Find Academic Supervisors

Saibal MukhopadhyayView profile

Professor

• VLSI Systems
• Nanotechnology
• Low-Power Electronics
• +5 more

Dr. Saibal Mukhopadhyay is a Professor in the Department of Electrical and Computer Engineering at the Georgia Institute of Technology, where he joined in 2007. He holds the Joseph M. Pettit Professorship and is recognized as an IEEE Fellow for his contributions to low-power and reliable VLSI systems. Education: BEng (Jadavpur University, India), Ph.D. (Purdue University) Labs: Gigascale Reliable Energy Efficient Nanosystem (GREEN) Lab His research focuses on VLSI Systems , Nanotechnology , and Low-Power Electronics , with emphasis on technology-circuit co-design for energy-efficient computing. Recent work explores Compute-in-Memory (CIM) architectures and Spiking Neural Networks for edge AI. Key article themes include Transformer Model Acceleration , Quantum Computing Calibration , 3D Object Detection , and Device Aging Analysis , reflecting his interdisciplinary approach bridging hardware design and machine learning. Scientific Awards IEEE Fellow (2018) ONR Young Investigator (2012) NSF CAREER Award (2011) IBM Faculty Awards (2009, 2010) Best Paper Awards (IEEE-Nano 2003, ICCD 2004)

• Data Mining
• Bioinformatics
• Machine Learning
• +3 more

Domingo Savio Rodríguez Baena is a Professor at Pablo de Olavide University, affiliated with the Department of Computer Languages and Systems. His research focuses on data mining, bioinformatics, and computational biology, with a particular emphasis on biclustering algorithms, gene co-expression networks, and high-performance computing applications. PhD in Engineering, Data Science, and Bioinformatics (2012) from Pablo de Olavide University His work spans interdisciplinary domains, including recommender systems , livestock behavior analysis , and biological data interpretation . Recent articles highlight his contributions to multi-GPU optimization , ensemble learning , and historical database construction . Key collaborations include the DATAi Intelligent Data Analysis and DASE Data Analytics Science & Engineering research groups. He has developed tools like the CyEnGNet–App for gene network visualization and BIGO for gene enrichment analysis. Contact: dsrodbae@upo.es

• Share structures of companies
• ISE
• Financing
• +2 more

Prof. Dr. Mehmet GENÇTÜRK is a Professor at the Faculty of Economics and Administrative Sciences , Mehmet Akif Ersoy University , with a dual departmental affiliation in Business Administration and Accounting and Finance . His career spans over two decades, with extensive publications and thesis supervision. Education: License: Selçuk University, Business Administration (1993) Master’s: Süleyman Demirel University (1996) Doctorate: Süleyman Demirel University, Business Administration (2002) His research focuses on share structures, financing, accounting practices, and corporate value , with recent studies on pandemic impacts on financial decisions , machine learning in hospital finance , and digitalization in accounting . The 15 most recent articles reflect interdisciplinary work in health economics, organizational behavior, and Islamic finance . Supervised theses include: Demirtaş İbrahim (PhD, 2024) on working capital and firm performance Işık Zeynep (Master’s, 2024) on digitalization in accounting Acar Duygu (Master’s, 2024) on e-applications in accounting

• Biomedical Engineering
• Neuroimaging
• Brain Imaging
• +3 more

Stephen Smith is a Professor of Biomedical Engineering at the University of Oxford . He leads the Analysis Group at the Wellcome Centre for Integrative Neuroimaging, focusing on advanced methodologies for functional and structural brain imaging data. His work bridges biomedical engineering and neuroscience, with a strong emphasis on population-scale neuroimaging. Research Highlights : Population neuroscience, brain aging, multimodal MRI, and software development (FSL package). Collaborations : Key contributions to UK Biobank and CONVALESCENCE studies. Recent Publications (2024–2025) demonstrate expertise in neuroimaging analysis, including fetal brain mapping, post-COVID cognitive deficits, and big data applications for clinical translation. Research spans both methodological innovation and clinical neuroimaging. Email : steve@fmrib.ox.ac.uk

Jorge CambaView profile

Associate Professor

• Intelligent Computer-Aided Design (CAD) Systems
• Digital Manufacturing Processes
• Digital Product Model Quality Assurance
• +5 more

Jorge Camba is an Associate Professor at the School of Engineering Technology and holds a courtesy appointment in the Department of Computer Graphics Technology at Purdue University . He also serves as a Senior Research Scientist (by courtesy) in the Department of Industrial Engineering at the University of Naples Federico II , Italy. PhD in Systems and Engineering Management (Universidad Politécnica de Valencia, Spain) MSc in Digital Media (East Tennessee State University) MSc in Computer Science (Universidad de Vigo, Spain) His research explores intelligent CAD systems , digital manufacturing , and mixed reality environments , focusing on model quality assurance , design intent communication , and collaborative design tools . Recent work investigates spatial cognition in CAD education , geometric variability analysis , and annotation-driven knowledge management . Key trends in his publications include parametric modeling strategies , 3D annotation systems , and XR applications in design evaluation. Awards include the Purdue Faculty Scholar (2021) and I3B Fellow (2021). He has presented at conferences on topics like Industry 4.0 , space habitat design , and digital product quality .

• Forecasting
• Time Series Analysis
• Inventory Management
• +5 more

Nikolaos Kourentzes is a Professor of Informatics at the University of Skövde , specializing in forecasting and operations research. His work bridges theoretical advancements in time series analysis with practical applications in supply chain management, tourism demand, and renewable energy forecasting. Academic Rank: Professor Department: Department of Information Technology Research Interests: His research focuses on hierarchical and temporal forecasting methodologies, integrating macroeconomic indicators into demand planning, inventory optimization, and machine learning applications. He explores forecast reconciliation, shrinkage estimators, and the role of expert judgment in predictive analytics. Recent Publications: Highlights include advances in hierarchical forecasting with leading indicators, probabilistic forecasts during crises like the pandemic, and complex smoothing techniques. His work spans journals such as Omega , International Journal of Forecasting , and European Journal of Operational Research . Collaborations: Kourentzes collaborates with researchers globally, including George Athanasopoulos, Rob Hyndman, and Robert Fildes, across domains like tourism analytics, tire industry forecasting, and public health modeling.

• Data Mining
• Privacy-Preserving Computation
• Artificial Intelligence
• +3 more

Onur KARDEŞ serves as Assistant Professor in the Department of Computer Engineering at Beykent University's Faculty of Engineering and Architecture since 2020. His prior academic appointments include: Research Assistant, Department of Computer Science, Stevens Institute of Technology (2004-2008) Lecturer, Department of Mathematics and Computer Science, Faculty of Arts and Sciences, Beykent University (2000-2004) His research integrates data mining, privacy-preserving computation, and artificial intelligence with practical applications in educational technology and urban systems. Key contributions include developing privacy-enhancing protocols for distributed data mining and pioneering generative AI solutions for automated student assessment and digital teaching assistants. Analysis of his 2023-2025 publications reveals accelerating focus on generative AI implementations, particularly in educational evaluation (automated examination paper analysis) and urban planning (AI-driven smart city frameworks). This evolution demonstrates strategic adaptation to emerging technologies while maintaining core expertise in secure computation and data mining.

Cen WuView profile

Associate Professor

• High dimensional data
• Statistical machine learning
• Bayesian sparse learning
• +7 more

Cen Wu serves as Associate Professor in the Department of Statistics at Kansas State University and Faculty Scientist at the Johnson Cancer Research Center. His methodological research focuses on developing robust statistical machine learning approaches for high-dimensional cancer genomic data integration, addressing challenges where measurement dimensions far exceed sample sizes. Dr. Wu earned his Ph.D. in Statistics from Michigan State University in 2013, followed by a postdoctoral fellowship in Biostatistics at Yale School of Public Health (2013-2015). He joined Kansas State University as Assistant Professor in 2015, was promoted to Associate Professor in 2021, and has maintained dual appointments in Statistics and Cancer Research since 2016. His research program centers on Bayesian sparse learning methods for cancer genomics, with particular emphasis on robust variable selection techniques that accommodate outliers and heavy-tailed distributions common in genomic studies. He develops integrative approaches for multi-platform genomic data (mRNA expression, copy number variations, DNA methylation) to elucidate cancer etiology and identify prognostic markers. His work bridges theoretical statistics with practical clinical applications, including adaptive prediction of patient recruitment in clinical trials. Analysis of his recent publications reveals consistent focus on gene-environment interaction modeling through advanced Bayesian frameworks, with increasing emphasis on longitudinal data structures and robust inference procedures. His methodological innovations frequently translate into practical R packages that implement these complex statistical techniques for broader research communities. Dr. Wu actively contributes to the academic community as Associate Editor for TEST and BMC Genomics, and previously served as Guest Editor for a special issue on Bayesian Learning in Entropy. He maintains active collaborations with cancer researchers at the Johnson Cancer Research Center, applying his statistical expertise to real-world cancer genomics problems. His laboratory develops and implements cutting-edge statistical methods through R packages including 'mixedBayes', 'pqrBayes', 'roben', and 'interep', which address specific challenges in high-dimensional data analysis for cancer research. Current projects focus on extending robust Bayesian frameworks to handle increasingly complex genomic data structures while maintaining computational efficiency.

Aoife GowenView profile

Professor

• Hyperspectral Imaging
• Sustainable Transport Policy
• Critical Thinking in Education
• +4 more

Professor Aoife Gowen is a leading academic at the UCD School of Biosystems & Food Engineering , specializing in hyperspectral imaging and its applications across medicine, food safety, and engineering. Her research, supported by prestigious European Research Council (ERC) funding, investigates water molecule interactions with surfaces to improve bone graft materials and develop innovative diagnostic tools for prostate cancer. She also leads Science Foundation Ireland (SFI)-funded projects on hyperspectral monitoring of bacterial growth for food safety. Beyond technical research, Professor Gowen has developed computational tools now integrated into commercial chemical analysis software. Her work spans interdisciplinary domains, including sustainable transport policy, critical thinking education, and promoting gender diversity in engineering. As a key figure in the Women on Walls initiative, she has enhanced visibility for women in STEM fields. Her recent publications focus on spectral technologies for food quality, microplastics characterization, and medical diagnostics, reflecting her commitment to addressing global challenges in health and sustainability. Scientific Awards: ERC Grant for water-surface interaction research Professor Gowen actively collaborates with European networks and industry partners, driving advancements in hyperspectral imaging applications. Her lab’s efforts to bridge computational science with real-world chemical analysis have positioned her as a pioneer in invisible chemistry visualization, impacting medicine, food, and environmental engineering.

M Reza KholghyView profile

Associate Professor

• Industrial Decarbonization
• Hydrogen Production
• Carbon Dioxide Conversion
• +7 more

Dr. M Reza Kholghy is an Associate Professor and Canada Research Chair in Particle Technology and Combustion Engineering at Carleton University's Department of Mechanical and Aerospace Engineering. He directs the Energy and Particle Technology Laboratory (EPTL) where he focuses on sustainable industrial solutions. His academic credentials include a BASc in Aerospace Engineering from Sharif University of Technology, and MASc/PhD degrees in Mechanical Engineering from the University of Toronto, followed by postdoctoral work at ETH Zurich. Research interests center on: Industrial decarbonization through metal fuel combustion (aluminum/iron) and carbon management Hydrogen production via methane pyrolysis and metal-water reactions Advanced material synthesis including flame spray pyrolysis for catalytic films and alumina production Nanoparticle engineering with focus on soot formation dynamics and optical properties His publications predominantly explore nanoparticle synthesis mechanisms, soot formation modeling, and sustainable fuel technologies, with recent emphasis on hydrogen cogeneration and metal combustion. Experimental and computational approaches are equally represented across combustion diagnostics, reactor design, and molecular dynamics simulations. Major scientific recognitions include: Canada Research Chair (Tier 2) Vanier Canada Graduate Scholarship NSERC Postdoctoral Fellowship He leads the Energy and Particle Technology Laboratory with industry partnerships focused on sustainable technology development. The lab specializes in flame spray pyrolysis reactors, nanoparticle characterization (surface area, porosity, composition), and high-pressure reaction systems. Dr. Kholghy actively mentors students through capstone projects and research positions, though specific PhD/Master's advisees aren't named in available sources.

Venkat N. KroviView profile

Professor

• Robotics
• Autonomous Vehicles
• Human-Robot Interaction
• +5 more

Prof. Venkat N. Krovi serves as the Michelin Endowed Chair Professor of Vehicle Automation in the Departments of Automotive Engineering and Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences (CECAS). He directs the Automation, Robotics and Mechatronics Laboratory (ARMLab) at the International Center for Automotive Research (CU-ICAR), focusing on smart embedded systems for autonomy in challenging environments. He earned his Ph.D. in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania in 1998. His research leverages distributed autonomy and human-robot synergy to extend human capabilities, with applications spanning plant automation, consumer electronics, automobile, defense, and healthcare. The work emphasizes lifecycle treatment (design through verification) of robotic systems under uncertainty. Recent publications (2024-2025) demonstrate strong trends in digital twin frameworks for autonomous vehicle validation, sim2real transfer via reinforcement learning, and integration of large language models for editable simulations. Key themes include scalable cloud-based architectures, Koopman operator theory for robustness, and containerization for reproducible robotics development. His accolades include: National Science Foundation (NSF) CAREER Award Petro-Canada Young Innovator Award Multiple best paper awards at conferences and journals ASME Dedicated Service Award (2024) Prof. Krovi has advised doctoral students including Dr. Srivatsan Srinivasan (2024). His research receives substantial funding from NSF, DARPA, ARO, and industrial partners like Michelin. He leads the NSF I/UCRC RoSeHuB center and the AutoDRIVE ecosystem for autonomous driving education. As ARMLab director, he oversees projects including OpenCAV, the Robotics for AV Systems Bootcamp, and containerized terramechanics simulations. The lab specializes in mechatronic design, verification/validation frameworks, and human-autonomy coexistence studies for next-generation mobility solutions.

• Discrete choice experiments
• Optimal experimental design
• Multivariate statistics
• +7 more

Martina Vandebroek is Full Professor of Statistics and Operations Research at KU Leuven, Faculty of Economics and Business, where she leads methodological work within the Operations Research and Statistics Research Group (ORSTAT). She also serves as senior academic staff on the Faculty Council and the Campus Councils for Leuven and Kortrijk, and is a member of the LStat General Assembly. Research Interests Discrete choice experiments (design & analysis) Optimal and sequential experimental design Multivariate statistics and modelling of preference heterogeneity Random regret minimisation and attribute non-attendance Applications in health economics, transport, marketing, and food science Her methodological innovations enable more efficient data collection and richer behavioural insights in stated-preference surveys, while her applied projects translate patient and consumer preferences into actionable evidence for policy makers and industry. Recent Publications Overview Between 2022 and 2024 Vandebroek (co-)authored 15 key articles. These contributions advance both the statistical machinery of choice modelling—such as mixed random regret models, design-efficient sample-size rules, and consideration-set heuristics—and substantive applications in oncology patient preferences, inflammatory bowel disease treatments, meat-substitute adoption, and food-quality valuation. The work repeatedly integrates sophisticated econometric techniques with real-world stakeholder data, reflecting an overarching commitment to methodological rigour and societal impact. Scientific Awards & Recognition No specific awards are listed in the provided material; however, her sustained publication record in top journals (Journal of Choice Modelling, Food Quality and Preference, Frontiers in Oncology, Stata Journal) attests to significant scholarly recognition. Advising & Grant Activities Promotor (PI) of FWO project “Discrete choice models including screening rules: modeling and design” (2017-2021) Promotor of KU Leuven project “Efficient Online Choice Experiments” (2017-2020) Co-promotor of IWT/FWO project “Empirical and methodological challenges in choice experiments” (2017-2023) Co-promotor of VLAIO project “Development, Validation, and Valorization of a Patient Preference Platform” (2023-2026) Teaching & Service Vandebroek teaches master-level courses “Applications of Statistics” (Dutch and English iterations) and contributes to the Leuven Statistics Research Centre (LStat) educational programme.

Sreenath Chalil MadathilView profile

Assistant Professor

• Operations Research
• Data Science
• Healthcare Systems
• +3 more

Sreenath Chalil Madathil is an Assistant Professor in the Department of Systems Science and Industrial Engineering at Binghamton University. His academic journey includes previous roles as Assistant Professor at the University of Texas at El Paso (2019–2021) and Research Scientist at the Watson Institute of Systems Excellence (WISE) at Binghamton University's Research Foundation (2017–2019). He holds a PhD and MS in Industrial Engineering from Clemson University, an MS in Data Science from the University of Texas at Austin, and a Bachelor of Technology in Electrical and Electronics Engineering from Mahatma Gandhi University, India. Dr. Madathil's research focuses on applying operations research, simulation modeling, and data science to address healthcare challenges such as patient-centered care and healthcare disparities. His work bridges theoretical methodologies with practical applications, including maternal healthcare equity, healthcare facility design, and supply chain resilience. He has secured funding from the National Science Foundation and the U.S. Department of Commerce. Prior to academia, he gained industry experience as a software consultant and client-side analyst at Cognizant Technology Solutions (2006–2011), alongside collaborations with Los Alamos National Lab and BMW Manufacturing. His research has been published in high-impact journals like Healthcare Management Science and Computers and Industrial Engineering . His professional expertise spans interdisciplinary projects, including optimizing healthcare workflows, analyzing social media data for public health interventions, and developing resilient microgrid systems. Current research trends include leveraging AI/ML techniques for healthcare analytics and improving operational efficiency in healthcare systems.

Lewis J. LeheView profile

Assistant Professor

• Transportation Engineering
• Urban Traffic Management
• Transportation Economics
• +3 more

Lewis J. Lehe is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign (UIUC), specializing in Transportation Systems. He holds a Ph.D. from UC Berkeley (2016) and prior degrees from UC Berkeley, University of Leeds, and University of Pittsburgh. His research focuses on transportation pricing, urban traffic dynamics, and the economic implications of transportation policies. Lehe's work includes groundbreaking studies on congestion pricing, parking policy impacts, and the integration of AI tools (e.g., GTFS Segments, TransitGPT) for transportation data analysis. He has been recognized with the Gordon F. Newell Award (2016) and Ove Arup Transport Prize (2012), and leads the Urban Traffic & Economics Lab (UTEL) at UIUC. Notable research contributions include modeling hyperdemand in traffic systems, analyzing bus stop spacing, and evaluating taxation strategies for ride-hailing services. His recent articles explore transit pricing determinants, parking lot segmentation via NIR datasets, and equilibrium tolling paradoxes. Lehe advises graduate students like Ayush Pandey and Shirin Qiam, advancing topics such as bus route optimization and GTFS data benchmarking.

Sjoerd van der HeideView profile

Researcher

• Optical Communication
• Quantum Cryptography
• Digital Signal Processing
• +3 more

Sjoerd van der Heide is a University Researcher at Eindhoven University of Technology, affiliated with the Electrical Engineering department and the Electro-Optical Communication group. His work focuses on advanced optical communication systems, with expertise in quantum key distribution, digital signal processing, and space-division multiplexing. Education: MSc in Optical Communication Systems (2017), thesis titled Low-complexity pre-compensation and advanced modulation techniques for high capacity intensity-modulated direct detection systems , supervised by Prof. C.M. Okonkwo. Research interests include: Quantum cryptography over free-space and fiber links GPU-accelerated real-time optical receivers Mode-division multiplexing techniques Holography-based fiber device characterization Atmospheric turbulence compensation Statistical modeling of mode-dependent loss Recent publications demonstrate trends in Continuous-variable QKD integration Co-propagation of classical and quantum signals Neural network applications for transmission High-capacity SDM systems Real-time GPU-based signal processing Off-axis digital holography techniques Scientific awards include: ECOC 2018 Student Paper Award Optica Student Paper Award (2022) OECC 2019 Best Paper Award Active in experimental validation of transmission systems, with collaborations on multi-core fiber implementations, turbulence generators, and software-defined optical receivers. Currently involved in the Zwaartekracht ECO project for integrated nanophotonics research.