Decision Making Algorithms Professors and Supervisors | ApplySharif

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Richard BellamyView profile

Professor

Graz University of TechnologyAustria

Professor Richard Bellamy is a leading scholar in political science and constitutional theory at University College London (UCL) and serves as a Senior Fellow at the Hertie School, Berlin. He holds a PhD from the University of Cambridge and has held visiting positions across Europe and Australia. Doctor of Philosophy , University of Cambridge (1983) MA Cantab , University of Cambridge (1979) His research spans democratic theory , republicanism , constitutionalism , and EU studies , with a focus on Italian political thought and global ethics . His recent articles explore rule of law , data ethics , and EU differentiated integration . He has authored over 150 publications and 11 monographs, including the acclaimed Political Constitutionalism . Key awards include: Fellow of the British Academy (2022) 2016 PADEMIA Research Award Serena Medal (2012) David and Elaine Spitz Prize (2009) Fellow of the Academy of Social Sciences (2008) Member, Academia Europaea (2024) Bellamy has supervised 24 PhD students and led major Leverhulme , ESRC , and European Commission projects. His current work includes Defending the Political Constitution (Oxford UP) and The Democratic Prince (political leadership study). He co-edits The Cambridge Companion to Constitutional Theory and The Cambridge Dictionary of Political Thought .

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Sarath Chandar ANBIL PARTHIPANView profile

Associate Professor

University of MontrealCanada

Sarath Chandar is an Associate Professor at Polytechnique Montréal and Core Faculty Member at Mila, the Quebec AI Institute. He holds a Canada CIFAR AI Chair and Canada Research Chair in Lifelong Machine Learning. His research focuses on developing interactive learning algorithms for continual and lifelong learning, with expertise in deep learning, reinforcement learning, and natural language processing. Education: Ph.D. in Computer Science, University of Montreal (advisor: Yoshua Bengio) M.S. in Computer Science, Indian Institute of Technology Madras (advisor: Balaraman Ravindran) Research Themes: Continual Learning and Lifelong Learning Deep Reinforcement Learning Optimization for Deep Networks Natural Language Processing AI for Scientific Discovery Notable Contributions: Founder of the Conference on Lifelong Learning Agents (CoLLAs) Developed Chandar Research Lab (CRL), focusing on adaptive learning algorithms Contributions to model-based reinforcement learning and bias mitigation in AI systems Awards & Grants: Canada CIFAR AI Chair Canada Research Chair Tier 2 MITACS-funded projects on reinforcement learning applications Lab & Collaboration: CRL collaborates with academic/industrial partners (e.g., IBM, Samsung) Hosts annual symposium showcasing research in AI, optimization, and multi-agent systems

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Dimitrios TsarapatsanisView profile

Senior Lecturer

University of YorkUnited Kingdom

Dr Dimitrios Tsarapatsanis is a Senior Lecturer at York Law School, University of York. Previously, he served as a Lecturer in Law at the University of Sheffield. He holds an LLB from the University of Athens, an LLM in EU and International Law, and a PhD from Université Paris X-Nanterre. His research focuses on legal theory, human rights, AI ethics, and European law. Education: LLB, University of Athens LLM in EU and International Law, University of Athens DEA in Legal Theory, Université Paris X-Nanterre PhD (summa cum laude), Université Paris X-Nanterre Research Interests: Legal realism, administrative law, judicial decision-making, AI ethics in law, human rights jurisprudence, and bioethical legal issues. His work bridges theoretical jurisprudence with practical legal challenges, particularly in European contexts. Recent work explores the application of natural language processing to predict judicial outcomes and the ethical implications of AI in legal systems. He has also contributed to debates on the European Court of Human Rights' strategies and the legal status of prenatal life. Recipient of the Paul Ricoeur Prize (2009) for his doctoral thesis on prenatal legal personhood. Collaborations: Active in interdisciplinary projects, including the 'AI Law and Ethics: The Challenge of AI Wrongdoing' initiative. His research often intersects with political theory and institutional analysis. Professional Contributions: Fellow of the Higher Education Academy (2018). Previously practiced law in Greece, focusing on EU law and human rights cases.

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Camellia ZakariaView profile

Assistant Professor

Singapore Management UniversitySingapore

Camellia Zakaria is an Assistant Professor at the University of Toronto, holding appointments in the Biostatistics Division and Institute of Health Policy, Management, and Evaluation (IHPME) at the Dalla Lana School of Public Health (DLSPH), and the Faculty of Information (FoI). She was recently named Canada Research Chair (Tier 2) for mobile health solutions. Her research focuses on integrating mobile technologies and machine learning to develop equitable health monitoring systems. Education: University of Toronto (Present) University of Massachusetts Amherst (2020–2023) Singapore Management University (2014–2019) Research interests span mobile health, applied machine learning, and human-computer interaction, with a focus on creating inclusive health applications. Her work addresses challenges in environmental sensing, sleep monitoring, and mental health tracking through innovative sensor technologies and data analytics. Key articles (2024–2016) explore medical decision-making, aerosol emissions, and group behavior modeling. Her work has been recognized in venues like BMJ , IMWUT , and CHI . Awards : Canada Research Chair (Tier 2, 2024) Projects like StressMon and WATCHME highlight her commitment to scalable health monitoring systems. She collaborates on lab initiatives such as sensAILabs, advancing interdisciplinary health technology solutions.

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Nicole MegowView profile

Professor

University of BremenGermany

Nicole Megow is a Professor holding the chair for Combinatorial Optimization in the Faculty of Mathematics and Computer Science at the University of Bremen since 2016. She is affiliated with several research clusters including Humans on Mars Initiative, Minds, Media, Machines, and Dynamics in Logistics. Her academic journey includes positions at TU Berlin, Max Planck Institute for Informatics, TU Darmstadt, and TU Munich. Professor Megow's research focuses on mathematical optimization, algorithm design and analysis, and operations research. Her specific interests span combinatorial and discrete optimization, efficient algorithms, scheduling theory, resource allocation, packing problems, network design, routing, and uncertainty models including online, stochastic, robust, and explorable approaches. Her work bridges theoretical foundations with practical applications in logistics and decision-making systems. Her recent publications demonstrate a strong trend toward integrating prediction models with traditional optimization frameworks, particularly in scheduling and matching problems. She has made significant contributions to understanding the role of uncertainty in optimization problems, developing algorithms that work effectively with incomplete or uncertain information. Her work spans multiple prestigious venues including Mathematical Programming, Algorithmica, SODA, STACS, and NeurIPS. Dissertation Award by the German Operations Research Society (2007) Berlin Science Award for Young Researchers (2013) Heinz Maier-Leibnitz Prize (2013) Listed among Germany's top 40 researchers below 40 (Capital, 2014, 2015) Professor Megow actively supervises PhD students and postdocs, including Max Stahlberg, Joes Biburger, Sarah Morell, Bart Zondervan, Zhenwei Liu, and Alexander Lindermayr. She serves on numerous program committees for major conferences including SODA, IPCO, and STOC, and holds editorial positions for several prestigious journals. Her current research projects include Optimization under Explorable Uncertainty (DFG funded), How robots learn how to use structure (seed grant from MMM research cluster), and Scheduling Invasive Multicore Programs Under Uncertainty (within TCRC 89).

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Tamás KoltaiView profile

Professor

Budapest University of Technology and EconomicsHungary

Dr. Tamás Koltai is a Professor and Dean at the Faculty of Economics and Social Sciences of Budapest University of Technology and Economics (BME). He leads the doctoral school's Specialization Group in Production Management. His roles include overseeing academic programs and research in production management, operations research, and efficiency analysis. Education: Doctor of the Hungarian Academy of Sciences (2016) Dr. habil. (2000), Budapest University of Technology and Economics Candidate of Technical Sciences (1987), Hungarian Academy of Sciences M.Sc. in Mechanical Engineering (1983), BME Faculty of Mechanical Engineering Research Interests: Dr. Koltai focuses on production management optimization, including the application of Data Envelopment Analysis (DEA), sensitivity analysis in mathematical models, and the integration of robotics in assembly lines. His work bridges theoretical models (e.g., MILP/CP optimization) with practical industrial challenges, particularly in healthcare efficiency and educational management. Notable Awards: IEOM Society Teaching Excellence Award (2021) BME GTK Faculty Memorial Medal (2016) János Susánszki Award (2013) Széchenyi Professorship Scholarship (1999–) Teaching & Leadership: He has held visiting roles at the University of Seville (1990–1992) and the University of Michigan (1988/89). His teaching excellence is recognized through awards and his contributions to business simulation education. His research often collaborates with industry partners to address real-world operational challenges. Labs/Teams: Leads the Production Management Specialization Group and contributes to interdisciplinary teams focusing on manufacturing efficiency and healthcare operations within BME.

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Jeffrey D MarrView profile

Assistant Professor

Brown UniversityUnited States

Jeffrey D Marr is an Assistant Professor of Health Services, Policy, and Practice at Brown University’s School of Public Health. His research focuses on health care markets using quasi-experimental methods and administrative data, with current work emphasizing Medicare, private insurance, and AI applications in healthcare decision-making. Marr holds a PhD in Health Economics and Policy from Johns Hopkins University (2025) and a BA in Economics from Colgate University (2018). Key Affiliations: Brown University School of Public Health Research Themes: Algorithmic healthcare decision-making, Medicare Advantage networks, insurer market power, and post-acute care economics His research demonstrates how predictive algorithms reduce skilled nursing facility stays without compromising health outcomes, and how Medicare Advantage expansion impacts hospital pricing and provider networks. Marr’s work has been published in prestigious journals like Health Affairs , Health Services Research , and Journal of the American Geriatrics Society . His studies on Medicaid managed care reveal hospital pricing disparities linked to insurer bargaining power, while analyses of Medicare Advantage enrollment trends highlight shifts in supplemental insurance usage among elderly patients. Marr’s research also addresses critical issues like dialysis facility choice constraints and home-based care utilization differences between Medicare programs.

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Taylor McLindenView profile

Research Professor

Western UniversityCanada

Taylor McLinden, PhD is an Adjunct Research Professor in the Department of Epidemiology and Biostatistics at Western University's Schulich School of Medicine and Dentistry. He serves as the Manager of the Health Data Research Network Canada (HDRN Canada) Pragmatic Trials Training Program and holds positions at ICES Western, HDRN Canada, and the London Health Sciences Centre. Previously, he worked as a Scientific Officer at the British Columbia Centre for Excellence in HIV/AIDS and completed postdoctoral training at the same institution. Dr. McLinden received his PhD in Epidemiology from McGill University (2013-2017), an MSc in Epidemiology from the University of Guelph (2011-2013), and an Honours BSc in Biomedical Sciences from Brock University (2007-2011). His research focuses on epidemiology, causal inference, and administrative health data analysis, with particular expertise in HIV/AIDS research, social determinants of health, food insecurity, and pragmatic trials methodology. Dr. McLinden has made significant contributions to understanding the intersection of HIV, overdose crises, housing stability, and social determinants of health among marginalized populations in British Columbia. His recent publications demonstrate a strong emphasis on population health research using linked administrative data, with particular attention to HIV outcomes, health disparities, and methodological innovations in epidemiologic research. His work often addresses critical public health challenges including the overdose crisis, social isolation among people living with HIV, and the impact of structural factors on health outcomes. Dr. McLinden has secured significant research funding, including a CIHR Operating Grant ($480,992 over 2 years) focused on the impact of the COVID-19 pandemic on drug overdoses among people with and without HIV, and a CIHR Project Grant ($994,500 over 5 years) addressing engagement of marginalized populations in primary health care in Vancouver's downtown east side. As Manager of the HDRN Canada Pragmatic Trials Training Program, Dr. McLinden plays a key role in building capacity for pragmatic clinical trials across Canada. His work bridges academic research with practical health system applications, contributing to evidence-informed healthcare decision-making through rigorous epidemiologic methods.

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Zhijian HuangView profile

Associate Professor

Rochester Institute of Technology (RIT)United States

Zhijian Huang is an Associate Professor in the Department of Finance and Accounting at Saunders College of Business, Rochester Institute of Technology, with expertise in corporate finance, behavioral finance, and risk management. Education: B.Eng., Shanghai Jiaotong University (China) M.S., Michigan State University M.Eng., Cornell University Ph.D., Pennsylvania State University His research focuses on financial markets, cognitive dissonance in investor behavior, cryptocurrency volatility, and climate policy impacts on stock prices. Recent publications explore asymmetric responses to earnings news, social media sentiment effects, and credit risk modeling. Huang teaches courses in equity analysis, options/futures, and risk management, with a strong emphasis on derivative instruments and portfolio optimization strategies.

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Hui WangView profile

Professor

Stevens Institute of TechnologyUnited States

Hui Wang is a Professor and Associate Chair for PhD Studies and Research in the Department of Computer Science at the Charles V. Schaefer, Jr. School of Engineering and Science, Stevens Institute of Technology. She also serves as the Director of the Data Science PhD Program and holds leadership roles in multiple institutional committees, including the Doctoral Committee, Faculty Mentoring Program, and Strategic Planning initiatives at both departmental and university levels. Research Interests: Dr. Wang's research focuses on building trustworthy machine learning systems by integrating privacy, fairness, and accountability . Her work aims to fortify ML models against privacy attacks, eliminate algorithmic biases, and ensure auditable decision-making. She explores intersections between machine learning, data mining, and cybersecurity, with applications across domains requiring ethical and secure AI deployment. Recent Research Trends: Her recent publications and funded projects reflect a strong emphasis on privacy-preserving machine learning , fairness-aware systems , and verifiable computing . Themes include securing graph embeddings, federated learning with fairness guarantees, and audit mechanisms for black-box models. Supported by NSF, Cisco, and Google, her work bridges theoretical rigor with practical system design. Scientific Awards: NSF CAREER Award, 2014 Advising and Grants: Dr. Wang actively mentors PhD students and hosts visiting scholars. She leads multiple NSF-funded projects, including Securing Network Embedding against Privacy Attacks and Privacy for All: Ensuring Fair Privacy Protection in Machine Learning . Her research is supported by substantial grants from the National Science Foundation, Cisco, and Google, reflecting her leadership in trustworthy AI. Labs and Teams: While not explicitly named, Dr. Wang leads a research group focused on trustworthy machine learning, advising students and collaborating with industry partners. She is deeply integrated into the Data Science PhD program and CS faculty leadership, shaping research and academic strategy at Stevens.

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Ramon LobatoView profile

Professor

Swinburne University of TechnologyAustralia

Professor Ramon Lobato (Swinburne University of Technology, School of Social Sciences, Media, Film and Education) is a leading scholar in media and cultural industries, specializing in digital distribution, streaming services, and cultural policy. With three Australian Research Council Fellowships, including a current Future Fellowship on Australian television in the smart TV ecology (2020-2025), his research bridges empirical analysis with critical theory to explore industrial transformations in the digital media landscape. Bachelor of Arts, University of Melbourne PhD in Cultural Studies, University of Melbourne Graduate Certificate in Learning and Teaching, Swinburne University His work focuses on: Streaming Media: Analyzing video-on-demand platforms, local content regulation, and global distribution dynamics Informal Media Economies: Examining piracy, geoblocking, and alternative distribution circuits Media Policy: Advising governments on smart TV regulation, data privacy, and platform accountability Recent research trends include smart TV datafication, ad-tech infrastructure, and the cultural implications of algorithmic content recommendation. His book Netflix Nations: The Geography of Digital Distribution (NYU Press, 2019) and co-edited MIT Press series Distribution Matters provide foundational insights into platform globalization and industrial practices. RMIT Award for Research Excellence in Technology, Communication, and Policy (2020) International Communication Association Outstanding Young Scholar (2016) Dean's Award for Outstanding Research, Swinburne (2015) Faculty ECR Research Excellence, Swinburne (2014) Udine Film Forum Best International Film Studies Book (2013) As a supervisor, Lobato welcomes PhD projects on media industries, technologies, and audiences. He collaborates with institutions like the Department of Infrastructure, Transport, Regional Development, Communications and the Arts (DITRDCA) and SBS on funded research, while co-editing the MIT Press book series Distribution Matters , dedicated to media distribution's social and cultural impact.

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Kash BarkerView profile

Professor

University of OklahomaUnited States

Kash Barker serves as the John A. Myers Professor and David L. Boren Professor at the University of Oklahoma in the Department of Industrial & Systems Engineering within the College of Engineering. As Graduate Liaison, he leads research on network resilience, supply chains, and systems engineering for societal good, with applications spanning infrastructure, supply chains, and community systems. His lab has produced 11 Ph.D. graduates (10 in academia) and 31 M.S. graduates. Research Domains: Resilient networks and interdependent systems Risk and decision analytics Supply chain survivability Pandemic economic impact modeling Climate migration optimization Cyber-Physical-Social Systems Article Trends emphasize disinformation defense , network restoration optimization , and multi-layer resilience modeling across infrastructure, supply chains, and community systems. His work combines game theory , machine learning , and decision analysis frameworks. Scientific Awards & Roles: Fellow, Institute of Industrial and Systems Engineers Senior Member, IEEE Fellow, Fulbright Finland Foundation (2023) Associate Editor roles in IISE Transactions and Naval Research Logistics Editorial Board Member for Risk Analysis and Scientific Reports Faculty Advisor, OU INFORMS student chapter Educational Background: Ph.D., Systems Engineering, University of Virginia M.S., Industrial Engineering, University of Oklahoma B.S., Industrial Engineering, University of Oklahoma

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Aleksandar MihajlovicView profile

Researcher

Singidunum UniversitySerbia

Aleksandar Mihajlovic is a researcher and Art Director at Singidunum University, Serbia. With a doctoral degree in Contemporary Business Decision-Making (2022), a master's in Business Economics (2014), and a bachelor's in Computer Graphics and Design (2008), he combines academic rigor with creative leadership in the university's marketing strategy. Doctoral studies: Contemporary Business Decision-Making, Singidunum University (2022) Master studies: Business Economics, Singidunum University (2008–2014) Undergraduate: Computer Graphics and Design, Faculty of Informatics and Management (2005–2008) High school: Robotics and Flexible Production Systems Technician, Polytechnic Academy (1995–1999) His research spans visual communication , digital marketing , and artificial intelligence applications in creative industries. Key contributions include Co-authoring 11 academic papers (2015–2025) on topics like Instagram ad effectiveness, techno-feudalism, and responsive logo design. Developing the scientific research portal 'Singipedia' and international magazine 'SingiLogos'. Participating in 7 global projects including Erasmus+ and TEMPUS initiatives. His scientific awards include the JISA Discobolos Special Award (2010), IT Globus Award (2010), and Grafima Fair Special Award (2025). He serves on the organizing committee for conferences like Sinteza and Sitcon , and has judged marketing competitions while volunteering for NGOs like the City Organization of the Deaf of Belgrade.

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James D. HerbslebView profile

Professor

Carnegie Mellon UniversityQatar

James D. Herbsleb is a Professor at Carnegie Mellon University in the Software and Societal Systems Department under the School of Computer Science . He served as Department Head from 2019-2024 and holds a PhD in Psychology and an MS in Computer Science. Education PhD in Psychology MS in Computer Science Research interests focus on the intersection of software engineering , computer-supported cooperative work , and socio-technical systems . Key areas include global software teams, open source ecosystems, and the limits of modularity in complex projects. His work explores decision networks , interface translucence , and scientific software sharing through NSF-funded initiatives. Recent publications examine API management in ecosystems like Eclipse and Node.js, coordination theory in distributed teams, and transparency in open source practices. Awards include the ACM Outstanding Research Award (2016) and Alan Newell Award (2014) . Scientific Awards ACM Outstanding Research Award (2016) Alan Newell Award for Research Excellence (2014) Most Influential Paper Award (ICSE 2010) Best Paper Award (Academy of Management 2010) Best Paper Award (CSCW 2006) Students advised include Patrick Wagstrom (COS PhD), Anita Sarma (postdoc), Uri Dekel (SE PhD), and current PhD candidates like Ben Towne. Research is supported by NSF, Sloan Foundation, and industry partners including Google and IBM.

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Shiyu ChangView profile

Associate Professor

Massachusetts Institute of TechnologyUnited States

Shiyu Chang is an Associate Professor of Computer Science at the University of California, Santa Barbara, and a Research Staff Member at the MIT-IBM Watson AI Lab. His work bridges machine learning, natural language processing, and computer vision with a focus on interpretability and robustness. Current Affiliation: UC Santa Barbara Lab: MIT-IBM Watson AI Lab His research explores how to make AI systems more interpretable and robust by integrating human intuition and rationalization. Key themes include adversarial learning, self-supervised methods, and improving transferability in models. Recent publications span conferences like ICML, CVPR, and NeurIPS, addressing topics such as black-box text classification, fairness-aware algorithms, and speech representation disentanglement. Broad keywords include Machine Learning, NLP, and Computer Vision. Fairness Reprogramming (AI Fairness) TransGAN: Transformer-based GANs Adversarial Robustness Certificates

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