Find Academic Supervisors

Koushik SenView profile

Professor

• Software Engineering
• Programming Languages
• Formal Methods

Koushik Sen is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on developing software tools and methodologies to enhance programmer productivity and software quality, with expertise in Software Engineering, Programming Languages, and Formal Methods. Education: B.Tech from Indian Institute of Technology, Kanpur M.S. and Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Research Focus: Professor Sen pioneers automated testing techniques including concolic testing and DART (Directed Automated Random Testing). His work bridges formal methods with practical software development, emphasizing bug detection, program synthesis, and AI-driven software analysis tools. Recent innovations include machine learning approaches for code recommendation and fuzzing. Publication Trends: His recent publications (2019-2023) demonstrate strong emphasis on fuzzing techniques, program synthesis, and AI/ML applications in software engineering. Notable domains include smart contract security, automated testing, and developer tooling, with frequent collaborations in top-tier conferences. Awards and Honors: NSF CAREER Award (2008) Sloan Foundation Fellowship (2011) IFIP TC2 Manfred Paul Award (2010) Okawa Foundation Research Grant (2015) Multiple ACM SIGSOFT Distinguished Paper Awards UIUC Distinguished Alumni Educator Award (2014) Leadership: Active program committee member for premier conferences (PLDI, ICSE, ISSTA) and keynote speaker. His research is supported by NSF, Okawa Foundation, and Sloan Foundation.

Kathleen R. McKeownView profile

Professor

• Natural Language Processing
• Text Summarization
• Natural Language Generation
• +4 more

Kathleen R. McKeown is the Henry and Gertrude Rothschild Professor of Computer Science at Columbia University and the Founding Director of Columbia's Data Science Institute (2012-2017). She has been a faculty member since 1982 and served as Department Chair (1998-2003) and Vice Dean for Research in the School of Engineering and Applied Science. Her research focuses on natural language processing , text summarization , natural language generation , and social media analysis . Current projects include neural methods for extractive/abstractive summarization, electricity usage message generation via reinforcement learning, and social media sentiment analysis in low-resource languages like Uyghur. She leads the Columbia NLP Group and developed the long-running Newsblaster system (2001-present) for automated news tracking and multi-document summarization. Key scientific awards include NSF Presidential Young Investigator (1985) NSF Faculty Award for Women (1991) AAAI Fellow (1994) ACM Fellow (2003) ACL Founding Fellow (2012) Columbia Great Teacher Award (2010) Anita Borg Woman of Vision Award (2010) She has held leadership roles in major academic organizations: President of the Association for Computational Linguistics (1992), Vice President (1991), Secretary-Treasurer (1995-1997), and board member of the Computing Research Association with secretary role.

• Graph Representation Learning
• Deep Learning
• Reinforcement Learning
• +7 more

Jian Tang is an Assistant Professor at HEC Montreal and the Montreal Institute for Learning Algorithms (MILA), as well as an Associate Professor at the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also affiliated with IVADO (Institut de valorisation des données) as a member. His research spans multiple institutions including collaborations with leading biology labs worldwide and access to extensive computational resources through industry partners. Ph.D. in Computer Science, Peking University (2009-2014) Visiting Ph.D. student, University of Michigan (2011.10-2013.8) B.S. in Mathematics, Beijing Normal University (2005-2009) Professor Tang's research focuses on the intersection of deep learning and graph theory, with particular emphasis on geometric deep learning, knowledge graph reasoning, and applications in drug discovery. His work bridges symbolic and neural approaches to create robust reasoning systems that can handle complex structured data. He has pioneered techniques in graph representation learning that have significantly advanced the field of molecular property prediction and protein design. His publication record shows a clear trajectory toward applying geometric deep learning to biological problems, with a growing emphasis on protein design, molecular conformation generation, and multi-omics analysis. Recent work demonstrates sophisticated integration of 3D geometry with deep learning architectures to model complex biomolecular interactions. Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) Tencent AI Lab Rhino-Bird Gift Fund Amazon Faculty Research Award Microsoft-Mila collaboration grant National Research Council Canada (NRC) Collaborative Research and Development Grant Professor Tang actively mentors doctoral and master's students, with six recent graduates working on cutting-edge topics including graph neural networks for reasoning, protein design, and molecular representation learning. His research is supported by substantial funding from industry partners including Microsoft, Amazon, and Tencent, as well as government agencies like NRC. He collaborates extensively with biology labs worldwide, applying AI to solve real-world biomedical challenges. He leads a research group focused on geometric deep learning for drug discovery, with active projects in protein design using geometric-aware models and large language models for multi-omics analysis. The group has access to thousands of GPUs through industry collaborations, enabling large-scale experiments in molecular simulation and generative modeling.

• Scientific Machine Learning
• Climate Science
• Earthquake Seismology
• +3 more

Karianne Bergen is an Assistant Professor of Data Science and Earth, Environmental & Planetary Sciences at Brown University, with a courtesy appointment in Computer Science. She leads the Scientific Machine Learning (SciML) Research Group, affiliated with the Data Science Institute (DSI), DEEPS, and the SciAI Center. Her research focuses on scientific machine learning (SciML), including surrogate models for climate science, explainable AI (XAI), and foundation models. She holds a Ph.D. and M.Sc. in Computational and Mathematical Engineering from Stanford University and a B.Sc. in Applied Mathematics from Brown University. Her postdoctoral training included a Harvard University HDSI fellowship in Computer Science. Education: B.Sc. Applied Mathematics, Brown University (2009) M.Sc. Computational and Mathematical Engineering, Stanford University (2015) Ph.D. Computational and Mathematical Engineering, Stanford University (2018) Research Interests: Dr. Bergen’s work bridges machine learning and Earth sciences, emphasizing scalable methods for climate modeling and geophysical data analysis. Her group develops emulators for Antarctic ice sheet dynamics, XAI frameworks for climate data interpretation, and scientific foundation models for geoscience applications. Recent projects include GAN-based sea ice resolution enhancement and flow-based neural networks for sea level projections. Awards: Harvard Data Science Initiative Postdoctoral Fellowship (2018–2020) Stanford Graduate Fellowship in Science and Engineering (2011–2015) Outstanding Student Paper Award, American Geophysical Union (2015) Advising & Collaborations: She mentors PhD students in Earth, Environmental, and Planetary Sciences and collaborates with institutions like MIT-Lincoln Laboratory, SciAI Center, and international geoscience groups. Her lab includes postdocs (e.g., Hilarie Sit) and alumni from data science practicum programs. Labs/Teams: The SciML group at Brown University focuses on multidisciplinary projects at the intersection of AI and Earth sciences, with recent presentations at AGU Fall Meetings and the AI for Science Workshop at ICML.

Kevin F. KellyView profile

Associate Professor

• Optics and Photonics
• Nanotechnology
• Imaging and Spectroscopy
• +7 more

Kevin F. Kelly is an Associate Professor in the Department of Electrical and Computer Engineering at Rice University. He was formerly the Chair of the Applied Physics Program and is affiliated with the Smalley-Curl Institute. Additionally, he has been a member of the Penn State Center for Nanoscale Science and the Mid-Infrared Technologies for Health and the Environment (MIRTHE) Center at Princeton University. Dr. Kelly co-founded Inview Technology Corporation as its Chief Scientist, focusing on commercializing compressive imaging technologies. He has also consulted for the Baker Institute for Public Policy regarding photovoltaics and taught courses in anthropology and history at Rice. Dr. Kelly holds a B.S. in Engineering Physics from the Colorado School of Mines (1993), followed by an M.S. (1996) and Ph.D. (1999) in Applied Physics from Rice University. His postdoctoral work included fellowships at the Institute for Materials Research in Sendai, Japan, and the Chemistry Department at Penn State University. His research interests span Optics and Photonics, Imaging and Spectroscopy at the nanoscale, and the role of mathematics in image acquisition. He develops Scanning Probe Microscopy techniques and studies Electronic Materials such as graphene and topological insulators. A major focus is Compressive Hyperspectral Imaging systems, including single-pixel camera innovations and advanced microscopy methods. He also pioneers molecular machines like the Nanocar and investigates charge transport in polymer photovoltaics. Over recent years, his work emphasizes interdisciplinary applications, such as integrating compressive sensing with neural networks for machine vision and exploring technological disaster analysis through history courses. His contributions have been recognized with awards like the IEEE Fellow (2022) and Technology Review’s Top 10 Emerging Technologies (2007). In addition to academic roles, Dr. Kelly has co-founded Inview Technology and contributed to grants and collaborations through his involvement in the MIRTHE Center and other institutes. While no formal advisees are listed, his teaching includes courses on nanotechnology since 2009 and he actively engages in policy consultations for photovoltaic commercialization. His lab at Rice and collaborations with the Smalley-Curl Institute drive advancements in nanotechnology and imaging, with a particular emphasis on practical applications of compressive sensing and molecular-scale devices.

Ngoc Cuong NguyenView profile

Researcher

• Computational Mechanics
• Molecular Mechanics
• Nanophotonics
• +7 more

Ngoc Cuong Nguyen is a Principal Research Scientist in the Department of Aeronautics and Astronautics at MIT and a member of the MIT Center for Computational Engineering. His research focuses on computational mechanics, numerical simulation, and advanced numerical methods such as hybridizable discontinuous Galerkin (HDG) methods for multi-scale and multi-physics problems. Education: PhD in High Performance Computation for Engineered Systems (2005), National University of Singapore BEng in Aeronautical Engineering (2001), Ho Chi Minh City University of Technology Research Interests: Computational Mechanics, Molecular Mechanics, Nanophotonics Numerical Simulation & Optimization, Scientific Computing, Machine Learning Reduced Basis Methods, High-Order Methods (e.g., HDG), Uncertainty Quantification Key Projects: Development of HDG methods for fluid dynamics, structural mechanics, and electromagnetics Plasmonic nanostructure simulations using quantum hydrodynamic models Space weather modeling via GPU-accelerated HDG approaches Optimization of photonic crystals and nanostructured materials Large-eddy simulation (LES) of hypersonic flows and buffet phenomena Labs & Teams: Active contributor to the MIT Center for Computational Engineering, leading projects in numerical methods, computational fluid dynamics, and interdisciplinary applications of advanced simulation techniques.

Mingming ChengView profile

Professor

• Social Media Analytics
• Sustainable Tourism
• Sharing Economy
• +4 more

Professor Mingming Cheng is a faculty member at Curtin University, serving as Professor in Digital Marketing and Director of the Social Media Research Lab within the School of Management and Marketing (Faculty of Business and Law). His research focuses on social media analytics, sustainable tourism, and Chinese outbound tourism, with a strong emphasis on data science and the sharing economy. He holds a PhD from the University of Technology Sydney and previously worked at the University of Otago. Professor Cheng has secured over AUD 650,000 in research funding and serves on editorial boards of top journals like Tourism Management and Annals of Tourism Research . He has been recognized with prestigious awards, including the 2021 Australia's Top 40 Early Career Researchers and the 2023 Young Tall Poppy Science Award. Research Interests: Social media analytics, sustainable tourism, sharing economy, digital transformation Key Roles: Editor for Tourism Management Perspectives and Tourism Geographies Affiliations: Collaborations with institutions like the Japan Foundation and Western Australian Department of Agriculture His teaching includes courses on digital marketing, big data analytics, and social media management. He actively engages with media, contributing insights to ABC News and South China Morning Post.

Daniela AndreiView profile

Associate Professor

• Workforce Management
• Employee Wellbeing
• Occupational Safety
• +5 more

Daniela Andrei is an Associate Professor at Curtin University's School of Management and Marketing, affiliated with the Faculty of Business and Law. She coordinates large-scale applied research projects addressing workforce aging, safety, and employee wellbeing. Her work focuses on understanding forces shaping work experiences across the lifespan and their impacts on performance and safety. She is an Associate Investigator with CEPAR Stream 3 (Organisations and the Mature Workforce) and a member of Cost Action - LeverAGE. Research interests include aging workforce management, safety culture in high-risk industries (e.g., maritime), and HR practices for age diversity. Her studies often involve collaborations with industry and government bodies like Safe Work Australia and the International Mining for Development Centre. Her recent articles analyze interventions for aged care job demands, safety leadership behaviors, and maritime industry fatigue. Notable contributions include frameworks for 'Include, Individualize, Integrate' HR practices and meta-strategies for age-diverse workforces. She has advised on organizational metastrategies for managing younger and older workers, emphasizing inclusive design. Labs/Teams: Active in CEPAR's benchmarking surveys of the Australian workforce and the Age in the Workplace research network. Engaged in cross-disciplinary projects linking occupational health, leadership, and sustainable work design.

Olaf SchmidtView profile

Professor

• Soil Science
• Agricultural Sustainability
• Stable Isotope Analysis
• +7 more

Olaf Schmidt is a Professor at the School of Agriculture and Food Science, University College Dublin (UCD). His academic career includes roles such as Senior Lecturer/Associate Professor (2008–2017) and extends back to a Post-doctoral Research Assistant position (1999–2001). He holds a Ph.D. from UCD (2000), an M.Sc. in Soil Science from the University of Aberdeen (1993), and a Diplom-Agraringenieur from Martin-Luther-University Halle-Wittenberg (1992). Research Focus: Schmidt specializes in soil ecology, earthworm biology, stable isotope analysis, and sustainable agriculture. His work addresses topics like soil restoration, invasive species impacts, food authentication, and ecosystem rehabilitation. He leads projects such as Edaphobase 2.0 and the Gunnera tinctoria invasion studies. His research integrates molecular techniques, isotopic tracing, and large-scale data synthesis. Awards & Recognition: Fellow of the Royal Entomological Society (2023) Honorary Member of the Association of Applied Biologists (2022) Fellow of the Institute of Soil Science (2023) Teaching & Outreach: Coordinates modules on soil science, environmental microbiology, and the circular bioeconomy. Engages in public lectures on topics like earthworm contributions to soil health. Collaborates internationally, including a visiting professorship at the University of Agriculture in Kraków (2022). Grants & Impact: Contributed to the SmartGrass project, enhancing sustainable agricultural practices through multispecies swards. His work on peat alternatives and mine tailings rehabilitation has practical implications for environmental management and policy. Labs & Collaborations: Active in the Global Soil Biodiversity Initiative and Soil BON network. Edaphobase 2.0 exemplifies his data-driven approach to soil biodiversity research.

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.

A. Erdem SariyuceView profile

Associate Professor

• Graph Mining
• Social Network Analysis
• Network Science
• +4 more

A. Erdem Sariyuce is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, part of the School of Engineering and Applied Sciences. His research focuses on graph mining, network science, and distributed computing. He holds a PhD from Ohio State University (2015) and a BS from Middle East Technical University (2010). His work explores temporal network analysis, social network dynamics, and algorithm design for large-scale systems. Notable contributions include methods for motif detection, core decomposition, and stream processing in bipartite networks. Sariyuce has received prestigious awards such as the NSF CAREER Award (2023) and the UB Exceptional Scholar—Young Investigator Award (2023). His research is supported by grants including the LDRD Project at Sandia National Laboratories (2016-2017). Education: PhD, Computer Science and Engineering, Ohio State University, 2015 BS, Computer Engineering, Middle East Technical University, 2010 Research interests emphasize graph algorithms, network resilience, and parallel computing. His recent projects include analyzing financial networks, identifying cohesive communities, and developing efficient algorithms for temporal data. His work bridges theoretical foundations with practical applications in social media, cybersecurity, and bioinformatics. Awards: UB Exceptional Scholar—Young Investigator Award (2023) NSF CAREER Award (2023) SEAS Early Career Researcher of the Year (2020) Advising and grants highlight his leadership in collaborative projects, including the NSF-funded Collaborative Research: OAC Core: Fast Tools for Complex Event Detection over Bipartite Graph Streams (2021). His research often involves industry partnerships and government labs, such as Sandia National Laboratories.

Melanie MorrisonView profile

Professor

• Stereotyping
• Prejudice and Discrimination
• Social Class
• +7 more

Dr. Melanie Morrison is a Professor in the Department of Psychology and Health Studies at the University of Saskatchewan's College of Arts and Science. Her research focuses on stereotyping, prejudice, and discrimination toward marginalized groups, with emphasis on social class, gender identity, and sexual orientation. She leads the Saskatchewan Equity, Equality, and Diversity (SEED) Lab, investigating the psychophysiological and behavioral manifestations of bias, particularly among dominant group members. Education: BA (Honours, Distinction) from the University of Victoria, MSc in Experimental Psychology from the University of Northern British Columbia, and PhD in Experimental Psychology from the University of Ottawa. Research interests include intersectional microaggressions, LGBTQ+ rights, food security, and the application of big data and machine learning to social justice issues. She employs mixed-methods approaches, including surveys, experiments, and content analysis. Teaching areas: Social Psychology, Stereotyping & Discrimination, Psychometrics, and Social Justice. She collaborates with students on projects addressing prejudice reduction and systemic inequities. Labs/Teams: SEED Lab focuses on marginalized groups' experiences, including Indigenous peoples, sexual minorities, and women. Current projects explore media framing of LGBTQ+ violence and the affordability of local food systems.

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.

• Applied Statistics
• Optimization
• Data Visualization
• +7 more

Zhongyuan (Annie) Yu is a Teaching Associate Professor in the Department of Systems and Enterprises at Stevens Institute of Technology. She holds a PhD in Systems Engineering (2014), and two MS degrees in Operations Research (2012) and Industrial Engineering (2010), both from Georgia Institute of Technology. Her research bridges applied statistics, optimization, data visualization, and socio-economic systems analysis. Key focus areas include healthcare systems engineering, defense acquisitions, and interactive decision support systems. She leads software engineering education initiatives, including serving as Program Director for the Software Engineering Program (2022-2024). Yu has secured major grants from DoD DASD(SE) and NIH, focusing on mega-project management, digital engineering integration, and healthcare policy simulations. Her 2023-2024 research explores HIPAA compliance challenges in software engineering and patient trust in electronic health records. Her publications span health informatics, portfolio optimization networks, and systems engineering methodology. Notable works include developing policy flight simulators for healthcare interventions and semantic integration frameworks for digital engineering interoperability. Teaching responsibilities include courses like Agile Methods for Software Development, Data Analysis & Visualization, and Engineering Economics. She advises capstone projects and maintains active roles in curriculum development for systems and software engineering programs.

Azizur RahmanView profile

Professor

• Data Science
• AI and Machine Learning
• Statistics
• +7 more

Professor Azizur Rahman is a leading academic in applied statistics and data science at Charles Sturt University (Australia), where he leads the Data Mining Research Group and directs the Data Analytics Lab. He holds a PhD in Economics and Applied Statistics from the University of Canberra, with postdoctoral experience at the University of Adelaide. His expertise spans statistical methodologies, microsimulation, AI/ML, and data-driven policy analysis. He has attracted over $4.03M in research funding and published over 200 scholarly works. Education: BSc (Honours) in Statistical Science, University of Chittagong (2000) MSc (Thesis) in Biostatistics, University of Chittagong (2003) PhD in Economics and Applied Statistics, University of Canberra (2010) Research Focus: Big data analytics and AI applications in health, environment, and policy Bayesian inference and multilevel modeling Spatial microsimulation for socioeconomic analysis Water quality modeling and environmental systems Public health challenges in developing nations Key Contributions: Developed novel computational methods for microsimulation modeling Founder of the Data Analytics Lab at CSU Editorial roles in journals like PLOS ONE and International Journal of Microsimulation Recipient of 2023 ANZRSAI Outstanding Service Award and Charles Sturt Excellence Awards Grants & Awards: $4.03M+ external research funding secured 30+ awards including travel grants, fellowships, and service recognitions Advising & Mentorship: Supervised 6 PhD/DIT students and co-supervised many more. Active in mentoring early-career researchers through editorial boards and conference organizing roles. Labs & Collaborations: Leads DaMRG (Data Mining Research Group) and collaborates with global institutions on interdisciplinary projects involving AI/ML, environmental science, and public health.