Manufacturing Institute (1990-2004). He holds a PhD (1986), MS (1983), and BS (1981) in Mechanical Engineering from the University of Wisconsin-Madison and Illinois Institute of Technology, respectively. Education: PhD, MS, and BS in Mechanical Engineering Roles: Department Chair, former Associate Dean, and co-founder of the Design and Manufacturing Institute Research: Focuses on additive manufacturing, computer-integrated design, and intelligent optimization His research integrates mathematical modeling, machine learning, and experimental studies to ensure product and process quality in manufacturing. Over his career, he has secured $30M+ in grants, authored 130+ publications, and supervised 30+ graduate students and 12 postdoctoral fellows. He is an ASME Fellow and recipient of awards including the ASME Design Engineering Division Award. Key research trends in his articles include real-time monitoring of additive manufacturing processes (e.g., melt pool analysis, acoustic emission sensors), machine learning applications in quality control, and optimization of manufacturing systems. His work addresses challenges in precision, defect detection, and process automation across 3D printing and microfluidics. Scientific Awards: ASME Fellow, ASME IDETC Award, DMC Best Presentation Grants: Over 50 contracts totaling $30M+; advising over 30 graduate students Labs/Teams: Co-founded the Design and Manufacturing Institute (DMI) at Stevens His contributions span academic leadership and industry-relevant innovation, emphasizing interdisciplinary solutions in advanced manufacturing.

• Reinforcement Learning
• Stochastic Dynamic Optimization
• Operations Research
• +7 more

Somayeh Moazeni is an Associate Professor at the School of Business, Stevens Institute of Technology. She holds a PhD in Computer Science from the University of Waterloo and has held academic appointments including Visiting Associate Professor at Northwestern University and Postdoctoral Research Associate at Princeton University. Her research focuses on Reinforcement Learning, Stochastic Dynamic Optimization, and applications in Energy Markets, Inventory Management, and Algorithmic Trading. She has authored over 30 peer-reviewed articles and serves as an associate editor for INFOR and PLOS One . Education: PhD (Computer Science, 2012), University of Waterloo; Postdoc (Operations Research, 2012-2014), Princeton University Industry Experience: Senior Risk Analyst at RBC (2011-2012), Risk Analyst at BMO (2010) Awards: IEEE Senior Member (2019), Anita Borg Institute GHC Faculty Scholar (2017), MITACS Poster Competition First Place (2009) Her research spans Bayesian Optimization , Resilient Network Design , and Energy Efficiency . Current funded projects include PSEG Foundation grants for energy resilience and NSF funding for distributed energy resource controls. She advises PhD students in Operations Research and Energy Systems and teaches graduate courses in Reinforcement Learning and Financial Engineering. Key Contributions: Developed stochastic optimization frameworks for energy storage, contact center reliability modeling, and risk-aware trading strategies. Her work on sequential learning for consumer-driven demand response programs has advanced smart grid applications.

Cheryl SealsView profile

Professor

• Artificial Intelligence
• Natural Language Processing
• Educational Technology
• +4 more

Cheryl Seals is the Charles W. Barkley Endowed Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. She holds a Ph.D. in Computer Science from Virginia Tech, with additional degrees from Virginia Tech, North Carolina A&T State University, and Grambling State University. Her research focuses on AI ethics, natural language processing (NLP), virtual reality (VR) applications in healthcare and education, and promoting diversity in STEM. She leads initiatives like the NSF-funded AI ethics education project and the Institute for African Americans in Computing Sciences (IAAMCS). Notable awards include the Charles W. Barkley Professorship (2020) and participation in Drexel’s ELATES fellowship program. Her work bridges technology and social impact, including VR-based empathy training for medical students and interventions to mitigate pandemic-induced learning loss. She collaborates on interdisciplinary projects, such as automated grading tools for speech therapy and pavement crack recognition systems. Education: Ph.D. Computer Science, Virginia Tech M.S. Computer Science, Virginia Tech M.S. Software Engineering, North Carolina A&T State University B.S. Computer Science, Grambling State University Research Interests: AI Ethics and STEM Education Reform NLP for Sentiment Analysis and Hate Speech Detection VR Applications in Healthcare Training and Empathy Development Computing Equity and Underrepresented Groups Interdisciplinary Tools for Education and Accessibility Awards and Recognition: Charles W. Barkley Endowed Professor (2020–2025) ELATES Fellowship Participant (2024) Grants and Collaborations: NSF Grant: Integrating AI Ethics into STEM Curricula Collaborative Research: Evolution of IAAMCS National Science Foundation (NSF) AI Ethics Initiative Labs and Affiliations: Center for Artificial Intelligence and Cybersecurity Engineering McCrary Institute for Cyber and Critical Infrastructure Security

Burhaneddin İzgiView profile

Associate Professor

• Game Theory
• Stochastic Differential Equations
• Numerical Analysis
• +3 more

Burhaneddin İzgi serves as an Associate Professor in the Department of Mathematics Engineering at Istanbul Technical University, where he maintains an active research profile with projects extending through 2025. His work bridges theoretical mathematics with computational applications, particularly in game-theoretic problem solving. Research interests center on Game Theory and Stochastic Differential Equations, with specialized focus on matrix norm-based solution methods for zero-sum, fuzzy, and stochastic matrix games. Recent work integrates Artificial Intelligence techniques to address large-scale game complexity, reflecting interdisciplinary innovation across Numerical Analysis and Fuzzy Mathematics. Article trends (2022-2025) reveal consistent advancement of matrix norm methodologies across diverse game types, increasingly incorporating machine learning for computational efficiency. Publications demonstrate strong theoretical grounding in Mathematics while addressing practical applications in finance (e.g., IPO modeling) and decision systems. Scientific recognition includes: 2210 - Yurt İçi Yüksek Lisans Burs Programı (2008) 2211 - Yurt İçi Doktora Burs Programı (2010) Matematik Bölüm Birinciliği (2008) Üniversite İkinciliği ödülü (2008) Research funding includes TUBITAK-supported projects such as 'Stokastik Oyunlar İçin Matris Norm Tabanli Yeni Çözüm Yöntemleri Ve Yapay Zeka Uygulamalari' (2021-2023) and current grants developing chaos theory approaches for stochastic matrix games (2024-2025), demonstrating sustained external validation of his research program.

Matthew SiegfriedView profile

Associate Professor

• Subglacial hydrology
• Ice-ocean interaction
• Grounding zone dynamics
• +4 more

Matthew Siegfried is an Associate Professor in the Department of Geophysics at Colorado School of Mines, where he leads the Mines Glaciology Laboratory . His work combines satellite remote sensing with field and airborne geophysical methods to study Earth's glaciers and ice sheets, focusing on subglacial hydrology, ice-ocean interactions, and grounding zone dynamics. PhD in Earth Sciences (2015) - Scripps Institution of Oceanography M.S. in Earth Sciences (2010) - Dartmouth College A.B. in Earth Sciences (2008) - Dartmouth College His research explores subglacial hydrology (subglacial lakes, groundwater), ice-ocean interactions (grounding zones, ice shelves), and geophysical surveying using air/spaceborne altimetry (ICESat-2, Operation IceBridge). Recent projects examine West Antarctic ice streams, Arctic coastal dynamics, and Ross Ice Shelf stability. Publications span topics in subglacial lake evolution , ICESat-2 algorithm development , and ice-ocean feedback mechanisms . Collaborative work includes biogeochemical studies of Mercer Subglacial Lake and Arctic permafrost-wildfire interactions. 2023 Mines Excellence in Research Award 2022 Undergraduate Student Government Faculty Award 2020 AGU Outstanding Reviewer He mentors through the Mines Glaciology Laboratory , supported by NASA and NSF grants (CAREER program, ICESat-2 projects). Outreach includes podcast appearances (e.g., Historians at the Movies) and K-12 collaborations.

Damien RafteryView profile

Lecturer

• Higher Education Pedagogy
• Generative AI in Education
• Academic Integrity
• +5 more

Damien Raftery is a Lecturer in the Department of Business at South East Technological University (SETU Carlow), where he has served since 1995. He holds roles as eLearning Development Officer in the Teaching and Learning Centre (since 2008) and External Examiner. His academic leadership includes Programme Directorships, Academic Council membership (2013-2015), and chairing the Teaching, Learning & Support Services Committee. Education: BSc (Hons) and MSc in Mathematical Science from University College Dublin (1994-1995), followed by an MA in Management in Education (Waterford Institute of Technology, 2003) and an MA in Educational Studies (University of Sheffield, 2017). Research focuses on higher education pedagogy, with emphasis on generative AI's impact on teaching/assessment, blended/flipped learning methodologies, and quantitative literacy. He actively contributes to educational technology through roles in the Irish Learning Technology Association (founding treasurer, conference organizer) and co-developed the StatsSkills web app for statistics education. His recent work addresses AI-driven challenges in assessment, VLE optimization, and digital pedagogy innovations. Collaborative efforts include organizing the 2024 Digital Education Conference and publishing on AI ethics in academic settings.

• Coding and K-12 education
• Inclusive mathematics education
• Learning through technology
• +3 more

Dr. Diane Tepylo is an Associate Teaching Professor at the Faculty of Education, Ontario Tech University. She is located at Charles Hall, Room 336 in the Downtown Oshawa campus. Her contact information includes the email diane.tepylo@ontariotechu.ca and phone number 905.721.8668 ext. 3768. Dr. Tepylo holds a PhD in Applied Psychology and Human Development from the Ontario Institute for Studies in Education (OISE), focusing on mathematics teaching and learning within STEM fields. Her research interests include Coding and K-12 education, Inclusive mathematics education (particularly for at-risk students, English language learners, and culturally responsive pedagogy), Learning through technology, Spatial reasoning in STEAM education, and STEAM teacher development for both pre-service and in-service educators. She emphasizes innovative pedagogical approaches, such as flipped classrooms and problem-solving challenges to enhance student engagement and learning outcomes. Dr. Tepylo teaches a diverse range of courses across multiple programs, including: Bachelor of Commerce: BUSI 1915U (Business Mathematics I) Bachelor of Education: EDUC 1311U and EDUC 2408U (Coding and Communication), CURS 4140U and CURS 4141U (Mathematics Curriculum Studies) Bachelor of Arts, Educational Studies: EDST 1000U (Foundations of Learning), EDST 1100U (Problem and Inquiry-Based Learning), EDST 3170U (Developing Numeracy), EDST 4000U (Inquiry and Professional Practice), EDST 4120U (Games and Simulations for Learning), EDST 4610U (Coding and Maker Pedagogies for Young Children) No scientific awards or grants are explicitly mentioned in the provided information. Her work focuses on bridging educational theory and practice, with a strong emphasis on student-centered learning and technology integration.

John MarshallView profile

Professor

• Mathematical modeling of infectious diseases
• Genetics-based strategies for mosquito control
• Malaria elimination
• +4 more

John Marshall is a Professor in the Department of Biostatistics and Epidemiology at the School of Public Health, University of California, Berkeley. His research focuses on using mathematical models to inform novel genetics-based strategies for mosquito control and to support efforts to control and eliminate mosquito-borne diseases such as malaria, dengue, and Zika virus. Dr. Marshall received his PhD in biomathematics from UCLA in 2008, with a dissertation on the use of GM mosquitoes to control malaria transmission. Prior to joining UC Berkeley, he worked on several aspects of this project as a PostDoc at the UCLA Center for Society & Genetics, the Malaria Research and Training Center in Mali, Caltech, and Imperial College London. His research interests include mathematical modeling of infectious diseases, genetics-based strategies for mosquito control, malaria elimination, dengue control, Zika virus control, gene drive systems, and vector control. He has developed the Mosquito Gene Drive Explorer (MGDrivE), a framework for testing gene-drive releases for mosquito-borne diseases control that models inheritance matrices, life-history, and migration across spatial landscapes. Dr. Marshall has secured significant funding for his research, including an $800,000 grant from the Bill & Melinda Gates Foundation in 2021 for genetics-based malaria mosquito control and an NIH grant in 2020 for mosquito movement and control research. He leads the Marshall Lab which collaborates with researchers from Tecnológico de Monterrey and IHME through the Mosquito Networks Taskforce (MoNeT). His recent publications demonstrate interdisciplinary work spanning epidemiology, genetics, computational biology, and vector control, with a focus on gene drive systems, surveillance methods, and cost-effectiveness analyses for malaria elimination. $800,000 grant from the Bill & Melinda Gates Foundation (2021) NIH grant for mosquito movement and control (2020) Dr. Marshall mentors numerous students and researchers including Héctor M. Sánchez C. (lead of MoNeT), Kevin Islas Abud, Luis Rodrigo Careaga Sotomayor, and others working on machine learning applications, network theory, and spatial analysis related to mosquito-borne diseases. He teaches courses PH 252B Modeling the dynamics of infectious disease processes and PH 295 Infectious disease modeling seminar at UC Berkeley.