جست‌وجوی استاد و پژوهشگر

Katja Dlouhyمشاهده پروفایل

استادیار

• Career Development
• Professional Mobility
• Career Success
• +۳ مورد دیگر

Dr. Katja Dlouhy is an Assistant Professor of Human Resource Management at the University of Mannheim Business School. Previously, she served as Acting Professor for Strategic Management at Technical University of Munich (2020–2022) and worked as a research assistant at the University of Mannheim (2013–2018). Doctorate in Management from University of Mannheim (2017) Diploma in Psychology from University of Koblenz-Landau (2012) Her research explores: Organizational career dynamics and professional mobility Cultural and institutional influences on workforce development Human-animal relationships in organizational contexts Recent publications examine: Boomerang mobility frameworks Career ecosystem perspectives Human-animal work engagement Occupational turnover patterns Scientific recognitions include: Best Reviewer Award (Academy of Management 2023 & 2024) Excellence in Reviewing Award (Human Resource Management 2023) Carolyn B. Dexter Award Nominee (2020) Dissertation Prizes (Karin Islinger Foundation, Volksbank Weinheim 2018) Multiple Academy of Management award finalists (2015–2018) She serves as Associate Editor for Applied Psychology and on the Human Resource Management editorial board. Her work integrates psychology and organizational studies to address contemporary workforce challenges.

• Statistical Design of Clinical Trials
• Adaptive Trial Methodology
• Optimal Trial Design
• +۷ مورد دیگر

Nigel Stallard is Professor of Medical Statistics and Director of Warwick Clinical Trials Unit at the University of Warwick. His work focuses on the statistical methodology for clinical trials, with particular expertise in adaptive designs and trial monitoring. He has made significant contributions to the field of medical statistics through his research, editorial work, and leadership of the Warwick Clinical Trials Unit. Professor Stallard's educational background includes: BA in Mathematics from Cambridge University MSc in Biometry from the University of Reading PhD in Applied Statistics from the University of Reading Professor Stallard's primary research interests are in the statistical design and analysis of clinical trials. He has worked extensively on optimal trial design and methodology for clinical trials with interim analyses and adaptations such as treatment or subgroup selection. His current work includes developing innovative design methods for clinical trials in small populations and in emerging disease epidemics, as well as methods for decision-making in clinical trials that have been interrupted. His research has significant implications for improving the efficiency and ethical conduct of clinical research across various medical specialties. His recent publications demonstrate a strong focus on adaptive clinical trial designs, particularly group sequential methods, seamless phase II/III trials, and enrichment designs incorporating biomarkers. There's a clear emphasis on practical applications in various disease areas including cancer, orthopedics, and neurological disorders. His work shows an increasing interest in addressing methodological challenges in trials for rare diseases and during public health emergencies. Professor Stallard has served as Editor in Chief of the journal Statistics in Medicine. His methodological contributions have been widely cited and implemented in clinical research practice. Professor Stallard has held major research grants from the National Institute of Health and Social Care Research, the Medical Research Council, and the European Union. His work has directly influenced clinical trial methodology and practice, particularly in the areas of adaptive designs and statistical monitoring. He has collaborated extensively with clinical researchers across multiple disciplines. As Director of the Warwick Clinical Trials Unit, Professor Stallard leads a team conducting high-quality clinical research across various therapeutic areas. The unit specializes in innovative trial designs and has been involved in numerous national and international collaborative research projects.

Aishwarya Agrawalمشاهده پروفایل

استادیار

• Computer Vision
• Natural Language Processing
• Deep Learning
• +۷ مورد دیگر

Aishwarya Agrawal is an Assistant Professor in the Department of Computer Science and Operations Research at the University of Montreal and a core academic member of Mila – Quebec AI Institute, where she holds the Canada CIFAR AI Chair. She also works as a Research Scientist at Google DeepMind, spending one day per week there. Her research sits at the intersection of computer vision, natural language processing, and deep learning, aiming to build AI systems that can both 'see' and 'talk'. Her research interests include vision-language reasoning, data-efficient pretraining, robust evaluation, improving model reliability and explainability, and aligning AI systems with diverse cultural values. She has made significant contributions to visual question answering, particularly in addressing dataset bias through work like the VQA-CP dataset. The recent publications show a strong focus on evaluating and improving vision-language models, especially in areas like cultural alignment, consistency, reliability, and robustness. There is a clear trend toward developing better evaluation methodologies and enhancing model behavior in complex, real-world scenarios. Her group actively publishes in top-tier conferences including CVPR, NeurIPS, ICML, and ACL. Canada CIFAR AI Chair Award Georgia Tech 2020 Sigma Xi Best Ph.D. Thesis Award Georgia Tech 2020 College of Computing Dissertation Award Runner-up, 2019 AAAI/ACM SIGAI Dissertation Award NVIDIA Graduate Fellowship 2018-2019 Rising Stars in EECS 2018 Aishwarya advises several PhD and MSc students, including Rabiul Awal, Qian Yang, Le Zhang, and Oscar Mañas. She has received research grants, such as one from Samsung for compositional reasoning in multimodal transformers. She is actively recruiting graduate students and postdocs through Mila’s supervision request process. She co-organized the annual VQA Challenge and Workshop from 2016 to 2021 and is involved in broader AI community initiatives, including panels and talks on leadership and AI research trends. Her lab, part of Mila, fosters a collaborative environment focused on advancing multimodal AI.

Aaron Courvilleمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۶ مورد دیگر

Aaron Courville is a Full Professor in the Department of Computer Science and Operations Research at the University of Montreal, and a Canada Research Chair in Learning Representations. He holds a PhD in Robotics from Carnegie Mellon University and degrees from the University of Toronto. His research focuses on deep learning models, probabilistic methods, and applications in vision and natural language processing. He co-leads the LISA lab and is Scientific Director at Mila, Quebec's AI institute. Education: PhD in Robotics, Carnegie Mellon University (2006) MSc in Electrical Engineering, University of Toronto BSc in Applied Sciences, University of Toronto Research Interests: Developing deep learning architectures, probabilistic models, and reinforcement learning techniques. Applications include computer vision, NLP, and generative models. His work emphasizes systematic generalization and scalable methods. Grants & Awards: Canada Research Chair (2022–2029) CIFAR Fellowship (Learning in Machines & Brains) NSERC Discovery Grants Mitacs Acceleration Funds Students & Collaborations: Supervised over 40 graduate students, many contributing to foundational AI work (e.g., Ian Goodfellow, inventor of GANs). Leads projects on generative models and reinforcement learning efficiency. Affiliations: Mila, IVADO, and member of CIFAR's AI program. Active in organizing conferences like ICLR and teaching at MIT/online.

Panagiotis Traganitisمشاهده پروفایل

استادیار

• Statistical Signal Processing
• Machine Learning
• Crowdsourcing
• +۴ مورد دیگر

Panagiotis Traganitis is an Assistant Professor in the Electrical and Computer Engineering (ECE) Department at Michigan State University (MSU), where he joined in August 2022. Previously, he was a Postdoctoral Researcher at the University of Minnesota’s Signal Processing in Networking and Communications (SPiNCOM) group under Prof. Georgios B. Giannakis. His research focuses on statistical signal processing, machine learning, crowdsourcing, weak supervision, and network science, with applications in big data analytics and distributed learning. Education: Ph.D. in Electrical Engineering, University of Minnesota (2019), Thesis: Scalable and Ensemble Learning for Big Data M.Sc. in Electrical Engineering, University of Minnesota (2015), Thesis: Large-scale Clustering using Random Sketching and Validation Diploma in Electrical & Computer Engineering, National Technical University of Athens (2013), Thesis: Reinforcement Learning Methods for Cognitive Radio Networks Research Interests: His work spans statistical learning, blind ensemble methods, weak supervision, subspace clustering, and graph-based algorithms. Current projects include blind ensemble learning, adversarial detection in crowdsourcing, and self-supervised learning. Awards & Honors: Gerondelis Foundation Graduate Scholarship (2015) Finalist, CAMSAP 2017 Student Paper Award Eurobank’s Award of Excellence in Greek Nationwide University Entrance Exams (2007) Teaching & Mentorship: He has served as a teaching assistant for courses on statistical methods and nonlinear optimization at the University of Minnesota. He is currently seeking motivated Ph.D. students to join his research group. His lab focuses on advancing robust, scalable learning algorithms with real-world applications. Labs & Collaborations: Active member of the SPiNCOM research group, collaborating on projects related to signal processing, machine learning, and network science.

Bing Yaoمشاهده پروفایل

استادیار

• Big data analytics
• Physics-informed machine learning
• Computer simulation and optimization
• +۱۲ مورد دیگر

Bing Yao is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Tennessee Knoxville, where she holds the Dan Doulet Early Career Assistant Professor title and serves as Director of the RME Program. She previously served as an Assistant Professor at Oklahoma State University's School of Industrial Engineering and Management from Fall 2019 to Summer 2022. Education: Dual-Title PhD in Industrial Engineering and Operations Research, The Pennsylvania State University, 2019 MS in Physics, The Pennsylvania State University, 2015 BS in Physics, University of Science and Technology of China, 2012 Dr. Yao's research centers on developing physics-informed machine learning models for decision optimization in complex systems, with applications in healthcare and advanced manufacturing. Her work integrates statistical learning with physical constraints to model spatiotemporal dynamics, particularly in cardiac electrophysiology and EHR-based disease prediction. She employs deep learning, simulation optimization, and signal processing to address challenges in data quality, missing values, and system complexity. Her recent publications demonstrate a strong trend toward AI-driven healthcare solutions, including personalized cardiac surgery planning, diabetic retinopathy screening, and sepsis prediction using longitudinal EHR data. The research consistently leverages multi-branching neural networks, physics constraints, and tensor-based imputation to improve model robustness and accuracy. Scientific Awards: Best Poster Award, NERCCS, 2018 Susan Schall Fellowship, Penn State, 2018 First Place, IISE Healthcare Systems Student Paper, 2017 IMS/ASA Spring Research Conference Scholarship, 2017 First Place, Penn State IEGA/IME Poster Competition, 2017 Best Poster Finalist, INFORMS MIF, 2016 Samsung Scholarship, USTC, 2011 Dr. Yao actively mentors PhD students, including Jianxin Xie, Zekai Wang, and others, many of whom have received prestigious awards such as the Gilbreth Fellowship and Best Paper Finalist recognitions. She is the Lead PI on a $1.1M NSF/NIH grant for cardiac surgical planning and a Co-I on a $1.2M grant for diabetic retinopathy screening. Her research is supported by the AI TENNessee Initiative as well. She leads a research group focused on spatiotemporal system dynamics, EHR analytics, and deep learning for medical diagnostics, currently recruiting PhD students with strong backgrounds in data science and engineering.

• Power Systems
• Energy Storage Systems
• Frequency Control
• +۷ مورد دیگر

Álvaro Ortega Manjavacas is an Assistant Professor at the Institute for Research in Technology (IIT), part of the School of Engineering at Comillas Pontifical University, Madrid, Spain. He has been a senior power systems researcher at IIT since September 2020, following prior research roles at LOYOLATech (Loyola Andalucía University) and University College Dublin. His academic background includes a PhD in Electrical Engineering from University College Dublin (2017) and a Bachelor's in Industrial Engineering from the University of Castilla-La Mancha (2013). His research focuses on the modeling, control, and stability of energy storage systems connected to transmission and distribution networks, frequency estimation and control in low-inertia power systems, and the design of advanced monitoring and control systems for flexible energy grids. His work bridges theoretical modeling with practical applications in renewable integration and grid resilience. The recent publications of Dr. Ortega span high-impact journals such as IEEE Transactions on Power Systems, Energy, and Sustainable Energy, Grids and Networks. These works predominantly center on virtual power plants (VPPs), robust optimization under uncertainty, frequency stability, and energy storage integration. Key themes include optimal bidding strategies in electricity markets, real-time operation of renewable-based VPPs, and coordinated control of energy storage with load shedding schemes. His research demonstrates a strong trend toward enhancing power system flexibility and stability in the context of increasing renewable penetration. 10th annual ISGAN Runner-up Award of Excellence Dr. Ortega advises PhD students including H. Nemati and O. Oladimeji, whose theses focus on the optimal operation of renewable virtual power plants. He has secured and contributed to significant research grants, including the Horizon 2020 POSYTYF project and the national PID2021-125628OB-C21 project on counteracting synchronous inertia reduction. He also participates in industry-funded projects with entities like Red Eléctrica de España and I-DE Redes Eléctricas Inteligentes. He is actively involved in the power systems research community, serving in IEEE and IEEE PES, and has organized scientific events such as the ICREPQ'23 conference. His work includes contributions to books and technical reports, further establishing his expertise in frequency control and energy storage modeling.

James Grantمشاهده پروفایل

مدرس

• Bandit Learning
• Reinforcement Learning
• Online Sequential Decision Making
• +۴ مورد دیگر

Dr. James Grant is a Lecturer in Statistics at Lancaster University's School of Mathematical Sciences since 2021, where he also serves as the EDI Lead for both the School and the ProbAI Research Hub. He is active in several roles at the Royal Statistical Society and serves as an Associate Editor for the ACM Transactions on Probabilistic Machine Learning. Grant completed his PhD in 2019 through the STOR-i Centre for Doctoral Training at Lancaster University under the supervision of Professors David Leslie, Kevin Glazebrook, and Roberto Szechtman. Prior to his PhD, he worked as a Machine Learning Research Student at Secondmind.ai and as a Research Associate within the STOR-i Centre for Doctoral Training. Dr. Grant's research focuses on online sequential decision making for problems with complex data structures, particularly in bandit learning and reinforcement learning. He specializes in multi-armed bandits, online optimization, and recommender systems where algorithms learn optimal decisions through iterative processes of decision-making, data observation, and model updating. His research emphasizes collaboration with industry, having worked on the Next-Generation Converged Digital Infrastructure project with BT. His publication record demonstrates strong expertise in bandit algorithms with applications spanning federated learning systems, anomaly detection in time series data, and phylogenetic tree analysis. Grant's work consistently bridges theoretical foundations with practical applications across telecommunications, surveillance systems, and decision-making frameworks. Notable Paper Award Winner at AISTATS 2020 for "On Thompson Sampling for Smoother-than-Lipschitz Bandits" Associate Editor for ACM Transactions on Probabilistic Machine Learning Active participant in Royal Statistical Society initiatives Dr. Grant currently supervises six PhD students working on diverse research topics including bandit learning, tropical statistics, anomaly detection, sequential simulation methods, safety in reinforcement learning, and Bayesian optimization. His former research associate, Changjiang He, has progressed to become a Lecturer in Computer Science at the University of Roehampton. As an EDI Lead, Grant actively promotes equality, diversity, and inclusion within the academic community while maintaining a robust research program that bridges theoretical statistics with real-world applications.

Chris Holmesمشاهده پروفایل

استاد

• Bayesian statistics
• Statistical genetics
• Machine learning
• +۵ مورد دیگر

Chris Holmes is a Professor of Biostatistics at the University of Oxford and Programme Director for Health and Medical Sciences at The Alan Turing Institute. His work bridges Bayesian statistics, machine learning, and genomic sciences, with cross-appointments in the Department of Statistics and the Nuffield Department of Clinical Medicine through the Wellcome Trust Centre for Human Genetics. He is affiliated with St Anne's College and actively leads research in Statistical Genomics under a Medical Research Council Programme Leaders Grant. PhD in Bayesian statistics from Imperial College London Former postdoctoral researcher and lecturer at Imperial College Industry experience in scientific computing for defense and SCADA systems Research Interests: Focus on Bayesian statistics , nonparametric methods , genetic epidemiology , and treatment effect heterogeneity . His methodologies address challenges in malaria pharmacodynamics, health equity in medical devices, and pandemic preparedness. Publication Trends: Recent work applies machine learning to reclassify multiple sclerosis progression , analyze genomic data for health equity , and develop tools for emergency admission prediction in Scotland. Collaborative efforts span Nature Medicine , Nature Reviews Genetics , and Trials . Scientific Awards: Medical Research Council Programme Leaders Grant in Statistical Genomics Advising: Mentors PhD students Oscar Clivio , Sahra Ghalebikesabi , and Natalia Garcia Martin in areas like Computational Statistics and Statistical Genetics .

Rolf Drechslerمشاهده پروفایل

استاد

• Circuit Design
• System Design
• Formal Verification
• +۵ مورد دیگر

Prof. Dr. Rolf Drechsler is a Full Professor at the University of Bremen since 2001 and Director of the Cyber-Physical Systems Group at DFKI Bremen since 2011. He leads the Computer Architecture working group in the Faculty of Mathematics and Computer Science and previously held adjunct/visiting roles at Duke University, IIT Kharagpur, and ISI Kolkata. Education: Diploma in Computer Science, Goethe University Frankfurt (1992) PhD (summa cum laude), Goethe University Frankfurt (1995) Habilitation, Albert-Ludwigs-University Freiburg (1999) His research focuses on formal verification , design automation , and data structures for circuit/system design , with applications in RISC-V processors , quantum computing , and in-memory computing . Recent studies explore LLM integration in hardware verification and polynomial methods for arithmetic circuits. Key awards include IEEE/ACM Best Paper Awards (ICCAD 2018, DATE 2025), the Berninghausen Prize (2018), IEEE Fellow (2015), and ACM Fellow. He co-founded the Graduate School "System Design" and led editorial roles at IEEE/ACM journals.

Ashutosh Guptaمشاهده پروفایل

دانشیار

• Formal Verification
• Software Verification
• Model Checking
• +۷ مورد دیگر

Ashutosh Gupta is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Bombay, where he has been a faculty member since 2018. His research focuses on formal methods for software verification, particularly in the areas of model checking, constraint solving, and automated reasoning for both sequential and concurrent programs. He teaches advanced courses including Automated Reasoning (CS433), Analysis of Concurrent Programs (CS766), and Logic for Computer Science (CS228). Dr. Gupta received his Ph.D. in Computer Science from Technical University of Munich (TUM) in 2011, with affiliations during his doctoral studies at TUM, Max Planck Institute for Software Systems (MPI-SWS), and École Polytechnique Fédérale de Lausanne (EPFL). Prior to joining IIT Bombay, he served as a faculty member at Tata Institute of Fundamental Research (TIFR) in Mumbai and completed post-doctoral research in the Henzinger group at IST Austria. His research interests span formal verification of sequential and concurrent software, modeling of biological systems, and constraint solving including constraint logic programming, decision procedures, and automated theorem proving. He has developed several verification tools including VAJRA, HSF, and InvGen. His work bridges theoretical foundations with practical applications, particularly in verifying safety-critical systems and biological processes. Dr. Gupta's publication record shows a consistent trajectory of high-impact research in top venues like POPL, CAV, TACAS, and AAAI. His recent work has expanded into neural network verification, reinforcement learning verification for medical devices, and applying formal methods to biological systems, demonstrating both depth in core verification techniques and breadth in application domains. Best Paper Award at TACAS 2015 Best Paper Award at TACAS 2009 Dr. Gupta actively mentors numerous students through course projects, research presentations, and specialized workshops like SATfest. He has supervised students working on SAT/SMT solvers, verification of concurrent programs, and applications of formal methods to biological systems. His teaching philosophy emphasizes hands-on experience with verification tools and encourages students to engage with cutting-edge research through reading and presenting recent conference papers. He maintains active research collaborations with institutions including TUM, MPI-SWS, EPFL, IST Austria, and TIFR, reflecting his continued integration into the international formal methods research community.

Leticia Arco Garciaمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Complex Networks
• Deep Learning
• +۵ مورد دیگر

Leticia Arco Garcia is a postdoctoral researcher at Vrije Universiteit Brussel working in the Department of Informatics and Applied Informatics with affiliations at Federated Labs AI and Robotics. Her research spans computational linguistics, complex network analysis, and more recently, interdisciplinary applications in archaeobotany. Her research interests focus on Natural Language Processing , Complex Networks , and Deep Learning methodologies. Early work centered on community detection in complex networks and customer behavior analysis, while recent publications demonstrate expansion into archaeobotanical applications using morphometric analysis of phytoliths. She employs advanced computational techniques including LSTM networks, case-based reasoning, and multi-objective optimization across diverse domains from smart power grids to electronic money systems. Analysis of her 15 most recent publications reveals an interdisciplinary trajectory moving from pure computer science applications toward biological/archaeological domains. The earliest works focus on network construction and community detection, while more recent papers apply similar analytical frameworks to botanical specimens, indicating methodological transfer between fields. Her work consistently applies computational rigor to complex data structures across diverse application areas. Her scientific contributions include: Development of novel network construction methodologies for large purchasing collections Advancements in overlapping community detection using rough clustering Application of deep learning to sentiment analysis and customer behavior prediction Recent innovative work applying morphometric analysis to archaeobotanical specimens Dr. Garcia actively presents her research at international conferences including INTERSPEECH and specialized meetings on phytolith research. Her collaborative network spans multiple institutions, with co-authorship on interdisciplinary projects connecting computer science with archaeology and plant science. While specific grant information isn't detailed in the available text, her consistent publication output across multiple domains suggests successful research funding.

Alon Peled-Cohenمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Statistical Learning
• Online Learning
• +۳ مورد دیگر

Dr. Alon Peled-Cohen is a senior lecturer at the School of Electrical Engineering - Systems within The Iby and Aladar Fleischman Faculty of Engineering at Tel Aviv University, and a researcher at Google Research Tel-Aviv. Dr. Peled-Cohen received his PhD in Machine Learning from the faculty of Industrial Engineering & Management at the Technion - Israel Institute of Technology under the supervision of Professor Tamir Hazan. Dr. Peled-Cohen's research focuses on the intersection of statistical learning, online learning, and decision-making under uncertainty. His work particularly emphasizes the connections between online learning and reinforcement learning, with applications in control theory and optimization. His research addresses fundamental challenges in learning from sequential data, making decisions with limited information, and developing algorithms with provable performance guarantees. He has made significant contributions to understanding regret bounds in various learning settings and has developed novel algorithms for challenging control and optimization problems. Analysis of Dr. Peled-Cohen's publication record reveals a strong focus on theoretical machine learning, particularly in online learning, reinforcement learning, and control theory. His work consistently addresses fundamental questions about algorithmic performance, often establishing tight regret bounds for various learning settings. A notable theme throughout his research is the application of optimization techniques to challenging learning problems, with several papers focusing on linear quadratic control and Markov decision processes. His collaborations span multiple institutions and include prominent researchers in the machine learning community. Dr. Peled-Cohen has published in top-tier machine learning conferences including ICML, NeurIPS, UAI, and AAAI. His research has addressed important problems in online learning, reinforcement learning, and control theory, contributing both theoretical insights and practical algorithms. As a senior lecturer at Tel Aviv University and researcher at Google Research Tel-Aviv, Dr. Peled-Cohen bridges academic research with practical applications. His work has implications for various domains requiring sequential decision-making under uncertainty, including robotics, recommendation systems, and autonomous control systems. While specific grant information is not provided in the available materials, his publications in top venues suggest successful research funding. Dr. Peled-Cohen is actively involved in both academic and industrial research, contributing to the advancement of machine learning theory while maintaining strong connections with practical applications through his position at Google Research.

Kelley M. Kidwellمشاهده پروفایل

استاد

• Clinical Trial Design
• Sequential Multiple Assignment Randomized Trials (SMARTs)
• Bayesian Methods
• +۵ مورد دیگر

Kelley M. Kidwell is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health. She specializes in clinical trial design and analysis, particularly through Sequential Multiple Assignment Randomized Trials (SMARTs) and small-sample methodologies. Her work bridges statistical innovation with real-world applications in chronic diseases, rare diseases, and mental health. Education PhD in Biostatistics, University of Pittsburgh (2012) BS in Mathematics, Bucknell University (2007) Research Focus Dr. Kidwell's research emphasizes adaptive clinical trial frameworks, including: Development of snSMART (small-sample SMART) designs with Bayesian and Frequentist methods Incorporation of patient preferences into dynamic treatment regimens Applications in adolescent risky behaviors, insomnia, chronic pain, suicide prevention, and oncology Article Trends Her recent publications demonstrate expertise in: Bayesian and Frequentist statistical approaches Adaptive trial design for rare and chronic conditions Telehealth and digital health implementation Pharmacogenetics in cancer treatment Alcohol use disorder interventions Physical activity and stroke rehabilitation

Matthew Schipperمشاهده پروفایل

استاد

• Biostatistics
• Cancer research
• Radiation oncology
• +۵ مورد دیگر

Matthew Schipper is a Professor in Radiation Oncology and Director of the Division of Biostatistics and Bioinformatics at the University of Michigan School of Public Health. He also holds a Research Professor appointment in the Biostatistics department. Dr. Schipper works to develop and apply statistical methods to improve the quality and length of life for patients with cancer or other illnesses. His research focuses on: Clinical trial design Data integration for prognostic models Statistical methods for personalized medicine Radiation therapy optimization Clinical decision-making tools He is particularly interested in building predictive models for the efficacy and toxicity of treatment that can inform optimal treatment decisions for individual patients. These models are implemented in web apps to facilitate ease of clinical use. Dr. Schipper has published extensively on these topics, with recent publications covering utility-based approaches to dose selection, life expectancy calculators for prostate cancer patients, improved designs for small sample clinical trials, predictors of pneumonitis after radiotherapy, Bayesian dose-finding methods, evaluation of predictive model performance with missing data, and the use of intermediate clinical endpoints in prostate cancer research.