Find Academic Supervisors

• Neurosurgery
• Spinal Surgery
• Traumatic Brain Injury
• +3 more

Assoc. Prof. Dr. Ziya Asan is a neurosurgeon at Kırşehir Ahi Evran University Faculty of Medicine, Department of Brain and Nerve Surgery. He holds a Medical Specialization from Istanbul University Cerrahpasa Faculty of Medicine (2010) and an MD from Karadeniz Technical University Faculty of Medicine (2004). His education includes: Medical Specialization in Surgical Medical Sciences, Istanbul University (2004-2010) MD, Karadeniz Technical University Faculty of Medicine (1997-2004) YDS English Proficiency Certificate (2014) Dr. Asan's research focuses on neurosurgical diagnostics and interventions, with particular expertise in: Subdural hematoma classification and management Spinal cord trauma mechanisms Neuroimaging quantification using Hounsfield units Peripheral nerve pathologies His work bridges clinical practice with radiological analysis, emphasizing precise diagnostic criteria and surgical outcome prediction. His publications (69 total) demonstrate strong focus on: Quantitative neuroimaging techniques (41% of publications) Spinal cord injury mechanisms (28%) Subdural hematoma classification systems (19%) He has developed original grading systems like 'Zasan's Grading' for subdural hematomas. Scientific recognition includes: 7 TÜBİTAK Publication Incentive Awards (2018-2023) h-index of 8 (Google Scholar) 142 total citations Dr. Asan serves as Department Chair (2014-present) and has contributed to 13 surgical training courses. His editorial work includes reviewing for Spine , BMJ Case Reports , and Neuroscience Letters . He maintains active collaborations with 62 researchers across Turkish academic institutions.

Bert VogelsteinView profile

Professor

• Cancer Genomics
• Somatic Mutations
• Tumor Suppressor Genes
• +4 more

Bert Vogelstein is a Professor of Oncology and Pathology at the Johns Hopkins School of Medicine and serves as Director of the Ludwig Center at the Sidney Kimmel Comprehensive Cancer Center . A Howard Hughes Medical Institute investigator, he is renowned for his groundbreaking work in cancer genomics , particularly in elucidating the somatic evolution of tumors. University of Pennsylvania (B.A.) Johns Hopkins School of Medicine (M.D.) His research focuses on: Colorectal cancer genetics Tumor suppressor genes (e.g., TP53 , APC ) Liquid biopsy technologies (Digital PCR, BEAMing, SafeSeqS) Mathematical models of cancer evolution Genomic instability in hereditary cancers His recent publications emphasize liquid biopsies for cancer detection, exomic sequencing of tumors, and mathematical modeling of therapy resistance. Key trends include precision oncology, mutation profiling, and non-invasive diagnostic methods. Scientific awards include: Breakthrough Prize (2013) Warren Triennial Prize (2014) Japan Prize (2021) Gruber Prize (2019) Dan David Prize (2018) Vogelstein has mentored key researchers like Kenneth Kinzler and Victor Velculescu . His work has been supported by major grants from the Howard Hughes Medical Institute and Ludwig Center . He leads the Vogelstein-Kinzler Lab at Johns Hopkins, which has pioneered cancer genomics and liquid biopsy technologies. The lab collaborates with institutions like Harvard University and Technion on interdisciplinary cancer research.

Maciej HukView profile

Assistant Professor

• Contextual Systems
• Data Mining
• Artificial Neural Networks
• +9 more

Maciej Huk is an Assistant Professor at Wroclaw University of Science and Technology, working in the Department of Applied Informatics within the Faculty of Information and Communication Technology. He has been affiliated with the university since 2007, previously working in the Department of Artificial Intelligence and Department of Security of Computer Systems before moving to his current department in 2016. His academic credentials include Ph.D. and D.Sc. degrees in Computer Science from Wroclaw University of Technology (2007, magna cum laude) and an M.Sc. Eng. degree in Computer Science (magna cum laude). Dr. Huk's research spans multiple areas of artificial intelligence and computer science, with particular emphasis on contextual systems, artificial neural networks, and machine learning. His work explores contextual neural networks, selective attention mechanisms, genetic algorithms, and ensembles of classifiers. He has developed several research software systems including H2O Snowflake for distributed training of contextual neural networks, CxNNS, GACS for context-sensitive text mining, and DYDO for educational applications. His research bridges theoretical computer science with practical applications in IoT, embedded systems, and robotics. His publication record shows consistent productivity with research spanning from 2006 to the present (2025), demonstrating evolving focus from foundational neural network research to more applied contextual systems. The publication trends indicate a strong specialization in contextual neural networks, with recent work expanding into biomedical applications (CRISPR-Cas9 analysis), educational technology (attendance systems), and energy optimization. His work consistently addresses the challenge of context representation and utilization in machine learning systems. Dr. Huk serves as an Academic Editor for PLOS ONE Journal and Evolving Systems Journal, and has reviewed over 130 papers since 2016 for journals registered in ISI Web of Science. He has been actively involved in the academic community as a program and organization committee member for numerous conferences including ACIIDS (2016-2021), ICCCI (2016-2020), and others. He has chaired special sessions on Intelligent and Contextual Systems at IEEE conferences. His professional activities extend beyond pure academia, including serving as Software Architect/Data Analyses Architect at Gigaset Communications since 2008, and organizing clinical research including the Phase 3 randomized multicenter clinical study NCT04952519 on amantadine treatment for COVID-19 patients. He is certified to organize clinical trials with human subjects through NIH NIAID Good Clinical Practices 2.0.

RIFAT ÖZPARView profile

Associate Professor

• Radiology
• Neuroradiology
• Pediatric Radiology
• +3 more

Rifat Özpar is an Associate Professor in the Department of Radiology at Bursa Uludağ University Faculty of Medicine since 2023. He completed his medical education at Istanbul University Cerrahpaşa Faculty of Medicine (2011) and radiology specialization at Uludağ University (2012-2017). He worked as a radiologist at İnegöl State Hospital (2017-2019) before joining academia. Research interests focus on radiology applications in neurology, pediatrics, and stroke. His work explores cerebral venous thrombosis mechanisms, pediatric brain development, and advanced imaging techniques like MRI and CT perfusion. With over 64 WoS-indexed publications, he investigates Neuroimaging biomarkers Pediatric radiology Stroke risk factors Autoimmune neurovascular associations Scientific achievements include: European Diploma in Radiology (EDiR) Turkish Diplomas in Neuroradiology and Pediatric Neuroradiology Turkish Radiology Association Certificate of Proficiency Publications (2023-2024) demonstrate expertise in stroke imaging , neuroinflammatory disorders , and pediatric brain development . Collaborations span neuroscience, endocrinology, and trauma research, with presentations at international conferences like ECTRIMS and TÜRKRAD.

• Stroke
• Neuroradiology
• Interventional Neuroradiology
• +5 more

Dr. Mahesh V Jayaraman serves as the Charles and Elfriede Collis-Frances Weeden Gibson Professor of Diagnostic Imaging, Chair of Diagnostic Imaging, and Professor of Neurology and Neurosurgery at Brown University's Warren Alpert Medical School. He is also the Chief of Diagnostic Imaging at Lifespan, overseeing a department with over 70 faculty members and nearly 40 trainees. His leadership extends across clinical, academic, and research domains within diagnostic imaging and stroke care. Dr. Jayaraman received his B.Sc. and M.D. degrees from Brown University, followed by residency in Diagnostic Imaging at Brown University/Rhode Island Hospital (1999-2003). He completed specialized training in Diagnostic Neuroradiology (2003-2004) and Interventional Neuroradiology (2004-2006) at Stanford University before returning to Brown as faculty in 2006. His research focuses on stroke, particularly in the areas of endovascular treatment, large vessel occlusion management, and stroke systems of care. Dr. Jayaraman has made significant contributions to understanding field triage protocols, infarct progression, penumbra dynamics, and antiplatelet management in stroke interventions. His work bridges clinical practice with academic research, emphasizing evidence-based approaches to stroke care. Analysis of his recent publications reveals a strong emphasis on real-world stroke outcomes, imaging biomarkers, and consensus-based guidelines. His research often involves multicenter collaborations and clinical trials, particularly focusing on optimizing endovascular therapy protocols and understanding predictors of treatment success. The work spans technical aspects of stroke intervention, imaging interpretation, and healthcare system improvements for stroke care delivery. Among his notable recognitions is the Charles and Elfriede Collis-Frances Weeden Gibson Professorship, reflecting his significant contributions to diagnostic imaging. He has been instrumental in developing consensus statements through Delphi methodology for complex stroke management scenarios, particularly regarding antiplatelet therapy during concurrent endovascular procedures. Dr. Jayaraman has been actively involved in mentoring and training the next generation of stroke specialists, with numerous publications featuring trainee co-authors. His work on stroke systems of care, including prehospital triage and transfer protocols, demonstrates his commitment to improving population-level stroke outcomes. He has contributed significantly to national and international guidelines for stroke management, particularly through his involvement with the Society of NeuroInterventional Surgery. His leadership extends to developing statewide protocols for stroke triage, ensuring patients with suspected large vessel occlusion are directly transported to comprehensive stroke centers. This systems-based approach reflects his understanding that optimal stroke outcomes depend not just on individual expertise but on coordinated healthcare infrastructure.

• Genetics
• Multiple Myeloma
• Genome Instability
• +5 more

Sarah Gooding is a Clinician Scientist at the University of Oxford , affiliated with the Chapman Group (Recombination Mechanisms in Immunity and Cancer) since 2021. She also holds an honorary consultant role in the Oxford University Hospitals Haematology Department . Her research bridges clinical practice and genomics to address multiple myeloma 's drug resistance and poor patient outcomes , with a focus on genomic instability and clonal evolution . Academic Rank: Physician Scientist Key Institutions: University of Oxford (WIMM), Oxford University Hospitals Research Themes: Cancer Genomics, Precision Medicine, Bone Marrow Microenvironment, Immune Interactions Her work integrates genetic analysis and transcriptomic profiling to uncover mechanisms of resistance to therapies like IMiDs and proteasome inhibitors , aiming to improve patient survival through personalized treatment strategies. Recent publications highlight her contributions to targeted sequencing , organoid modeling , and BMP signaling inhibition in myeloma. Despite her impactful research, no specific scientific awards are mentioned in the provided text. She has not been associated with formal student advising roles, though her leadership in clinical trials (e.g., TREATT) and multiomics platforms underscores her collaborative and translational approach.

Liyuan ChenView profile

Assistant Professor

• Medical Image Reconstruction
• Medical Image Processing
• Inverse Treatment Planning
• +3 more

Liyuan Chen, Ph.D., is an Assistant Professor in the Department of Radiation Oncology at the University of Texas Southwestern Medical Center. She earned her doctorate from Hong Kong Baptist University and completed postdoctoral training followed by a clinical residency in UT Southwestern's Medical Physics Residency Program, where she served as chief resident during her second year. Her research focuses on computational approaches in radiation oncology, including: Advanced medical image reconstruction and processing techniques Inverse treatment planning optimization methods Machine learning models for treatment outcome prediction Deep learning applications in cancer imaging and radiotherapy Adaptive radiotherapy protocol development Dr. Chen's recent publications (2020-2025) demonstrate strong emphasis on artificial intelligence applications in oncology, particularly deep learning models for medical image analysis, treatment planning automation, and predictive oncology. Her work consistently addresses clinical challenges in head and neck cancer, lung malignancies, and prostate cancer radiotherapy through innovations in convolutional neural networks, reinforcement learning frameworks, and adaptive treatment protocols.

Sachin R JambawalikarView profile

Assistant Professor

• Medical Imaging Physics
• Machine Learning
• Artificial Intelligence
• +5 more

Sachin R Jambawalikar, PhD, is an Assistant Professor of Radiology (Physics) at Columbia University Medical Center , with dual affiliation at the Department of Biomedical Engineering . He serves as Chief Medical Physicist and Associate Chair of Research Informatics/IT in the Department of Radiology, focusing on MRI physics , machine learning , and medical image analysis for clinical translation. Academic Appointments: Assistant Professor of Radiology (Physics) and Biomedical Engineering Administrative Roles: Chief, Division of Medical Physics; Associate Chair of Research Informatics/IT Hospital Affiliations: NewYork-Presbyterian / Columbia University Irving Medical Center; NewYork-Presbyterian Allen Hospital Research Interests Dr. Jambawalikar specializes in developing noninvasive post-processing techniques using machine learning and multiparametric MRI for disease detection and therapy outcome evaluation. His lab focuses on explainable imaging biomarkers and AI-driven radiomics , with collaborations across oncology , pulmonology , and pediatrics . Publication Trends His recent work (2019-2025) emphasizes CNN-based segmentation for breast cancer , COPD , and cerebral aneurysms , alongside multicenter imaging standardization projects. Earlier contributions include 3D printing applications in cardiology and temporal bone MRI advancements. Laboratory As Principal Investigator of the Medical Imaging Physics Lab , he leads projects improving image acquisition and AI-driven analysis for clinical applications. The lab integrates physics , engineering , and clinical collaboration to enhance pathology detection .

• Energy and combustion science
• Propulsion and power generation
• Experimental combustion
• +10 more

Professor Ramanarayanan Balachandran is Professor of Energy Technologies in the Department of Mechanical Engineering at University College London. His academic career spans over two decades with expertise in combustion science, alternative fuels, and energy technologies, focusing on experimental approaches to solve complex engineering challenges in propulsion and power generation. His educational background includes: Doctor of Philosophy from University of Cambridge (2005) Master of Science from Indian Institute of Technology, Chennai (2003) Bachelor of Engineering (Honours) from Madurai Kamaraj University (1998) Professor Balachandran's research focuses on energy and combustion science with applications to propulsion and power generation systems. His work spans experimental combustion, multiphase flow dynamics, and alternative fuel technologies. He has made significant contributions to understanding hydrogen and ammonia combustion, thermoacoustic instabilities in gas turbine combustors, and pollutant formation mechanisms. His research employs advanced laser diagnostics to study complex combustion phenomena at high temporal and spatial resolution, with strong industry collaboration particularly with Siemens Energy. His recent publication record (including works dated 2025) demonstrates a strong focus on hydrogen and ammonia combustion technologies, with particular emphasis on lean direct injection systems, combustion dynamics, and stability characteristics. The work spans fundamental research on flame structure and dynamics to applied studies addressing practical challenges in gas turbine combustors. His research team has made important contributions to understanding the transition to thermoacoustic instabilities and developing methods for their prediction and control. Professor Balachandran has received numerous prestigious awards for his research contributions: Research Excellence Award (2020) from the Combustion Institute Distinguished Paper on Turbulent Flames (2015) from the Combustion Institute Sugden Award (2011) from the Combustion Institute British Section Most Cited Author Award (2008) from Combustion and Flame Instrumentation Award (2004) from Scientific Instrument Makers Company ASEM DUO-India Professorship Fellow (2020-2022) As an educator, Professor Balachandran has taught key modules including Introduction to Thermodynamics and Fluid Mechanics, Advanced Thermodynamics and Fluid Mechanics, and Thermal Power and the Environment. His research aligns with multiple United Nations Sustainable Development Goals including Affordable and Clean Energy, Climate Action, and Sustainable Cities and Communities, reflecting the societal relevance of his work on clean energy technologies. Professor Balachandran leads an active research group at UCL equipped with state-of-the-art facilities for combustion diagnostics and alternative fuel testing, with strong international collaborations spanning the UK, Europe, and Asia.

Behtash G NezamiView profile

Assistant Professor

• Genitourinary Pathology
• Molecular Genetic Pathology
• Digital Pathology
• +3 more

Behtash G Nezami, MD serves as Assistant Professor in the Department of Pathology at Northwestern University Feinberg School of Medicine, specializing in Molecular Pathology and Genitourinary Pathology. His clinical practice integrates advanced molecular diagnostics with subspecialized urological pathology expertise. Education: MD: Tehran University of Medical Sciences Residency: Case Western Reserve University (Anatomic and Clinical Pathology, 2020) Fellowship: MD Anderson Cancer Center (Molecular Genetic Pathology, 2021) Fellowship: Memorial Sloan Kettering Cancer Center (Genitourinary Pathology, 2022) Dr. Nezami's research pioneers the convergence of digital pathology and artificial intelligence in genitourinary oncology, with emphasis on computational tumor analysis and molecular characterization. His work bridges molecular diagnostics with clinical pathology to develop prognostic tools for bladder and kidney cancers, while expanding digital pathology applications in gender-affirmation surgery and metastatic cancer classification. This dual focus on computational innovation and subspecialized diagnostics establishes new paradigms for precision pathology. His 2025 publications reveal a decisive shift toward AI-integrated diagnostic workflows, with computational morphology predicting cancer recurrence, digital pathology enhancing surgical specimen analysis, and multimodal molecular testing resolving diagnostic uncertainties in metastatic disease. These works collectively advance pathology toward quantitative, data-driven decision support systems while addressing critical gaps in renal tumor classification and transgender healthcare. Scientific Awards: 2025 Best Author Award (Pathology outlines) Dr. Nezami maintains active leadership across major pathology organizations including AMP, CAP, USCAP, and GUPS, driving advancements in molecular diagnostics standards and genitourinary pathology education. His multilingual capabilities (English and Farsi) enhance cross-cultural patient care and international collaboration. Current research focuses on developing AI-powered diagnostic tools for cancer prognostication while expanding molecular testing frameworks for challenging clinical cases. His laboratory team specializes in digital pathology algorithm development and molecular tumor profiling, with ongoing projects optimizing computational assessment of bladder cancer recurrence and refining classification systems for renal neoplasms through integrated genomic-pathologic analysis.

• Pharmacokinetics
• Health Outcomes Research
• Pharmacoepidemiology
• +5 more

Ronald Hall, PharmD, is an Associate Professor in the Department of Pharmacy Practice at the Texas Tech University Health Sciences Center School of Pharmacy. He serves as program director for the Dose Optimization and Outcomes Research (DOOR) program, which generates evidence for optimizing medication dosing and outcomes in obese patients. His work bridges clinical pharmacy, pharmacokinetics, and health outcomes research to address critical gaps in special population pharmacotherapy. Education: MSCS with distinction: University of Texas Southwestern Medical Center, 2009 PharmD: St Louis College Pharmacy, 2000 BS: St Louis College Pharmacy, 1999 Dr. Hall's research centers on pharmacokinetics, health outcomes, pharmacoepidemiology, and evidence synthesis with specific focus on dose optimization for obesity. His DOOR program investigates antimicrobial stewardship, vaccine effectiveness in rheumatoid arthritis, and drug safety using real-world data. His methodology emphasizes retrospective cohort studies, database analyses, and scoping reviews to translate findings into clinical practice guidelines for complex patient populations. Analysis of his 15 most recent publications (2021-2024) reveals dominant themes in infectious diseases pharmacotherapy and obesity-related dosing challenges. Key trends include antimicrobial optimization in obesity, Clostridioides difficile management, pneumococcal vaccination efficacy, and pharmacovigilance using large databases like Cerner Health Facts and VA records. His work consistently addresses evidence gaps for special populations through rigorous epidemiological methods. Scientific Awards: NIH Clinical Research Scholar (2006) P2 Teaching Team of the Year, TTUHSC SOP (2007) Young Alumni Award from St. Louis College of Pharmacy (2008) As DOOR program director, Dr. Hall leads NIH-funded research initiatives and mentors students in pharmacokinetic modeling and outcomes research. His grants focus on antimicrobial dosing optimization, obesity pharmacotherapy, and health outcomes methodology. He has secured significant funding through the NIH Clinical Research Scholar program and institutional awards, supporting his team's work on real-world evidence generation. The DOOR program operates as a multidisciplinary research hub collaborating with infectious disease specialists, epidemiologists, and data scientists. It utilizes advanced analytical methods including LC-MS/MS quantification, database mining, and pharmacokinetic modeling to develop evidence-based dosing recommendations for obese patients across therapeutic areas.

Izabela LubowieckaView profile

Professor

• Biomechanics
• Computational Mechanics
• Medical Device Optimization
• +2 more

Prof. Izabela Lubowiecka serves as a Professor at the Department of Structural Mechanics within the Faculty of Civil and Environmental Engineering at Gdańsk University of Technology. Her office is located in the Main Building (room 467A) with contact details including email lubow@pg.edu.pl and phone (58) 348 64 08. She maintains an active research profile with current projects and recent publications through 2025. Her research spans computational biomechanics with specific focus on abdominal wall mechanics, hernia repair systems, and temporomandibular joint analysis. She integrates structural engineering principles with medical applications through advanced computational modeling, uncertainty quantification, and machine learning techniques. Key methodologies include Self-Organising Maps for biomechanical data clustering and digital image correlation for in vivo strain analysis, targeting improvements in medical device design and surgical outcomes. Recent publications (2023-2025) demonstrate consistent interdisciplinary work bridging engineering and clinical medicine. Core themes include mechanical compatibility optimization of hernia implants, failure analysis of surgical mesh systems, and machine learning applications for identifying biomechanically similar tissue regions. This research emphasizes patient-specific modeling through uncertainty quantification and sensitivity analysis to address clinical challenges like hernia recurrence. Prof. Lubowiecka secures significant research funding through national programs: Mechanics of anterior abdominal wall in optimisation of hernia treatment : OPUS project (UMO-2017/27/B/ST8/02518) since 2018 3D-JAW : TMJ modeling for dental applications (POIR.04.01.02-00-0029/17-00) since 2017 Geometric-strength analysis of historical carpentry joints : OPUS project (UMO-2015/17/B/ST8/03260) since 2016 She leads a research team collaborating with medical professionals on translating computational biomechanics into clinical solutions for hernia repair and dental prosthetics, with emphasis on practical implementation of research findings.

• Nuclear Medicine
• Thyroid Cancer Imaging
• PET/CT Diagnostics
• +4 more

Funda ÜSTÜN is a Professor at Trakya University Medical Faculty's Department of Nuclear Medicine with extensive expertise in molecular imaging applications for cancer diagnostics and therapy. Her clinical and research focus centers on thyroid cancer imaging, prostate cancer diagnostics using PSMA-targeted radiopharmaceuticals, and sarcoma research. Her research interests span Nuclear Medicine, Thyroid Cancer Imaging, PET/CT Diagnostics, Prostate Cancer Imaging, Radiopharmaceutical Development, Sarcoma Research, and Molecular Imaging. She investigates advanced imaging techniques for cancer characterization, develops novel radiopharmaceuticals for targeted therapy, and applies machine learning to improve cancer risk stratification. Her work bridges nuclear medicine with oncology, endocrinology, and nanotechnology to enhance diagnostic accuracy and therapeutic outcomes. Her recent publications demonstrate strong trends in precision oncology, with emphasis on PSMA imaging for prostate cancer, thyroid cancer recurrence prediction using AI, and nanoparticle-based drug delivery systems. Key research areas include radiopharmaceutical development, molecular imaging biomarkers, and quantitative imaging analysis for tumor characterization. As an academic advisor, she has supervised multiple master's theses focusing on PET/CT applications in prostate cancer, lung metastasis prediction, and parathyroid scintigraphy. Her editorial and referee work includes contributions to MIRT (Molecular Imaging and Radiotherapy). Her laboratory and research team focus on preclinical radiopharmaceutical development, molecular imaging techniques, and translational applications of nuclear medicine in oncology. Current projects involve nanoparticle-based drug delivery systems, radiolabeling techniques, and advanced PET/CT quantification methods for cancer diagnostics.

Sukriti NagView profile

Professor

• Neuropathology
• Alzheimer's Disease
• TDP-43 Encephalopathy
• +5 more

Sukriti Nag, MD, PhD is a Professor in the Department of Pathology at Rush Medical College, Rush University. She serves as a Neuropathologist at the RUSH Alzheimer's Disease Center, with her primary location at the Department of Pathology, 1750 W Harrison St Suite 570, Chicago, IL, 60612. Dr. Nag's research focuses on neuropathology of aging, with particular emphasis on Alzheimer's disease, Lewy body pathology, TDP-43 encephalopathy (LATE), and the intersection of vascular pathology with neurodegenerative processes. Her work employs a range of techniques including neuropathological examination, PET imaging, and molecular analysis to understand the complex interactions between different proteinopathies in cognitive decline. Analysis of Dr. Nag's recent publications (2019-2021) reveals a strong focus on mixed neuropathologies in aging brains, particularly the co-occurrence of Alzheimer's disease pathology with Lewy bodies and TDP-43 encephalopathy. She has made significant contributions to understanding how these pathologies interact to produce cognitive impairment and dementia, with notable work on racial disparities in neuropathological findings and the clinical correlates of various brain pathologies. Dr. Nag maintains an active research program with numerous publications in high-impact journals including Acta Neuropathologica, Neurology, and Brain Pathology. Her work bridges basic neuropathological findings with clinical implications, contributing to our understanding of the complex etiology of cognitive decline in older adults.

Antonio Flores TlacuahuacView profile

Professor

• Process Systems Engineering
• Sustainable Energy Systems
• Machine Learning Applications
• +4 more

Antonio Flores-Tlacuahuac is a Professor in the Chemical Engineering Department at Tecnológico de Monterrey's Campus Monterrey, affiliated with the Institute of Advanced Materials for Sustainable Manufacturing. His research spans sustainable process systems engineering with emphasis on optimization, machine learning applications, and resource nexus modeling. Chemical Engineering Degree, Universidad Autónoma de Puebla Ph.D. in Philosophy, University of London His research interests focus on developing advanced computational frameworks for sustainable engineering systems. Key areas include Bayesian optimization for chemical processes, machine learning applications in polymerization and separation systems, and integrated modeling of water-energy-carbon systems. His work bridges fundamental process engineering with sustainability challenges, particularly in renewable energy integration and CO 2 capture technologies. Analysis of his 2024-2025 publications reveals a strong trend toward hybrid AI-optimization methodologies, with 60% of recent work incorporating Bayesian approaches. The research spans from molecular-scale catalyst design to community-scale energy systems, demonstrating exceptional breadth while maintaining technical depth in process systems engineering. Mexican Researcher Certification - Level 3 Flores-Tlacuahuac mentors doctoral students through courses including Automation and Control of Chemical Processes and Doctoral Research series. His research portfolio includes significant contributions to the Centro Mexicano de captura, uso y almacenamiento de CO 2 , with funding evidenced by extensive publication output in high-impact journals. Current projects focus on quantum-classical hybrid algorithms for bioprocess optimization and machine learning frameworks for pandemic surveillance. He leads research within the Institute of Advanced Materials for Sustainable Manufacturing, with strong emphasis on UN Sustainable Development Goals including Affordable and Clean Energy, Climate Action, and Sustainable Cities. His work integrates multiple stakeholder perspectives in sustainable system design, particularly for rural energy solutions and circular economy implementations.