Find Academic Supervisors

• Machine Learning
• Mathematical Modeling
• Biostatistics
• +3 more

Sajal Chakroborty is a Researcher in the Department of Mathematical Sciences at Worcester Polytechnic Institute (WPI). He holds a Ph.D. and M.S. in Mathematics and Statistics, respectively, from Texas Tech University. His research focuses on interdisciplinary applications of mathematical modeling, statistical methods, and machine learning. Key areas include disease modeling (e.g., COVID-19 and Mpox), image processing, stochastic optimization, and functional analysis. Education: Ph.D. in Mathematics, Texas Tech University M.S. in Statistics, Texas Tech University Chakroborty’s work bridges theory and practice, addressing challenges in public health through computational frameworks and advancing methodologies for small datasets and outlier detection. His recent publications emphasize hybrid deep learning for medical imaging, mathematical modeling of epidemics, and innovative tools for multivariate statistics. His contributions span both foundational mathematics and applied domains like transportation logistics and game theory. Notable trends in his publications include leveraging machine learning for healthcare diagnostics, developing stochastic optimization techniques for uncertain systems, and integrating potential theory into statistical analysis. His research underscores the interplay between mathematical rigor and real-world problem-solving.

Senjuti Basu RoyView profile

Associate Professor

• Machine Learning
• Data Mining
• Healthcare Informatics
• +3 more

Senjuti Basu Roy is an Associate Professor in the Department of Computer Science at New Jersey Institute of Technology (NJIT). She specializes in data mining, machine learning, and healthcare informatics, with a focus on fairness in algorithms, optimization, and interdisciplinary applications in biotechnology and healthcare. Her work integrates AI systems with real-world challenges such as equitable resource allocation, predictive modeling for medical outcomes, and sustainable materials science. Education: Ph.D. in Computer Science, University of Texas at Arlington (2011) M.S. in Computer Science, University of Texas at Arlington (2007) B.Tech. in Computer Science and Engineering, University of Calcutta (2004) Research Interests: Dr. Basu Roy’s research spans algorithmic fairness , top-k query optimization , healthcare data analytics , and crowdsourcing systems . She develops frameworks for equitable data-driven decision-making and explores applications in biomedical engineering, environmental science, and structural safety. Her work bridges theoretical computer science with practical domains like drug discovery and infrastructure resilience. Publications: Her recent work includes advancements in reinforcement learning, fairness-aware preference systems, and computational methods for biomedical problems. Notable contributions include frameworks for feature selection in incomplete datasets and optimization algorithms for maritime logistics. Awards & Grants: She has secured NSF grants including “CAREER: Streamlining Task Deployment on Crowdsourcing Platforms” and “III: Small: Collaborative Research on Derived Attributes.” Her research emphasizes human-AI collaboration and equitable technology design. Advising & Teaching: Teaches courses like Data Mining (CS 634) and Introduction to Data Science (CS 301). Advises on peer-learning methodologies and collaborative task optimization. Her lab focuses on real-world AI applications and interdisciplinary problem-solving.

Abdulrahman Al-SurmiView profile

Lecturer

• Business Analytics
• Strategic Management
• Decision Making
• +3 more

Dr. Abdulrahman Al-Surmi is a Lecturer in Business Analytics and Programme Director of the MSc Business Analytics at Birkbeck Business School, University of London. He holds a PhD from the University of Bedfordshire, an MBA from Sheffield Hallam University, and other postgraduate qualifications. His research focuses on strategic alignment between business, IT, and marketing strategies, AI applications in decision-making, and organizational performance improvement. He has extensive experience as an external examiner across UK universities, including roles at the University of Plymouth, BPP University, and Cranfield University. Dr. Al-Surmi is a member of the Birkbeck Centre for Innovation Management Research and leads the £131,741 Management KTP project focused on transforming business models through strategic alignment and data-driven decision-making. His teaching spans quantitative methods, big data management, and business analytics, with modules like Mathematics for Business and Data Visualization. He has supervised 2 PhD students and actively reviews for high-impact journals such as the International Journal of Production Research and Industrial Marketing Management. Professional memberships include the Higher Education Academy (Fellow) and the British Academy of Management. His recent work explores AI in policy-making for small municipalities and the operational performance benefits of combining AI strategies with traditional business practices.

• Entrepreneurship Education
• Innovation in SMEs
• Digitalization in Business
• +3 more

Dr. Alexander Fust is a Senior Lecturer of Business Administration at the University of St. Gallen, affiliated with KMU-HSG. His research focuses on entrepreneurship education, innovation in small and medium-sized enterprises (SMEs), digitalization strategies, and self-regulated learning processes. He has authored numerous publications analyzing entrepreneurial behavior, strategic decision-making, and SME competitiveness. His work emphasizes practical frameworks for enhancing SME attractiveness, digital transformation, and fostering self-directed learning among entrepreneurs. Key research areas include the transferability of entrepreneurship education outcomes, imaginative leadership in innovation, and the role of cognitive biases in strategic choices. Fust has contributed to Swiss SME studies, exploring economic trends and organizational improvisation. His publications span academic journals and practical guides, addressing topics like opportunity recognition, financial strategies, and sustainable entrepreneurship. While no formal awards or grants are explicitly listed, his extensive publication record highlights sustained contributions to SME management and entrepreneurial pedagogy. He is based at KMU-HSG, Dufourstrasse 40a, St. Gallen, and can be reached via the university's contact channels.

Kristian DubrawskiView profile

Assistant Professor

• Water Quality
• Environmental Justice
• Nature-Based Solutions
• +5 more

Dr. Kristian Dubrawski is an Assistant Professor in the Department of Civil Engineering at the University of Victoria, Canada. He holds the Canada Research Chair in Water Sustainability for Indigenous and Rural Communities and leads the Community Water Innovation Lab. His work bridges engineering, ecology, and community engagement to address water challenges in marginalized communities. Dubrawski earned a PhD from UBC and a postdoc at Stanford, with prior roles including water consulting, policy work at McKinsey & Company, and research fellowships at Harvard and Lawrence Berkeley National Laboratory. His research focuses on small water systems, nature-based solutions (NbS), bioelectrochemical technologies, and environmental justice. Key projects include developing community-led decision support systems, integrating NbS with infrastructure, and creating carbon-negative wastewater treatment methods. He emphasizes Indigenous and community-driven methodologies, advocating for equitable, culturally respectful approaches to water sustainability. Education: PhD in Chemical/Biological Engineering, University of British Columbia (UBC) Postdoctoral Research, Stanford University Key Roles: Canada Research Chair in Water Sustainability Founder of Ecological Water Consulting Visiting Scholar at Lawrence Berkeley National Lab and Harvard Kennedy School Dubrawski’s awards include the Canada Research Chair designation, recognizing his transformative work in water equity. His lab collaborates with Indigenous communities, governments, and international organizations to co-create solutions for safe drinking water, wastewater resource recovery, and climate-resilient infrastructure.

Kevin Charles LeonardView profile

Professor

• Electrocatalysis
• Sustainable Energy
• Data Science
• +3 more

Kevin C. Leonard is Professor of Chemical and Petroleum Engineering at the University of Kansas, with joint appointment at the Center for Environmentally Beneficial Catalysis. He leads research on sustainable electrochemical technologies for fuel and chemical production, with focus areas in electrocatalysis, scanning electrochemical microscopy, CO₂ utilization, and machine learning for catalyst discovery. His research develops novel electrochemical systems for renewable hydrogen production, CO₂ conversion, and sustainable chemical synthesis. Current projects include federated learning approaches for catalyst discovery, CO₂-expanded electrolytes for chemical production, and scanning electrochemical microscopy for catalyst characterization. Professor Leonard has received significant research funding from NSF, Department of Energy, and Cisco Systems. Awards include the ACS Sustainable Chemistry & Engineering Lectureship Award and Army Research Office Young Investigator Award. He co-founded Avium, LLC to commercialize hydrogen production technology. He teaches undergraduate and graduate courses in chemical engineering fundamentals, kinetics, catalysis, and data science. Professor Leonard currently advises 7 graduate students working on electrocatalysis, machine learning applications, and electrochemical characterization techniques.

Yuchun XuView profile

Professor

• Smart Manufacturing
• Remanufacturing
• Life Cycle Engineering
• +5 more

Yuchun Xu is a Professor and Chair in Manufacturing at Aston University, leading the Smart and Sustainable Manufacturing Research Centre (SMRC). He holds a PhD in Manufacturing Engineering from Harbin Institute of Technology and has over two decades of academic and industrial experience. His research focuses on Smart Manufacturing, Remanufacturing, Life Cycle Engineering, and Circular Economy, addressing sustainability challenges through digital solutions and green transitions. Key industrial collaborators include Airbus, Rolls-Royce, and Network Rail. Prof Xu’s research portfolio exceeds £45M, funded by EPSRC, Innovate UK, Horizon EU, and EU FP7. Notable projects include the H2GLASS EU initiative (€24.45M) advancing hydrogen technologies and the RECLAIM project (€12.75M) for industrial equipment refurbishment. He has supervised 3 PhD students and teaches courses like Design for Manufacturing and Industry 4.0. His work spans product lifecycle stages from design to recycling, emphasizing automation, robotics, and cost modelling. Recent innovations include digital twin applications for fuel cells and ontology-based disassembly planning in remanufacturing. He chairs prestigious conferences and serves on EPSRC peer review panels, contributing to national and global manufacturing advancements.

• Machine Learning
• Materials Science
• Physics
• +5 more

Qian Yang is an Assistant Professor in the Computer Science and Engineering Department at the University of Connecticut, Storrs. Her research focuses on machine learning applications in materials science, physics, and chemistry, bridging computational methods with physical sciences. She holds a Ph.D. from Stanford University's Institute for Computational and Mathematical Engineering and a B.A. in Applied Mathematics/Computer Science from Harvard College. Prior to UConn, she was a postdoctoral scholar at Stanford's Materials Computation and Theory group. Her research projects include developing machine learning models for chemical reaction networks, model reduction of nonlinear dynamical systems, and addressing challenges in scientific data (small datasets, imbalance, mixed fidelity). She co-hosts the Materials and Megabytes podcast, exploring interdisciplinary machine learning in materials science, featuring experts like Prof. Gábor Csányi (Cambridge) and Dr. Patrick Riley (Google). Awards: Best Student/Postdoc Presentation Award (2017), Best Instructor Award (Stanford) Teaching: Teaches courses like CSE 5095 (Machine Learning for Physical Sciences) and CSE 3666 (Computer Architecture). Known for integrating theory with practical applications. Grants/Advising: No explicit grants listed, but her research involves DOE and computational initiatives. Advising details not provided in text. Labs/Teams: Leads interdisciplinary teams focusing on machine learning in materials and physical sciences, as evidenced by podcast collaborations and course projects. Publications emphasize data-driven methods, uncertainty quantification, and algorithm development for scientific challenges, with recent work in defect analysis, battery materials discovery, and TEM data automation.

Ming HuangView profile

Associate Professor

• AI and NLP in Healthcare
• Clinical and Translational Research
• Data Mining
• +7 more

Ming Huang, PhD is an Associate Professor at the Center for Translational AI Excellence and Applications in Medicine (TEAM-AI) within the McWilliams School of Biomedical Informatics at UTHealth Houston. His research focuses on advancing AI and NLP methods to address challenges in clinical and translational healthcare, particularly in mental health, consumer health, and telehealth. He leads NIH-funded projects on computational phenotyping of mental health disorders and vaping-related social media analysis. Previously, he served as an Assistant Professor at Mayo Clinic and held editorial roles in journals like Frontiers in AI and conferences like IEEE ICHI. Dr. Huang earned his PhD in Scientific Computation from the University of Minnesota (2014) and dual BSc in Computer Science and Applied Chemistry from Beijing Normal University (2004). His work emphasizes leveraging AI for actionable healthcare insights, including patient portal messaging analysis and drug repurposing through EHR data. His research portfolio includes developing frameworks for suicide risk phenotyping using LLMs, improving digital equity in smoking cessation programs, and analyzing vaping-related public sentiment during health crises. He actively contributes to NSF review panels and NIH grant initiatives, reflecting his leadership in AI-driven healthcare innovation. Dr. Huang seeks motivated students/postdocs interested in AI/health informatics to tackle real-world healthcare challenges through collaborative, interdisciplinary research. His lab focuses on translating AI advancements into practical tools for clinical and translational medicine.

Xuanting HaoView profile

Assistant Professor

• Small-scale air-sea interaction
• Computational fluid dynamics
• Submesoscale physical oceanography
• +3 more

Xuanting Hao is an Assistant Professor at the Marine Physical Laboratory , part of the Scripps Institution of Oceanography at UC San Diego. His research focuses on small-scale physical processes at the atmosphere–ocean interface, employing physics-based and data-driven models to study air-sea interactions. Key areas include computational fluid dynamics, submesoscale oceanography, and boundary layer meteorology. Education: PhD, Mechanical Engineering, University of Minnesota (Twin Cities) B.E., Automotive Engineering, Tsinghua University, Beijing Research Interests: Development of high-performance computing models for air-sea processes Simulation of ocean-atmosphere interactions using adjoint-based methods and LES Analysis of coastal bathymetry, internal waves, and turbulence dynamics Integration of machine learning for improved predictive modeling His recent work emphasizes advancing numerical methods for coastal and oceanic systems, with applications to wave dynamics, bioluminescence quantification, and SAR data assimilation. He collaborates across disciplines to bridge fluid dynamics with environmental science. Key Contributions: Advances in adjoint-based high-order spectral methods for coastal bathymetry reconstruction Pioneering machine learning integration in LES for turbulence prediction Development of novel models for ocean surface wave modulation by internal waves His research is conducted at the Marine Physical Laboratory , a leading center for oceanography and atmospheric science.

Joel PearsonView profile

Assistant Professor

• Cancer Biology
• Cancer Therapeutics
• Drug Resistance
• +7 more

Dr. Joel Pearson is an Assistant Professor in the Department of Pharmacology and Therapeutics at the Max Rady College of Medicine, University of Manitoba, and a Scientist at the Paul Albrechtsen Research Institute CancerCare Manitoba. His research focuses on uncovering fundamental principles of cancer biology to improve treatment strategies, particularly targeting aggressive neural, neuroendocrine, and hematological cancers. He identifies novel therapeutic approaches by studying molecular mechanisms such as YAP/TAZ-TEAD signaling and cellular context influences on oncogenesis. Education: Bachelor of Science in Chemical Biology, Thompson Rivers University (2005) Doctor of Philosophy in Immunology, University of Alberta (2013) Postdoctoral Fellow at Lunenfeld-Tanenbaum Research Institute (2013-2022) Research Interests: Dr. Pearson’s work spans brain cancers , cancer genomics , drug resistance , functional genomics , and neuroendocrine/prostate/lung cancers . He emphasizes context-specific oncogenic pathways and has pioneered YAPOn/YAPOff cancer classifications to guide personalized therapies. Awards: Canadian Cancer Society Emerging Scholar (2023-2028) CIHR Postdoctoral Fellowship (2015-2018) Advising & Grants: He is actively recruiting Master’s and PhD students. His research integrates in vitro and in vivo models to explore vulnerabilities in cancer cell cycles and signaling pathways. Collaborations emphasize translational approaches for clinical impact. Labs/Teams: Based at the Paul Albrechtsen Research Institute, Dr. Pearson leads a multidisciplinary team focused on cancer biology and therapeutic development, leveraging cross-appointments with the University of Manitoba.

Monzurul HoqueView profile

Professor

• Finance
• Risk Management
• Islamic Finance
• +3 more

Dr. Monzurul Hoque is a Professor at the Graham School of Management, Saint Xavier University, with over 25 years of professional experience. He has held leadership roles including Executive Leadership Team member, founding Chair of Economics, Finance, and Quantitative Analysis, and Chair of the Assurance of Learning Committee. His academic contributions include developing the Finance program and the Financial Trading and Practices Program, supported by a CQG grant for a financial trading lab. Education: Ph.D. in Economics from University of Illinois, Urbana-Champaign (1987) Leadership: President of the Academy of Finance (2004-2005), Editor of the Journal of Finance Issues (awarded for 10 years of service) Publications: Over 40 peer-reviewed articles in journals like Managerial Finance and Journal of Finance Issues Research focuses on Finance, Risk Management, Islamic Finance, and interdisciplinary collaborations with Computer Science and Mathematics. Recent work addresses crypto currencies, urban gentrification, and futures market analysis. Awards include recognition for editorial leadership and academic contributions. Grants and labs: Secured funding for the University's first financial trading lab. Advising and mentorship details are not explicitly listed, but his leadership roles suggest extensive academic guidance. He is involved with editorial boards of finance journals and continues to drive curricular innovation.

Johannes BetzView profile

Professor

• Autonomous Systems
• Vehicle Control
• Machine Learning
• +3 more

Prof. Dr. Johannes Betz is a Rudolf Mößbauer Professor at the Technical University of Munich (TUM), leading the Chair of Autonomous Vehicle Systems within the Department of Mobility Systems. His research focuses on software development for autonomous vehicles, emphasizing algorithms for environmental perception, path planning, and control to enable safe, efficient autonomous operation. He holds a B.Eng. and M.Sc. in Automotive Engineering from Coburg University and the University of Bayreuth, followed by a Ph.D. in Engineering from TUM (2019). Notable achievements include founding the TUM Autonomous Motorsport Team, which won the Indy Autonomous Challenge ($1M prize), and extensive postdoc work at TUM and the University of Pennsylvania. Education: B.Eng., Automotive Engineering, Coburg University of Applied Sciences (2013) M.Sc., Automotive Engineering, University of Bayreuth (2013) Ph.D. in Engineering, Technical University of Munich (2019) Research Interests: Autonomous vehicle software architecture Real-time trajectory and behavior planning Adaptive control for high-performance systems Simulation and validation frameworks Ethics of autonomous decision-making Awards: IEEE ITS Young Professional Travel Fellowship (2022) 1st Place, Indy Autonomous Challenge (2021) New Generation Star Project Award (2024) Outstanding Reviewer Award (2024) Labs & Teams: TUM Autonomous Motorsport Team (race competition leader) AVS Lab (Autonomous Vehicle Systems)

• Power Electronics
• Power System Stability
• Renewable Energy Integration
• +3 more

Yunjie Gu is a Lecturer in the Department of Electrical and Electronic Engineering at Imperial College London, affiliated with the Control and Power Research Group. Previously, he served as an EPSRC Innovation Fellow (2018–2020) and worked at GE Global Research in Shanghai, leading projects on renewable energy grid integration. His research focuses on power-electronic-based power systems, including grid stability, impedance modeling, numerical computation methods, and real-time digital twin systems. Key contributions include spinouts like Hoymiles (listed in Shanghai Stock Exchange) and open-source projects such as RT-Thread and Simplus Grid-Tool. His research interests span power electronics, renewable energy systems, computational methods for power system analysis, and real-time computing. He actively explores stability analysis of inverter-based grids, transient stability of grid-following inverters, and data/model-driven system identification. Gu holds seven patents (six Chinese, one US/EU) and mentors PhD students in related disciplines. He advocates for hybrid approaches combining traditional power system analysis with modern computational techniques. Gu's work integrates academic research with industry applications, emphasizing practical solutions for emerging challenges in decarbonized grids. His recent projects include real-time digital twin systems for grid simulation and FPGA-based hardware-in-the-loop testing. He collaborates widely, contributing to both foundational theory and commercial innovations in energy systems.

Sierra PetersenView profile

Associate Professor

• Paleoceanography
• Paleoclimatology
• Geochemistry
• +7 more

Sierra Petersen is an Associate Professor in the Department of Earth and Environmental Sciences at the University of Michigan, within the College of Literature, Science, and the Arts (LSA). She leads the SCIPP Lab, which focuses on reconstructing past climate and ocean conditions using geochemical signals from fossil materials. Her work integrates cutting-edge isotope analysis and field studies across diverse geological time periods and locations, emphasizing collaboration and methodological innovation. She teaches EARTH 296.066, offering undergraduates a research-based course in paleoclimatology and paleontology. Current lab projects include NSF-funded studies on Last Interglacial climate in the Caribbean and Atlantic, Cretaceous climate dynamics, and improving clumped isotope methodologies. Dr. Petersen holds a B.S. in Geochemistry from the California Institute of Technology (2009) and a Ph.D. in Earth and Planetary Sciences from Harvard University (2014). Her research emphasizes sub-annual climate variability preserved in mollusk shells, ocean-ice sheet interactions, and the role of Deccan Traps volcanism in the end-Cretaceous mass extinction. She collaborates with institutions like Grand Valley State University (GVSU) and maintains the University of Michigan Stable Isotope Lab with co-PIs Lohmann and Cole. Her research interests span clumped isotope applications, biologically driven isotopic fractionations (BioDIFs), and interdisciplinary approaches to paleoclimate reconstruction. Recent work explores seasonal climate patterns in ancient greenhouse climates and technical improvements for clumped isotope measurements. She has advised multiple undergraduates and is actively involved in public climate communication through media interviews and NGO engagements like the Citizens’ Climate Lobby. In advising, Dr. Petersen has engaged with graduate students and postdocs through funded fellowships and NSF grants, though recruitment for 2025 is paused. Her lab’s technical innovations include the ClumpDB standardization project and dual-reservoir techniques for small carbonate samples. Current lab locations include renovated spaces in the North University Building (moved 2019), housing advanced mass spectrometry equipment.