Reinforcement Learning and Stochastic Optimization

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2	This spreadsheet has been set up to collect the names of people interested in a complementary (electronic) review copy of:
3	Reinforcement Learning and Stochastic Optimization: A unified framework for sequential decisions
4	Priority will be given to academic departments or programs interested in the book for teaching purposes, either within an existing course or a new course. Decisions on who will receive complementary copies will be made by the publisher.
5	Audience: This is a graduate level book, but for a broad audience: any department in engineering or the sciences (including the hard sciences as well as economics, psychology, sociology, ....). For an overview of the book, go to https://tinyurl.com/RLandSO/
6						Did WIley provide examination e-book?
7	Name	email	School	Department/program	Brief description how you anticipate using the book.
8	Jayash Koshal	j.koshal@northeastern.edu	Northeastern University	Data Science and Analytics	Teaching and research in optimization and ML
9	Warren Powell	wbpowell328@gmail.com	Princeton University	Operations Research and Financial Engineering	The book was used in a graduate level course that attracted about 30 students from eight different departments.	Yes
10	Edgar Gutierrez-Franco	edfranco@mit.edu	MIT	Supply Chain Management	Teach AI/ML applications in logistics and Supply Chain Management.
11	Pierre Pinson	ppin@dtu.dk	DTU	DTU	Teach decision making under uncertainty and sequential decision-making
12	Amit Karamchandani	amit.karamchandani@imi.edu	IMI Delhi	Operations Management and Quantitative Techniques	Teach sequential decision making
13	Kiefer Joe Burgess	kjburgess@uwaterloo.ca	University of Waterloo	Management Sciences	Self-study and teaching dynamic game theory with sequential decisions
14	Nikolaos Pappas	nikolaos.pappas@liu.se	Linkoping University, Sweden	Communication Networks	Stochastic Optimization - advanced course
15	Tahir Ekin	tahirekin@txstate.edu	Texas State University	CISQM	for a graduate elective course on (sequential) decision analytics (with uncertainty)
16	Marco Huber	marco.huber@iff.uni-stuttgart.de	University of Stuttgart	Cognitive Production Systems	Treaching the advanced course "probabilistic planning", which covers MDPs, optimal control and RL. The course has a strong focus on unifying optimal control with RL. Further, it provides many coding examples. I would to use the book to improve and expand many aspects of the course, particularly the RL part, which currently is rather ad hoc.
17	Dennis Janka	dennis.janka@h-ka.de	Karlsruhe University of Applied Sciences	Data Science	Teaching advanced undergrad elective course on sequential decision analytics; self-study.
18	Ehsan Rezaei	Ehsan.rezaei@polymtl.ca	Polytechnique Montreal	Industrial Engineering and Applied Mathematics	Self-study and using to teach RL methods and its application in a graduated course regarding energy mangment in buildings and microgrids
19	Leonardo Massai	leonardo.massai@polito.it	polytechnic of Turin	Mathematics	Planning a PhD course on stochastic optimization.
20	Sebastian Souyris	ssouyris@illinois.edu	University of Illinois Urbana-Champaign	Information Systems, Operations, Supply Chain, and Analytics	PhD course
21	Can Li	canl1@alumni.cmu.edu	Purdue University	Chemical Engineering	Teaching PhD level course on sequential decision analytics
22	Andy Chow	andychow@cityu.edu.hk	City University of Hong Kong	Systems Engineering	Teaching graduate course on dynamic transportation and logistics system optimization with use of reinforcement leanring.
23	Josue Prado	josue.camposdoprado@wsu.edu	Washington State University Vancouver	Electrical Engineering	Teaching and research
24	Rajendra Prasad Sirigina	srprasad@nus.edu.sg	National University of Singapore	school of Computing	Teaching Reinforcement Learning
25	Matt Bailey	mdb025@bucknell.edu	Bucknell University	Analytics and Operations Management	Incorporate into existing Predictive Analytics course and/or Independent Study courses. Also for Research.
26	Javier Tovar Facio	jfacio@andrew.cmu.edu	Carnegie Mellon University	Chemical Engineering/Fulbright Scholar	Planning a course on stochastic optimization
27	Vigneshwar Pesaru	Per35@pitt.edu	University of Pittsburgh	Operations Research	This was my course work and will leverage my knowledge more
28	Jinan Zhang	jinan.zhang@ruc.edu.cn;jnzhang.sta@gmail.com	Renmin University of China	Institute of Statistics and Big Data	Textbook for 'Machine Learning' course.
29	Andre Fioravanti	fioravanti@fem.unicamp.br	University of Campinas	School of Mechanical Engineering	Teaching Reinforcement Learning for MSc and PhD students
30	Erick C. Jones Jr.	erick.jones@uta.edu	University of Texas at Arlington	Industrial, Manufacturing, Systems Engineering	Help Develop and Enhance Stochastic Optimization Class, Train Graduate Students, Inform Research
31	Ömer Faruk YILMAZ	omerfarukyilmaz@ktu.edu.tr	Karadeniz Technical University	Industrial Engineering	It is planning to be used in the course of "Optimization techniques in production systems"
32	Gökhan Özçelik	gozcelik@ktu.edu.tr	Karadeniz Technical University	Industrial Engineering	Using in my course "Decision Analysis"
33	José Eduardo Pécora Junior	pecora@ufpr.br	Federal University of Paraná (Brazil)	Numerical methods Graduation Program.	I teach Data Analytics and Machine learning, also do research in healthcare systems.
34	Rui Liu	ruixliu@umich.edu	University of Michigan, Ann Arbor	Computer Science and Engineering, EECS Department	I am a PhD candidate of CSE working on applying sequential decision making techiniques for accelerating ML model training.
35	Kaushik Pradhan	kpradhan@miners.utep.edu	University of Texas at El Paso	Earth, Environmental, and Resource Sciences	I am planning to use the book to make better earthquake predictions.
36	Mehmet Kürşat Öksüz	mkoksuz@erzincan.edu.tr	Erzincan Binali Yıldırım University	Computer Engineering	I'm planning to use this book in the undergraduate course of "Optimization Techniques" and the graduate course of "Advanced Optimization Techniques". The book will also be helpful for me in my humanitarian logistics studies.
37	Pedro Munari	munari@dep.ufscar.br	Federal University of São Carlos (Brazil)	Production Engineering	Teach decision making under uncertainty to graduate students
38	Nicolò Michelusi	nicolo.michelusi@asu.edu	Arizona State University	Electrical, Computer and Energy Engineering	I teach the EEE598 graduate class "Learning-based sequential decision-making under uncertainty." I taught it in Fall'21 and I will teach it again in Spring'23.
39	Jia Liu	jialiu@xjtu.edu.cn	Xi'an Jiaotong University, China	Department of mathematics and statistics	I worked on stochastic optimization, recently i am interested in an extended framwork for reinforecement learning based on stochastic optimization. I am planning to build a new course on 'stochastic optimization and reinforcement learning'. This book is helpf for the new course as well as my current course "stochastic optimization’。
40	Alp Arslan	a.arslan1@lancaster.ac.uk	Lancaster University Management School	Management Science	Teach decision making under uncertainty and sequential decision-making
41	Aaron Glassburner	aaron.glassburner@afit.edu	Air Force Institute of Technology	Logistics and Supply Chain Management	Self-study and incorporating reinforcement learning into graduate-level SCM courses
42	Lars Relund Nielsen	larsrn@econ.au.dk	Aarhus University, Denmark	Department of Economics and Business Economics	Course: RL for Business
43	Richard Saldanha	r.saldanha@qmul.ac.uk	Queen Mary University of London	School of Economics and Finance	MSc Module – ECOM193: Statistical Machine Learning for Finance. Lecture 9 of this course introduces RL.
44	Ranjeev	ranjeev@perdanauniversity.edu.my	Perdana University, Malaysia	School of Data Science	Considering for syllabus update for graduate students and teaching
45	Yasin Gocgun	yasin.gocgun@istinye.edu.tr	Istinye University, Turkey	Industrial Engineering Dept.	I plan to use it for teaching dynamic optmization-based courses as well as for MDP-based research
46	Marco Pruckner	marco.pruckner@uni-wuerzburg.de	University of Wuerzburg, Germany	Computer Science	Teaching and Research in the field of computer science and sustainability
47	Michael Römer	michael.roemer@uni-bielefeld.de	Bielefeld University, Germany	Decision Analytics Group,Faculty of Economics and Business Administration	Teaching the graduate-level course "Combining OR and Data Science" as well as the PhD course "Decision-Focusetd Analytics"
48	Amr Mohamed	amr.adel@ryerson.ca	Ryerson University- Cairo Campus	Electrical and Computer Engineering	Teaching and research
49	Georgios Tsaousoglou	geo.tsaousoglou@gmail.com	National Technical University of Athens	Electrical and Computer Engineering	Teach decision-making under uncertainty and sequential decision-making
50	Jayeeta Jagannath	jj87@rice.edu	Rice University	electrical and computer engineering	Graduate study and research purposes , self study
51	Siva Sankar Udaya Kumar	siva.s.u@ntnu.no	Norwegian University of Science and Technology	Industrial Economics and Technology Management	I am a PhD candidate and I am looking use this for my doctoral research as I am working on Stochastic Optimization with sequential decisions.
52	Spandan Rout	spandanr@mit.edu	MIT	Supply Chain Management	Self study and learning AI/ML in decision making
53	Shervin Khazaeli	shervin.khazaeli@polymtl.ca	Polytechnique Montreal	Civil engineering	My research area lie within the coupleing of state-space models and Reinforcment Learning for anomaly detection in infrastructure health monitoring
54	Suchismit Mahapatra	suchismi@buffalo.edu	University of Buffalo	Machine/Reinforcement Learning	ML/RL research
55	Soumojit Kumar	soumojit.kumar@ifim.edu.in	IFIM, Bangalore, India	Artificial Intelligence	ML/RL research
56	Anubhav Sachan	anubhav_ug@ece.nits.ac.in	National Institute of Technology Silchar, India	Electronics and Communication Engineering	Self-study for future research
57	Padma Chitturi	ppchitturi@indeed.com	Jawaharlal Technological University Kakinada	Computer Science Engineering	Self-study and apply the concepts solving some of decisioning problems at work
58	Rohit R V	rohit.17ee@kct.ac.in	Kumaraguru college of technology	Electrical Engineering	Self-study for future research
59	Napoleon Lazardi Macias	nrlazardim@gmail.com	UCEMA University	School of Finance / School of Computing	Teaching Data Structure
60	Ignacio Erazo	iien3@gatech.edu	Georgia Institute of Technology	Industrial and Systems Engineering/ Operations Research	Ph.D. Candidate that has done research in stochastic optimization and is interested in enhancing his knowledge on sequential decision making.
61	Husain Aziz	azizhusain@ksu.edu	Kansas State University	Civil Engineering	Self-Learning and Research in Connected-Automated Transportation Systems
62	Joseanne Viana	josi_ocroch@hotmail.com	Lisbon university institute	Eletronics engineering	Self study for future research
63	Engin Tekin	etekin@ucsc.edu	University of California Santa Cruz	Computer Science Engineering	Research for Safe RL
64	Stavya Bhatia	S.Bhatia-2@student.tudelft.nl	tu Delft	OR	self study
65	Beopsoo Kim	sparky3275@inha.edu	Inha University	Department of Electrical and Computer Engineering	I'm planning to use this book to make developing renewable energy and energy storage operation optimization operation
66	Zahi Matta	zahi.matta@alumni.enac.fr	ENAC (french civil aviation school)	OR applied to air transport	self study and research
67	Hamed Mehranfar	mehranfar@ibi.baug.ethz.ch	ETH	Department of Civil, Environmental and Geomatic Engineering	Optimising intervention plans for the railway infrastructure.
68	Pham Luu Trung Duong	luu_pham@sutd.edu.sg	Singapore University of Technology and Design	Engineeering Product Development Pillar	Iam a postdoc at SUTD. Self-Learning and Research in AM /Manufacturing
69	Jayeeta Jagannath	jj87@rice.edu	Rice University	electrical and computer engineering	student research purposes
70	Aleksander Molak	a.molak3@student.uw.edu.pl	University of Warsaw, Poland / IRONSCALES	Experimental Psychology / R&D Machine Learning	I am interested in exploring the methodologies described in the book for for malicious email and phishing campaigns detection
71	Zisis Maleas	zisismaleas@certh.gr;zisismaleas@meng.auth.gr	CERTH/HIT, Greece (Mechanical engineer AUTH PhD)	mechanical/Industrial Engineering Dept./Research institute	I am an associate researcher in CERTH/HIT (Greece), while also doing my Ph.D. in reinforcement learning and decision making for industrial engineering applications. I find this book extremely interesting and suitable for my research, so I hope to get access to it.
72	Nour-Eddin El Faouzi	nour-eddin.elfaouzi@univ-eiffel.fr	Gustave Eiffel University - Paris	Transport and Traffic Engineering	Decision Suport Systems, Uncertainty Modeling, RL, Multiagent modeling and learning
73	Jason Dong	zdong@uw.edu	University of Washington	Statistics	Building up notes on RL for broad audience
74	El Mehdi Ainasse	elmehdi.ainasse@stonybrook.edu	Stony Brook University
75	Alexander Becker	apbecker@bu.edu	Boston University	Adminstrative Sciences
76	Premkumar K	kpk@iiitdm.ac.in	Indian Institute of Information Technology	Electronics and Communication Engineering	We use RL for a variety of problems in Communications and Data Science
77	Zhihong Yuan	zhihongyuan@mail.tsinghua.edu.cn	Tsinghua University	Department of Chemical Engineering	Teaching and research
78	Yunping HUANG	yunping.huang@connect.polyu.hk	The Hong Kong Polytechnic University	Department of Civil and Environmental Engineering	Applying and teaching RL for optimizing on dynamic transportation and logistics system
79	JP Sabini	jpsabini@ix.netcom.com	Netcom and Columbia University	Data Science	RL for course within firm largely to secure networks.
80	Mohammad Ranjbar	m_ranjbar@um.ac.ir	Ferdowsi University of Mashhad	Department of Industrial Engineering	Teaching and research in the field of SO and RL
81	Rami Dmaithan Halloush	rami.h@yu.edu.jo	Yarmouk University	Department of Telecommunications Engineering	Learn how to model optimization problems that arises in telecom using the framework suggested and explained in the book.
82	Saket Deshmukh	Sdeshmuk@lakeheadu.ca	Lakehead university	Electrical engineering	Use case in self driving cars
83	Charles Malack Oloo	cmalack@daystar.ac.ke	Daystar University Kenya	Management Science
84	Alex Pang	alex.pang@temple.edu	Temple University	Computer and Information Science	Teach Reinforcement learning
85	Sundaravalli Narayanaswami	sundaravallin@iima.ac.in	IIM Ahmedabad	Public Systems Group	Teaching, and publication in public policy analytics
86	Jefferson Huang	jefferson.huang@nps.edu	Naval Postgraduate School	Operations Research	Master's-level seminar on sequential decision-making
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